WP6: D6.1 Guidelines for the future massive integration of DRES in distribution grids

Transcription

1 Guidelines for the future massive integration of DRES in distribution grids This project has received funding from the European Union s Seventh Framework Programme for research, technological development and demonstration under grant agreement no

2 ID & Title : D6.1: Guidelines for the future massive integration of DRES in distribution grids Short Description (Max. 50 words): Number of pages : 107 This document delivers guidelines for the future massive integration of DRES in distribution grids, including solutions to better integrate DRES and recommendations to the most relevant stakeholders in the field based on the experience gained of IGREENGrid project. Version Date Modifications nature Author V1.0 18/03/2016 Version submitted to the consortium approval IBERDROLA Accessibility: PU, Public PP, Restricted to other program participants (including the Commission Services) RE, Restricted to other a group specified by the consortium (including the Commission Services) CO, Confidential, only for members of the consortium (including the Commission Services) If restricted, please specify here the group: Owner / Main responsible: IBERDROLA Reviewed by: GNF V /03/18 2/107

3 Authors Version Date Modifications nature Author name (s) Company V /04/09 Document creation & draft IBERDROLA V /11/20 Change of Index IBERDROLA V /12/11 Insert of contributions for solutions chapter V /12/18 Draft with partners comments EAG, SAG, TEC, IBD, GNF, ERDF, RWE, ENEL, RSE, AIT, HEDNO, ICCS-NTUA. EAG, SAG, TEC, IBD, GNF, ERDF, RWE, ENEL, RSE, AIT, HEDNO, ICCS-NTUA. V /12/30 New draft according to new table of contents IBERDROLA V /01/15 V /02/12 V /03/04 Draft with new chapter Criteria to establish Hosting capacity and to manage curtailment procedures New draft with Recommendations and Methodologies and tool to develop economic assessments chapters Draft including ERDF comments, updated Recommendations and new chapter for technical assessments. EAG, SAG, TEC, IBD, GNF, ERDF, RWE, ENEL, RSE, AIT, HEDNO, ICCS-NTUA. EAG, SAG, TEC, IBD, GNF, ERDF, RWE, ENEL, RSE, AIT, HEDNO, ICCS-NTUA. IBERDROLA V /03/18 Version submitted to the consortium approval IBERDROLA V /03/18 3/107

4 Abstract The deliverable D6.1 Guidelines for the future massive integration of DRES in distribution grids is focused on delivering rules of thumbs for the effective integration of DRES in MV and LV distribution networks. This report introduces a set of solutions grouped in a matrix, which is divided in four different functionality groups: prosumer side, network operation, network planning and asset management & economic matters. For each solution, it is introduced the concept, the actual state of the art per country or demo s implementations, barriers to be overcome, best practices for future deployment, stakeholders involvement and exploitation time horizon. The implementation of smart grid solutions needs new roles for DSOs and other stakeholders involved in the integration of DRES. To reach this goal, are presented a set of recommendations addressed to the most relevant stakeholders: prosumers, generators, investors, manufacturers, aggregators, retailers, research centres, EEGI, European Commission, Standardisation working groups, regulators, DSOs and TSOs. The transformation of distribution grid towards a smart grid, due to the transition to a low carbon economy, requires new approaches to the successful integration of DRES. In response to this challenge, IGREENGrid project has developed its own criteria to establish hosting capacity and has considered different criteria to manage curtailment procedures. To figure out what are the best practices for the future distribution grid, methodologies and tools to develop technical and economical assessments for the cost-effective DG connection are also introduced. To sum up, this deliverable provides guidelines grouped by stakeholders to facilitate the integration of DRES in distribution grids. V /03/18 4/107

5 Table of contents AUTHORS... 3 ABSTRACT... 4 TABLE OF CONTENTS... 5 LIST OF FIGURES & TABLES INTRODUCTION, SCOPE AND STRUCTURE OF THE DOCUMENT Introduction Scope of the document Document structure Notations, abbreviations and acronyms SOLUTIONS USED TO BETTER INTEGRATE DRES Prosumer side (Generation, storage & demand) Non-firm connection contracts Inverters providing reactive power at any time (inductive and capacitive) EV-charging optimisation by DSO/TSO Development of cheap storage Network operation Harmonized network operation data format for exchange Interface DSO / Customer Improve the observability of the distribution network Control of DRES' Reactive power Use of devices for Voltage control (AVR or STATCOM) Stakeholders involvement & exploitation time horizon Curtailment of DRES Network planning Harmonised connection rules Voltage & Load Monitoring (MV & LV) Advanced planning (more data used) including new operation concepts and tools Asset management & regulatory matters Assess the suitability of a traditional replacement compared to smart grid solutions Allow the proper allocation of the costs (infrastructures and systems) among the agents V /03/18 5/107

6 2.4.3 Adaption of DSO cost recovery/ remuneration framework to foster innovation RECOMMENDATIONS Prosumers Generators Investors Manufacturers Aggregators Retailers Research Centres EEGI European Commission Standardisation working groups Regulators DSOs TSOs GUIDELINES TO ASSESS HOSTING CAPACITY Calculation/Measurement of Hosting Capacity GUIDELINES TO MANAGE CURTAILMENT DG Curtailment procedures Criteria for curtailment Suggested compensation mechanisms GUIDELINES TO PERFORM TECHNICAL ASSESSMENTS (METHODOLOGIES AND TOOLS) Methodologies to develop technical assessments Step 1 Feeder screening and classification Step 2 - Determination of the expected hosting capacities for the case-studies Step 3 Detailed analysis of case-studies Tools to develop technical assessments Data management PowerFactory tools Parallelisation of the simulations Matlab tools GUIDELINES TO PERFORM ECONOMIC ASSESSMENTS (METHODOLOGIES AND TOOLS) V /03/18 6/107

7 7.1 Methodologies to develop economic assessments Approach for the economic evaluation Concept for the evaluation of costs and benefits in IGREENGrid: CA&BA 73 Part 1: Description of the most promising solutions Part 2: Cost Analysis (CA) Part 3: Benefits Analysis (BA) Part 4: Overall Assessment Tools to develop economic assessments Excel file for the Costs Analysis (CA) CONCLUSIONS REFERENCES Project Documents External documents ANNEX Annex 1: Glossary of terms - Stakeholders Annex 2: List of JRC-functionalities grouped in six services (Annex III of [28]) Annex 3: List of main benefits that smart grids solutions could provide (Annex I of [28]) Annex 4: List of formulas to the calculation of main benefits (Annex II of [28]) Annex 5: List of other potential benefits that smart grids solutions could provide (Annex IV of [28]) V /03/18 7/107

8 List of figures & tables Figure 1 (Matrix of the solutions to better integrate DRES) Figure 2 (Connection between IGREENGrid and EEGI KPIs) Figure 3 (Resulting Hosting Capacity in the BAU and R&I scenarios) Figure 4 (Hosting Capacity KPI) Figure 5 (DG curtailment cases) Figure 6 (General overview of the three steps for SRA) Figure 7 (Detailed overview of the three steps for SRA) Figure 8 (Two examples of horizontal DRES scenarios. Left: generation dominantly at the beginning. Right: generation dominantly at the end) Figure 9 (Illustration of the hosting capacity calculation for two type of networks) 65 Figure 10 (Cumulative distribution function CDF of the hosting capacity) Figure 11 (Implementation of Step 1) Figure 12 (Implementation of Step 2) Figure 13 (Implementation of Step 3) Figure 14 (Overview of the samples generation process) Figure 15 (Data preparation process for the samples generation) Figure 16 (Samples generation process) Figure 17 (Work flow proposed by EC JRC for a CBA of a smart grids project) Figure 18 (Methodology of evaluation of costs and benefits of solutions CA&BA considered in IGREENGrid) Figure 19 (Workflow of the Excel file used for the Costs Analysis (CA) of the solutions under analysis in IGREENGrid) Figure 20 (Solutions to integrate DRES) Figure 21 (Roadmap for the prosumer side) Figure 22 (Roadmap for Network operation) Figure 23 (Roadmap for Network planning) Figure 24 (Roadmap for Asset management and regulatory matters) Figure 25 (Recommendations to Prosumers) Figure 26 (Recommendations to Generators) Figure 27 (Recommendations to Investors) Figure 28 (Recommendations to Manufacturers) Figure 29 (Recommendations to Aggregators) Figure 30 (Recommendations to Retailers) Figure 31 (Recommendations to Research centres) Figure 32 (Recommendations to EEGI) Figure 33 (Recommendations to European Commission) Figure 34 (Recommendations to Standardisation working groups) Figure 35 (Recommendations to Regulators) Figure 36 (Recommendations to DSOs) Figure 37 (Recommendations to TSOs) Table 1: Acronym Table 2 (Actual state of the art - Harmonised Connection Rules [10]) Table 3 (Incentive schemes for long-term investment in distribution grids. [11], [12] and [13]) V /03/18 8/107

9 Table 4 (Adaption of DSO cost recovery / remuneration framework to allow and foster innovation (OPEX/CAPEX => TOTEX) [11], [12] and [13]) Table 5 (Network scenarios for KPIs calculation) Table 6 (Interconnected electricity distribution networks - DG curtailment procedures) Table 7 ( Non-interconnected electricity distribution networks - DG curtailment procedures.) Table 8 (Suggested compensation mechanisms) Table 9: Grouping of most promising solutions by functionality Table 10 (List of potentially attributable CapEx and OpEx) Table 11 (Stakeholder categories definition) Table 12 (List of JRC-functionalities grouped in 6 services (Annex II of [28])) Table 13 (List of main (quantitative) benefits that smart grids solutions could provide (Annex I of [28])) Table 14 (List of formulas to the calculation of main benefits (Annex II of [28]) not used on the IGREENGrid project) Table 15 (List of other potential benefits that smart grids solutions could provide (Annex I of [28])) V /03/18 9/107

10 1 Introduction, scope and structure of the document 1.1 Introduction Electricity distribution networks are a key enabler for a European low carbon future. The widespread connection of Distributed Renewable Energy Resources (DRES) requires new approaches to design, construct and operate current power networks [1]. IGREENGrid project addresses this challenge [2]. This European project uses experience gained from demonstration projects in six different countries for the deployment of novel smart solutions to address DG integration in distribution grids. The main objective of this deliverable is to develop a set of guidelines for the future massive integration of DRES in distribution grids, delivering a feedback to improve current activities of the DSO s demo projects, collecting know-how related to the integration of DRES and providing a response to the stakeholder s requirements to figure out solutions for a large-scale integration of DRES in MV and LV networks. Moreover, this report delivers recommendations to reduce connection problems and to help stakeholders to deploy the exploitation plan taking advantage from know-how obtained. Additionally, a criterion to establish hosting capacity and to manage curtailment procedures of DRES in distribution grids is introduced. And finally, the methodologies and tools used during IGREENGrid project to develop technical and economic assessments of smart grid solutions are proposed. 1.2 Scope of the document This deliverable summarises the guidelines for the massive and effective integration of DRES within European electricity distribution networks, in order to increase the hosting capacity and also improve the quality of service and the grid efficiency. The goal of this deliverable is to be a baseline document for future projects and initiatives regarding to the massive integration of DRES. 1.3 Document structure This document is structured as follows: Chapter 2 describes the suggested solutions to integrate DRES in four different categories: prosumer side, network operation, network planning and asset management & economic matters. Chapter 3 includes a set of recommendations by stakeholder. Chapter 4 describes the criteria to establish hosting capacity Chapter 5 introduces guidelines to manage curtailment procedures. V /03/18 10/107

11 Chapter 6 introduces the methodologies and tools to develop technical assessment. Chapter 7 introduces the methodologies and tools to perform economic assessment. Chapter 8 presents the conclusions of this deliverable focusing on guidelines and recommendations to better integrate DRES. Annexes include glossary of terms for stakeholders, list of JRC-functionalities grouped in six services, list of main benefits that smart grids solutions could provide, list of formulas to the calculation of main benefits and list of other potential benefits that smart grids solutions could provide. 1.4 Notations, abbreviations and acronyms AMI AVR BA BAU BRP B2C CA CAPEX CBA CDF CHP CIM CO 2 CPP DER DG DMS DRES DSE DSO EC EEGI EMS EPRI EU EV FILO GIS GOOSE GSSE HC HV Advanced Metering Infrastructure Automatic Voltage Regulator Benefit Analysis Business As Usual Business Protection from Misleading Marketing Regulations Business to Customer Cost Analysis Capital Expenditures Cost Benefit Analysis Cumulative Distribution Function Combined Heat and Power Common Information Model Carbon Dioxide Critical Peak Pricing Distributed Energy Resources Distributed Generator Distributed Management System Distributed Renewable Energy Sources Distributed State Estimator Distribution System Operator European Commission European Electricity Grid Initiative Energy Management System Electric Power Research Institute European Union Electric Vehicles First In Last Out Geographic Information System Generic Object Oriented Substation Events Generic Substation Status Events Hosting Capacity High Voltage V /03/18 11/107

12 ICT IEC IED IEEE IP ISO IT JRC KPI LTE LV MDM MMS MV NIC NRA OLTC OPEX OPF PLC P PV PVTC Q QoS R&D R&I RD&D RES RoR RT SCADA SE SRA STATCOM SUT SVR TCP ToU TOTEX TSO VPP WACC Information and Communication Technology International Electrotechnical Commission Intelligent Electronic Device Institute of Electrical and Electronics Engineers Internet Protocol International Standards Organization Information Technology Joint Research Centre Key performance indicator Long Term Evolution Low Voltage Mobile Demand Management Multimedia Messaging Service Medium Voltage Network Interface Card National Regulatory Authority On Top Load Changer Operating Expenditures Optimal Power Flow Programmable Logic Controller Active Power Photovoltaic Present Value of Total Costs Reactive Power Quality of Service Research and Development Research and Innovation Research Development and Deployment Renewable Energy Sources Rate of Return Real Time Supervisory Control And Data Acquisition State Estimator Scalability and Replicability Analysis Static Synchronous Compensator Solution Under Test Static VAr Compensator Transmission Control Protocol Time-of-Use Total Expenditures Transmission System Operator Virtual Power Plant Weighted Average Cost of Capital Table 1: Acronym V /03/18 12/107

13 Prosumers Generators Investors Manufacturers Aggregators Retailers Research centres EEGI European Commission Standardisation working groups Regulators DSOs TSOs WP6: D6.1 2 Solutions used to better integrate DRES The challenges presented by the transition to a massive integration of DRES will directly impact the distribution grid. By 2020, it is expected that each country will generate at least 15% of its total electricity demand from RES with a 20% reduction in greenhouse emissions. Current electricity distribution networks need to be upgraded in a smart way, in order to allocate the expected increase of DRES through the avoidance of high traditional reinforcement costs. In response to this issue, DSOs and most relevant stakeholders are developing, assessing and deploying novel smart techniques to allow the increase of DRES penetration in distribution grids. In this chapter, it is introduced a Solutions Matrix with suggested smart solutions to be implemented in order to integrate more DRES, including the parties to be involved in each one. Stakeholder involved Solutions to integrate DRES Prosumer side (Generation, storage & demand) Non-firm connection contracts X X X X Inverters providing reactive power at any time (inductive and capacitive) X X X X X EV-charging optimisiation by DSO/TSO X X X X X X X X X X Development of cheap Storage X X X X X X Network operation Harmonised network operation data format for exchange (including TSO, DSO) X X X X X Interface DSO / Customer X X X X X X X X X X Improve the observability of the distribution network X X X X X X X X X Control of DRES' reactive power X X X X X X Use of devices for voltage control (STATCOM or AVR) X X X X Curtailment of DRES X X X Network planning Harmonized Connection rules X X X X X X Voltage & Load Monitoring (MV & LV) X X Advanced planning (more data used) including new operation concepts X X Asset management & economic matters Assess the suitability of a traditional replacement compared to smart grid solutions X X X X Allow the proper allocation of costs (infrastructures and systems) among the agents X X X X X X X X Adaption of DSO cost recovery/remuneration framework to allow foster innovation X X X Figure 1 (Matrix of the solutions to better integrate DRES) The suggested solutions are clustered by functionality in the following categories: Prosumer side. Network operation. Network planning. Asset management & Regulatory matters. V /03/18 13/107

14 For each solution a clear description is presented, including: actual state of the art by country, barriers to overcome and best practices to be implemented in the near future in order to address DRES integration, highlighting stakeholders involvement and exploitation time horizon. The exploitation time horizon is classified as follows: short-term (less than 5 years), medium-term (between 5 and 10 years) and long term (more than 10 years). 2.1 Prosumer side (Generation, storage & demand) From the prosumer side, there are several solutions to be implemented in the near future for the purpose of massive DRES integration. Down below some suggested solutions to be deployed regarding to power generation, energy storage and demand side management are introduced Non-firm connection contracts Concept In many cases, the connection of additional generators requires network reinforcements which might be costly and time-consuming. The constraint can for example be the transformer capacity at a primary substation to which a significant amount of generation is connected or a current or voltage constraint on an existing feeder. In some cases, the network reinforcement is only needed for a short duration (few tens of hours per year) which poses the question of the real meaningfulness of such reinforcement. An approach to this is the use of non-firm connection contracts, meaning that the DSO would tolerate the connection of the generator as requested, leading to a violation of its planning rules but keeping the right to curtail the injection of the generator for a given number of hours per year to prevent constraints on the distribution network. This allows the DRES to connect to the distribution network while minimising its connection costs and time. Such a solution will generally lead to a very small loss of revenues for the generation owner but it compensates with the savings of the reinforcement costs that would be necessary to guarantee the possibility to inject on the network all the time its generation Barriers The way to distribute the risk must be clarified. The question is whether such contracts would mention a cap for the curtailed energy over the year (and how this is to be evaluated (average of 3 years?)) and the way to calculate the penalty in case the curtailment exceeds the cap as in any connection contract. The uncertainty for the DSO to estimate the total amount of energy to be contracted is generally high. This estimation must consider all the network users connected to the considered network (loads, generators) for which standard or historic load/generation profiles must be used. In case of major changes (e.g. bankrupt of a large consumer), additional constraints might appear. An accurate determination of the actual curtailed energy is usually not possible. V /03/18 14/107

15 Since the amount of curtailed energy is expected to be low (a few percents), the accuracy is critical. This amount of curtailed energy would be necessary for non-firm connection contracts stipulating a curtailment cap. For biomass or CHP installations in general, the problem is more complex since the revenues or the earnings depend on the cost of the fuel. Current regulatory rules are insufficient. Suitable mechanisms are needed to handle the evolution of the network (e.g. connection of further generators at a stronger connection point for a voltage-constrained feeder). Current approaches tend to follow the FILO (First In Last Out in order to ensure stable conditions for investors) approach which is in general could not be the optimum (optimum = least total amount of curtailed energy). Non-firm connection contract is a conservative solution to operate the network, which does not allow the massive integration of DRES by using only this solution Best Practices & recommendations Perform further analysis work on the challenges to implement non-firm connection contracts to decide if the benefits are actually as high as currently considered taking into account the challenges previously mentioned. If the added value is confirmed, develop suitable regulatory mechanisms to address fairness vs. optimality Stakeholders Involvement & time horizon Party taking action: Regulators, DSO, Generators, research centres. Party to be involved in exploitation process: DSO, Generators. Exploitation time horizon: Short-term (2018) Inverters providing reactive power at any time (inductive and capacitive) Concept Some customers demands are causing significant reactive power flows, inductive as well as capacitive. Furthermore reactive power demand is caused by transformers and lines of the grid itself, depending on the active load flow. DG-units should contribute to the required reactive power on DSO s request like centralized plants do at the transmission network level Barriers Besides the general increase of losses caused by the reactive power provision which is very small and therefore uncritical, the provision of reactive power on demand requires the inverters to be always in operation and therefore this might have a negative impact on the inverter lifetime. Lack of clear regulatory and legal framework (obligation). Actual available inverters provide reactive power depending on active power- thus the availability of reactive power can be volatile. Only a small share of the inverters available on the market is able to operate independently from the available PV power (also at night). V /03/18 15/107

16 This is especially true for small inverters connected to the LV network which still represent the main share of the installed PV power in several countries Best Practices & recommendations To avoid specific configuration a unique solution should be harmonized all over Europe. DG-units should provide a certain capacitive or inductive reactive power (based on rated active power) at any time and independent from actual active power generation. To enable additional hosting capacity and to avoid any resulting constraint due to voltage levels a characteristic Q(U) could still be applied in any case with top priority. The proposed approach would follow two objectives: compensate the voltage rise caused by the PV infeed if necessary (e.g. Q(U)) to increase the hosting capacity and at the same time, when possible, to operate according to the DSO needs for an optimal network operation, Stakeholders Involvement & time horizon Party taking action: Prosumers, DSO, Manufacturers, Generators. Party to be involved in exploitation process: Generators, Regulators. Exploitation time horizon: Short-term (2018) EV-charging optimisation by DSO/TSO Concept The widespread connection of DRES requires the optimisation of EV charging to facilitate RES integration. Smart charging flexibility can be used to balance supply and demand by the market and also to adjust the power network to unexpected constraints by DSOs and TSOs [3]. To achieve this goal, EV charging connection should be incentivised or penalised by the market through a price signal for network tariff, in order to integrate more DRES at a time that the consumptions are optimised and the operating costs are reduced [4]. Furthermore, to facilitate the integration of EVs with limited impact on the network and fostering DRES penetration, manufacturers of charging stations should develop systems allowing a controlled charging (and discharging) of EVs following standardised ISO A set of harmonised functionalities (e.g. reduction of the charging power in case of low voltage, remote activation and deactivation via broadcasting to network areas behind congestion) shall be implemented into the charging equipment Barriers All the local controllers have to be coordinated and behave in the same manner. The behaviour of the devices should be detailed in advance and gathered in a standard. The use of EV charging for voltage control worsens the performance of the devices due to additional losses. Additional costs of the control box and the necessity of ICT to optimise EV charging points are constraints for its economic viability. EV integration is not viable yet because oil and carbon prices are more cost-effective and V /03/18 16/107

17 current incentives are not large enough to compensate this issue. Interaction rules with new players, such as EV charging infrastructure operators, are not clearly defined Best practices & recommendations To develop EV charging points able to perform intelligent charging management. Allowing DSOs to interact with EV through charging points to perform intelligent charging management results in integrating a greater number of DG units. Use state of the art of the technology by supporting ISO principles on charging infrastructure. Controllable EV charging points could be used to obtain a higher hosting capacity and less disconnection events due to overvoltage. As a consequence of this technique, a larger DRES penetration and a greater usage of distribution assets could be reached. Arrange contracts between DSOs, aggregators and EV owners. Future work should be focus on the integration of EV charging along with demand side response to achieve a cheaper electricity dispatch. To integrate EV into network planning processes and regulate EV based provision of ancillary services. Use EV charging points to control frequency at transmission level Stakeholders Involvement & Exploitation Time Horizon Most involved parties: DSOs, EV Charging infrastructure operators and EV Service Providers (Prosumers), TSO. Party taking action: Regulators. Party to be involved in exploitation process: Costumer (Prosumer), Investors, Manufacturers, Aggregators, Retailers, EEGI, European Commission. Exploitation time horizon: Short-term (2020) Development of cheap storage Concept Energy storage solutions deliver flexibility to distribution grids and can be used to provide power when conditions are not favourable and to store power when there is a generation excess. In this way, implementing new storage solutions can increase DRES penetration and reduce the effects of the energy intermittency. Until now, it has been proposed several storage solutions such as batteries and biogas storage, although economic factors and regulatory issues are still being barriers for these technologies [5] Barriers Rules for interaction with new actors are not clearly defined. Regulation does not allow DSOs to control energy storage solutions (should be managed by a third party). Batteries are still being very expensive solutions. Furthermore, weight and space are going V /03/18 17/107

18 against their utilisation. Some social concerns exist regarding to this infrastructure Best practices & recommendations To work in batteries cost reduction, because battery storage can be used for network operation as a back-up system. Storage solutions for DRES should have an incentive to participate in the provision of ancillary services. Incorporate energy storage solutions to help aggregators and DSOs to increase current DRES hosting capacity. Energy storage systems can provide more flexibility to the control of distribution grid and therefore will be able to integrate a higher number of intermittency DG units [5]. Increasing the power flow control (P and Q) using DRES along with storage, would decrease voltage fluctuations Stakeholders Involvement & Exploitation Time Horizon Most involved parties: Research Centres, Storage operators (Prosumers). Party taking action: Manufacturers. Party to be involved in exploitation process: EEGI, European Commission, Regulators. Exploitation time horizon: Medium/Long-term (Not before ). 2.2 Network operation From the point of view of the network operation there are several solutions that can be implemented to allow a better integration of DRES in distribution grids. Down below the most important ones in this field are introduced Harmonized network operation data format for exchange Concept Generic network studies (for several networks of several DSOs) are strongly hampered by the lack of standard network data exchange format. The problem of data conversion is recurrent in projects involving several DSOs (e.g. IGREENGrid). In the absence of a standard interface/format between network simulation tools, special converters must be written and tuned to the special situations. This process is time consuming, prone to errors and not scalable. IGREENGrid experience shows that the data exchange can be complex, needing in some cases the conversion from one format to an intermediate format and then into the final format. Some V /03/18 18/107

19 special converters are sometimes also needed (e.g. Neplan->PowerFactory) since manufacturers support very poorly data exchange Barriers No established exchange format between tools for distribution networks (CIM is basically only available for the transmission profile) Best practices & recommendations Work on the adoption of a common interface format for network data Stakeholders Involvement & time horizon Party taking action: Standardisation working groups, Software manufactures, DSO, Research centres. Party to be involved in exploitation process: Standardisation working groups, DSO, research centres. Exploitation time horizon: Short-term (2020) Interface DSO / Customer An interface between DSOs and customers is needed in order to facilitate the active demand management and DRES penetration. In this context, several solutions related to the implementation of this solution are presented Ripple control for dynamic use of remote switching Concept The interface between DSO and customer is required to be: Simple. Secure. Robust. Compatible to existing systems. Low CAPEX and low OPEX (at installation, maintenance and operation). For example, typically at one control unit five switches can be used to set one of 32 statuses to control inverters controllable loads or smart home system in Germany and Austria Barriers Contracts related to switched supply are related to fixed time schedules. Switches are used directly switching on and of the circuit of the load. Interaction with the retailers, the aggregators and the balancing responsible party Best practices & recommendations V /03/18 19/107

20 Harmonized standard for use of five switches for different components and general load limitation. Define regulatory framework for dynamic price signals for loads regarding interests of markets and possible constraints in distribution grid Stakeholders Involvement & time horizon Party taking action: DSO, Consumer (Prosumer), Retailer, Aggregator. Party to be involved in exploitation process: Manufactures, Standardisation working groups, Prosumers, Regulators. Exploitation time horizon: Short-term (2020) Mains signalling for dynamic used remote switching Concept The deployment of smart meters shall not require any changing of installations at the customer site. Ripple control units are replaced by full compatible. In Austria, typically at one control unit five switches could be used to set one of 32 statuses to control inverters controllable loads or smart home system. A simple approach for large deployment of demand side management as first step approach is the dynamic use of these five switches Barriers Contracts related to switched supply are related to fixed time schedules. Interactions with retailers, aggregators balance responsible party. Switches are used directly switching on and off the circuit of the load Best practices & recommendations Harmonized standard for use of five switches for different components and general load limitation. Define regulatory framework for dynamic price signal for loads regarding interests of markets and possible constraints in distribution grid Stakeholders Involvement & time horizon Party taking action: DSO, Aggregator, Retailer, Consumer (Prosumer). Party to be involved in exploitation process: Manufactures, Standardisation, Prosumers, Regulators, Retailers and Aggregators. Exploitation time horizon: Short-term (2020) TCP IP connection: IEC Concept The implementation of communications using the IEC protocol can provide substantial cost savings over legacy Ethernet based systems. Installation costs can be reduced by limiting the need for separate links for each relay and utilising the existing bandwidth using services such as GOOSE and GSSE. The standardisation of communications using the IEC protocol instead V /03/18 20/107

21 of many proprietary protocols will also provide interoperability of devices produced by different manufactures, reducing installation costs where additional interface devices would have been required. It has also the potential to increase competition, opening up the market with all manufactures producing standard communications equipment that is compatible with the communication systems on all DSO networks. Since communications is a key feature to grid automation and in many of the smart grid solutions that are demonstrated in the IGREENGrid demonstration projects, the industry wide adoption of the IEC protocol for DSOs and manufactures across Europe would play a significant role towards reducing the cost of DG integration. The benefits of using standardised solutions by the implementation of IEC MMS (client/server) and GOOSE messaging has been demonstrated by Enel in several pilot and large scale demonstration projects. The current applications for this protocol include remote control, automation, voltage control and fault detection implementations. The deployment of this solution is ongoing in several large scale demonstration projects using the new LTE and WIMAX technologies Barriers Manufactures are still using proprietary protocols Best practices & recommendations Increased dissemination of application and benefits of IEC standard Stakeholders Involvement & Exploitation Time Horizon Most involved parties: DSOs, Manufactures, Standardisation working groups. Party to be involved in exploitation process: Costumer (Prosumer), Investors, Manufacturers, Aggregators, Retailers, EEGI, European Commission, Standardisation working groups. Exploitation time horizon: Short-term (2018) Improve the observability of the distribution network The rapid deployment of DG generation in distribution grids has resulted in a need to better understand, predict, and manage variable generation. RES generation and load forecasting are lately seen by DSOs as a valuable tool under the general framework of generation management and voltage problems mitigation strategies, to facilitate the integration of higher RES penetrations. One of the most important capabilities to be improved in distribution grids is the supervision and the observability of the network, in order to understand its behaviour Enhanced prognosis of DG and load Concept V /03/18 21/107

22 Numerical weather prediction and historical information about demand profiles are the major components of DG and load prognosis. Advanced statistical models and artificial intelligence algorithms have been employed to enhance forecast accuracy and provide exploitable outputs for the DSOs. The latter are using enhanced prognosis for several decisions and actions (e.g. situational awareness, operational planning, probabilistic energy management and dispatch), which allow the increase of DGs integration Barriers As input data may be provided by remote sources communication reliability and noise can be a problem for the implementation of the solution. Regulator does not reimburse to DSOs their investments in innovation. Accuracy and uncertainty of forecasting algorithms should be improved to provide performance verification. Weather forecasts cost. Implementing this kind of solutions implies the use of ICT infrastructures that are not installed today Best practices & recommendations GIS information should be available to improve spatial granularity of forecasts. DSOs should integrate forecasting modules in their Energy Management Systems (EMS) and Mobile Demand Management (MDM) platforms. Smart meters should be employed to provide demand profiles data Stakeholders Involvement & time horizon Party taking action: DSOs. Party to be involved in exploitation process: Consumers (Prosumers), Generators (of all sizes), Aggregators, Retailers, Research centres, EEGI. Exploitation time horizon: Short-term (2018) MV: OPF based on SE or further sophisticated algorithms Concept Currently, distribution networks are in the middle of a transformation within the electricity system, redefining the operation of the grids including technological advances. Conventional passive networks are substituted by active, smart, secure, flexible infrastructures that ensure a reliable power supply in an environment where new assets are present: DER, local storage, electric vehicles and demand response. The concept of this solution is to start from the knowledge of the actual state of the network which has been calculated thanks to the State Estimator (SE), and afterwards optimise the power flows of the grid by using the available infrastructure to where are connected, for example: OLTC, Capacitor batteries, DRES, etc. OPF or further sophisticated algorithms will help us to achieve an optimal solution of the network, taking into account different requirements for its optimisation, such as voltage constraints, the reduction of losses, etc. V /03/18 22/107

23 Barriers Measurements from generators are needed to have a real-time monitoring of the importation and exportation of energy. The integration of all active elements into a central control system is essential to have an optimal management of the distribution network. Monitoring and control of end-users devices. There is the need to settle agreements with the customers on their usage. The current installations must be also updated and reinforced to support the new functionalities. Cybersecurity must be taken into account. Data privacy is an important issue in social acceptance of smart grids, so it is necessary to adopt proper mechanisms that ensure the security of consumer and network data. ICTS to enable the proper communication between SE and the control centre. Need to use real-time data from generators. DRES do not have any incentive to take part in the network operation Interactions with the new actors resulting from DRES integration are not clearly defined There is a lack of a standard Smart Grid solution components and communication protocols. Distribution network processes are not adapted to the realities of the integration of DRES. There is a lack of experience of the DSO in the operation of new devices and systems. The power system reliability may be affected by the massive DRES penetration. The ICT solutions for remote areas may be unaffordable. There is a lack of an adequate remuneration for DSO services Best practices & recommendations Development of advanced tools which allows the implementation of the new functionalities of the smart grid, using a standard data model. Deployment of an advanced communication infrastructure able to monitor and control all the flexible resources of the grid. The standardization of components and communication protocols enhances the integration of DG. A new regulatory framework should consider CAPEX-OPEX of DSOs developing the new smart grid functionalities Stakeholders Involvement & time horizon Party taking action: DSO, Generators. Party to be involved in exploitation process: DSOs, Manufactures, Research centres, Regulators. Exploitation time horizon: Short-term (2018) Control of DRES' Reactive power DSOs can contribute to power flows management through the control of DRES reactive power. Afterwards, two different techniques to the control reactive power of DRES are presented.. V /03/18 23/107

24 Reactive power from generators on request (balance at connection point DSO/TSO) Concept Some customers demands are causing significant reactive power inductive as well as capacitive. Furthermore reactive power demand is caused by transformers and lines of the grid itself, depending on the active load flow. Voltage control solutions based on reactive power consumption of DG-units have to be regarded as additional reactive load. DG-units should contribute on request reactive power like centralised plants do at the transmission network level. If generators with renewable energy sources like wind or solar energy are providing reactive power based on their intermittent active power profile DSOs might be challenged by managing the balance at the connection point to TSO. DSOs can contribute to reactive power flows management through the control of MV DRES reactive power. For generation units from renewable sources reactive power is available at any time independent on active power can provide a useful network service to DSO. For a large number of small scale generation units a simple remote control (e.g. remote switching) solution has to be applied. This remote control system can be linked to MV-SCADA and be used to support MV-OPF (see ) in case the DG units are organized in proper groups. This concept could be further used for providing reactive power to TSO on request Barriers Reactive power causes increased currents possibly leading into constraints. Reactive power causes increased losses in network. Voltage rise or drop caused by reactive power flow can cause constraints in respect to voltage and reduce the hosting capacity for DG and load. Most of the inverters are not ready to provide reactive power on request at any time. Inverters are not ready for simple remote control due to lack of standard Best practices & recommendations DG-units should support the distribution system by providing capacitive or inductive reactive power at any time and independent from actual active power generation. Harmonise communication with DRES around Europe. The DSO should use a simple control solution (e.g. switches controlled by ripple control, radio, PLC etc.) to set the DG-unit to provide inductive, capacitive or zero reactive power. Organizing plants in groups certain steps of reactive power could be switched providing in total a service like distributed STATCOM To avoid impact on hosting capacity a characteristic Q(U) is applied in any case with top priority blocking the request by local control Stakeholders Involvement & time horizon Party taking action: DSO, Generators. Party to be involved in exploitation process: Generators, Prosumers, Manufactures, DSO, TSO. Exploitation time horizon: Short-term (2018). V /03/18 24/107

25 Voltage control by DRES' reactive power Concept The flow of reactive power provided by DRES is used to compensate voltage drops. For instance, the regulation of reactive power provision can be done with a DG power factor control (cos (P)) or with a characteristic (Q (U)) Barriers Reactive power causes increase currents and losses in inverters (low) and network components. The absorption and/or injection of reactive power might require installation and operation of SVC, coils, capacitor banks etc. at the primary substation. The use of any Q(U) or cos (P) settings can require network reconfigurations. Reactive power provision depends in some cases on the generation (Active power) profile Best practices & recommendations To enable additional hosting capacity and to avoid any resulting constraint due to voltage levels a characteristic Q(U) shall be applied in any case with top priority. Follow solutions and in order to avoid any need of reconfiguration due to balancing reactive power and to balance reactive power demand at primary substation bus bar level. To avoid specific configuration efforts, a unique set of characteristics should be harmonized all over Europe Stakeholders Involvement & time horizon Party taking action: Generators. Party to be involved in exploitation process: Generators, Manufactures, Regulators. Exploitation time horizon: Short-term (2018) Use of devices for Voltage control (AVR or STATCOM) Regarding voltage control, there are several techniques to regulate the voltage supplied to consumers within statutory limits Concept The main problem caused by DRES generation is voltage constraints. For this reason, it is relevant to provide solutions to control the voltage in such a way that the voltage profile can be managed. Voltage issues caused by DRES generation are a real problem in many grids. European Standard DIN 50160, which sets voltage limits at ±10 percent of nominal voltage, are not fulfilled by some grids and in the near future is going to be more restrictive. V /03/18 25/107

26 In order to examine the effects of direct voltage regulation units (OLTC, AVR) on the operational state of distribution grids, the conventional distribution transformer is replaced by a general direct voltage regulation unit. Whereas to examine the effects of indirect voltage regulation via reactive power management (VAr Compensation, STATCOM) on the operational state of distribution grids, the conventional distribution transformer is maintained and a general source of inductive and capacitive reactive power is added at the end of the line. Automatic Voltage Regulator (AVR) is a voltage control solution based on physical devices exhibiting a local control in charge of performing the voltage regulation function. The AVR dynamically and continuously (theoretically 20 milliseconds time-cycle, adjusted to 700 milliseconds in real conditions) correct the grid voltage by up to ±10 percent. The AVR incorporates a bypass system. If the inverter trips, the bypass system shunts the injection transformer, relieving the load from the inverter and providing a direct connection between the grid and the load without interruption. Static Synchronous Compensator (STATCOM) is a static VAr generator, whose output is varied to maintain or control specific parameters of the electric power system. In general terms, a STATCOM is an electronic converter shunt-connected with the grid, able to compensate non-lineal load effects responsible of the harmonic distortion. This device is able to control reactive power in the connection point with the grid, capable to inject or absorb reactive power depending on the grid status at each moment Barriers Lack of experience of DSO operating new devices and systems, such as AVR and STATCOM. Nowadays STATCOM is not a cost-effective solution and AVR is not always a suitable solution compared to OLTC voltage control. This kind of solutions is not in some cases a cost-effective solution compared with the classical approaches to counteract voltage problems, replace cables and build new substations. AVR needs a clear protocol to use the services and measurements provided by the device Best practices & recommendations Implementing voltage control solutions, the hosting capacity is increased as a side effect. As a consequence of the higher hosting capacity and the less disconnection events due to overvoltage, higher DRES penetration and increased use of distribution assets will be reached. A proper economic retribution of the voltage control services should be adopted to allow the DSO implement voltage control strategies. Controlling the line voltage in a more precise way can help to avoid line investments. As a result, line investments can be deferred because voltage limits are not reached as soon as without the control. Implementing voltage regulation solutions will lead to a reduction in the cost of the ancillary services. The control centre should send voltage and reactive-power set points to generators and power electronic devices (STATCOMs) which take part on the centralised voltage control. In this way, optimal set points are calculated by a voltage control algorithm fed by a state V /03/18 26/107

27 estimator. By using AVR, the stability of the voltage can be enhanced by the local control of the devices, improving the QoS at the same time. The AVR system should maintain the voltage levels as close as possible to the nominal value in the output of the device Stakeholders involvement & exploitation time horizon Party taking action: DSO, Manufactures. Party to be involved in exploitation process: Manufactures, Prosumers, Regulators. Exploitation time horizon: Short-term (2020) Curtailment of DRES Concept Curtailment is the action of reducing the injection of active power to avoid the violation of a limit (loading or voltage). Within network operation instantaneous curtailment on special demand requires proper ICT solutions fulfilling real time requirements. The current European directives limit the possibility of DER curtailment to system security or security of supply reasons, and force grid operators to take grid measures to minimize the curtailment of electricity produced from renewable energy sources. However, one of the results of the discussions in this project is that curtailment is a technical solution which can make sense from a global economic point of view if the compensation to the RES agent for curtailment is lower than the cost of the reinforcements required for preventing it. Otherwise the network should be expanded or reinforced. For this solution to be applied, it is necessary to open a fair debate on the use of curtailment of renewable electricity. This debate should cover the determination of: 1) a national cost-benefit analysis methodology 2) boundary conditions and 3) adequate compensation rules for the DER agent. DSO driven curtailment should only be considered when congestion or voltage problems arise in the local network and when all other available measures have been evaluated and utilized if it s possible. In any case, curtailment should be kept as low as possible. An example of a quantitative indicative measure is that for instance it should not exceed 3% (status of discussion in Germany) of the annual production of each single installation. Although identified as a technical solution, it is possible that curtailment can put DER market growth at risk, bringing investment insecurity. To prevent this, it should only apply to new installations Barriers Since this solution directly affects the revenues of generators, remuneration for this energy curtailed would be expected by the generator. Cost (especially OPEX) for proper ICT in respect to reliability and performance will exceed the benefit from increase of hosting capacity. V /03/18 27/107

28 A very clear framework is needed (the current framework is not suitable) to avoid conflicts when implementing curtailment. Computing the added value of curtailing the generation in terms of hosting capacity is not always trivial, event for the solution fix Curtailment. For different types of generation and depending on the coincidence of the load, the peak reverse power flow might appear at the peak generation (and even out of the curtailment time slot). In such cases, curtailing the DRES power to e.g. 70 % (fix curtailment) does not allow to increase the hosting capacity by 1/0.70 = +42 % but to a smaller amount. In order to ensure the meaningfulness of this solution, the overall curtailment (not only per generator but per feeder / network) shall be monitored. If the overall amount of energy curtailed is too high, this solution is not meaningful anymore and network reinforcement is necessary. A limit should be set Best practices & recommendations Those generators installing high performant ICT for taking part in the market could also be included in curtailment due to grid constraints even in case there are actually no constraints to be prepared for later upcoming constraints. When implementing this solution at pilot stage, extensive monitoring should be used to ensure that all the effects are well monitored and understood Stakeholders Involvement & time horizon Party taking action: DSO, Generators. Party to be involved in exploitation process: DSO, Generators, Regulators. Exploitation time horizon: Short-term (2020). 2.3 Network planning From the point of view of the network planning, there are several solutions that can be implemented to optimise network investments and to foster DRES penetration. Hereafter the most significant ones are introduced Harmonised connection rules Concept The massive integration of DRES requires a uniform and clear regulation for distribution grid connection, independently of country or utility. Under current regulation and planning rules [7], it is necessary that the DSO plans the network to cater for worst case scenario and therefore could potentially oversize its reinforcements, in order to allow new DG connections [8]. The aim of the European commission is to deliver a set of rules to achieve a harmonised distribution grid connection, which enable DRES penetration and a more efficient and secure system operation. The Framework Guidelines on Electricity Grid Connection [9], was adopted in 2011 and defines the requirements to be considered by different network codes. V /03/18 28/107

29 Country Grid Connection Barriers Grid Connection Consequences Austria Spain France Germany Greece Italy Insufficient grid capacity and also for single phase feed-in. Long waiting time for meter change Authorization request Lack of transparent information. Time response. Insufficient grid capacity DSO waiting periods. Certificate of non-opposite and DSOs Technical connection conditions Delay by grid operator and grid expansion. Grid operator charges fee for connection study Connection agreement with DSO Grid connection request Long waiting time Grid operators delay Barriers Engage arbitrator Upgrade of existing electrical installations. Clear and transparent information by the utilities Agile respond by the utility. Submission to the standing Committee for disputes and actions (CoRDiS) Reclaiming connection study costs. Provisional injunction grid connection Compensation claim missing grid upgrade and expansion Application for treatment Objection or quasi-judicial action Litigation Delays in obtaining the estimate Compensation (connections) Table 2 (Actual state of the art - Harmonised Connection Rules [10]) Currently, there are several barriers to be overcome to reach a harmonised connection. The most common constraints are the insufficient grid capacity and the long waiting times to connect DRES, whereas, potential solutions are compensation claims and improved regulation framework. No uniform regulatory framework. Complex integration of a large number of DG in MV and LV networks simultaneously. Insufficient grid capacity and long waiting periods. The costs of network upgrades are commonly paid by customers Best practices & recommendations Unify European regulation to deploy a common strategy for DSOs on DRES connection requirements. Regulated compensation claims to facilitate DRES penetration. Implementing harmonised connection rules and anti-islanding relay, will be reduced the islanding phenomena. Updating current regulation framework (to allow a higher DRES integration and the cost recognition for DSO services) will increase DG hosting capacity and will improve the grid efficiency Stakeholders Involvement & Exploitation Time Horizon V /03/18 29/107

30 Party taking action: DSOs, TSOs, Generators, European Commission, Regulators. Party to be involved in exploitation process: EEGI, European Commission, Regulators. Exploitation time horizon: Short-term (2017) Voltage & Load Monitoring (MV & LV) Concept Voltage and load monitoring systems provide information about some network points in order to improve the knowledge of the network state. Afterwards, using this data statistical analysis can be done to forecast DRES generation and network behaviour. A good knowledge of network state in real time and evolution at short-term allow grid operator to be more confident with his systems and permissive with the limits of DRES penetration, given that he feels being able to control any situation. Monitoring systems are cheaper solutions than control systems and moreover, it is a good approach for conflictive areas (where there are high DRES penetration) before deciding whether it is required a network reinforcement, a control system, voltage regulation, etc. Furthermore, historical data obtained in this way, can also be used for network planning. On the other hand, whether it is decided to install control systems (for example in some MV networks) inserting alternatively control and monitoring systems, deployment costs are reduced and good results are delivered, due to a certain capability to control and a good knowledge of the network state Barriers Implementing this kind of solutions implies the use of ICT infrastructures that are not installed today. As the monitored nodes can be located in remote areas the communication can be a problem for the implementation of the solution. The cost of these elements should be taken into account on the DSO remuneration Best practices & recommendations Monitoring the grid voltages in different locations can help the system to choose the most appropriate configuration and minimise the voltage fluctuations. Implementing smart voltage monitoring, DER can be installed in no critical nodes (voltage or current) increasing DRES hosting capacity. LV and MV monitoring shows actual availability of network capacity with respect to its standard value, providing a clear knowledge of the network performance [6]. Increasing voltage monitoring in the distribution grid enables a higher penetration of the DER generators at once that efficiency in day-to-day grid operation is enhanced. An optimised use of capital and assets will increase the hosting capacity, as a side effect, because the same infrastructure is able to manage a higher number of DER generators due to a better voltage control. Improved monitoring facilities of the network distinguish between PV production and V /03/18 30/107

31 demand power flows, enabling the identification of reverse flows. Grid monitoring can be used to delay asset replacements and to reduce reinforcement costs. Through closer monitoring points on distribution feeders, may potentially extend the time before upgrades in the assets and also facilitate the integration of more DG units. LV and MV monitoring combined with forecast algorithms could show actual availability of network capacity with respect to its standard value, providing a clear knowledge of the network performance and facilitating the calculation of the set points for controls and a clear view of the grid assets loading. Grid monitoring can be also important to delay asset replacements and reinforcement costs. Monitoring the grid in different locations can help system adopt the most appropriate configuration and minimise the voltage variations Stakeholders Involvement & Exploitation Time Horizon Party taking action: DSOs. Party to be involved in exploitation process: Manufacturers. Exploitation time horizon: Short-term (2018) Advanced planning (more data used) including new operation concepts and tools Concept Current planning and operation paradigms of electrical distribution networks are facing fundamental challenges. Distribution network operators require more advanced planning tools to deal with the challenges of future network planning. Network planning optimization tools can help network planners to select among the numerous alternatives for the optimal future distribution network layout. Future network planning tools will need to accommodate the improved information on local network conditions provided by advanced technologies, i.e. make use of more precise information on actual network conditions, as envisaged to be applied in distribution networks according to the EEGI Roadmap and several other research activities. On the other hand, future planning tools will also have to include the interaction with other segments in the electricity supply chain, in particular with the integration of intermittent renewable generation, but also with the ICT infrastructure needed to operate the network. Probabilistic methods are necessary to capture the intrinsically stochastic behaviour of renewable generation, whereas the multi-objective programming is recognized to be the most effective, transparent and objective way for planning the system evolution, taking into account the multiple needs of different stakeholders. Finally, the integration of smart grid operation within planning algorithms is the key point for a proper distribution planning that allows the integration of renewable resources and minimizes the cost for new electrical infrastructures. The OPF-based techniques are a valuable addition to the set of planning tools potentially available to DSOs. They provide a rapid, adaptable and objective means of examining the connection of DGs and will provide information regarding the most suitable sites to connect DGs. SE has become a key function in supervisory control and planning of electric power grids. It serves to monitor the V /03/18 31/107

32 state of the grid and enables the Energy Management Systems (EMS) to perform various important control and planning tasks such as establishing near real-time network models for the grid, optimizing power flows, and bad data detection/analysis. The common practice for the estimation of maximum hosting capacity of grids is a deterministic procedure, which typically examines extreme conditions with maximum DER production/minimum load. This conservative approach limits the capacity of DGs that are allowed to be connected. Advanced functions and algorithms, exploiting network data already available (smart metering, load and production time-series, voltage measurements on critical nodes, etc) can determine an upper safe limit of hosting capacity, without need of new investments. In addition to identifying the location and magnitude of available network capacity, the OPF allows the limiting factors to be highlighted, which may be equipment thermal ratings or the specified voltage constraints. It is anticipated that the identification of the limiting factors could be used to provide an efficient and effective means of determining network upgrades and reinforcement that allow further DGs to be accommodated Barriers Distribution network modelling: Distribution networks are dimensioned for the worst-case (loading) situation. The loads in the MV Distribution network model are traditionally specified as single maximum values: peak loads. This is also due to the fact that detailed measurement data in distribution networks is in general still quite rare (unlike in transmission networks). Data requirements: data acquisition from the network (substations) and data from business IT systems (Network data, lines, lengths, breakers, etc), load data (hourly data, telemetered, etc) are necessary to perform network planning based on advanced algorithms and tools. Big data integration: The sampling rate of automated field measurements is higher than the conventional measurements, resulting in a huge amount of data for data processing. Apart from the different sampling rate, data set are often not synchronized with each other. Many of the telemetered measurements at the feeders are current, rather than power, which also complicates the measurement functions. Identification of data management and exploitation levels. Determination between central or decentralized data collection and management for further control and planning purposes. The requirements are often conflicting. For instance, increasing reliability requires investment to spare capacity, which increases the network cost. Similarly, a simple and clear network structure, that facilitates maintenance, is seldom the minimum cost network. Planning rules require design criteria to be defined and network constraints to be identified. ICT infrastructure availability and cost of deployment: Implementing this kind of solutions implies the use of ICT infrastructures that may not be installed today and require an important cost of investment. Well-trained personnel are required to operate and be able to comprehend advanced planning tools operation. Complexity in algorithms and partial information can lead to misleading results. Lack of incentives or regulatory instruments: Regulatory framework must be amended in order to include new operational concepts (PQ control) in grid planning. Regulation does V /03/18 32/107

33 not allow DSOs to control DRES; there are not standards for the control of the DRES units. Regulator does not reimburse to DSOs their investments in innovation Best Practices & recommendations Development of high-fidelity modelling and simulation tools can improve the accuracy of grid planning, operations and decision making. Wide installation of monitoring devices will enhance network observability and be fundamental for the application of advanced planning methods. LV and MV monitoring combined with forecast algorithms could show actual availability of network capacity with respect to its standard value, providing a clear knowledge of the network performance and facilitating the calculation of the set points for controls and a clear view of the grid assets loading. Grid monitoring can be also important to delay asset replacements and reinforcement costs. Unify European regulation to deploy a common strategy for DSOs on DRES connection requirements, flexibilities etc. Determination of priority rules and hierarchy, among central and decentralized planning actions. DSOs should be more receptive to new tools and methodologies for hosting capacity calculations and operation concepts and also give incentives to their personnel to be informed and trained on these new techniques. Strong collaboration with manufacturers and DSO movers to share knowledge and experiences Stakeholders Involvement & time horizon Party taking action: DSOs. Party to be involved in exploitation process: DSOs, Manufacturers. Exploitation time horizon: Short-term ( ). 2.4 Asset management & regulatory matters Assess the suitability of a traditional replacement compared to smart grid solutions Concept Asset status (age, condition, events etc.) determines the date of replacement. Applying e.g. voltage control solutions in regions with significant aged components might be not economic as the hosting capacity can easily be increased within replacement (change to optimized type of line) combined with optimizing topology. Even premature replacement might be economic from this point of view. V /03/18 33/107

34 Barriers Insufficient data about asset status. Lack of performant and effective tools Best practices & recommendations Set up or improve assets database. Improve quality of prognosis and scenarios. Adopt organization of departments inside the DSO in order to put in place the assetmanagement strategies Stakeholders Involvement & time horizon Party taking action: DSO. Party to be involved in exploitation process: Research centres, EEGI, Manufactures. Exploitation time horizon: Short-term (2020) Allow the proper allocation of the costs (infrastructures and systems) among the agents Concept This chapter will mirror the current regulation on the integration of DRES into the DSO grids and highlight the identified barriers of that regulation from a DSO perspective. A smart grid solution can bring about many advantages, such as a more sustainable, efficient and secure electricity supply to customers. However, each of these benefits is accompanied by costs related to the purchase, the operation and maintenance of the required components, and the management of the information and communication infrastructure associated with them. Careful consideration of both costs and benefits will be required. National regulators should discuss with all relevant stakeholders the adaptation of national regulatory frameworks in order to concretely promote smart grid investments. A stable and transparent regulatory framework (avoiding frequent changes), and an ex-ante approach should also be established in order to favour such evolution. If the conclusion of careful analysis suggests the implementation of smart grids to support integration of renewables and where necessary, explicit (pecuniary) incentives should also be established. Incentives can apply to innovative projects in smart grids, approved by the national regulators. There is a need to incentivise the avoidance or reduction of conventional reinforcement measures if economical feasible. The trend towards more OPEX needs consideration in the regulatory framework. In case that these incentives are to be generalized, it would be required to clearly V /03/18 34/107

35 define a smart grid in terms of what are the services it has to provide, and (in the cases in which such a fixed list of equipment exists) its architecture and components Barriers The mirroring up upcoming necessities in infrastructure and system developments and current European regulation revealed certain challenges to overcome. Those challenges have been identified: Rules forbidding DER energy curtailment except for security of supply issues. Insufficient self-consumption frameworks. Insufficient regulatory framework for prosumer storage solutions. Insufficient regulatory and technological framework for Demand Response. Incoherent metering frameworks regulatory or technological? Regulatory frameworks discouraging Smart Grid development Best practices & recommendations DSO investments to foster DRES integration and to promote smart grid should be remunerated in a cost-effective way, taking into account the added-value Stakeholders Involvement & Exploitation Time Horizon Party taking action: Generators, Regulator, Aggregators, Retailers, TSO, DSO. Party to be involved in exploitation process: Prosumers, European Commission, DSO. Exploitation time horizon: Short-term (2020) Adaption of DSO cost recovery/ remuneration framework to foster innovation Concept Research and Development projects are not equally remunerated in each European country and moreover, some pioneering initiatives do not receive any income during their progress. These incentives in pilot projects can be useful for making the technology ready for broad adoption, but they are not sufficient for achieving the recovery of this type of investments by the DSO. For these reasons, it is required to unify regulation within the European Union, allowing DSOs to recover their costs fostering innovation. To increase DG hosting capacity in distribution grid, a long-term incentive scheme will be required for DSOs to enable the widespread connection of DRES. For that purpose, it is needed to quantify the cost of connecting DG from a passive behaviour of demand and DG to an active demand response and generation control, to reimburse afterwards this service via incentive. Costs CAPEX = Network Investments + Innovation. OPEX = Operation and Maintenance (O&M) + Innovation. TOTEX = CAPEX + OPEX. V /03/18 35/107

36 Reference grid model: Revenue = function [Distributed Energy Output + O&M + Network investments + Incentives (efficiency and quality of service improvements) + Innovation] Innovation schemes in Europe are introduced in the Table 3 and Table 4: Country Regulation System Regulation period Current efficiency requirements Austria Cap Regulation (Price or Revenue Cap) Incentive regulation (DSO) Cost plus (TSO) 5 years ( ) In Austria, the regulatory system provides incentives for cost reductions as companies have to follow a regulatory/efficiency path. This results in companies choosing smart solutions whenever they are more cost efficient than other solutions. These incentives are explicit investment incentives which do not differentiate between traditional and smart incentives. Spain Hybrid: Revenue Cap / RoR 4 years Coefficient reducing inflation. Difficult estimation as a percentage of the total cost. Method: Reference grid model is used to determine CAPEX. OPEX are adjusted due to standard cost and negotiations with the National Regulatory Authority. Impact: Significant adjustment of CAPEX due to reference grid model. France Revenue Regulation with target values for investments 4 years General efficiency requirement of 2 % on OPEX considered as predictable and manageable by the DSO. Germany Revenue Cap Regulation (including quality regulation) 5 years : 1.5% p.a. referring to total cost Method: DEA/SFA using book values and annuities for capital cost respectively (4 methods). Efficiency score depends on the best result. Impact: Requirement is determined by TOTEX and referring to TOTEX. Greece RoR Not officially defined, currently 1 year period OPEX clearance term in the regulation formula (difference between actual and budgeted OPEX only if this difference exceeds 3%). Italy Hybrid: Revenue Cap/RoR, including benefit sharing mechanism 4 years : 2.8% referring to OPEX. No individual efficiency requirement but benefit sharing mechanism. 50% of the differences between OPEX recognised in the tariffs and actual OPEX have to be given back to customers in the first year of the next regulation period. The remaining 50% have to be given back within 8 years. V /03/18 36/107

37 Country Austria Spain France Table 3 (Incentive schemes for long-term investment in distribution grids. [11], [12] and [13]) Treatment of R&D and Pilot Projects In Austria, Smart Grids projects are funded by industry, public institutions and national budgets; the Climate and Energy Fund (Klima- und Energiefonds - KLIEN) was created by the Federal Government with the aim of supporting the implementation of the climate strategy. No specific program to fund Smart Grid pilots. Some basic R&D projects are partially financed in the national and European R&D programs. R&D and pilot projects are excluded from incentive mechanism for cost reduction on OPEX. Recognition of Capital expenditures Investment factor reducing the CAPEX time shift to two years. A markup on the WACC is granted. TOTEX used for regulation in AT (DSO). Revenues partially reflect CAPEX Delay of 1 year. Hybrid Regulation: revenue-cap regulation with a cost-plus mechanism. Capital expenditures are forecasted at the start of the regulatory period.. The gap between the forecasting and the effective investment is covered ex post. Real expenditures are fully recognized in the capital calculation. Effect of smart grid investments Remaining costs (if adequate) are audited and covered by network charges. The remaining investment costs are checked and considered as passthrough costs during the regulatory period, with efficiency targets applied in the following period. Additional costs are currently not taken into account in the reference grid model. Hybrid Regulation: Revenue-cap regulations with a costplus mechanism for unpredictable or not manageable OPEX Investments are not subject to a regulatory mechanism such revenue cap. The investments are covered ex post ( costplus mechanism). Specific assessment of investments by source of financing and risks incurred by the DSO. Innovation Awareness Rather hamper. In Austria, the funding institution (KLIEN) is the responsible party for granting (or not) most demonstration projects funding. Rather hamper. Neutral. Germany No specific Revenues partially Additional cost for Rather V /03/18 37/107

38 Greece Italy mechanism As other cost Investment made in Smart grid pilots selected by the NRA is treated in the same way as other investment but receiving a premium on the WACC (+2%). Eight pilot projects have been selected by the regulator. reflect CAPEX (efficiency requirements refer to CAPEX). Delay up to 7 years for a major part of investments (mainly replacements). Revenues fully reflect CAPEX. Without delay Revenues fully reflect CAPEX. Delay of 2 years but WACC is adjusted to take this into account. smart grid would usually not be approved by the NRA (National Regulatory Authority) and would therefore have no negative effect on the benchmarking results. N.A. No impact. Gained efficiency from the smart meter rollout was reflected in a higher sharing benefit and a higher X-factor for metering activity. hamper. Rather hamper. Rather foster. Table 4 (Adaption of DSO cost recovery / remuneration framework to allow and foster innovation (OPEX/CAPEX => TOTEX) [11], [12] and [13]) R&D CAPEX is recognised in most of the countries. However, the effect of smart grid investments on the OPEX evolution is not yet recognised in the vast majority of countries. On the other hand, UK is the benchmark country in terms of innovation recognition, with a stable regulatory framework each eight years and Network Innovation Competitions (NICs) to foster innovation Barriers Regulator does not reimburse to DSOs all their investments in innovation. Furthermore, DG integration extra-cost is not explicitly considered in the remuneration. Increasing costs for DSOs due to high DG penetration. For instance, network reinforcements, energy losses or active management of the grid. Financing risks and income fluctuations Best practices & recommendations Ensure consistency between policy and regulation [11]. Uniform regulation framework, defining a long-term policy not only for producers and consumers but also for network operators [13]. Establish a regulated rate of return in a way that is transparent and based on long-term stable cost of capital, consistent with the assets lifetime. Incorporate incentive schemes to support DRES integration, will be achieved a higher hosting capacity. V /03/18 38/107

39 Incentivise DSOs to make efficient long-term investments rather than focus short-term optimisation. Include specific impact of DG on DSO targets, for example for energy losses. Implement explicit mechanism to recognise incremental cost due to DG and particularly incremental OPEX. Reward rather than penalise innovation. Remove legal and regulatory barriers for active distribution system management. Foster large-scale smart grid demonstrations projects. Efficiency targets should not hamper innovative solutions. Ensure a timely cost recovery for the smart metering roll-out by DSOs Stakeholders Involvement & Exploitation Time Horizon Party taking action: Regulator, DSO, Generators. Party to be involved in exploitation process: DSOs, Regulator. Exploitation time horizon: Short-term (2018). V /03/18 39/107

40 3 Recommendations The cost-effective integration of DRES in distribution grids requires new smart grid solutions, methodologies and tools to improve current activities of DSOs and to provide a response to stakeholders requirements for a large-scale integration of DRES in MV and LV networks. This chapter delivers recommendations to facilitate DRES integration in Europe to the most relevant stakeholders in the field (see Annex 1), by taking advantage of know-how obtained from IGREENGrid project. Hereafter, the mentioned recommendations are grouped by stakeholder. 3.1 Prosumers Be smart grid ready Prosumers but also customers (without generation) should be ready to be connected to future smart grid systems. Relevant appliances in respect to potential load shift should support start-up operation when they are reconnected or provide proper interfaces to be controlled by smart home systems. The given lack of standardization for interfaces of household appliances of course is certainly one of the main barriers and causes economic risks. Use generated surplus for local supply Technically and economically a local demand fitting with the locally generated electrical energy reduces network requirements supporting the total hosting capacity but also reduces losses. Therefore prosumers should locally supply other customers without any generation. Implement technology supporting increased self-consumption Households or enterprises acting as prosumers are technically and economically best performing by consuming their own generation. The potential load shift is strongly limited in case there is no electric heating, cooling and the charging of electric vehicles. The supply of other applications only can be shifted by using battery systems for home storage. Following this approach less problems for the integration of DRES will be potentially produced. Install cheap storage like cooling, heating or boilers Increasing the use of renewables requires the integration of loads to be potentially shifted. Energy storages like boilers but also cooling and heating systems which are typically much cheaper than batteries should be installed at first. The adoption of heating systems to be able to control their power consumption in balance with the generation supports increased self-consumption. (Please read also recommendations to generators) V /03/18 40/107

41 3.2 Generators Allow DSO to manage reactive power with power electronics The use of reactive power for the voltage control of the grid can have a positive impact on the DRES integration. The control of reactive power has only limited impacts on the produced energy and in this case its acceptance should be high. Reactive power support and power factor control can be provided either through a built-in capability or through a combination of switched capacitor banks and power electronic based transmission technologies such as Static VAr Compensator (SVC) and static synchronous compensator (STATCOM). Accept that DSOs can control DG in case of necessity and for unexpected congestions and/or actions devoted to the electricity system stability. The amount of Distributed Renewable Energy Sources (DRES) connected to the distribution network is increasing. Generators should understand and make better use of their flexibility by embracing smart grid technologies, expected to be beneficial for all parties. Network operators can use flexibility, supplied by system users (generators, prosumers and consumers) or by intermediaries such as aggregators, for congestion management as a substitute of infrastructure investments. Participate in new flexibility mechanisms/market to provide services to the electricity system Generators can provide flexibility services to help manage the emerging complexity. Flexibility services offer potential value to parties across the energy system. They could enable suppliers to optimise their portfolios; network operators to delay or avoid network reinforcement; and system operators to balance the system and manage constraints at an efficient cost. Providers of flexibility, on the other hand, can benefit from providing a service through direct payments or savings on energy purchases. Achieving the full potential benefit of flexibility will require market arrangements that reward the provider of a flexibility service for the benefits it brings to the system. Grouping into one large generator (aggregator) and participate in energy markets composing virtual power plants A Virtual Power Plant (VPP) consists of multiple smaller decentralized plants (usually producing energy from renewable sources) that are bundled and controlled by centralized steering software. VPPs can assist small-scale renewables in the transition to the energy market, and they provide flexibility and transparency as far as the supply points are concerned. Provide ancillary services The focus here is in generators providing services critical to system restoration. RES generation should play a greater role in helping to maintain system reliability and stability, and this may be increasingly required by interconnection standards. Technologies have been developed and are continuously improving at the generating unit, plant and plant cluster level to make RES generation more predictable, controllable and dispatchable, or in other words more grid-friendly. V /03/18 41/107

42 3.3 Investors Stay well-informed and invest in new technologies. Investors should stay well-informed about the available technologies and prefer to invest in new technologies (e.g. inverters with integrated voltage support capabilities), even if it is not absolutely necessary by the regulation. These devices include advanced functionalities, which can be activated later during the life of investment, creating an added-value to it (e.g. lower disconnection times due to overvoltage). Invest in innovation. Innovation will be the core of the advancements for increasing the hosting capacity. Smart buildings, home automation, intelligent appliances, sophisticated algorithms for DG prognosis, cheap storage devices are only a few examples of the innovative sectors that investors should take into account. Invest in telecommunication infrastructure. Telecommunication operators or other investors should consider electricity grids and their customers as a potential market to provide their services and create a set of new ICT-based services allowing the increase of hosting capacity as a by-product. Adapt to changing regulation and market framework. Investors should remain flexible and adaptable to changing market conditions due to technical limitations. They should consider mostly the hazards for their investments if they do not accept the new trends in electricity market. For example, it is preferable to accept production curtailment in rare occasions as part of a non-firm connection contract, then to be entirely cut-off due to a generalised overvoltage event. 3.4 Manufacturers Develop inverters able to provide reactive power at any time Actual available inverters provide reactive power depending on active power- thus the availability of reactive power can be volatile. It is however, necessary to develop inverters able to provide a certain capacitive or inductive reactive power (based on rated active power) at any time and independent from actual active power generation. Currently only a small share of the inverters available on the market is able to provide this functionality. This is especially true for small inverters connected to the LV network which still represents the main part of the installed PV power in several countries. Harmonization of the solution all over Europe is also required in order to avoid the necessity of specific configurations. Develop EV-charging stations able to optimise of the charging/discharging processes V /03/18 42/107

43 The widespread connection of DRES requires the optimisation of EV charging to facilitate RES integration. Smart charging flexibility can be used to balance supply and demand by the market and also to adjust the power network to unexpected constraints by DSOs and TSOs. In this sense, it is necessary that manufacturers of charging stations develop systems allowing a controlled charging (and discharging) of EVs. A set of harmonised functionalities (e.g. reduction of the charging power in case of low voltage constraint, remote activation and deactivation via broadcasting to network areas behind congestion) shall be implemented into the charging equipment (standardization is required). The cost of these systems should be also taken into account because it can represent a constraint for their development. It is already technically state of the art, for instance CHAdeMO is capable of feeding back the energy. Moreover, ISO is on its way and thus we should promote already existing functionality. Develop cheap storage Energy storage solutions deliver flexibility to distribution grids and can be used to provide power when conditions are not favourable and to store when there is a generation excess. In this way, implementing new storage solutions can increase DRES penetration and reduce the impacts of the energy intermittency. However, the high cost of these devices represents a barrier for their final deployment. Consequently, manufacturers should focus on developing efficient storage devices with reduced costs. Develop control systems for Smart home and buildings Home and building automation can exploit new functions as accumulating energy in heating systems, load shifting, etc.in this sense, it is necessary that manufacturers develop control systems able to optimize the operation of the different appliances of the end-users in order to provide the mentioned benefits. These can be local controllers installed at the individual appliances and/or central controllers able to control the operation of several appliances in a coordinated way at house/building level. Develop advance tools which allow the implementation of new functionalities of the smart grid Transformation from conventional passive distribution networks to active ones requires redefining the operation of the grid. Main changes are related to the ability to exploit the increasingly supply of DERs like DG, Demand Response and Storage Systems and to the enable of new technologies in energy grid planning and operation, making grid management an active process. Manufacturers should participate in this process by developing the required new and advance tools which allow the implementation of the new functionalities of the smart grid, using standard data models. These include tools for optimising planning and operation of the distribution network (e.g. probabilistic forecasting tools, OPF, DSE.). Take part in the development of harmonized network data format for exchange Generic network studies are strongly hampered by the lack of standard network data exchange format. In the absence of a standard interface/format between network simulation tools, special V /03/18 43/107

44 converters must be written and tuned to the special situations. So, software manufacturers should work on the adoption of a common interface format for network data 3.5 Aggregators Take part in the definition of common interfaces for the use of flexibility. There is a necessity to discuss/create a common communication interface between the market and the grid for developing the best economic solutions. Without a combining element in regulation DSO recommendations towards aggregators, like the establishment of such a flexibility platform for avoiding grid problems, are just nice. Be prepared for new contracts of prosumers. Aggregators should be prepared for all kind of possible regulations, restrictions and guidelines that their contracted prosumers can be subject to. Be prepared for curtailment solutions Duration curves are clearly demonstrating that a significant part of network requirements caused by generation from renewables e.g. PV-systems are occurring only for short times. Curtailing instead of network reinforcement is more economical. To avoid economic losses, aggregators should be aware of possible curtailment causing a few percent less supply of generation. Avoid a geographical concentration of contracted prosumers. The prosumers which are contracted by an aggregator should not be concentrated in a small region when services (energy, power,..) are tendered on a market to avoid restrictions or curtailment by a DSO. Smart technologies and appliances will enable flexibility users and procurers to develop grid and retail products and services tailored to the needs of the flexibility service providers. These products and services will need to be clear and simple so that customers can easily evaluate and compare them. This will support choice and promote competition in the market by helping customers to find the product and services that suits them best. Aggregators and suppliers should have the same ability to extract the value of flexibility services on behalf of their customers. Contractual arrangements are necessary between market parties (BRP, Supplier, DSO, TSO, aggregator), they should be streamlined and simple, and reflect the respective benefits, costs and risks for all parties. These arrangements should also allow consumers to access any service provider of their choosing without previous permission of other market parties. Standard contracts V /03/18 44/107

45 should ensure smooth contractual process, fair financial adjustment mechanism and standard communications procedures between aggregation service provider and the BRP/supplier. 3.6 Retailers Generally speaking, it is hard to give recommendations from a DSO perspective into the direction of a retailer. As both domains are unbundled and their interfaces are driven by either the technical connection rules (DSO->Retailer) or regulation/legislation (DSO <-> Retailer) both actors are independent in the regard of Distributed Energy Resources. Thus, Retailers assuming the grid to be a copper plate to fulfil the needs of integrating DER, especially if the flexibility is qualified as an ancillary service. Offering of easy-to-use flexibility products. Retailers are requested to create B2C products which require minimal interaction of end customers to facilitate their flexibility. Many studies reveal that customers are not willing to have active participation on a daily basis. Take part in the definition of common Communication Interfaces for the usage of flexibility Retailer shall anticipate the fact that the dimensions of future grids will be smaller, or to fit it into the frame - the copper plate will get thinner. An example is the 3% rule regarding curtailment of DRES. Thus there is a necessity to discuss/create a common communication interface between the market and the grid for developing the best economic solutions. Without a combining element in regulation DSO recommendations towards retailers, like the establishment of such a flexibility platform for avoiding grid problems, are just nice. Therefore the subsequent recommendation is more general. Retailers should focus on CO 2 -Potentials of products Since there is a strong momentum for decarbonisation (especially since COP21), retailers should focus their product communication on the CO 2 -potential for attracting potential customers. 3.7 Research Centres Development of tools for the optimal planning of distribution networks The traditional planning procedures often do not include a detailed study of the impact of DRES on the distribution system, in particular when several assumptions have to be taken in terms of future penetration of distributed generation. According to this, IGREENGrid recommends to research centres to develop tools supporting the planning of distribution grids, able to optimally select and allocate grid asset and equipment, even in presence of large uncertainties associated to future scenarios. V /03/18 45/107

46 Development of tools for the optimal operation of distribution networks DRES units, as well as smart grid technology dedicated to their safe integration, in many occasions do not participate to network operation, except for the management of local congestion issues. However, these devices have higher potential that can be exploited for more advanced functions: island operation mode in case of blackout, optimization of network energy losses, provision of ancillary services, etc. IGREENGrid recommends to research centres to update the currently adopted operation tools in order to integrate the newest technology and DRES advanced functions. These tools should be based on optimization processes, particularly aimed maximization of DRES energy injection in addition to the minimization of operational costs. Improvement of storage cost/benefits ratio Storage systems have one of the highest potential for the operation of distribution grids: it increases the management possibility of both non programmable DRES and loads. Unfortunately, the current status of the art of storage technologies does not seem to satisfy the cost effectiveness requirement for their massive integration in electricity grids. IGREENGrid recommends to research centres to investigate innovative storage technologies in order to develop devices at lower costs and able to feature multiple functions simultaneously (power profile flattening, frequency/voltage regulation, etc.). These objectives should guarantee lower cost/benefits ratios and higher convenience in the exploitation of storage devices in distribution grid. Identification of new renewable sources/energy converters to be exploited for distributed generation Renewable resources connected to the electricity network are very few typologies. In addition their conversion systems are characterized by low efficiency. IGREENGrid recommends to research centres to investigate other sources of green and renewable energy to be converted into electricity and injected in distribution systems. In addition, new materials and systems can be studied in order to increase the conversion efficiency of currently exploitable energy resources. Provision of foreseen future scenarios of DRES penetration One of the largest sources of uncertainty in the planning of electricity networks is represented by the poor knowledge of the future situation of the energy market. The currently available predictions are very general and do not have a sufficient level of details for the investment decision process. According to IGREENGrid, research centres have the role of identifying the most probable scenarios according to the foreseen increase of DRES penetration, customers change of habits and evolution of regulatory and market frameworks. V /03/18 46/107

47 3.8 EEGI The European Electricity Grid Initiative (EEGI) is one of the European Industrial Initiatives under the Strategic Energy Technologies Plan (SET-PLAN) and proposes a nine year European research, development and demonstration (RD&D) programme to accelerate innovation and the development of the electricity networks of the future in Europe. The programme focuses on system innovation and addresses the challenge of integrating new technologies under real life working conditions and validating the results. Key Performance Indicators (KPIs) were developed by the GRID+ project to monitor the contribution of each single innovation project to achieving the specific objectives of the European Electricity Grid Initiative (EEGI) R&I Roadmap The goals identified by EEGI and the definition of their Key Performance Indicators (KPIs) are proposed with an external approach since these decisions are made separately and prior to the development of projects. As a result, the feedbacks concerning the use of their KPIs on projects are important for EEGI in order to see if their guidelines are in line with the realities of the field situation. IGREENGrid project has selected three KPIs from the list defined by the EEGI team, in order to evaluate technical aspects of the solutions related to the main expected benefits of IGREENGrid solutions: Increased RES and DER Hosting Capacity. Power quality and quality of supply. Reduction of Energy Losses. A common problem identified is the difficulty to gather large amount of data and to overtake the issues introducing uncertainty in KPI calculation procedures. The main recommendations made by the IGREENGrid team to overcome the encountered problems are: Rely on adequate simulations to reconstruct realistic situations of network operation. The experience from the different demonstration projects involved in IGREENGrid shows that the use of KPIs to compare demonstrations requires complex analysis. Indeed, R&I solution A demonstrated in Demo 1 cannot be simply compared to R&I solution B demonstrated in Demo 2. KPIs can only be used to compare the performance of a R&I solution in different networks or to compare the performance of different R&I solutions in a considered network. A common problem identified is the difficulty to gather large amount of data and to overtake the issues introducing uncertainty in KPI calculation procedures. According to this, the only way to use KPI is to rely on simulations allowing to consider cases in which the R&I solutions are really needed. Of course, the simulations should ideally be based on real measured inputs data and, for this reason; the network monitoring plays a fundamental role for the accurate reproduction of the grid real behaviour. To define a standard way to calculate KPIs in order to obtain results in line with realistic network situations. V /03/18 47/107

48 Thanks to the experience gained within IGREENGrid, the applicability of EEGI indicators has been evaluated by considering the actual DEMOs scenarios, which represent a field for the application of R&I technologies. Particular attention should be focused on the applicability of KPIs on real demonstrators, because it is difficult to calculate KPIs from the field. 3.9 European Commission These recommendations are focused on what actions could have benefits in terms of cost-effective integration of DRES in European networks. It is recognized that some of these recommendations might be actions that are already underway by the commission, they are intended to share what knowledge has been gained from IGREENGrid and to support either ongoing or new actions. Support the R&I projects (Pilots, demonstrators, research, etc.) FP7 and H2020 initiatives are key enablers to promote R&I at the European level. These kinds of projects contribute to the EU targets in terms of renewable integration, energy efficiency and CO 2 emissions reduction. For these reasons, the funding provided to these research projects is essential to permit the collaboration among the most relevant stakeholders in order to improve the current European electricity grid. Focus on the cost of new technologies Within the IGREENGrid demonstrations projects, in some cases it was observed that similar problems were addressed using different technologies. It was considered that for a range of networks throughout Europe, a range of solutions using different technologies could be technically feasible. Ultimately the deployment potential of a solution will be a question of cost. However, it is expected that the cost of these technologies would be reduced as the technology is optimized both in terms of design, manufacturing process and economies of scale during the market uptake phase. At European level, the evaluation of the economic viability and risks, over a typical planning time horizon (at least 10 years), could be benefit to European network operators, regulators and other key stakeholders in supporting the investment decision process which could ultimately reduce the cost of DG integration. More focus on expert knowledge and less on methodology based approach for evaluating the deployment potential of different solutions Within the IGREENGrid project a number of different approaches were used to evaluate the deployment potential of different solutions on a European scale. One of the main challenges in completing this task was the definition of methodologies that could be used to assign ratings or scores to different solutions. Workshops with experts from the demonstration projects were also used to understand the different solutions that were being demonstrated and the potential for deploying other solutions, from different demonstration projects. It became apparent that due to the complexities involved, an appraisal using an investigative approach was more appropriate than using a standardised methodology that would produce ratings or scores. V /03/18 48/107

49 Support process for establishing new regulation and addressing commercial aspects that need to be addressed to realising the full benefits if some of the available technologies To realise the benefits of some of the solutions currently being demonstrated regulatory and commercial aspects would have to be addressed. One example would be storage using Li-ion batteries. Uncertainty exists over who will operate and control the dispatch of the storage facilities and under what market or regulatory conditions. Another example would be for solutions within the IGREENGrid demonstration projects where, in several cases, the control of the power (reactive or active) injected by the DG inverter was subject to individual contracts between the generator and DSO that are hence on a voluntary basis. This creates some uncertainty in the ability to fully achieve the potential benefits of deploying this type of solution. Promote standardisation process at European level for new technologies The promotion of the standardisation of new technologies at a European level could reduce the economic risk and encourage more investment from the private sector Standardisation working groups Define a uniform and clear regulation framework for distribution grid: Harmonised connection rules. The massive integration of DRES requires a uniform and clear regulation for distribution grid connection, independently of country or utility. Under current regulation and planning rules, it is necessary that the DSO plans the network to cater for worst case scenario and therefore could potentially oversize its reinforcements, in order to allow new DG connections. Smart grids features, such as voltage control, reactive power control and remote monitoring and controllability, need to be standardised in order have the chance to be widely used. To agree on a standard for DRES communication with DSOs (to homogenize interfaces) Encourage the standardization of the ICTs (Information and communication technologies). The implementation of standardized automation, control, and communication systems is a prerequisite to many smart grids applications. To agree on a common network data exchange format Generic network studies (for several networks of different DSOs) are strongly hampered by the lack of standard network data exchange format. The problem of data conversion is recurrent in projects involving several DSOs (e.g. IGREENGrid). In the absence of a standard interface/format between network simulation tools, special converters must be developed and tuned to the special situations. This procedure will be time consuming, prone to errors and not scalable. To develop a standard for the provision of reactive power by DRES This solution could be used not only for voltage rise mitigation purpose but also for optimal operation of the distribution network: losses, TSO-penalties, etc. To reach this goal, it is required to provide a harmonised set of parameters for voltage control by DRES (with reactive power) to avoid error in the local parametrisation of inverters during commissioning. It is also necessary to develop V /03/18 49/107

50 conformance testing procedures for the provision of reactive power by DRES. To propose a standard-compliant automation approach using the IEC interoperability concept for power systems together with the distributed automation model IEC Many of these devices can be remotely controlled allowing the inverter function to be presented to a utility operator as an IEC compliant IED, by using other protocols, often proprietary and vendor specific solutions. To pursue information standards to enable plug & play system interoperability provide application examples of this standard for testing purpose Regulators European DSOs have to cope with demanding investment requirements, driven by distributed energy resources, quality of supply and smart grids. Eurelectric analysis of different European countries regulatory systems shows that DSOs today are facing lower investment incentives than in Both the achievability and the adequacy of the regulated rate of return seem to have been decreased since then, as has planning reliability. Network operators are obliged to perform extension investments, including the connection of renewables without delay, in order to ensure non-discriminatory connection and access to the network. Regulators should grant DSOs suitable revenues to cover their costs and make necessary investments while providing the required quality of supply. Promote Smart Grids Initiatives Existing hosting capacity of the distribution network can be used more optimally if other options including ICT, connection & operational requirements guaranteeing adequate performance of DER towards the system. Implementing smart grids solutions can help also to increase the hosting capacity. Testing and deploying smart grid technologies is indispensable to develop efficient network solutions. In addition more research and innovation project are needed at EU level, namely for demonstration projects in order to promote them. Regulation framework should be revised to incentivize this kind of initiatives. Adapt the regulatory framework for steering the most cost-efficient solutions Regulatory mechanisms for steering when the investment deferral is more cost-effective than active distribution approach should be elaborated. Active system management will affect the amount and structure of operational expenditure and would replace some CAPEX with OPEX. DSOs should be able to look at the business case for both the investment solution (CAPEX) and the service-based solution (OPEX), or a mixture of the two, and decide which is preferable. Adoption of a regulatory mechanism is necessary to integrate this approach in the grid fee calculation. DSOs need to be provided with adequate remuneration for the most adequate solution: investment or active system management tools including procurement of flexibility services from network users. V /03/18 50/107

51 Define a proper allocation of network costs Distribution users must pay fair and cost-reflective network tariffs. Situations in which one group of network users covers the costs generated by other user groups must be avoided. To achieve this and to enable DSOs to better cope with the changing operational challenges more capacitybased network tariffs should be introduced in order to allocate properly the costs. Adapt the regulatory framework to enable RES integration First of all and based on a country s RES goal and policies for increasing its penetration, it has to be determined if any action is required regarding the distribution grid hosting capacity. If so, policy makers need to determine where RES is supposed to be connected, as for example uniformly distributed or only in certain regions. Network connection studies and schemes for generators are designed to guarantee that under normal operation all capacity can be injected at any time of the year (fit and forget approach). The current European regulatory framework provides for priority and guaranteed network access for RES electricity. Non-firm network access rights could be offered as a discounted connection contract for generation customers, with pre-defined mechanisms for DG to reduce their output to a predefined limit in infrequent situations, expected only for few hours per year. If only several hours of re-dispatching per year are needed to limit peaks of production and use network capacity more efficiently, those would be more than offset by an additional DG output in all other hours due to a higher installed DG capacity up to a certain point where the cost of net losses and curtailed generation become relevant to justify network reinforcement. Regulation framework must be changed in order to allow the non-firm connection contracts. Regulate the DRES reactive power contribution The traditional approach to voltage control includes reinforcing the grid or installing preventative measures. Voltage control has been traditionally done by transformers and capacitor banks that inject reactive power into the grid. As the penetration of DG in networks increases, it is no longer possible to ensure sustained system security without some dynamic resources, including reactive power compensation and active voltage control. DG contribution to voltage control, probably in combination with network solutions, is likely to play an important role in keeping distribution networks stable. Where this solution proves to be the most cost-effective one, generators connected to the distribution network above a certain capacity should be required to have reactive power capabilities adapted to the connection level and capacity. Where it goes beyond maintaining local system security as impacted by the generation connection, a market-based approach should be adopted for procuring additional amounts of reactive power such as DG contribution to minimise losses. For example, voltage control by inverters providing reactive power at any time could be an economically viable alternative to grid reinforcement at a fraction of the cost. Other possibility could be the provision of an amount of reactive power asked by the DSO V /03/18 51/107

52 Regulate the DRES active power curtailment Generation curtailment is used in some countries in cases of system security related events. The regulatory basis for generation curtailment in such emergency situations differs across Europe. Curtailment of DRES, not only in an emergency situation, but also before a grid constraint is reached, is technically and economically suited to complement reactive power control. It can be used to limit overload on feeders and transformers and can help to manage the voltage on distribution feeders. By accepting a very small decrease of the annual energy yield, active power control can contribute to significantly increase the hosting capacity of a network. Create an appropriate regulatory and market framework allowing/incentivising the use of distributed flexibilities by the DSO In many countries, DSOs are obliged to design their networks according to peak demand. As consumption and production patterns change, other solutions might be more cost-efficient. DSOs should be free to consider both the traditional investment solution (building up new capacity) and the flexibility service-based solution, or a combination of the two, depending on what is most efficient. It s necessary to create an adequate regulatory framework that allows other solutions beyond the traditional approach of investing in copper. They should implement the solution which is most efficient and be remunerated via appropriately designed grid fees. Regulators must be adapted the regulatory and market framework in order to design operational rules and facilitate the procurement of flexibility from the market. The network codes on system operation and balancing will affect the development of flexibility markets, as a means of supporting distribution grid operation. They should be designed with a view to facilitating such flexibility markets without foreclosing any market design options DSOs Implement new non-firm connection contracts for generators. These contracts enable DSOs to connect the DG as requested and keeping the right to curtail the active power of the generator a given number of hours or a given amount of energy per year to prevent constraints on the distribution network and to ensure the proper network operation. This solution allows the generator to connect to the distribution network while minimising its connection costs (reducing the need of reinforcement) and the time needed to ensure the connection to the grid. Pro-active system management. A proactive approach is much preferred, rather than waiting until DRES penetrations are high, and reacting to the cost and maintenance complexity associated with incompatible devices and applications. Involve prosumers participation (consumers, storage operators and EV charging infrastructure operators, etc.) to provide balancing services via incentives or smart tariffs whereby smaller consumers reduce their consumption or increase their injection to help balance supply against demand on the local grid, flexibility and thrifty contribution to resource adequacy. V /03/18 52/107

53 Forecast the generation and demand-side at distribution grid level. Develop sophisticated tools for stochastic and deterministic forecasting, operations scheduling and grid optimization. Integrate smart algorithms handling the uncertainty of DRES and the associated problems to the grid elements. Consider the implementation of ICTs to improve the management of network flexibilities. ICT is a key enabler to improve the quality of service, the energy efficiency and to reduce network constraints. It can be used to support network flexibilities. Improve investment models considering new necessities and scenarios. New DSO business models need to be upgraded gathering new customers needs and the evolving technical, economic and regulatory conditions in distribution grids. Develop a scenario investment model for decision making could be a great approach to optimise investment planning. Optimise the use of flexibilities to solve network constraints. A decision-making tool taking into account the potential use of flexibilities at distribution grid performance will enhance to solve network constraints in a more cost-effective way. Use the reactive power provided by DRES for voltage control and congestion management. To develop a mechanism for the voluntary provision of reactive power by DRES (not only for voltage rise mitigation purpose but for optimal operation of the distribution network: losses, reduce the TSO-penalties...). Improve network planning and operation. To update planning strategies taking into account the possibility to exploit services from energy players. Update the network planning rules and the constraints taken into account when a new DRES is going to be connected in the distribution grid. Improve data management and remote switching. Implement network voltage and loading monitoring. Implement dynamic asset rating for cables and transformers to avoid their peak capacity and reduce the number of replacements and the incurred costs. By the deployment of ICT and smart sensors, DSOs will have the capacity to monitor the assets in order to avoid their capacity peak and moreover to reduce the number of potential failures and replacements. Facilitate the access to the electricity markets by flexibility operators (aggregators, storage operators, EV operators, etc.). The ability to facilitate the access to the electricity markets, in a neutral and transparent way, is a V /03/18 53/107

54 key enabler to manage competitive mechanisms for the procurement of flexibilities directly connected to the distribution grid, such as aggregators, storage operators and EV charging infrastructure operators. Flexibility support DSO-TSO for voltage issues and for frequency issues. DSOs can contribute to the proper operation of the transmission network by using DRES reactive power to solve voltage issues. DSOs can help TSOs to manage power flows to solve unexpected constraints like frequency issues. By this support, it is ensured the reliability and stability of the power network but also fostering DRES integration in distribution grid TSOs Reinforce TSO-DSO cooperation. A more collaborative working procedure is needed in order to exchange network planning and operation data between DSOs and TSOs for system security. This role may provide more costefficient solutions to solve network constraints by responding to TSO s planning and requests. Adapt interfaces with DSO to improve the monitoring of DRES connected MV networks. Develop compatible interfaces to monitor the DG units connected to the MV networks at any time. Efficient real-time data exchange to reach this goal. Accept ancillary services provided by DRES. DRES can be used for ancillary services in order to contribute to the proper electricity dispatch by the TSO. V /03/18 54/107

55 4 Guidelines to assess Hosting Capacity The demonstration activities grouped by IGREENGrid have played an important role in the testing of Smart Grid technologies, which have been particularly devoted to the safe integration of DRES in distribution grids. The effectiveness of the investigated solutions have been evaluated by means of Key Performance Indicators which have been selected (Figure 2) by the ones proposed by the European Electricity Grid Initiative (EEGI) in line with the current challenges in the field of Smart Grid [15]. Integration of DRES B1 Increased RES and DER hosting capacity Improvement of voltage quality B2 Reduced energy curtailment of RES and DER Reduction of energy losses B3 Power quality and quality of supply B4 Extended asset life time B5 Increased flexibility from energy players B6 Improved competitiveness of electricity market Percentage reduction in energy losses B7 Increased hosting capacity for new loads Figure 2 (Connection between IGREENGrid and EEGI KPIs) The evaluation of the solution performance has been carried out by computing the selected indicators for all the IGREENGrid demonstrators. In particular, in terms of integration of DRES, the increase of Hosting Capacity has played a central role for the selection of the most promising solutions. According to the standard calculation procedure adopted for KPIs, the performance of a Smart Grid solution is computed by comparing the resulting Hosting Capacity of distribution networks (in which the technologies have been tested) in two separate scenarios (Table 5). Business As Usual (BAU) scenario The first scenario consists in the network operated as usual, therefore without the support of any Smart Grid solution. This working condition determines the potential of the currently-employed asset (electric lines/cables, transformers, etc.) and it is adopted as reference for the evaluation of the incremental benefits introduced by the Smart Grid solutions. Table 5 (Network scenarios for KPIs calculation) Research and Innovation (R&I) scenario The second scenario represents the situation in which the investigated Smart Grid solution is fully operating in the selected grid. From this working condition, once it has been compared with the BAU case, it is possible to determine the incremental benefits which can be attributed to the solution under test. V /03/18 55/107

56 4.1.1 Calculation/Measurement of Hosting Capacity As mentioned above, the assessment of DRES integration performance has been focused on the evaluation of the Hosting Capacity increase. The common definition adopted for Hosting Capacity consists in the maximum penetration of Distributed Generation (DG) for which the power system operates satisfactorily [16] and, from the IGREENGrid prospective it has been calculated for renewable-based DG connectable to electricity distribution networks. It is of significance for both DSOs and DGs developers to quantify the maximum available headroom in the network to accommodate new DG according to given technical or commercial objectives while subject to a set of predefined limits. Nowadays, this index is commonly employed for the planning and operation of electrical systems. In fact, despite DG seems to introduce several benefits [17] and [18], it has to be considered that distribution grids are still designed in order to be mostly passive, then a massive integration of generators may produce technical issues [17] and [19], such as voltage rise and consequent voltage limits violation, overload of electric lines and cables, etc. Because of the continuously growing penetration of DRES in distribution grid, the necessity of Hosting Capacity calculation procedure is evident and, in the last decade, several approaches for its evaluation have been proposed and exhaustive surveys on them are detailed in [20] and [21]. From the analyses reported in these documents, the most recurrent solution for an effective evaluation of the Hosting Capacity (HC) is based on Optimal Power Flow (OPF) [22]-and [23]. In fact, it is clear that the hosting capacity estimation is an optimization problem since, as stated by its definition, it should return the maximum connectable generation. However, from the IGREENGrid experience, it has emerged that OPF-based approaches have a strong limitation: the returned value of literature approaches is often referred to an optimal and/or fixed allocation of generators within the network under study. In fact, since normally the DRES position has a relevant impact on the Hosting Capacity itself and that the network operators often do not have the complete control on the allocation of generators [24] and [25], these HC evaluation methods may lead to unrealistic results. According to this, in order to obtain a more significant evaluation of the HC, the uncertainty introduced by the unknown position of generators has to be taken into account. For this purpose, the approach proposed by EPRI (detailed in [26] and-[27] and further developed in IGREENGrid has been considered more appropriate for an exhaustive calculation of the Hosting Capacity and of its increase: it is based on the simulation of a large number (Monte Carlo approach) of possible scenarios of DRES allocation returning, for each value of total generation, the probability that it corresponds to the Hosting Capacity (Figure 3). V /03/18 56/107

57 Figure 3 (Resulting Hosting Capacity in the BAU and R&I scenarios) As state above, the performance of the Smart Grid solution can be easily deduced from the comparison of the two considered situations (BAU and R&I), which returns the KPI associable to the application of the studied technology on the selected network. Of course, since multiple values of Hosting Capacity are obtained, the same amounts of possible Hosting Capacity increase are returned (Figure 4). Figure 4 (Hosting Capacity KPI) The consideration of multiple situations is a relevant added value for the evaluation of Smart Grid solution performance. In fact, the resulting probability function may highlight some peculiar situations in which the tested solutions have not the expected behaviour. In particular, from the IGREENGrid experience, it has emerged that some technologies (with positive KPIs in the majority of the cases) could feature negative performance in few situations. This means that, contrarily to single-value Hosting Capacity calculation methods, the level of details of the adopted procedure allows the identification of peculiar cases and deeper investigation of the results can be conducted in order to increase the solution performance. V /03/18 57/107

58 5 Guidelines to manage curtailment Generation curtailment is the action of reducing the active output power to avoid the violation of a limit (current or voltage) according to the electricity dispatch. Within network operation instantaneous curtailment on special demand requires proper ICT solutions to fulfil real-time requirements. The loss from curtailing generation based on RES is generally seen as an unacceptable solution, due to the loss of green energy and an economic loss to curtail generation with near zero marginal costs. However, this view could lead to overinvestment in grid infrastructure and underinvestment in renewable energy sources. DSO s claim the right to curtail generation, ex-ante or in real-time, in order to manage the congestion in the distribution grid under certain circumstances such as an unavoidable intervention to guarantee the stability and security of the grid. Curtailment of a generation facility means that the concerned facility is obliged to reduce its output to a lower level or to zero upon request of the DSO. The regulation applied in several EU countries disallow the DSO requesting active or reactive power changes to DRES while in other countries DSOs are enabled to somehow control them to a certain extent or within some limits. However, DSOs are legally obliged to reinforce their grids thus the curtailment solution can only be used until grid expansion is executed. Therefore, in some cases and countries, generators connected to MV or LV network may not be a controllable variable for network operation. Integration of a large amount of small DRES may require alternative approaches to be explored. For instance it would be helpful to expand the usage of curtailment on a few hours during the year if it helps to reduce grid expansion costs. This makes it necessary that DSO is allowed to dispatch active and reactive power commands to DRES to address issues related to voltage control, congestions, fault isolation, service restoration, etc DG Curtailment procedures Depending on the type of the network, there are different procedures to manage curtailment. For interconnected electricity distribution networks, when DG connection request exceeds network capacity, there are three possible cases to integrate DRES: Procedure Description Regulation framework No curtailment Limitation of the contractual power Producers pay for network adaption Curtailment Optimised curtailment Disconnection of DG in case of congestion Optimal modulation of DG Power DSOs are allowed to operate occasional curtailment DSOs are allowed to operate curtailment on the basis of optimisation Table 6 (Interconnected electricity distribution networks - DG curtailment procedures) V /03/18 58/107

59 For non-interconnected electricity distribution networks, when energy production endangers grid security, there is only one possible case to integrate DRES: Procedure Description Regulation framework Optimised curtailment Optimal modulation of DG Power DSOs are allowed to operate curtailment on the basis of optimisation Table 7 ( Non-interconnected electricity distribution networks - DG curtailment procedures.) Down below in Figure 5, the procedures to manage DRES integration by type of network are shown. From left to right there are three possibilities to manage curtailment procedures: no curtailment case, curtailment case and optimised curtailment case. Figure 5 (DG curtailment cases) Criteria for curtailment The most important reasons to curtail generation are the following: Network constraints. Network security. Excess of generation relative to load levels. Strategic bidding. Network constraints Curtailment due to network constraints can be managed voluntary and involuntary. The most common procedure is involuntary curtailment due to new DRES capacity connection. In many countries, RES producers have priority network access at their nominal capacity, also referred to as firm access. It is distinguished the voluntary curtailment from involuntary curtailment by an exante agreement between the RES investor and the network owner specifying the rules regarding to the amount of curtailment and the possible compensation. V /03/18 59/107

60 Involuntary curtailment caused by permanent network constraints could be combined with an obligation for the network owner (DSO or TSO) to compensate the generator at least partly for the loss incurred. This is important in order to provide an incentive for the network owner to build more capacity, and do it at a speed that balances the costs of network investment with the value of curtailed energy represented by the compensation payments. Voluntary curtailment in networks is mostly associated with investments where the investor directly or indirectly finances the connection lines to the network. The investor thereby voluntarily accepts some curtailment due to a constraint in his own connection cable. Naturally, there is no compensation associated with this type of curtailment, and it will not appear in any statistics. Optimally, the marginal value of expected curtailed energy over the lifetime of the cable should be balanced with the marginal cost of increasing the cable capacity. Network security Curtailment due to network security is produced when there is not a capacity limit causing the curtailment, but there are limitations in other factors such as reactive power or potential risks regarding to a fast change in variable generation. Grid faults and scheduled grid maintenance will cause occasional curtailment of primarily generators connected to the distribution grid levels. The DSO should optimally balance the effort to reduce grid faults with the compensation it has to pay to the generators in these cases. Security concerns in relation to possible grid faults and voltage concerns play an important role in determining how much variable generation can be allowed at certain points in the grid. A separate concern are fast changes in fluctuating generation, when curtailment also takes place as a precautionary action in cases where there is a high share of fluctuating generation expected and the system would lose too much capacity too quickly, for instance if wind generation shuts down during storms or local network faults spreading. In principle, this is a systems reserves problem and was especially relevant before fault overriding capabilities became a general property of wind turbines in new installations. However, with high shares of wind relative to load nowadays, there are times when the expected reduction of wind generation from forecasts coincides with low demand levels and requires considerable spinning reserves. Therefore, it can be necessary to curtail wind generators in hours ahead of the expected drop in generation. As for all reserves, the cost for this is shared among use-of-system charges contributors which can be consumers only or both consumers and generators depending on national legislation. Excess generation Curtailment due to excess generation relative to load levels is produced when the level of variable generation reaches a high penetration of renewables and exceeds a certain maximum load regarding to an upper border for system stability. The effect of different curtailment thresholds, such as for the penetration of wind power, has been quantified. As a result, for a system with a large amount of installed wind power, large amounts of curtailment can be avoided if this threshold can be raised. Spatial levelling effects could provide a V /03/18 60/107

61 similar effect, although this requires distant and large-scale interconnection. As the share of variable generation sources in the power systems increases with investments to comply with the EU 2020 RES share targets, such excess generation situations will occur in more areas and for more hours. The involuntary curtailment in this situation is more problematic for the individual RES operator than from a system operation point of view since the value of the generation and the market price is low with excess generation. Regarding to this, voluntary curtailment is also more likely to take place, especially if some generators are not receiving production-dependent market support. Strategic bidding Curtailment due to strategic bidding takes place when market benefits could be obtained. Directly related to curtailment, capacity withholding from the market can drive up the market price. Withholding generation capacity with low marginal costs only makes sense for society when market prices are low. The largest gain from withholding capacity is at times of high prices because a small capacity at the steep part of the merit-order curve makes a big difference. In such a situation, this form of curtailment to exercise market power leads to socio-economic losses Suggested compensation mechanisms The aim of generation curtailment is to minimize costs and to determine the optimal states of the controllable DG units. To achieve this goal, a proper regulation framework is required, as well as load consumption forecasting, DG production forecasting and other quantities affected by uncertainty. Hereafter different categories of curtailment of active power with possible compensation mechanisms to be established by regulators are shown. Categories of curtailment Network constraints System security concerns Excess generation Strategic Bidding Possible compensation DSO or TSO compensate curtailment loss based on market price and/or incentives Separate market or compensation from TSO and grid users based on regulation Compensation by TSO through an incentive for voluntary curtailment (regulation), whereas no compensation for involuntary curtailment No compensation Table 8 (Suggested compensation mechanisms) V /03/18 61/107

62 6 Guidelines to perform technical assessments (methodologies and tools) The aim of this chapter is to introduce methodologies and tools used during IGREENGrid project to develop technical assessments. The approaches used to carry out these evaluations are based on the analysis of the scalability and replicability of smart grid interventions. These smart solutions are evaluated under different scenarios to assist decision makers in future distribution network planning, taking into account different type of networks, DRES penetration levels and the limitations on data availability and confidentiality among other factors. The general approached followed in the technical assessment has been a top-down approach. This approach is based on the analysis of artificial scenarios defined by the hosting capacity. This approach has been selected since it allows a common comparison between solutions independent on questionable scenarios about DRES deployment in the next decades. For example, roadmaps from industry organisations or from international organisations ([30]-[31]) present a very large uncertainty due to unknown political and economic developments. 6.1 Methodologies to develop technical assessments The methodology proposed by IGREENGrid project is based on three steps with an increasing level of complexity. It is interesting to note that the results of each one of the three steps provides some answers to the main question of the actual deployment potential of smart grids solutions for the considered networks. Figure 6 and Figure 7 provide a general and detailed overview of the chosen methodology respectively. S3: detailed case-studies S2: HC=f(SUT,Grid) S1: feeder screening Figure 6 (General overview of the three steps for SRA) V /03/18 62/107

63 Step 1: Feeder screening and classification Inputs: network data Assumptions: no SUT no load no temporal analysis (only installed capacity) Outputs: HC of each feeder (categorised by U/I constraint) selection of one DRES scenario (location along the feeders): the median scenario (rather uniform DRES penetration) Step 2: Determination of the HC for: AsIs, Max and the SUTs Inputs: network data DRES scenario load / generation profiles/samples Assumptions: limited temporal analysis (only critical times) max. HC with 100% observability and controlability Outputs: AsIs HC Max HC expected HC per solution Step 3: Detailed case studies Inputs: network data DRES scenarios load / generation profiles/samples Assumptions: detailed SUT model full temporal analysis Outputs: first level KPI (losses, reactive power flows, curtailment) second level KPI (e.g. observer accuracy ) number of necessary sensors and actualtors for the economic assessment Figure 7 (Detailed overview of the three steps for SRA) Step 1 Feeder screening and classification This first step aims at selecting one DRES scenario, i.e., to select a common reference distribution of generation units along the feeders. Indeed, the hosting capacity of a feeder heavily depends on the location of the generation along the feeder (high hosting capacity when the generation is connected at the beginning and low when it is connected at the end in the case of a voltage constraint). While the determination of the horizontal DRES distribution could be based on a priori considerations (e.g. generation profile along the feeder), the scenario is defined on the basis of its implication on the hosting capacity since this is the main KPI. This definition allows a fully automated implementation (see Figure 11). V /03/18 63/107

64 For the whole study, one single DRES distribution has been proposed: an average hosting capacity scenario, corresponding to a rather uniformly located generation along the feeders and leading to the median hosting capacity. In the reality, each feeder might experience specific conditions (many small highly distributed generators or large generators at the beginning or at the end of the feeder). In some countries, depending on the subsidies in place to support DRES, some may prevail. For example, the largest share of the installed PV power is located in LV networks in Germany 1 while most of the PV generation in Spain is connected at MV level. In any case, the purpose of the technical assessment of smart grid solutions is to perform a comparative study, even if the selected scenario is only one of many possible scenarios in terms of DRES distribution along the feeder. To determine such a scenario, Monte-Carlo simulations 2 are used to generate randomly different distributions of generation along each feeder (e.g. 0.33/0.33/0.17/0.17 for the feeder on the left and 0.17/0.17/0.33/0.33 for the feeder on the right of Figure 8). Figure 8 (Two examples of horizontal DRES scenarios. Left: generation dominantly at the beginning. Right: generation dominantly at the end) For each single distribution of generation, the hosting capacity is evaluated by scaling up the power of each generator according to the distribution along the feeder until one of the constraints (voltage/current) 3 is reached. This is done by a script which uses an own programmed Secant Method algorithm 4 (the script finds the scaled generation power leading to one of the two constraints). Loads are not considered in this step since the objective is only to screen all the possible scenarios in terms of distribution of the generation power along the feeders. Out of this procedure, the hosting capacity (in fact a lower limit of it since load is not considered and the coincidence factor is assumed to be 100 % for all the generators) as well as the limiting constraint are determined. Figure 9 illustrates, for two types of feeders with a given random DRES distribution, the calculation process leading to the hosting capacity: in the Feeder A, identified as rural, the voltage constraint is reached before the loading (thermal) constraint when the PV power increases; in the Feeder B, the thermal limit is reached before the voltage limit as it is semi-urban. Thus Feeder A is classified as voltage constrained (blue point) and Feeder B is classified as loading constrained % according to Stetz, Autonomous Voltage Control Strategies in Distribution Grids with Photovoltaic Systems: Technical and Economic Assessment, Kassel University press GmbH, See D5.2 for more details on the Monte-Carlo simulations. 3 Note that only the loading of cables and lines are considered here (not the transformers in primary substations). 4 Secant method is a root-finding algorithm based on Newton s method with an approximation of the derivate. V /03/18 64/107

65 Thermal constraint 100 % HC PPV PInit PInit PPV HC 100 % Voltage constraint Figure 9 (Illustration of the hosting capacity calculation for two type of networks) This procedure is repeated for each DRES distribution generated in the Monte-Carlo simulations and the hosting capacity Cumulated Distribution Function (CDF) is created. In addition to the hosting capacity figures, the type of constraint (voltage or current) is stored and shown on the CDF. Finally, the average hosting capacity distribution can be extracted from it. Figure 10 shows an example of the outcomes from Step 1: The hosting capacity Cumulated Distribution Function (CDF) coloured according to the constraint (blue: voltage / red: current). The 50 %-point on the CDF-curve corresponding to the DRES scenario selected for further study. cdf (u,i) 1 0 HC (MW) Figure 10 (Cumulative distribution function CDF of the hosting capacity) The implementation of the procedure used in Step 1 is explained in Figure 11. Note that this definition of the DRES distribution is based on a uniform probability of having generators connected along the feeders. It does therefore not consider the probability of having generators mostly at the end of the feeder, which might be observed in regions in which the penetration of PV installations on farms which are usually connected to remote nodes is higher (e.g. in the South of Germany). V /03/18 65/107

66 Generate DRES distributions generate N random numbers summing 1. (N = number of nodes of the feeder) allocate these N random numbers to the N generators Determine the hosting capacity for each DRES distribution scale-up the power of each generator to reach the voltage or current limit (hosting capacity) with a Secant Method algorithm store this value as the hosting capacity of the corresponding DRES scenario store the limiting constraint (u or i) Select the median DRES distribution build the cumulated density function (cdf) of the HC select the horizontal PV distribution corresponding to the median on the cdf Figure 11 (Implementation of Step 1) Step 2 - Determination of the expected hosting capacities for the case-studies The second step aims at determining a more realistic hosting capacity value for the following cases: Without any modification: AsIs hosting capacity (network as it is, without reinforcement and without smart grids solutions). With a perfect control assuming 100 % observability and 100 % controllability (active and reactive power at generators and tap changers at transformers) and without reinforcements: Max hosting capacity. With the solution under tests (or families of solutions): Expected hosting capacity. Contrary to Step 1, the time characteristics of load and generation are considered in this step. In a first phase, a probabilistic power flow is computed considering load and generation samples, with the DRES distributions previously determined (Step 1). The probabilistic power flow is based on Monte- Carlo simulations and it uses ( samples). If a violation caused by the load is observed inside a feeder, i.e. an under voltage or an overloading (due to inaccuracies in the provided feeder load profiles), the installed load power is reduced in order to respect the planning rules set by the DSO 5. After this phase, another probabilistic power flow is computed with the (possibly modified) load 5 For each network, the applicable voltage limits (from the DSO) have been considered for medium and low voltage networks. V /03/18 66/107

67 values. In order to limit the computation burden, critical times have been introduced: for each solution which does not involve the OLTC, the critical times correspond to the occurrence of the highest voltage among all the nodes and highest loading among all the lines of the feeder. For OLTCbased solutions, the critical time corresponds to the occurrence of the maximum voltage spreading inside the network. Once these critical times are determined (two critical times per feeder or one critical time for the network), the hosting capacity is evaluated by considering these critical times. The procedure to calculate the hosting capacity for a given solution is the following: firstly, the system is parametrised accordingly to the study-case (e.g. DSO-limits, SUT-parametrisation); then a snapshot is made at the critical times determined previously. Finally, the hosting capacity (to reach one of the limits) is determined, as for Step 1, by scaling the installed power with a Secant Method algorithm. Note that for the solutions which do not involve any OLTC, two critical times are determined for each feeder, leading to two possible values of the hosting capacities in case the maximum voltage and maximum loading don t occur at the same time. In this case, the selected hosting capacity is the smallest. Correct the installed load power run Monte-Carlo simulations (power flow) for all the samples ( ) correct the installed load power if any violation occured Determine the critical times run Monte-Carlo simulations (power flow) for all the samples ( ) with the corrected load values determine the critical times for each case-study (time of maximal voltage and time of maximal loading or time of maximum voltage spreading) Determine AsIs HC for each feeder, select the critical times values and scale-up the power of the generators to determine the hosting capacity until a limit is reached (voltage or current) store the voltage and loading values at the hosting capacity Determine Max HC implement an optimal power flow (with 100% observability and controllability) select the critical time leading to the maximum voltage spreading and scale-up the power of generator to determine the hosting capacity until a limit is reached (voltage or current) store the voltage and loading values at the hosting capacity Determine expected HC implement the solutions under test scale-up the power of generator to determine the hosting capacity until a limit is reached (voltage or current) store the voltage and loading values at the hosting capacity Figure 12 (Implementation of Step 2) Step 3 Detailed analysis of case-studies This last step presents the highest complexity in terms of simulations. For the detailed case- V /03/18 67/107

68 studies, more accurate models of the solutions under tests are used and the full temporal analysis (i.e. using load and generation samples generated from time series) is done in order to be able to evaluate integral values (e.g. annual network losses or curtailment). By using a detailed model of the solutions, their actual performance (e.g. accuracy) can be assessed. First, the solutions are implemented and the expected hosting capacity is then used and the solution is simulated for the full amount of samples. The simulation results are then analysed and the results are analysed (e.g. losses, curtailment, monitoring accuracy ). In addition to the pure technical evaluation of the results, some key results are forwarded to the economic analysis. Moreover, the maximum voltage and loading of each feeder are analysed to validate the HC calculated in Step 2. The individual subtasks of Step 3 are summarised in Figure 13. SUT simulations run monte-carlo simulations with the solutions implemented in a higher level of details for all the samples ( ) Result analysis results analysis validation of the hosting capacity evaluation of first level KPIs evaluation of second level KPIs Figure 13 (Implementation of Step 3) 6.2 Tools to develop technical assessments The tools used for the technical assessment of the S&R potential of smart grids solutions have been selected on the basis of the following criteria: Flexibility. Performance. Compatibility between each other. Compatibility with programs used by partners. The network simulations are all done with the simulation package DIgSILENT PowerFactory and the data handling, processing and analysis is done with Matlab. The following sub-chapters present the approach selected to manage the data and to implement the simulation and analyses presented in the previous chapters Data management The work with a high number of network files, load and generation files and scenarios requires a well-structured and organized platform to ensure an efficient and reliable work within a project team. For this reason a Sub-Versioning System (SVN [23]) was used. Files that were included into V /03/18 68/107

69 the versioning system are network files, sample files, source code for results analysis and documentation. With this system, all versioned files have a documented history of changes allowing an accurate tracking of changes PowerFactory tools As previously mentioned, the simulation package PowerFactory has been selected for the network simulations. The following sub-chapters provide a short description of the tools from this simulation package which have been used. The description is not detailed; it aims at giving the reader an idea of the tools used for the simulations. Several Built-in tools of PowerFactory are used in the studies, including DSL (DIgSILENT Simulation Language), DPL (DIgSILENT Programming Language) as well as standard functions such as Optimal Power Flow (OPF) and voltage control functions which are available (e.g. Q(U)). For LV networks, detailed three-phase four wires network models allowing an accurate modelling of unbalanced conditions will be used. Two programming environments have been used to carry out the automation of calculations for a high number of networks and/or scenarios. Indeed, considering the large number of networks and scenarios, an automation of the simulations is absolutely necessary. The built-in PowerFactory programming environment DPL allows scripting every interaction with the Program. However, it offers only a limited built-in standard library that can be extended by own libraries. On the contrary, the Python interface offers a high flexibility and an access to higher level functions such as optimisation Parallelisation of the simulations The necessity to cover a high number of networks and simulation scenarios leads to the problem, that the computation power of a single computer or a couple is insufficient for the purpose of the studies. In order to speed-up the simulations efforts were taken to setup a parallelization environment for the different tasks: Preparation: Import of network data on several virtual machines. Execution: Split of simulation tasks assigned to virtual machines and/or (virtual) users. Analysis: Aggregation and processing of result files. For the parallelisation, the cluster available at AIT is used. It consists of 24 operative nodes and one Central Management/Storage Node which is connected via 40 GBit/s Infiniband and 10 GBit/s Ethernet (24 nodes with 12 cores per node and 128 GB per node). With this parallelisation environment, a substantial gain of time has been achieved and has made the work possible Matlab tools Matlab has been primary used to prepare the input data (Monte Carlo samples) and to analyse the results. This section gives a non-complete overview of the different tasks done in Matlab for the SRA. The objective is to provide the reader an idea of the type of tools used to prepare, process V /03/18 69/107

70 and analyse the data. Samples generation The generation of samples is an important task of the probabilistic network simulations. It consists in the generation of samples for the stochastic inputs (in our case the DRES and the loads) from the raw data provided by the DSO. It can be divided into two sub-steps presented in the Figure 14. Figure 14 (Overview of the samples generation process) Since the raw data provided by the DSOs is very heterogeneous and might have singularities such as missing or abnormal values; a preparation step is necessary before generating samples from it. The raw data differs according to the following characteristics: Format of the file:.xls,.mat,.xml. Level of measurements: LV consumer, aggregated data (feeder, group of N consumers). Time characteristics of the measurements: time frame, period, rate. In the preparation step, the raw data is first converted into Matlab and reshaped. Statistical indicators are then calculated, in order to identify singularities and to control whether the data can be used in a further step. For example, if the percentage of missing values is too high, the raw dataset is removed and an alternative is searched. Once controlled, the data can be resampled if necessary in order to have 15-min values, and normalised in the case of the DRES. Between each step, controls are made to verify that the modified data conserves its statistical properties. The preparation tasks are summarised in Figure 15. Figure 15 (Data preparation process for the samples generation) After this preparation, the samples can be generated. For each 15 time frame, one cdf is calculated with the Matlab function ecdf. Then samples are generated with the LHS method; the vector U of stratified numbers is generated with the Matlab function lhsdeign. From it, a sample of the variable is obtained by the reverse cdf evaluated with an interpolation (interp1). At this stage, the samples are created for the same variable of the raw data (a LV consumer, a MV consumer or a group of consumer). It has been necessary in some cases to distribute the power among the loads belonging to a group, according to their contracted power and a random parameter. V /03/18 70/107

71 Once the samples are generated into Matlab, they are saved in a text format which can be read by PowerFactory (see Figure 16). One file is created per for each load of the reference network under study. For DRES, only one file containing a normalized power is created for the entire network. Results analysis Figure 16 (Samples generation process) As previously explained, the network simulations of each of the three steps are all made in PowerFactory and then exported in a text format to be analysed by Matlab. Analysis of the results for Step 1 In the Step 1, the hosting capacity of each feeder for different distributions of DRES along them is exported. A Matlab script is used to plot the distribution function of the hosting capacity and to extract the horizontal distribution for the three values (20/50/80). These distributions are exported in a text format and used for Steps 2 and 3. Analysis of the results for Step 2 and 3 In the Step 2 and 3 in which the samples are used, the results of each simulation run are exported in the text format. A Matlab script reads processes and calculates the parameters described and plots figures. V /03/18 71/107

72 Smart Grid project overall assessment WP6: D6.1 7 Guidelines to perform economic assessments (methodologies and tools) The aim of this chapter is to introduce methodologies and tools used during IGREENGrid project to develop economic assessments. The approaches used to carry out these evaluations are based on the analysis of the scalability and replicability of smart grid interventions. These smart solutions are evaluated under different scenarios to assist decision makers for future investment planning. 7.1 Methodologies to develop economic assessments Approach for the economic evaluation The economic approach is based on the reference report for CBA on smart grid projects, proposed by the European Commission (EC) Joint Research Center (JRC). These European guidelines have the advantage of considering two types of benefits in the assessment: Quantitative benefits that can be monetised. Qualitative benefits, those represented by KPI that qualitatively capture the deployment merit of the smart grid project towards the achievement of the ideal smart grids and of the policy goals behind it. Down below is presented on Figure 17the workflow proposed by EC JRC to undertake a CBA of smart grids projects: Define boundary conditions and set parameters Cost-benefit analysis Sensitivity analysis Economic analysis (monetary appraisal) Evaluation on externalities and social impacts Qualitative impact analysis Figure 17 (Work flow proposed by EC JRC for a CBA of a smart grids project) V /03/18 72/107

73 7.1.2 Concept for the evaluation of costs and benefits in IGREENGrid: CA&BA The methodology to evaluate costs and benefits for the IGREENGrid project is based on the EC JRC CBA methodology. However, several modifications have been performed with the aim of simplifying the scope and making it more suitable for the characteristics of the project. The main difference between the methodology followed in IGREENGrid and the CBA methodology proposed by the JRC is that costs are not compared with benefits (not monetized), i.e., in the IGREENGrid project Cost Analysis (CA) and Benefits Analysis (BA) are carried out instead of Cost-Benefit Analysis (CBA). Here after, it is introduced the CA&BA methodology to evaluate the costs and benefits of the different solutions included in this study: Methodology of evaluation of costs and benefits of solutions in IGREENGrid Most promising solutions 1. Description and general context 2. Identification of assets & functionalities 3. Grouping Costs Analysis (CA) 4. Identification and estimation of costs 5. Comparison of costs of different solutions 6. Sensitivity analysis of costs Benefits Analysis (BA) 7. Identification of main benefits 8. Formulation of main benefits 9. Identification of other benefits Assessment 10. Assessment of costs analysis and benefits analysis Figure 18 (Methodology of evaluation of costs and benefits of solutions CA&BA considered in IGREENGrid) Then, the different steps by section for the evaluation of the solutions are introduced. Part 1: Description of the most promising solutions Step 1: Description and general context The very first step is the description of the solution under study. It is also necessary to summarise the elements of the solutions, taking into account voltage levels, control type, control system tools, V /03/18 73/107

74 measurements nodes, controlled devices and solution requirements, among other factors. In addition, the overall project assessment should be tailored to local conditions, considering different geographies and contexts that may have different impacts on costs and benefits. Therefore, these specified conditions, such as regulatory framework, relevant macroeconomics factors and the characteristics of regions, should be described in this point. Step 2: Identification of assets & functionalities The first task at this step is to identify and categorise the main components / technologies deployed in each solution according to the location of assets: Low voltage (LV) line. Medium-Low voltage (MV/LV) secondary substation. Medium voltage (MV) line. High-Medium voltage (MV/HV) primary substation. Distribution management system (DMS). After that, the main smart grids functionalities 6 ( JRC-functionality onwards) that are activated by the identified assets need to be identified. These JRC-functionalities are taken from the list of 33 functionalities grouped in six services proposed by the EC Task Force from smart grids 2010a see Table 12 in the Annex 2: List of JRC-functionalities grouped in six services (Annex III of [28]). Step 3: Grouping Some of the most-promising solutions identified in WP4 solve the same problem, i.e. they have the same main functionality or objective to accomplish. In the third step, these most-promising solutions are clustered by functionality (main objective of the solution): Functionality LV Voltage Monitoring MV Voltage Monitoring LV Voltage control MV Voltage control Congestion Management Table 9: Grouping of most promising solutions by functionality Part 2: Cost Analysis (CA) The objective of Costs Analysis (CA) is to analyse the costs related to the most-promising solutions 6 The word functionality in this step refers to the capability activated by the smart grid solution and therefore we will call these capabilities JRC functionalities onwards, as in this report the word Functionality is used to refer to solution groups and means main objective pursued by the solution. V /03/18 74/107

75 and to compare them with the costs of reinforcing the network to reach the same hosting capacity (just negative cash flow or costs incurred by DSOs are considered). The savings of deploying smart solutions instead of the wire solution can be understood as the main or monetized benefit of the solutions under study in IGREENGrid project. Step 4: Identification and estimation of costs The use cases analysed in this evaluation of costs and benefits are characterized in technical simulations and thus this analysis is performed and limited to cases that are technically feasible. Once use-cases are characterized and validated technically, the costs incurred by DSOs when implementing the technical configurations of the solutions resulting from the simulations can be classified into two categories: Capital Expenditure (CapEx): it refers to the capital amount which has been dedicated to the acquisition/development/deployment of the assets under test. It represents the investment related to the realization of the R&I solutions and it includes the installation and replacement costs of the related assets. Operational Expenditure (OpEx): it considers the capital amount dedicated to the operation and management of the solution under test. It includes the scheduled maintenance operation, the primary energy supply (fuel for active assets), control resources, etc. A list of potentially attributable CapEx and Opex is provided in Table 10. Cost type Cost name Brief description of the cost CapEx OpEx Investment I+D costs Field testing costs Installation Bureaucracy Other CapEx Corrective maintenance Preventive maintenance Components replacement Other OpEx Components acquisition Attributable research and development costs Costs related to necessary and/or compulsory testing in case of the solution operability Costs related to work performance, including costs of construction management, civil engineering... Administrative paperwork: permits, licenses Any other attributable CapEx Costs related to activities undertaken to detect, isolate, and rectify a fault so that the failed equipment, machine, or system can be restored to its normal operable state Costs related to systematic inspection, detection, correction, and prevention of incipient failures, before they become actual or major failures Costs related to replace components which useful time is shorter than the installation s lifetime Any other attributable OpEx (such as primary fuel, if needed) Table 10 (List of potentially attributable CapEx and OpEx) V /03/18 75/107

76 After the identification, the estimation of costs of the solutions is performed. Unit costs and certainty/uncertainty of unit costs values are determined and applied to the technical configurations of solutions or use cases that result from the simulations. In this manner, an average (or probable) cost and a reasonable range of variation of the costs are estimated/calculated for each solution and solution group in the IGREENGrid project. Step 5: Comparison of costs of the different solutions The objective in this step is to compare the costs of different smart grids solutions included within a solution group among them and also with the business as usual case (network reinforcement). In other words, the aim in this task is to arrange or prioritize the solutions with a common objective (intended to solve the same problem) in terms of the costs associated to them and incurred by the DSO. The Cost Analysis (CA) proposed in IGREENGrid project consists on the application of the following two exercises or methods: The Present Value of Total Costs (PVTC) method consists of estimating the sum of net present value of annual costs (CapEx + OpEx) of the smart grids solution for the entire study period, in other words, the PVTC can be understood as the total costs brought back to the first year (commonly called year zero ) by applying a discount rate (thereby accounting for the time value of money). The PVTC is calculated as shown by equation (1): t i R t n R t PVTC = (1 + i) t n t=0 Time discount rate Cost incurred by the DSO in time t Total number of periods considered (1) The Annual Costs Comparison method consists of compiling the annual costs of the solutions over the study period (20 years) in order to make annual comparisons and identify individual years in which costs are higher and lower. Step 6: Sensitivity analysis of costs In this step a sensitivity analysis of the costs is carried out. The purpose of performing this analysis is to assess the impact of changes in project variables on the project s performance; this is to evaluate whether the smart grids project would be economically feasible in the case that some changes in the project variables occurred. A sensitivity analysis can aim at varying a type of costs, one at a time or in combination. This technique helps to assess whether and how project decisions could be affected by such changes and helps to identify actions that could mitigate possible adverse effects on the project. Carrying out some kind of sensitivity analysis seems logical and necessary in a costs analysis of a smart grids project, because (1) depending upon each national context the return on investment V /03/18 76/107

77 can essentially change, and (2) the calculations rely on estimated data, simulations and predictions. In order to develop a quality work, it is necessary to identify key variables that most influence the project s costs. While the selection of the variables to be varied is not straight forward, the proposal is to select variables of subjective nature (e.g. the discount rate), in order to standardize the sensitivity analysis. It is necessary to identify key variables that most influence the costs incurred by the DSOs for the solutions under analysis. In IGREENGrid project two variables have been identified so that the sensitivity analysis will be carried out varying these two variables: Discount rate (i). This sensitivity analysis is intended to reflect the impact that the economic situation of the markets may have on the costs associated with the implementation of the solutions. In addition to the average discount rate considered in the analysis, a large range of discount rate is considered in order to ensure that the final and real costs of the implementations will very probably be within the total costs ranges estimated. Number of DG units to be retrofit. Some of the DSOs have indicated that retrofitting of already connected DG units to include P&Q control is not planned in their countries. In order to take into consideration the impact that this possible cost of retrofitting DG units may have in the total costs of a solution, some scenarios have been defined and a sensitivity analysis is carried out. If in any case, if any other variable were also considered a key variable for the cost of the project, an ad doc sensitivity analysis should be defined and developed varying that key variable. Part 3: Benefits Analysis (BA) The purpose Benefits Analysis (BA) is to identify benefits (taken from the list of 22 potential benefits and the list of 54 KPIs/Benefits proposed by JRC) that smart solutions could provide to the system, such as the deferment of distribution capacity investments or a lower environmental impact of electricity grid infrastructure. Step 7: Identification of main benefits In this step the potential main benefits of the solution have to be identified. This task is done in cooperation between the partners, as long as reliable benefit identification is pursued. In this step within the CA&BA methodology of the IGREENGrid project, with the aim of identifying benefits, the solutions under study are considered as a whole. This assumption means: (1) Only those JRC-functionalities and services related to the overall solution will be considered instead of taking into account the whole set of JRC-functionalities / services associated to each asset. (2) Only those expected benefits resulting directly from the solution will be identified, ignoring partial effects related just to an asset or a JRC-functionality. There are two main reasons to undertake this task in this way: V /03/18 77/107

78 Direct relationship between the objective of the solution and the benefits provided by it: it is conceptually easier to understand the benefits of the whole solution through the problems it is considered to solve rather than evaluating the side benefits or impacts procured by each single asset being part of the solution. Different JRC-functionalities can have side effects on the same benefit but in opposite directions, so that the resulting net benefit could be insignificant and yet require a complex analysis. The main benefits are identified from the list of 22 potential benefits of smart grids projects (put forward by the EPRI methodology and included in Annex I of [28]) see Table 13 in the Annex 3: List of main benefits that smart grids solutions could provide (Annex I of [28]). It is important to understand why each identified benefit actually occurs or is provided by the solution (or the kind of solution within the solution group ). So that, an explanation of the reasons or causes of each main benefit is provided, thanks to the cooperation of all the partners within the IGREENGrid project. Step 8: Formulation of main benefit Once main benefits are identified, formulas to potentially calculate the monetised value of these main benefits will be proposed, as visualising how to monetise or valuate benefits may help to better understand their causes. It is important to clarify that these formulas will just be exposed for information purposes but not actually used, i.e. no value will be given to benefits. These formulas will be selected from Annex II A guide to the calculation of benefits of [28] see Table 14 in the Annex 4: List of formulas to the calculation of main benefits (Annex II of [28]). Step 9: Identification of other benefits The identification of main benefits is complemented by the identification of additional benefits brought by the project towards the achievement of the smart grids and of the policy goals behind it. These benefits are selected from the list of KPIs / Benefits defined by EC Task Force for smart grids 2010C and reported in Annex IV of [28], and shown in Table 15 in the Error! No se encuentra el origen de la referencia.. Part 4: Overall Assessment Step 10: Assessment of costs analysis and benefit analysis In this last point costs analysis and benefits analysis are already carried out and the two results or assessments are obtained for each solution. So, finally, these two analyses are combined with the aim of obtaining an overall assessment of the smart grids solutions proposed within the IGREENGrid project. The combination implies fulfilling a form that summarizes the main results of the costs analysis (CA) and the aimed benefits identification (BA). V /03/18 78/107

79 1.10 [p.u.] [km] Voltage, Magnitude Y = p.u. Voltage path Feeder(04) Date: 12/18/2015 Annex: / DIgSILENT WP6: D Tools to develop economic assessments This subchapter presents the main tools used for the economic analysis of the selected smart grids solutions. Although the technical and economic evaluations have been presented separately in this report, they are of course strongly related as explained in chapter Excel file for the Costs Analysis (CA) To undertake the Costs Analysis (CA), an Excel file has been developed and used. It is an Excel file from which PVTC values and graphical comparison of the annual costs of the solutions are obtained as results. This is the conceptual structure of the Excel file: Workflow of the Excel file used for the Costs Analysis (CA) of the solutions under analysis in IGREENGrid Input data Calculation engine Output: Results Input data needed: Assets and costs elements included within each solution under analysis PVTC ( ) of solutions within Solution Group A Costs data (CapEx & OpEx) provided DSOs BaU - Network reinforcement Solution A.1 Solution A.2 Solution A.3 Solution A Solution A.n Network data (results of technical simulations) Annual costs ( ) of the solutions within Solution Group A BaU - Network reinforcement Solution A.1 Solution A.2 Solution A.3 Solution A Solution A.n year 1 year 2 year 3 year year 20 Figure 19 (Workflow of the Excel file used for the Costs Analysis (CA) of the solutions under analysis in IGREENGrid) This MS Excel workbook is used to generate the summary figures and the graphical representation of the main results. A set of customized sheets are in charge of adding the individual CapEx and OpEx of the main assets required for each of the considered solutions to the target network applying the cost figures supplied by DSOs and the outcomes of the technical simulations (i.e. approximate reinforcement). V /03/18 79/107

80 8 Conclusions This document provides guidelines for the future massive integration of DRES from the experience gained of IGREENGrid project. This project has assessed several smart grid solutions in six different countries to facilitate DRES penetration in the European Union at distribution level. The most relevant knowledge gained from this project is covered within this deliverable as a set of guidelines to address DRES integration. In this context, IGREENGrid project has provided answers to the main challenges that nowadays are been arising for the increasing connection of DRES at distribution grids. This research introduces solutions to better integrate DRES clustered in four different categories: prosumer side, network operation, network planning, asset management and regulatory matters. Figure 20 (Solutions to integrate DRES) Down below, for each category is presented the expected roadmap to be followed in order to facilitate DRES integration. These roadmaps are presented as a time horizon and have been ordered in order of priority taking into account the urgency of the solution and the maturity of the available technologies. Figure 21 (Roadmap for the prosumer side) V /03/18 80/107

81 Figure 22 (Roadmap for Network operation) Figure 23 (Roadmap for Network planning) Figure 24 (Roadmap for Asset management and regulatory matters) As a result of IGREENGrid work, a set of recommendations is delivered to facilitate the massive integration of DRES in Europe at distribution level. These recommendations are separately addressed to the most relevant stakeholder in the field, in order to transmit the key message for DRES integration more clearly and consciously to the concerning agents. V /03/18 81/107

82 Prosumers Be smart grid ready Use generated power exceeding the self-consumption for local supply Implement technology supporting increased self-consumption Install cheap storage like cooling, heating or boilers Figure 25 (Recommendations to Prosumers) Generators Allow DSO to manage reactive power with power electronics Accept that DSOs can control DG in case of necessity and for unexpected congestions and/or actions devoted to the electricity system stability. Participate in new flexibility mechanisms/market to provide services to the electricity system Grouping into one large generator (aggregator) and participate in market composing virtual power plants Provide ancillary services Figure 26 (Recommendations to Generators) Investors Stay well-informed and invest in new technologies. Invest in innovation. Invest in telecommunication infrastructure. Adapt to changing regulation and market framework. Figure 27 (Recommendations to Investors) V /03/18 82/107

83 Manufacturers Develop inverters able to provide reactive power at any time Develop EV-charging stations able to optimise of the charging/discharging processes Develop cheap storage Develop control systems for Smart home and buildings Develop advance tools which allow the implementation of new functionalities of the smart grid Take part in the development of harmonized network data format for exchange Figure 28 (Recommendations to Manufacturers) Aggregators Take part in the definition of common interfaces for the use of flexibility Be prepared for new contracts of prosumers. Be prepared for curtailment solutions. Avoid a geographical concentration of contracted prosumers. Smart technologies and appliances will enable flexibility users and procurers to develop grid and retail products and services tailored to the needs of the flexibility service providers. Aggregators and suppliers should have the same ability to extract the value of flexibility services on behalf of their customers. Figure 29 (Recommendations to Aggregators) Retailers Offering of easy-to-use flexibility products. Take part in the definition of common Communication Interfaces for the usage of flexibility Retailers should focus on CO 2 -Potentials of products V /03/18 83/107