Short general description of use cases and process for demonstration of the proposed solutions

Transcription

1 THEME [ENERGY ] Integration of Variable Distributed Resources in Distribution Networks (Deliverable 1.5) (Deliverable 9.5) (Deliverable 9.7) Short general description of use cases and process for demonstration of the proposed solutions Common Deliverable

2 Authors Authors Organization Jesus Varela IBERDROLA (IGREENGrid) David Rubio IBERDROLA (IGREENGrid) Daniel Henriquez ITC (SiNGULAR) Salvador Suárez ITC (SiNGULAR) João P. S. Catalão UBI (SiNGULAR) Vera Nunes EDPD (SUSTAINABLE) Aires Messias EDPD (SUSTAINABLE) Pedro Godinho Matos EDPD (SUSTAINABLE) Access: Project Consortium X Family Projects within topic ENERGY European Commission Public X X X Status: Draft version Submission for Approval Final Version X 2/88

3 Executive summary IGREENGrid, SiNGULAR and SuSTAINABLE project teams strongly believe that close collaboration and interaction among the family of projects funded under the EC call "ENERGY : Integration of variable distributed resources in electricity distribution networks", is vital towards the fulfilment of their mutual goals, the wide dissemination of their expected outcomes and the consistent exploration of their results. This deliverable, which is common to the three projects, explains the collaboration made between SiNGULAR, SuSTAINABLE and IGREENGrid projects until the middle of their lifetime. 3/88

4 Table of content AUTHORS... 2 EXECUTIVE SUMMARY... 3 TABLE OF CONTENT INTRODUCTION AND SCOPE OF THE DOCUMENT Scope of the Document Notations, abbreviations and acronyms PROJECTS OVERVIEW SiNGULAR SuSTAINABLE IGREENGrid USE-CASES IGREENGrid Use-cases Spanish demo Use-cases Italian demo Use-cases Austrian demo Use-cases German demo Use-cases French demo Use-cases Greek demo Use-cases SiNGULAR Use-cases Greek demo Use-cases Spanish demo Use-cases Portuguese demo Use-cases Italian demo Use-Cases Romanian demo Use-Cases SuSTAINABLE Use-cases Portuguese use-cases Greek use-cases German use-cases UK use-cases EXPERIENCES AND LESSON LEARNED PROCESSES TO DEMONSTRATE THE PROPOSED SOLUTIONS /88

5 5.1 SINGULAR process to test the tools developed Crete Sao Miguel Braila Pantelleria La Graciosa El Hierro IGREENGrid process to test the solution evaluated Henares Corridor, La Herrera and LITER Großlangenfeld/Dahlem and Lower Saxony Vendeuvre-Sur-Barse Carpinone Lungau, Köstendorf, Eberstalzell and Littring Sperchiada SuSTAINABLE process to test the solution evaluated COMMON ACTIVITIES Meeting with External Advisors IGREENGrid Stakeholder Committee Meetings SiNGULAR Advisory Board Committee Meetings SuSTAINABLE Advisory Board Committee Meetings Web Presence and Interaction Events CONCLUSION /88

6 1 Introduction and scope of the document Regarding the expected strong-interaction between IGREENGrid, SuSTAINABLE and SiNGULAR projects, this document explains the collaborations made within these projects. This Deliverable has common contents for these three projects. Figure 1 (Project logos) 1.1 Scope of the Document This document is divided in four main sections: - Projects Overview: This section describes the three projects. - Use-cases: This section describes Uses-cases of the three projects and their relation. - Experiences and lesson Learned: This section describes the experiences and lesson Learned and the possible application in the other projects. - Common Activities Report: This section presents a report for the common activities carried during the first eighteen (18) months of the three projects. 1.2 Notations, abbreviations and acronyms AI ANN AMR BEA CBA CCS CHP DER DG DMS DRD DRES DSM DSE DSO Adaptive Interference Artificial Neural Network Advanced Meter Reading Building Energy Agent Cost Benefit Analysis Customer Control System Combined Heat and Power Distributed Energy Resources Distributed Generation Distribution Management System Dynamic Response of the Demand Distributed Renewable Energy Sources Demand Side Management Distribution State Estimator Distribution System Operator 6/88

7 DTC EC EDA EEGI ESCO ESS EU EV FLIR GOOSE GPRS HV ICT IEDs KPI LOM LTE LV MV OLTC PCC PLF PV QoS R&D RES RMS RTDS RTU SCADA SCS SE SG STATCOM TSO TVPP VPP WP Distribution Transformer Controller European Commission Electricidad dos Acores SA European Electricity Grid Initiative Energy Services COmpanies Energy Storage System European Union Electrical Vehicle Fault Location Isolation Restoration Generic Object-Oriented Substation Event General Packet Radio Service High Voltage Information Communication Technologies Intelligent Electronic Devices Key Performance Indicator Loss of Mains Line Termination Equipment Low Voltage Medium Voltage On-Load Tap Changer Point of Common Coupling Probabilistic Load Flow Photovoltaic Quality of Supply Research and Development Renewable Energy Source Root Mean Square Real Time Digital Simulator Remote Terminal Unit Supervisory Control And Data Acquisition Substation Control System State Estimator Smart Grids Static Synchronous Compensators Transmission System Operator Technical Virtual Power Plant Virtual Power Plant Work Package Table 2 (Acronyms) 7/88

8 2 Projects Overview 2.1 SiNGULAR The main objective of the SiNGULAR Project is to develop tools for the optimal planning distributed energy sources in insular grids as well as to create innovative tools for improving the operation of island power systems under high RES penetration. The planning and operation of island power systems is more complex than those of continental power systems, given the grid stability problems caused by the massive integration of non-dispatchable and dispersed energy sources. These tools have been developed in different areas: RES forecasting, power flow analysis, energy storage management, scheduling algorithms, market issues and DSM strategies. All of them are key tools for the optimal operation of island electrical systems under high RES penetration scenarios, and will contribute to overcome existing technical, regulatory and market barriers to the massive integration of RES in insular systems from technical, regulatory and market point of view. In all cases, the stochastic nature of RES and its operation is a key common factor, and the uncertainty has to be managed by the insular grid operation. Meteorological Data Thermal Units Data RES Units Data Consumers Data Load & RES Uncertainty Power Analysis Tools Short-Term Scheduling Tools Load & RES Forecasting Tools Insular DSO Planning Procedures Demand Response Tools Energy Storage Models Operation and Planning Tools & Solutions System Data KPI Analysis Anticipative RES Uncertainty Management Long-term evolution of electricity grids Customer Engagement Figure 2 (SiNGULAR Approach) The final result expected is to demonstrate that the algorithms and tools developed in SiNGULAR to improve RES integration. These algorithms and tools will be tested and validated in various pilot sites, and under different meteorological conditions, allowing for testing the 8/88

9 robustness and general performance of these solutions under real conditions with respect to what can be done in conventional laboratory setups. Their application at the different proposed sites will allow comparisons of data gathered during the systems operation in different islands. Some of these pilot sites like S. Miguel (Azores), Crete (Greece) and Braila (Romania) will test in real or near-real operation thanks to the direct participation of grid operators as project partners in the SiNGULAR consortium: EDA, HEDNO and ELECTRICA. 2.2 SuSTAINABLE The SuSTAINABLE project will develop and demonstrate a new operation paradigm, leveraging information from smart meters and short-term localized predictions to manage distribution systems in a more efficient and cost-effective way, enabling a large-scale deployment of variable distributed resources. The SuSTAINABLE concept is based on the cloud principle, where the Distribution System Operator (DSO): I. collects information from smart metering infrastructure and other distributed sensors, and communications from external partners, market operators, and maintenance staff; II. III. IV. processes the information using tools such as distribution state-estimation, prediction tools, data mining, risk management and decision-making applications; communicates settings to power quality mitigation devices, protection relays and actuators, distribution components and distributed flexible resources; assesses its market strategy as a provider of ancillary and balancing services. The SuSTAINABLE concept also involves an active management of distributed flexible resources by DSOs. A multi-objective decision-making scheme will be designed to keep network voltage inside operational constraints, to minimize DG energy spillage related to network constraints, to minimize operational expenditures related to high reliability and continuity of service for loads and generators, to minimize aging of automatic tap changers subjected to sudden variations of power flows, and to maximize the balancing and ancillary services to be provided to TSOs when necessary. It will also focus on flexible protection schemes, to avoid failures in selectivity and reliability of the protection plan in low-impedance earthed neutral MV networks related to DG integration. Finally, the market strategy to be defined by the SuSTAINABLE concept encompasses aspects related to flexibility pricing for distribution network users and distribution system operators strategies with respect to balancing and ancillary services markets. 9/88

10 Measurements Power Quality Management Forecasts Requirements Data Processing Forecasting Tools State Estimation Control Functionalities Advanced Tools Protection Management Flexibility Management Offers Market Operation Figure 3 (SuSTAINABLE Approach) 2.3 IGREENGrid In the IGREENGrid project, six world-class DRES integration Demo Projects in low and medium voltage grids are being developed in the European Union. These projects are led by some of the most relevant DSO members of the EEGI. Based on sharing the outputs of these experiences and evaluating their results using the IGREENGrid KPIs defined on the project (according to the EEGI guidelines) and the results of the scalability and replicability analysis, a set of recommendations will be produced in order to identify the most promising solutions for an appropriate integration of small and medium size variable renewable resources in distribution grids. The Project focuses on increasing the hosting capacity for DRES in power grids without compromising the reliability or jeopardizing the quality of power. Figure 4 (IGREENGrid KPIs) 10/88

11 The main final result will be a set of guidelines, consisting in a portfolio of accurate methodologies and tools for an appropriate integration of small and medium size variable renewable resources in distribution grids. In particular, different climatic, cultural and technical frameworks (focused on European networks) will be considered. Furthermore, the expected outputs will be related to the results of the projects, and will consist of sharing knowledge and promoting best practices and initiatives for the integration of DRES. The different solutions will be brought from the individual demo projects results and then validated thanks to models/simulation/testing in the rest of network environments in order to assess the replicability and scalability at EU level. The technical, regulatory and economical aspects will be carefully taken into account. 11/88

12 3 Use-cases This section describes the use-cases that have been considered for the analysis of the three projects. 3.1 IGREENGrid Use-cases The use-cases defined for each associate demo of IGREENGrid taking into account that they are related to similar functions (e.g. voltage regulation). These groups are: - Voltage control in LV/MV/HV networks: The scope of this function is to keep the voltage profile on a LV/MV/HV network with DRES connected within the specified limits (voltage band) in compliance with the existing regulation. - Anti-islanding on MV networks: The scope of this function is to avoid unintentional islanding operation in MV grid with DERs. - Load/Generation/Storage management: The scope of this function is to optimize the energy management by controlling in a coordinated way the existing generation/consumption/storage devices. - Customer engagement: The scope of this function is to make customers active players on Smart Grids. - DER generation forecasting: The scope of this function is the short-term forecast of the active power generated by DER (e.g. wind power, photovoltaic plants ) on MV networks. - Network constraints management in MV networks (overloads): The objective of this function is to detect grid congestion among the distribution grid when significant DERs are connected to a circuit. Country Use-cases Voltage control in networks Anti-islanding on MV networks Load/Generation /Storage management Customer engagement DER generation forecasting Network constraints management in MV networks (Overloads) Spain Italy MV and LV power flow monitoring, including DG x x Distribution network state estimation x x Voltage control in LV networks x Voltage control in MV networks x Voltage control in HV networks x Voltage regulation x Anti-islanding on MV grids x Complex node x 12/88

13 Austria Germany France Greece Customer engagement x Monitoring and control of active distribution grid x Substation automation MV Voltage Control with State Estimator x MV Congestion Management x MV Voltage Control with dedicated Field Measures x LV Local voltage Control x LV Distributed Voltage Control x LV Coordinated Voltage Control x LV and building/e-car self-supply maximization x LV Load/Generation management x Customer Engagement in Buildings x Regulation of low voltage x Regulation of medium voltage x Wide area control x Use of storage for grid optimization x Local voltage regulation x Storage use to solve network constrains x x Storage Volt VAr Control Contribution x Centralised Volt VAr Control x x DER Generation Forecasting x Distribution State Estimator x Congestion /Volt. limits violation mngt. (monitoring) x x Improved network condition monitoring x Planning of RES hosting capacity x Planning of PV control strategies /set points for DERs x x Table 2 (IGREENGrid Use-cases) It has to be noted, that sometimes, the technical solution implemented by two different demos to solve a similar problem is not exactly the same. For instance, one voltage control is carried out by using OLTCs in the German demo. However, the solution provided by the Spanish demo is based on STATCOM. However, both of them have been grouped under the same functionality: voltage control in HV/MV/LV networks Spanish demo Use-cases MV and LV power flow monitoring, including DG The objective of this use-case is to define the data acquisition frame and the data processing in order to obtain the inputs for the control system. Specifically, there will be different inputs from the main sources: voltage control devices, DG, secondary substations and primary substations. Data acquisition requires ICT which comprises systems, the interface with the data collection devices and data transmission (physical channel, standard protocols, and data specification). Distribution network state estimation From the monitored variables of the network, the SE calculates the most probable status of the network in real-time. The output of the SE is a power flow model which feeds the voltage control algorithm, trying to avoid the typical problem of numerical instability on the distribution networks. The developed state estimation algorithm comprises a load estimation tool which feeds the state estimation algorithm with pseudo measures at load buses of the MV networks. These pseudo 13/88

14 measures increase the visibility of the distribution network and provide an appropriate behave of the state estimation algorithm. Voltage control in LV networks A 50 kva power electronic equipment (STATCOM) will be connected to the LV network. The STATCOM aims to stabilize the connection bus voltage by means of controlling the injected or withdrawed reactive power following commands from the control system. In the same way, generators connected to the network will be adjusted in order to make them respond to the control system actions. Voltage control in MV networks A 1 MVAr power electronic equipment (STATCOM) will be connected to the MV network. The STATCOM aims to stabilize the connection bus voltage by means of controlling injected or withdrawed reactive power from the control system. In the same way, generators connected to the network will be adjusted in order to make them respond to the control system actions. Voltage control in HV networks Although the effect of voltage control by generators is a well-known issue in HV power systems control, it is not extended the participation of MV DG in voltage control services. This analysis will determine the contribution of DG participation in HV voltage control. In addition to steady state voltage stability, the impact of increasing DG penetration in LV and MV on reactive power flows through the substations between transmission and distribution systems and HV/MV substations will be determined Italian demo Use-cases Voltage regulation The scope of this use-case is to keep the voltage profile on a MV grid with DERs in the range defined by the norm EN minimizing operating costs. The voltage control algorithm resides into DMS. Anti-islanding on MV grids The scope of this use-case is to avoid unintentional islanding operation in MV grid with DERs. In case of local balance between generation and load, LOM on a section of MV grid with DERs could 14/88

15 not be detected by DERs local protection. Generators remain then connected to the MV grid creating an unintentional island. In order to avoid this situation, the SCS sends a message to interested DERs for remote tripping of DER protections. Complex node The scope of this use-case is to optimize the energy management and the load profiles and offer, when necessary, ancillary services to the distribution network. The photovoltaic plant, ESS and the EV recharge infrastructure are used and coordinated to optimize the Energy management, load profiles and give ancillary services to the distribution network, such as active/reactive energy for voltage control purposes and loss reduction. Customer engagement The scope of this use-case is to make customers active players on Smart Grids. The objective is to help customers to reduce their energy demand. With this aim, they are provided with real time and historic indicators to support them to make a decision and/or to modify consumption behaviours toward a reduction in the energy demand. The Smart Info kit offers the information to customers at any time. It monitors the client energy profile combining information coming from upper layers of the electric system. This feedback allows end users to modify demand energy behaviours driven by economic and environment factors. Monitoring and control of active distribution grid The scope of this use-case is forecasting the DG on MV networks. Aggregating data according to the different sources (solar, wind, hydro, thermal and others), forecasted production data and realtime measurements will be provided to the operators of DSO and TSO control centres to help them in Active network operation. Substation automation The scope of this Use-case is to provide MV grid automation, failure localization, failure isolation, alternative feed proposal and its realization, and return to standard conditions after failure recovery. Then, the objective is to demonstrate that automation equipment of the MV grid, called IEDs, together with grid operator of superior SCADA supported will resolve failures on MV grid, minimize impact of the failure providing the alternative feed, and based on proposal of regional SCADA will return operation of MV grid to standard conditions after recovery from the failure. The MV grid 15/88

16 automation of failure management consists basically in the localization of failure and its isolation by turning-off of the affected part of MV grid Austrian demo Use-cases MV Voltage Control with State Estimator MV grid voltage data are sent to the voltage control algorithm on regular basis, or dynamically (change of switching status). The voltage control algorithm checks if an action is necessary and calculates appropriate settings for the OLTC (position) and controllable DERs in the field (P and Q set-points). This data is then transferred to the relevant actors. The sequence of the commands to the actors considers intermediate states in the grid in order to avoid unstable system states. MV Congestion Management As an additional feature of the voltage control use-case, it is possible to reduce the load in a MV network by reducing the voltage within the band allowed by operation rules. In order to do so, the voltage controller is set into a specific state with the objective function to try to keep the network voltages at the lower limit. MV Voltage Control with dedicated Field Measurements Voltage information is gathered from a few at critical points in the MV grid using voltage sensors. This data is sent to the voltage control algorithm on regular basis. The voltage control algorithm checks if an action is necessary and calculates appropriate settings for the OLTC (position) and controllable DERs in the field (P and Q set-points). This data is then transferred to the relevant actors. LV Local voltage Control The key premise of this and also the later stages is that local actuators like PV inverters, transformer with OLTC and e-vehicle charging stations have to ensure their local voltage limits according EN LV Distributed Voltage Control The enhancement in comparison to LV local voltage control is the usage of voltage measurements within the network in a simple optimization algorithm for transformer tapping. Here, Smart Metering Infrastructure is used to get information of the actual voltage band violations to control the voltage 16/88

17 level in the system. So only a unidirectional communication from the measurement points in the grid to the voltage controller necessary. Additional distributed actuators like PV inverters and e- mobility charging units are still in local operation as described before. If the control actions (tap changing) cannot eliminate voltage band violations, the distributed actuators have to compensate them by themselves. LV Coordinated Voltage Control In this use-case enhancement in comparison to basic LV local voltage control is twofold: First, just as in the LV distributed control use-case, the usage of voltage measurements within the network is added. Secondly, the operation of DERs and e-mobility is optimised in terms of voltage control and maximum infeed from renewables by sending out Q(U) and P(U) characteristics to these units. This can either be realised as a broadcast to all units (simple solution) or by addressing units in groups or individually (advanced solution). Also the current grid topology can be taken into account (most advanced solution). Consequently, there is bidirectional communication, from the measurement points in the grid to the voltage control algorithm and from the voltage control algorithm to the DER and e-mobility units. LV and building/e-car self-supply maximization In order to maximize the share of the locally produced energy that is locally consumed in this usecase a component called BEA uses data from the local renewable generation (PV-inverters) and measurements from the local Smart Meter and controls home automation components, e-car charging station. In times of high renewable generation all flexible load is activated in order to use most of the renewably generated energy. When the generation from renewable sources is low all shiftable loads are deactivated. By maximizing the consumption of locally generated renewable electricity the integration of DRES in distribution networks is supported because every locally generated and simultaneously consumed kwh does not affect the grid and other DRES units can be integrated in the network. LV Load/Generation management In order to support the grid the building offers its flexibilities to the grid controller. By using the flexibilities of the building energy consumption and generation can be shifted to suitable time frames for the grid. Also compare with the description of the previous use-case. The BEA uses data from the local renewable generation (PV-inverters) and measurements from 17/88

18 the local Smart meter and controls home automation components, e-car charging station and if available controllable local generation (micro CHP). In times of high renewable generation all flexible load can be activated to support grid operation. When the generation from renewable sources is low all shiftable loads can be deactivated and if needed flexible generation activated. Customer Engagement in Buildings The customer needs information from the energy system in order to change his use of energy and his potentially counterproductive habits, in terms of not supporting the integration of DRES into the grid, of using energy in the household. This use-case describes how consumers are informed in order to achieve customer engagement. The intention is to utilize the customer s freedom of action in using the household devices for supporting the grid operation in order to improve the situation for integrating DRES in the network. The customer is provided with information on good times and bad times for using energy by an ambient device, called PEEM-Clock, as well as a monthly newsletter including his energy consumption and furthermore is able to evaluate his energy consumption by accessing the energy feedback website. The customers are also equipped with certain so called Smart Grid Ready household devices (e.g. washing machine) that are automatically activated in times when further consumption is good for the grid in case the customer prepared the operation of the device and enabled the Smart Grid operation mode by pressing a button on the device German demo Use-cases Regulation of low voltage The objective of this uses-case is to regulate the voltage in the LV grid isolating it from the MV voltage. A power electronics based device called Automatic Voltage Regulator (AVR) compensates and establishes the LV voltage supplied to the connected customers.. Regulation of medium voltage The objective of this use-case it to regulate the MV voltage within certain limits applying a MV AVR connected in series in some point of one MV feeder and sized accordingly to the expected power delivery capacity. Wide area control In this use-case a permanent monitoring of voltage levels in medium grid is targeted. The wide area control consists of three major parts: 18/88

19 1. Collecting data from measure points: this points, called critical nodes, are defined in advance (Critical nodes means that a high deviation of the nominal voltage in normal operation or fault could be predicted). 2. The collected data is transferred to a central unit, to process the data and calculate a set point to produce Switching orders that are transferred to the central voltage regulator. 3. The central voltage regulator initiates the switching operations. Use of storage for grid optimization The objective of this uses-case is to defer power generated during peak production periods to periods of high demand. A biogas storage is used to store biogas which is produced by fermentation of agricultural waste. Normally the biogas would be directly used to generate electrical/ thermal energy by CHPs. In this case, in times with a high PV feed in or low demand the biogas is stored instead of burned immediately. In times of higher demand the previously stored gas is used to buffer this peaks. In addition information of the grid is used to generate a timetable for the operating of the CHP French demo Use-cases Local voltage regulation Nowadays the DER contribution to the voltage regulation is based on a static law (tan φ = constant). This control law proved not to be the best to facilitate the DER integration in the MV network. This uses-case aims test different voltage control laws, in other words, the use-case objective is to determine the best control law for each DER taking into account the network characteristics and state. At the end, these control laws will be integrated as a new lever in the centralized VVC function. Storage use to solve network constrains The purpose of the storage use to solve the possible network constraints enabling an increase in DG s connection on existing distribution circuits instead of building dedicated MV feeders or reinforcing the existing MV network. In fact this use-case aims to use the storage to provide/storage active power in case of network constraint violation to solve it. Storage Volt VAr Control Contribution The purpose of the storage to the VVC contribution is to enable an increase in DG s connection on existing distribution circuits instead of building dedicated MV feeders or reinforcing the existing MV 19/88

20 network. This functions aims to use the storage to provide/storage energy (active or reactive) in case of voltage violation to solve this network constraints. Centralised Volt VAr Control (VCC) This use-case describes the sequence of activities required for a centralized voltage control over the OLTC installed in the primary substation. This active control of the automatic voltage regulation existing in the primary substation will rely on a state estimator located in the DMS. DER Generation Forecasting The main input of this use-case is the weather forecast and includes one or several algorithms that will be improved taking into account the gaps real/forecasted generation. This use-case could be uses at different time horizons from long term to real time. This description is focused in a real-time use. For a more long term use the network operator can use the generation forecasting database. Distribution State Estimator From the P, Q, V, I measurements at the primary substation level, I, P, Q measurement at MV feeders and, V measurement at some critical nodes and at the DER injection point, the estimation state function calculates the voltage profile of each MV feeder taking into account the network topology and characteristics (impedance). It is implemented in the DMS system. It will be launched cyclically (periodicity specified) or on demand. It supplies the centralised "VVC function Greek demo Use-cases Congestion / Voltage Limits Violations Management In the DMS contribution of dispersed generation is not calculated and taken into account so far. Whenever a MV line or MV/LV substation becomes congested it is common practice to be entirely cut off to avoid stability issues. The congestion management uses the PV forecasting and the load forecasting and calculates the PLF, giving information about the sensitive nodes and the specific PV installations and/or loads, which can be controlled or reconfigured to avoid overloads. Using stochastic short-term (12-24 hours ahead) PV and load forecasting and applying PLF techniques, the operator is warned of expected congested lines with a certain probability. Using sensitivity techniques he can be alerted about critical PV injections or sensitive loads which can be potentially controlled avoiding complete disconnection of large parts of the network. 20/88

21 Improved Network conditions monitoring Real-time measurements of flows available at the MV bus of the primary distribution substations do not provide any indication about the level of consumption and PV generation or the direction of flows. This function uses the stochastic short-term (12-24 hours ahead) PV and load forecasting and provides the flow components due to consumption and PV production. In this case, the operator is warned about reverse flows and is alerted of potential overvoltages. The added value of the use-case is the improvement of power quality. Moreover, reverse power flows lead to increased losses. Planning of RES Hosting Capacity The common practice for the estimation of maximum hosting capacity of MV grids is a deterministic procedure, which typically examines extreme conditions, i.e. if the voltage and fault level limits are violated at low load high DG power productions. This limits the capacity of DGs that are allowed to be connected. The advanced function of DER hosting capacity estimation uses the operational states and possibilities of the grid and determines the safe upper limit of hosting capacity, without need of new investments. The function takes as input typical PV production and load and uses the PLF function to provide probabilistic density functions of the output variables with the corresponding uncertainty intervals for assumed DER capacities in selected nodes of the network. This allows more accurate estimations of the effects of high RES capacities based on realistic production and consumption time-series, rather than a single worst case condition of maximum DER output. Planning of PV control strategies / set-points for DERs P-V control strategies / set-points for DERs may allow a higher DER penetration. This use-case provides control suggestions to the operator of the grid on the way the grid could be managed in an optimal way with increased DER penetration. The suggestions are the outcome of steady state simulation tools (probabilistic load flows) and include control of the PV active and reactive outputs, allowing the installation of increased RES capacity. The operation of on-load tap changing at the origin of the feeder can also be simulated. 3.2 SiNGULAR Use-cases SiNGULAR project has a specific Work Package (WP9) dedicated to the implementation, testing and validation of all algorithms, models, and tools already developed in the previous work 21/88

22 packages (WP2-WP8). In this context several islands have been considered as pilot demo sites to enable the demonstration of the developed tools and algorithms and, therefore, fulfil the project objectives Greek demo Use-cases Greek demo use cases take place in the power system of Crete, which is the largest autonomous (non-interconnected) insular power system in Greece and is managed by the Hellenic Distribution Network Operator (HEDNO). In this pilot site, tools related to the RES forecasting, short-term scheduling, real-time operation and demand response will be tested and evaluated.. Load and RES Forecasting Load, Wind and PV forecasting are operational and relevant forecasts are provided every 6 hours and used as input by the scheduling tool. An additional load forecasting tool is embedded within the scheduling tool providing load forecasts for the next hours and updated hourly, using most recent SCADA measurements and weather forecast information. Load and RES forecasts will be validated by comparison to measurements. There is connection with SCADA for load forecasting, while no connection with SCADA for wind and PV forecasting is available. PV measurements are created by appropriately upscaling PV output measurements in twenty 80kW PV parks. This is due to the fact that PV parks are connected to the distribution system and are not available to the control room as SCADA measurements. Power Analysis Power analysis functionality is integrated within the scheduling tool and anticipated power flows of future hours within the scheduling horizon are provided. Nodal electricity prices will be calculated and communicated to the Demand Response tool. Activation of network constraints, in which case the nodal electricity prices are differentiated, will be flagged. Since the real-time node-level SCADA measurements are not reliable, nodal demands are estimated through distribution of the total system load (which is reliably provided by SCADA) to the system nodes using distribution factors calculated based on historical nodal demand SCADA measurements (missing real-time data are smoothed on averaging). The same logic is used to forecast the nodal demands over the scheduling horizon to be used as input to the scheduling tool. Scheduling The scheduling tool is the heart of this pilot site. An integrated software tool that operates as standalone application and aims at providing significant assistance for the optimal short-term operation 22/88

23 of insular electricity grids under increasing renewable penetration characterized by limited predictability and high variability is used. This software tool has been developed in line with the particular needs of the insular power system of Crete, Greece. However, it can be easily adapted and parameterized in order to operate in any other insular power system. The scheduling tool is integrated with load, wind and PV forecasting, and power analysis functionalities. The ultimate goal is the minimization of the total operating cost of the conventional (thermal) generating units in the insular power system for the entire scheduling horizon, while respecting all system and generating units operating constraints. The network representation is properly incorporated in the scheduling models under a DC power flow model. The output is the calculation of the short-term scheduling of the entire insular generation system in terms of the short-term operation of conventional thermal units (e.g. start-up, shut-down and commitment schedules, dispatch levels, reserves contribution, etc.), wind energy curtailment, network loading, and total system production cost. The minimization of the thermal unit cost results in the maximization of the zero variable cost RES injection. Key performance indices such as production cost, CO2 emissions, RES curtailment, etc. will be reported and compared with real historic system operation data. Demand Response Demand response (DR) platforms have been developed and are currently operational, while access has been provided to 100 users so far. The real nodal electricity prices generated by the short-term scheduling tool will be used to formulate appropriate DR programs that will be implemented over all users, thus enabling the DR efficiency to be measured and evaluated. The willingness of end-consumers to participate in the DR programs and provide feedback is crucial for the success of this use-case. However, the development of an incentive mechanism to encourage participants to respond to dynamic price signals may present problems Spanish demo Use-cases In Spain, several tools will be tested in the islands of El Hierro and La Graciosa in the Canary Islands. The use cases implemented try to contribute to increase the integration of RES in these pilots: - El Hierro pilot:operation of a Wind-Hydro power plant that supplies the 80% or energy demand of an insular electrical grid without interconnection with a larger system. - La Graciosa: Operation of a small island with electrical connection to a main insular grid. Load and RES Forecasting 23/88

24 This tool will be tested and validated in both islands. In El Hierro the forecasting tools will be validated for load and the energy production of the wind farm. The validation will be made using real data from the Meteo station installed in the wind farm and its energy production information. The data will supplied by Gorona del Viento. The connection with the SCADA is not possible due to technical restrictions imposed by the power plant operator. In La Graciosa pilot site, the forecasting tool will be validated for load and solar. The island of La Graciosa has been monitored installing two power meters in the island s transformers and a meteorological station that measures the solar resources of the location. A SCADA is being implemented in the frame of SINGULAR to receive this information and use for validation purposes Storage management In La Graciosa, (where a smart-microgrid is being promoted by ENDESA, the Island Authority and ITC), SiNGULAR project proposes the validation of the EES management system that has been developed for this type of applications (Community Storage system connected to a Distribution Grid). However, the microgrid at this moment is still not a reality, for this reason, with the objective of fulfilling the objectives of the DoW, a simulation tool and a SCADA will be developed to test virtually the energy management system of the expected microgrid. The system will be validated through an integrated software. In this case, the heart of the microgrid is the Storage Management strategy, being the objective that the microgrid to show the participation potential of this type of systems in insular markets and operation. The sizing and topology of the microgrid has been calculated. The final microgrid emulated in the frame of SINGULAR is a grid-connected microgrid with a PC capacity installed of 1 MW and Energy storage of 4 MWh / 600 kw. Planning tools Planning tools are being implemented for this microgrid. The result of this planning will be taken into account for final demo use case simulation. There is no planning for the grid extension and location of DER in La Graciosa. The results of WP7 will be validated analyzing the dynamic response of the proposed extension and compared with DSO Res integration regulation Portuguese demo Use-cases Scheduling tools and Forecasting will be tested and validated in the island of Sao Miguel in Azores. This island has a diversity of renewable power sources (geothermal, hydro and wind). Software tools developed will be prepared for a future integration in the power system control centre of S. Miguel. RES Forecasting 24/88

25 Online interactive forecast web platform will be integrated in the operation process of the island. The forecast platform, will allow the DSO operators to visualize and import to other internal systems the hourly week ahead forecast for: wind, hydro, geothermal, consumption and net load of conventional generation. The platform also allows the visualization and management of all historical forecasts, with the possibility of download and upload of real SCADA values. All forecasts are stochastic, providing the support information for risk based scheduling approaches, to be integrated in the generation system operation. Scheduling In this pilot, an innovative risk based approach was implemented and is presently permanently in operation, presenting 24h ahead scheduling solutions for the dispatch center operators of S. Miguel. The risk based scheduling approach, provides suggestions for hourly commitment of generators (8 generators), risk of wind shed, risk of load shed, risk of operation below generator minimum. The risk type information includes probability of occurrence and expected value of the occurrence and associated cost. In each hour the dispatch center operators have access to specific stochastic dispatch information, with detail information for each generator, about individual suggestions for dispatch generation and associated costs and risks. Additionally, for each hour ahead, the risk scheduling provides the specific and marginal generation costs, generating price signal and RES penetration signals that can be used as predictive signals for predictive demand response programs in the island. The tool is implemented in web platform, accessible for all operators inside and outside the dispatch center, an approach that is especially useful in islands and remote dispatch situations. The platform is linked to the forecast system using this information as input. The platform is prepared for integration with SCADA, but this is a sensible topic that requires authorization from high level administration of the DSO. Figure 5 (Short Terms scheduling tool for Sao Miguel. Screen capture of the software developed) 25/88

26 3.2.4 Italian demo Use-Cases The demonstration in the Italian site is in progress in the island of Pantelleria an autonomous electrical system not interconnected with the mainland and managed by a local distribution company. In this pilot site, some forecasting and power analysis tools are under testing. An innovative wave energy system called ISWEC (Inertial Sea Wave Energy Converter) is being installed in the sea close to the island and will generate energy for the island. When operational, this system will provide data to validate the models of wave energy system operation and grid connection. Forecasting tools The forecasting models have been developed for wind, solar and wave energy. Using real data coming from a wave gauge system will validate the wave energy models. The wave meter is able to record data for a period of three months. At the end of this period it has to be recovered in order to change the internal battery pack and download the recorded data. The forecasting tool providing the power production from ISWEC will be evaluated with actual data gathered from the ISWEC system. ISWEC energy production in grid-connected mode The data on the electrical network of Pantelleria and of the ISWEC model are under testing to run the tools for power flow analysis, and network analysis for estimating integration of RES in the grid. Before grid connection, the performance of the sea wave energy system will be tested on an artificial impedance load connected to the ISWEC terminals. After grid connection, the ISWEC system will be tested in operational conditions with its connection to the Pantelleria network. The equivalent electrical network of Pantelleria seen from the node of grid connection and the details of the ISWEC model are under testing in order to run short circuit calculations and contingency analysis, by considering faults at given points of the system. The results also include the assessment of the fault ride-through (FRT) capability of the ISWEC system, taking into account the characteristics of the inverter control, and comparing the results with the limits set up by the present FRT standards Romanian demo Use-Cases The demonstration in the Romanian site is in progress in the Grand Island of Braila (Insula Mare a Brailei) and in to a similar site (Scanteiesti). In this pilot site, some forecasting and power analysis 26/88

27 tools are under testing. Forecasting tools The forecasting models have been developed for loads, wind and solar energy. Using real data coming from smart meters of AMM Converge and A1800 meters of AMR Meridian/ or records SCADA systems of producers will validate forecasting models, by using SiNGULAR forecasting models. All smart meters are able to record data for a period of three months. SINGULAR platform can receive data for last two years from SCADA producers for forecasting, while no connection with SCADA for wind and PV forecasting is available. Wind and PV measurements are created by appropriately up-scaling Wind and PV output measurements in (1000kW+1500kW) wind plant and 500kW PV parks. This is due to the fact that Wind plant and PV parks are connected to the distribution system and are not available to the control room as SCADA measurements. Power Analysis Since the real-time node-level SCADA measurements are not reliable, nodal demands are estimated through distribution of the total system load (which is reliably provided by SCADA) to the system nodes using distribution factors calculated based on historical nodal demand SCADA measurements (missing real-time data are smoothed on averaging). The equivalent electrical network the Grand Island of Braila (Insula Mare a Brailei) and in to a similar site (Scanteiesti) are under testing in order to run short circuit calculations and contingency analysis, by considering faults at given points of the system. The results also include the assessment of the fault ride-through (FRT) capability of the SMA Sunny Tripower TL17000 PV parks and Nordex 54 with asyncron generator 1MW and Vensys77 syncron generator 1.5MW Wind Power plant, taking into account the characteristics of the inverter control, and comparing the results with the limits set up by the present FRT standards. 3.3 SuSTAINABLE Use-cases In Sustainable project there are two Work Packages (WP5 and WP6) related to the demonstration of concepts and functionalities developed within the scope of the project. The purposes of the usecases are: - Data collection for training and posterior evaluation of tools developed in WP3, including Advanced Forecasting Tools and Advanced Local Distribution Grid Monitoring and State- Estimation Tools; 27/88

28 - Testing the Advanced Coordinated Voltage Control developed in WP3; - Proving the TVPP concept as support of TSO/DSO, as defined in WP3; - Testing the operation methodologies developed in WP3 for power quality mitigation devices; - Using the network reinforcement planning tools developed in WP4 to investigate several real scenarios Portuguese use-cases Évora Advanced Forecasting Tools Data from EDP Boxes concerning PV micro-generations will be combined with weather information to train and test the Advanced Forecasting Tools. More than 2 years (between 2011 and 2013) of historical records are available, from which one-year is used for fitting the forecasting tools and the remaining data for evaluating the forecasting performance. The new forecasting tools will be compared with standard forecasting techniques. Advanced local distribution grid monitoring and state-estimation tools Data of MV distribution network (electric characteristics of network equipment, topology information and measurement data) and data of some LV distribution feeders (historical data of all consumers and all producers, and some real time measurement data) will be collected to train and evaluate the Advanced Local Distribution Grid Monitoring and State-Estimation Tools. The state estimation algorithm will be evaluated using measurements of a period of time of at least a week, with a sampling interval of 15 minutes. Advanced Coordinated Voltage Control The coordinated voltage control algorithms developed will be tested using the network of Évora, at low voltage and medium voltage levels. The reduction of energy losses, reduction of DER cut-off due to technical problems, reduction of CO2 emissions, increase in DER hosting capacity and the power quality improvement are quantified. Also the hardware prototype developed by INESCP will be tested in LV. Distribution Reinforcement in networks with large shares of RES Flexible network reinforcement planning is explored by simulating the MV distribution network and one LV feeder. At MV level, several scenarios for high DG deployment, distribution storage devices and flexible loads will be tested, with typical days being investigated for each stage of the planning horizon. At LV level, simplified models will be used for representing the load diagram, PV and other 28/88

29 RES, μg, distributed storage devices, electric vehicles (with smart charging), and flexible loads connected at the MV/LV substation level. INESCP Lab Advanced Coordinated Voltage Control The Advanced Coordinated Voltage Control will be tested in laboratory for LV using the setup depicted in Figure 4. The setup simulates a week LV distribution grid that without appropriated voltage control cannot accommodate the voltage rising effect introduced by a high penetration of micro-generation. The prototypes of the power electronic interfaces between micro-generations and the LV grid (developed by INESCP), employing a droop control strategy, will be tested as possible hardware interfaces to ensure adequate voltage profiles in the micro-generation output Greek use-cases Figure 6 (Schematic of the INESCP Lab setup) Advanced Forecasting Tools The advanced forecasting tools developed will also be validated in the Greek Pilot Sites of Rhodes and Meltemi. The data collected includes demand profiles and recorded online-weather data. The DG production forecasts are evaluated using actual power outputs of solar installations and wind turbines connected at the distribution level. Advanced local distribution grid monitoring and state-estimation tools 29/88

30 The validation of the advanced local distribution grid monitoring/ state estimation will be made in the Greek Pilot Site of Rhodes considering two scenarios: - Scenario 1: Each LV load is modelled individually and a one day or time interval ahead forecast is carried out. The consumptions are then aggregated at their feeding MV/LV substation (and the MV node). - Scenario 2: All LV consumptions under the MV/LV substation are aggregated and the aggregated MV load is modelled and forecasted for the next day or time interval. Advanced Coordinated Voltage Control The purpose of this use-case is to assess the effect of coordinated voltage control in increasing the hosting capacity of the feeder to accommodate a progressive integration of DG. For this, the distribution feeders in Rhodes, with assumed high penetration of distributed PVs and wind, will be studied. Flexible loads, control of PVs and storage devices will be used to control the voltage profiles on the distribution grid. TVPP concept as support of TSO/DSO The concept of TVPP as support of TSO/DSO will be tested using actual data from networks in Greece, where installation licenses for Hybrid Stations have been granted and a number of wind farms are in operation. Several functionalities will be tested including the TVPP as a provider of load-frequency-related ancillary services to the TSO/DSO. Distribution Reinforcement in networks with large shares of RES The impact of the integration of large scale RES (particularly, wind power and PV) in the decisions regarding the reinforcement of the HV/MV substation and the MV distribution network will be investigated by ICCS in collaboration with HEDNO. For this purpose, a computer program is being developed by ICCS for the long-term (20 years) reinforcement of the primary (MV) distribution network of Rhodes under large scale integration of RES (particularly, wind power and PV). This computer program will optimize the network reinforcement, in a multi-objective fashion, under two different scenarios: 1) business as usual, and 2) smart grid concept, i.e., coordinated voltage control, control of active and reactive power of RES. Evaluation of the Advanced System Protections The advanced protection strategies (identified in WP2) will be verified in the ICCS and the INESCP laboratories by simulating the operation of the protections installed in the substations that feed the distribution grid and the interconnection protection of the local DG units (model of a small part of the Greek MV network). For this purpose the RTDS hardware / software simulation platform will be 30/88

31 used, either by connecting the new protection hardware to the simulator via current amplifiers or via implementation of the models and strategies of the protections under test in the simulation environment. Models of part of the MV network and fault scenarios will be used German use-cases TVPP concept as support of TSO/DSO The distribution-transmission co-coordinative actions via TVPP will be validated using renewable generation data, load demand data of representative networks in Germany, and the grid data. cases with high penetration of RES in the distribution networks will be the focus of the study. The TVPP resources are placed in different locations of the network. Temporal differentiated quality of supply The role of the VPP in providing temporal quality of supply will be studied. More specifically, a tariff based provision of quality of supply from the VPP will be studied and implemented, with the goal of enhancing relevant reliability indices of the system. This problem will be studied by developing a demand side management approach. Based on the infrastructure provided in the TUB SENSE laboratory, voltage imbalance will be tested in the LV network of the laboratory. The mitigation solution will be tested and verified by experimental results. As some of the loads, such as an electric vehicle, generation resources, and storage systems, can be connected to single phase, it is possible to test imbalance conditions and the mitigation method. Optimal Planning of Distribution Networks with differentiated QoS A study contemplating Optimal Planning of Distribution Networks with differentiated QoS will be performed in the German distribution networks harmonic. Different scenarios will be considered in order to plan future development and make recommendations for limits on harmonic content UK use-cases Optimal Planning of Distribution Networks with differentiated QoS The University of Manchester will use DIgSILENT and Matlab to test in simulation a greedy searching algorithm for Optimal Planning of Distribution Networks with differentiated QoS. The main purpose is to demonstrate the applicability of the concepts developed in WP4. A model of part of the UK distribution network will serve as basis for this demonstration, which includes three types of DGs: wind generators, photovoltaic and fuel cells. 31/88

32 4 Experiences and Lesson Learned The need for enacting policies to support renewable energy deployment in Europe is often attributed to a variety of barriers that prevent investments from occurring. These barriers to maximize the penetration of renewable energy in electrical grid have to be properly identified and assessed, in order to define an effective strategy to overcome them. These barriers include technical, economic, political, social and environmental restrictions of different kind, that vary from region to region. Regarding these barriers, a diagnosis has been made and barriers have been identified in IGREENGrid, SiNGULAR & SuSTAINABLE, also some actions to overcome them have to be proposed in these projects. These actions shall consider: - Thorough analysis of the existing situation, and identification of existing technical, economic and political (regulatory) barriers. - Analysis of possible solutions to overcome each of the barriers identified. - Estimation of the cost associated to the implementation of each of the proposed solutions. - Estimation of the impact of each implemented solution, towards the goal of maximizing RES penetration. Also, the need to have back-up conventional power on standby, to provide energy when natural sources are not available, is an important cost that has to be supported. Besides high percentage penetration of intermittent RES generated electricity induces stability problems in electrical systems. There are technical limitations and difficulties to manage these power fluctuations in electrical grids due to the intermittence and variability of the RES, such as solar and wind, it limits their maximum penetration in electrical grids, e.g.: Rapid reductions in the power output from these sources, caused by the wind dropping off or a cloud passing overhead must be managed with appropriate control or short term storage systems to maintain constant power output. Weather forecasting and a more efficient Power Systems operation, integrating Storage systems, may contribute to increase RES penetration. Reliable wind and solar prediction, through the development of climate models for 48h forecast, is possible, enabling a more efficient use of available RES. Furthermore, distribution grids need to be further developed, not only increasing hosting capacity but also via advanced ICT infrastructure, communication and control platforms. New roles of DSOs in managing the active operation of the network have to be allowed (oriented to active power, frequency and voltage control) and regulation needs to be adapted to enable DSOs to manage generation resources under clearly defined procedures either as market based services either as a regulated obligation. New control concepts need to be defined. 32/88

33 Actual requirements and new control operation require a more active participation between TSOs and DSOs in the effective management of the power system. Programmable loads to cope with RES over-generation may be a solution to curtailment. Water desalination or H2 production for transport fuel could be two possible applications of variable loads capable of consuming excess electrical power from RES at valley hours of the demand curve. Additionally, the lack of standardization in devices, solutions and the large base of already installed systems may become a barrier for the future. Work is in progress but, by the time a standard or requirement is ready, some devices will be already deployed and upgrading those systems could be unfeasible (technically and/or economically). For instance, bringing additional requirements of authentication, authorization and accounting issues will need to be considered from the technical, regulatory and economic point of view. The regulation seems to be behind the real needs of the Smart Grids. On one hand the remuneration of distribution activity does not encourage the adaptation of distribution networks to the massive DERS penetration scenario. On the other hand, promising solutions as taking advantage of demand data and flexibility are difficult to put into practice if access to metering devices for operation purposes (i.e. restoration) is disallowed unless it is aggregated. Long lead time to obtain necessary permits. Exact time to obtain a license is legislative set but always much more time is needed. Especially spatial planning related to permits can take many years. Permits for new renewable energy plants are difficult to obtain due to not optimized administrative processes. They often include unnecessary requirements and lack transparency of information In the absence of a legal framework, independent power producers may not be able to invest in renewable energy facilities and sell power to the utility or to third parties under so-called power purchase agreements. A high number of authorities at local, regional and national level are involved in the authorization processes. For both permitting and financial support, and RES developers should submit the same or very similar information multiple times to different authorities. Besides complex, long, expensive and non-transparent procedure for obtain RES licences and permits, there are many organizations and intermediate structures of consultation without having a coordination and common direction between them. The current situation demonstrate confuse and imprecise procedures for the customer, also due to the existing non-organic legislation for authorization and certification. To this aim, some EU countries are trying to simplify their legislation by the creation of unique tests. Nevertheless, it persists too many relevant documents. Finally we summarize below some of the first finding related to the solutions proposed by these three European Projects to overcome the barriers described above. Some of them have been 33/88

34 finished or well advanced, being able to show significant results: - Voltage VAr Control: One of the major difficulties encountered during the implementation of coordinated voltage control in MV networks was the topology changes after the installation of the devices, due that the performance of the majority of the implementation of this solution depend a lot from the network topology; moreover generators have the most of the times a connection contract which does not allow to perform additional requirements or the reluctance from the owner to change the performance of a running system, then the implementation that don t use additional devices in the network are not possible. Moreover centralized and decentralized solutions have been explored among the demo projects. In any case, Voltage control is possible to be implemented by inverters of PV systems, which may increase hosting capacity by reactive power control; moreover these PV inverters could provide active power control, that also contributes to increase the hosting capacity as well as distribution transformers with OLTC which provides additional flexibility to regular operation of the network. - Storage: The business case of storage systems seems to be related to the provision of several services simultaneously. Individually taken, services such as peak saving, frequency regulation, intermittency smoothing, etc. may not offer enough incomes as to justify the whole storage plant. More clear specifications are still missing which would be translated in less cost and time. Then, the lack of maturity has a heavy impact both on the integration of devices and systems and on the ICT side with communication protocols, custom developments, etc. unless turnkey solutions are applied. Storage systems are developed to explore economic benefits of the technology when renewable sources are not available. - Monitoring Systems: DRES control requires larger amount of monitoring devices. Monitoring system is translated in new ICT requirements, which leads to problems due to the lack of standards and private communication protocols. Even when devices are supposed to be using compatible protocols, interoperability problems appear due to separate interpretations of the standard and company strategies; the firmware upgrades may also introduce integration problems due to partial backwards compatibility or the need to reconfigure each device. New ICT solutions require new expertise from the field crew, to be satisfied either by trained and multidisciplinary personnel either by complete operation procedures guiding them. 34/88

35 - Management tools with on-line dynamic security assessment tools: Using such software tools can provide aid to the operators of an island power system to quickly evaluate the danger of frequency deviations that could lead to a collapse of the power system. In such a way, they can place limits to RES penetration even more efficiently for various potential disturbances. The power of such tools is reinforced if they are combined with uncertainty management methods. Possibilities to remote control production of wind farms and other RES production systems, to adjust their output to the needs and grid capacity, could be important instrument to support the curtailment policy that could allow for increase of the installed RES power. - Advanced Control Functionalities based on distributed intelligence: in order to achieve an efficient coordinated control of the distribution system, it is necessary to develop specific advanced control and management functionalities. These functionalities, under the control of the DSO, will aim at exploiting local resources whenever possible in order to overcome technical problems that may occur in the distribution network. This is particularly important as it is now essential that the problems that may arise at the distribution level are not passed to the upstream transmission level and translate into an uncertainty seen by the transmission system. In this regard, the distribution system should not be seen as a burden to the transmission system and use its own resources to manage the technical difficulties that may be encountered. - DSO/TSO coordination: the TVPP (Technical Virtual Power Plant) concept opens new perspectives towards improving the coordination between the DSO and the TSO and the way how the distribution system is presented at the transmission network boundaries. This will enable defining a new role of the DSO which involves mitigating the impact on the transmission network of variable generation connected to the distribution grid under very high penetration of RES. In this context, the DSO is expected to operate its set of distribution networks (normally divided by HV/MV primary substation) based only on the direct-control over its own assets (such as OLTC transformers and other reactive power compensation devices). Concluding, full value of distributed generating technologies is not always well assessed. Small renewable energy systems for distributed generation can help avoid, not only investment in new conventional generation power and fossil fuel consumption, but also investments to upgrade transmission or distribution lines. Renewable technologies are sometimes cost-effective when this integrated value is considered, and it is important that this vision be shared by regulatory authorities, which are able to contribute to define a framework that gives priority to RES development, and to transfer these benefits to the involved actors. 35/88

36 5 Processes to demonstrate the proposed solutions 5.1 SINGULAR process to test the tools developed SiNGULAR has developed during the first half of the project different software tools that are being implemented in the pilot sites selected. This testing will allow the evaluation of the tools performance. Each tool is framed in a specific work package but with a robust relationship between them, particularly those tools which aims to support the power system operation: probabilistic forecasting (RES and Load), storage management in distribution grids, stochastic power flow and advanced scheduling algorithms. Other tools, like optimal planning stochastic algorithms that take into account RES penetration, electrical mobility and demand side management will be implemented in some pilots to analyse the performance. This chapter explain how the tools will be implemented in the sites selected identifying the issues that limit their implementation on the proposed sites. It is important to bear in mind that the project consortium is composed by several utilities that have the responsibility of operate insular power systems guarantying the power supply to their customers and, at same time, test and validate some of the tools developed. It means, that some tools are not going to be applied directly on the real dispatching, but in parallel avoiding a direct integration on the actual dispatching processes implemented in the power system control. The islands selected to implement the solutions developed in a greater or lesser degree are: - Crete (Greece) - Sao Miguel (Portugal) - Braila (Romania) - Pantelleria (Italy) - La Graciosa (Spain) - El Hierro (Spain) El Hierro was added in the project few months ago. The idea is to demonstrate to Gorona del Viento (owner of the wind-hydro power plant) and REE (transmission system operator in Spain) that the work developed in SINGULAR can be useful to operate this innovative system that will supply the 70% of the island annual energy needs with Renewable Energy. The utilities from Crete, Sao Miguel, Braila and Pantelleria are partners of the project. These are: 36/88

37 HEDNO, EDA, ELECTRICA and Pantelleria community that manage the power system of this small Mediterranean island located at 70 km of the African coast. Figure 7 (Short Term Scheduling for Crete. Screen capture of the tool implemented) Crete In Crete, the tools that will be tested are: Forecasting tools (developed in WP2), Scheduling tools (developed in WP5), Power Analysis tools (developed in WP4) and Demand Response (WP8). The following table depicts the implementation plan to test the solutions proposed: Tools that will be tested Probabilistic Forecasting Demo plan 1. Forecasting algorithms training using historical data from Crete 2. Put into operation the Load, Wind and PV forecasting 3. Relevant forecast will be provided every 6 hours (to be used by scheduling tool) 4. Load forecasting will be embedded within the scheduling tool. It will provide load forecasts for the next hours updated hourly, using most recent SCADA measurements and weather forecast information. 5. Performance analysis of the Load forecasting will be made comparing with the observed value provided by the SCADA. 6. In case that a variable cannot be provided in real time, the 37/88

38 forecasting performance will be made using historical data. 7. In case that RES production is not available in the SCADA because they are connected to the Distribution Grid the measurement will be appropriately synthesized upscaling some RES production measurements. Scheduling algorithms 1. Implementation of the scheduling tool available to Distribution System Operator (HEDNO). 2. The scheduling tool will be integrated with load, wind and PV forecasting developed in WP2 and with power analysis tools developed in WP4. 3. The performance analysis of the scheduling results will be analysed on a quarterly basis. Key performance indices such as production cost, CO2 emissions, RES curtailment, etc., will be reported and compared with real system operation data. Power Analysis tools 1. Electrical grid data gathering and system modelling with the tool developed. 2. Execution of Power Flow & Optimal Power Flow for testing. 3. The forecasted power flows will be compared to actual power flows. 4. Nodal electricity prices will be calculated and communicated to the Demand Response tool. 5. The Power Flow will be integrated within the scheduling tool providing anticipated power flows (power flow forecasting). 6. If real time SCADA measurements at node level are not reliable, the nodal demands will be estimated distributing the total system load per node using factors calculated on historical node demand. Demand response 1. Installation of the demand response platforms (100 customers). 2. Demand response programs will be implemented over all users. 38/88

39 5.1.2 Sao Miguel 3. Measuring and evaluation of Demand Response efficiency. Table 3 (Crete tools) In the Portuguese island of Sao Miguel several tools will be implemented following the processes described below: Tools that will be tested Probabilistic forecasting and scheduling Demo plan 1. Software application: Elaboration of an integrated application to integrate scheduling and forecasting according the data and suggestions given by EDA. 2. With the support of the system operator, improvement of the conventional generations models for scheduling. 3. The connection with the SCADA is not possible due to technical restriction imposed by EDA, But data can be gathered: o o Forecasting will be validated with the actual information sent by EDA. Scheduling performance will be validated in terms of economic signals comparing the proposed scheduling results with the actual schedule. The software cannot operate in parallel with the current SCADA but a posterior validation of the results can be made. Power Analysis 1. Electrical grid data gathering and system modelling with the tool developed. 2. Execution of Power Flow & Optimal Power Flow 3. In case of any constraint due to unavailability of load data by node the nodal demands will be estimated. Planning tools 1. Gathering of the current electrical distribution grids and model parameterisation. 2. Model execution and test. It will be tested calculating the long term planning for new generation expansion (DER). 3. Results will be compared with the existing expansion plans 39/88

40 5.1.3 Braila provided by EDA and constrained by its availability Table 4 (Sao Miguel tools) Tools that will be tested Forecasting tools Demo plan The forecasting models have been developed for loads, wind and solar energy. The forecasting tool are provided by SINGULAR will be evaluated with actual data gathered from all smart meters of AMM Converge and A1800 meters of AMR Meridian / or records SCADA systems of producers. Load and RES forecasts will be validated by comparison to measurements. Wind and PV measurements are created by appropriately up scaling Wind and PV output measurements in (1000kW+1500kW) wind plant and 500kW PV parks. This is due to the fact that Wind plant and PV parks are connected to the distribution system and are not available to the control room as SCADA measurements. Power Analysis The data on the electrical network of Grand Island of provided by smart meters are under testing to run the tools for power flow analysis developed in WP4 and network analysis for estimating integration of RES in the grid. With the data collected short circuit calculations and contingency analysis will be run, by considering faults at given points of the system. The results also include the assessment of the fault ridethrough (FRT) capability of the SMA Sunny Tripower TL17000 PV parks and Nordex 54 with asyncron generator 1MW and Vensys77 syncron generator 1.5MW Wind Power plant, taking into account the characteristics of the inverter control, and comparing the results with the limits set up by the present FRT standards. Real-time node-level SCADA measurements are not reliable, nodal demands are estimated through distribution of the total system load (which is reliably provided by SCADA) to the system nodes. Table 5 (Bralia tools) 40/88

41 5.1.4 Pantelleria In this pilot site, some forecasting and power analysis tools are under testing. An innovative wave energy system called ISWEC (Inertial Sea Wave Energy Converter) is being installed in the sea close to the island and will generate energy for the island. When operational, this system will provide data to validate the models of wave energy system operation and grid connection. Tools that will be tested Probabilistic forecasting Demo plan The forecasting models have been developed for wind, solar and wave, but only the last one can be validated. The wave forecasting tool will be evaluated with actual data gathered from ISWEC system (wave energy). After the installation of the ISWEC system and the wave gauge data will be gathered to validate the wave energy models developed. The wave meter is able to record data for a period of three months. At the end of this period it has to be recovered in order to change the internal battery pack and download the recorded data. So, real time validation is not possible. The forecasting tool providing the power production from ISWEC will be evaluated with actual data gathered from the ISWEC system. Power Analysis The data on the electrical network of Pantelleria and of the ISWEC model are under testing to run the tools for power flow analysis, and network analysis for estimating integration of RES in the grid. Before grid connection, the performance of the sea wave energy system will be tested on an artificial impedance load connected to the ISWEC terminals. After grid connection, the ISWEC system will be tested in operational conditions with its connection to the Pantelleria network. The equivalent electrical network of Pantelleria seen from the node of grid connection and the details of the ISWEC model are under 41/88

42 5.1.5 La Graciosa testing in order to run short circuit calculations and contingency analysis, by considering faults at given points of the system. The results also include the assessment of the fault ride-through (FRT) capability of the ISWEC system, taking into account the characteristics of the inverter control, and comparing the results with the limits set up by the present FRT standard. Table 6 (Pantelleria tools) In La Graciosa, the SiNGULAR project proposes the validation of the EES management system that has been developed for this type of applications (Community Storage system in a Distribution Grid). La Graciosa is a small island located at the North of the main island of Lanzarote, where a smart-microgrid is being promoted by ENDESA, the Island Authority and ITC. However, the microgrid is not a reality currently, for this reason, with the objective of fulfilling the objectives of the DoW, a simulation tool and a SCADA will be developed to test virtually the energy management system of the expected microgrid. Figure 8 Different scenarios of PV integration in La Graciosa In the pilots sites proposed in SiNGULAR there are no Storage systems except in the islands of El Hierro. However, there is no possible to validate the models in that site, due to the restrictions imposed by Gorona (Power plant owner), since the system is currently testing the components of the power plant. 42/88

43 Figure 9 (Power meters installed in La Graciosa) The island of La Graciosa has been monitored installing two power meters in the island s transformers and a meteorological station that measures the solar resources of the location. A SCADA will be implemented to receive this information and use for validation purposes. At same time an emulation model of the microgrid will be implemented in order to have a tool to validate the Storage Energy management. Tools that will be tested Probabilistic forecasting Demo plan 1. Installation and data gathering of the meteo station and the power meters in the island. 2. Development of the SCADA to share the data with the tools to test. No PV systems are installed in the island, for this reason, a scenario of PV integration will be taken into account to validate PV forecasting results. 3. Validation of the forecast algorithms (solar and load) comparing forecasting results with actual data. Storage management 1. Development of the battery models and the microgrid models 2. Development of the interface between SCADA and the microgrid models 3. Development of the interface between the storage management tool, the SCADA and the microgrid model. 4. Finally, the results ( /kwh consumed by the island) will be compared between the emulated system and current situation. 43/88

44 Planning tools There is no planning for the grid extension and location of DER in La El Hierro Graciosa. The results of WP7 will be validated analyzing the dynamic response of the proposed extension and compared with DSO Res integration regulation. Table 7 (La Graciosa tools) Only Forecasting tools for demand prediction and wind power generation can be validated in this pilot due to the technical restrictions imposed by the power plant operator since the system is just now in the commissioning phase of the project. Gorona del Viento power plant operator, has accepted the use the system only for this case. Tools that will be tested Probabilistic forecasting Demo plan Phase 1: - Collect the information required to develop the forecasting tools (WP9 effort) and tool validation. Phase 2: - Implement the forecasting tool (load and wind energy generation) (WP2 effort) Phase 3: - Validation will be made comparing the forecasts with the actual data gathered Connection with the SCADA Power Plant in real time is not possible due to restrictions imposed by the power plant operator. Table 8 (El Hierro tools) 5.2 IGREENGrid process to test the solution evaluated Although the implementation of the solution is not in the scope of IGREENGrid, the associated demos have developed the solutions that implement the use-cases defined in the chapter 3. It is enabling to compare different approach to solve the same problem and/or to implement the same functionality. In some cases the same solution covers different functionalities (e.g.: a battery could be used for voltage control and congestion management), or in other cases implement only one functionality but with a high performance. Additionally, this approach has permitted to implement the same solution in different environments (climatic conditions), and/or different types of networks (rural/urban, overhead/cable lines...). 44/88

45 The viability of these solutions has been tested by real deployments in the following places (obviously not all the solutions are implemented in all sites): Country Site Network Weather Henares Corridor (GUADALAJARA/ MADRID) Semi-Urban/Urban Spain La Herrera (ALBACETE) Rural Mediterranean LITER laboratory (MADRID) Urban Germany Großlangenfeld and Dahlem (Rhineland- Palatinate) Rural Continental Lower Saxony Rural France Vendeuvre-Sur-Barse (Champagne-Ardenne) Rural Continental Italy Carpinone (ISERNIA) Rural Mediterranean Lungau (Salzburg) Rural Austria Eberstalzell and Littring (Upper Austria) Urban Continental Köstendorf (Salzburg). Urban Greece Sperchiada (Fthiotida) Rural Mediterranean Table 9 (IGREENGrid Demo Sites) With the information obtained and the experience gained by the demos during the implementation of the solutions, IGREENGrid will produce a set of guidelines to increase the penetration of DRES in the distribution networks Henares Corridor, La Herrera and LITER In the Henares Corridor, Iberdrola and Gas Natural Fenosa have deployed the devices to implement the Monitoring and Voltage control uses cases. These deployments have been also complemented by the installation of a STATCOM also in La Herrera (in feeder with a high penetration of PV) and the use of the DRES installed in the LITER laboratory. The following table depicts the implementation plan to test the solutions proposed: Solution to test Demo plan 45/88

46 LV & MV Monitoring and LV & MV Monitoring with DSE The status of the MV network is monitored using Intelligent Electronic Devices, also called MV supervisors. The MV supervision system consist of voltage and current sensors and fault passage indicators located in MV lines feeding secondary substations. MV supervisors have computing capabilities to calculate power flows, as well as RTU functionality that provide them with communication capabilities. Collected measures, alarms from fault passage indicators, as well as alarms related to threshold violation, are sent to SCADA. 1. Grid monitoring solution consists on deploying metering infrastructure to the secondary substations and the DRES resources. PRICE has deployed smart grids around 400 metering, and over 88 advanced monitoring secondary substations. These secondary substations mix different types of typologies of standardized substations. (i.e in-line, out-line plus transformer, ) The measurements at the MV side of the sec. subs. Includes P, Q, I and V. 2. To choose the most significant sub stations. The PSS/E was used to perform a study analyzing different faults and studying where was the best location to place MV measurements and automation. 3. The SCADA retrieves information from the MV monitoring infrastructure, secondary sub stations, when the value changes above the threshold and, at least once an hour, in this way, the communication requirements are smaller and the cost of the solution can be reduced. The performed tests show that the accuracy of the method is satisfactory. The Advanced LV Monitoring extends the measures to each LV feeder with the same mechanism. The main steps of this functionality are: 1. Voltages and currents are measured with LV sensors from the LV output cables of secondary substations. 2. LV supervisor collects these data from sensors and perform calculations on power flows as well as power quality issues, generating also events reports. 3. The state estimation calculates the most probable status of 46/88

47 the network from the monitored variables of the network trying to avoid the problem of typical numerical instability on the distribution networks. The state estimation algorithm application requires a load estimation creating pseudo measures at load buses of the medium voltage networks. These pseudo measures increase the visibility of the distribution network and provide an appropriate behavior of the state estimation algorithm. The load estimation is based on typical daily load curves of different types of client and real measures at the feeders to guess each supplied load pseudo measurement. The output of the state estimator is a power flow model which can be used directly for operator information purposes (voltages, flows, out of limits equipment ) through a graphical interface or to feed some other application. To test the monitoring efficacy different levels of monitoring and automation have been set up in different areas. A KPI indicating the level of monitoring have been calculated. (LV-MV) Centralised (SE & OPF) Voltage Control with STATCOM The LV and MV quality of supply can be improved taking advantage of devices based on power electronic interfaces and connected distributed generation. - In LV networks: o o o o Deploy a STATCOM device aimed to stabilize the LV bus voltage by means of the control of produced/consumed reactive power. Installation of STATCOM in LINTER grid. Monitoring and controlling installed DG. Testing the voltage control system in LINTER. - In MV networks: o o Deploy a STATCOM device aimed to stabilize the MV bus voltage by means of produced/consumed reactive power. Field installation of STATCOM 47/88

48 o o Monitor and control installed DG Testing the voltage control system in field. The voltage control is performed by means of the generation dispatch control centre. This control centre includes a state estimator and a voltage controller application, receives the real time measurements and manages the distributed generators as well as the present voltage control devices (STACOMs and distributed generators). 1. The application is based on the monitoring and control of the STATCOM producing set points (on/off, reactive power production/consumption, ramp configuration, control mode) and DG devices located in the smart grid laboratory producing suitable commands (on/off, active power limit, reactive power production/consumption). These commands aim to the optimal network state and are offered to the network operator for approval and delivery. 2. The voltage control should be done in two steps, the first one consist on the real time monitorization of a group of network status variables (voltage, current, active power, reactive power) on relevant network nodes every 15 minutes and on the second step the optimal set points are produced. The control centre, including a state estimator and a voltage controller application, receives the real time measurements and manages the distributed generators as well as the present voltage control devices. 3. The voltage control algorithm considers two possible scenarios adapted to different computation capabilities available at the generation dispatch control centre. The online mode is based on an optimal power flow while the off line mode is based on pre-generated decision matrixes considering multiple network status under several generation and demand scenarios. 4. In practice, as the current regulation imposes that the distribution network operator is neither allowed to receive real time measurements nor to issue commands to DG (even 48/88

49 in demo projects), the pilot project only sends set points to DSO owned devices (STATCOM) and power system simulations are needed to learn about the network final state should DG commands have been sent. The equipment is connected to the real network (in a connection point) and it tries during all time to compensate for the power factor of the load or generation equipment adjacent imbalances and harmonics. In addition to this compensation, takes control voltage by applying a voltage ramp reactive power. Table 10 (Spanish Solutions) Großlangenfeld/Dahlem and Lower Saxony This chapter includes a description of the solutions implemented in the German demonstration project. Solution to test (LV-MV) distributed voltage control with AVR Demo plan Voltage is corrected with a series injection transformer and a power electronics converter: - The MV voltage control is based on a MV/MV active voltage regulator in charge of compensating the voltage drops along the line so the supplied quality of service to the customers is guaranteed. The autonomous local control is in charge of following the scheduled target without requiring a remote control to issue the set points. - The LV voltage control is based on a LV/LV active voltage regulator in charge of compensating the voltage drop due to demand variations as well as the changes on the MV supplied voltage resulting from network operation and the intermittency of the connected DRES. The voltage regulation is done by means of power electronics injecting a voltage vector after the transformer secondary winding while other solutions are based on conventional OLTC and discrete steps. 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 49/88

50 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. The AVR LV can be installed next to an existing secondary substation, or it can be supplied as an integrated part of a new substation. It is connected in series between the low voltage side of the distribution transformer and the load feeders. For medium voltage applications the AVR MV is installed in series with the supply and provides continuous and dynamic voltage correction of up to ±10% percent for loads to 10 MVA and beyond. The optimal location, layout and dimension of an AVR depend on the degree of voltage violation. If voltage violations are of a long-range nature, an AVR in a MV feeder might be the best solution. If the voltage violations are seldom and located in the LV network, an AVR in the LV feeder is optimal. With respect to filed results, the first measurements in the secondary substations after the voltage regulator was installed show an equalisation of the voltage curves. This can be clearly seen in the following diagram. The graphic shows 10-minute averages of the phase-to-earth voltage of Phase L1. The input voltage is shown in green, while the output voltage is in purple. In the next figure the resulting voltages in the input (blue) and output (red) sides of a LV AVR are shown. Upstream in the MV network is installed a MV AVR that starts to regulate voltage at 18:00. In this 50/88

51 way can be observed the effect of each AVR separately and joint. MV Distributed Voltage Control with field measurements This solution of voltage control is based on the optimized control on the OLTC of the primary substation based on a few critical remote measurements from extreme points of the MV grid (Wide Area Control). This approach is intended to reduce grid expansion needs through the enhanced network monitoring and optimal control of the transformer. Generally, voltage controllers set the voltage on the secondary side of the equipment to a fixed value. In contrast, a wide-area controller monitors several critical nodes in the supplied grid via remote control. The objective of the wide-area controller is not to regulate the operating voltage of a single node to a constant value, but to lead the operating voltage of all critical nodes within a determined band width. To realise the voltage control, critical nodes are identified from measures but can be also detected at the planning stage of the network because it is already know which feeders will hardly be within the limits with a few load flows under maximum generation minimum load and minimum generation maximum load scenarios. The demo process should be summarized with the next steps 1. In the demonstration project, five secondary substations have become clear, at which online measurements should be taken. 2. Measures managed by RTUs are only sent to the central controller when a change happens over the configured threshold. After receiving a new measurement, the Wide 51/88

52 Area Control mechanism is launched only when some measurement reaches the limit. 3. Due to the measurements, an optimal target voltage is calculated, which is sent to the central voltage regulator in the transformer substation. 4. The central voltage regulator compares the target voltage with the actual voltage and sends a grading command to the transformer via the telecontrol system in the case of inadmissibly high deviations (while keeping voltage within limits, it is desirable to minimize the number of OLTC changes to obtain the same life expectancy). Voltage fluctuations are reduced by 30 % (average) as it is shown in the Figure below. MV distributed voltage control with biogas storage The solution is focused on a biogas power plant that includes a PV system and which energy evacuation line cannot handle both generations at the same time without reaching voltage limits, to introduce an alternative energy storage system aimed to voltage control The system is sized so that the PV feed-in is imitated in reverse, i.e. during the period with increased PV feed-in the electricity generated from the continually produced biogas is throttled down or stopped. The largest storage period for the biogas storage system is required during the summer months. In the winter months, on the other hand, this method of operation makes possible to generate electricity from the biogas almost without having to use the storage system. The /88

53 kwel produced at the site (160 Nm3/h raw biogas) has to be distributed during the day and year in such a manner that the biogas produced can also be completely used for generating electricity. Coupling the biogas storage system to the electricity injected by renewable energy sources such as photovoltaic and wind power balances out deviations in the electricity grid. The farm-produced biogas is used for generating electricity and heat in a CHP plant when insufficient photovoltaic or wind energy is available in the grid; conversely the production of electricity from biogas stops when the solar and wind energy can meet demand. With an efficiency of 98 %, this makes it possible to store renewable electricity produced from wind, photovoltaics and biomass The storage is activated remotely depending on the line current (MV feeder from the HV/MV primary substation). It is know that when line current exceeds a limit, voltage excursions outside limits start to appear. In the figure below it is shown the power flow in the MV feeder from the HV/MV primary substation. Most of the time there is a feed-in from the MV feeder. When the feed-in exceeds 6 MW the biogas is stored in the tank. When power flow is above 2 MW all the biogas is flowed to the CHP. In the range between 2 MW and 6 MW the local energy manager can control the CHP and storage systems depending on local constraints (state of charge of the storage system, etc). Table 11 (German Solutions) 53/88

54 5.2.3 Vendeuvre-Sur-Barse The French (VENTEEA) demonstration project focuses on DRES and energy storage. This chapter includes a description of the solutions implemented. Solution to test MV Distributed Voltage Control Demo plan In this solution the idea is to test a control schemes of the type Q = f(u) in order to compare the results with the actual French static approach (tan φ = -0.2). The controller is implemented locally in the DER, adapting the reactive power injection to the actual voltage at the connection point. The procedure is presented in the figure below: And can be summarized in the following steps: 1. Measuring the voltage at the connection point. 2. Calculation of the Q set value (Q REF). 3. Sent the calculated value to the generation facility to reach the set point (the control cycles every 10 seconds). The function Q = f(u) applied is: The figure above presents the results of applying Q(U) control on a wind farm. In the upper part of the figure the voltages in the primary substation (green) and in the interconnection or controlled point (blue) are plotted. In the bottom part of the figure the active and 54/88

55 reactive power are presented (the reactive part is consequence of applying the Q(U) control). MV Centralised (SE) Voltage Control This solution is based in the DSE using a few additional measurements from the distribution network (these measurements are synchronized on 10 minutes average values): - Over the normal current (I) measured at feeder level, active (P) and reactive (Q) power measurements are acquired. - 3 additional RTUs per feeder are necessary for testing and validation purposes. Simulations have been performed to verify the more appropriate placement of these RTUs. - At locations with generation, voltage and current measurements are in place to identify voltage problems. Then, the estimated network state is used to calculate the optimal set point for the primary substation OLTC minimizing the number to changes. At the same time, a control of the type Q = f(u) is also implemented locally by the DER installation. Although this function aims to increase the amount of DG through solving the voltage rise issue, it also has to satisfy the constraints of the network LV limits. The wind farms connected on a dedicated feeder used in the demo can also receive commands to minimize the reactive power drained from the transmission network (there is not any local Q(U) control running). MV Power Flow & Ancillary Services with Storage This solution aims to use the storage to provide/storage active power 55/88

56 in case of network constraint violation to solve it (2 MW and 1.3 MWh based on a Lithium-ion battery; 2 MW / 30 minutes down to 0.5 MW / 2 hours). The focus of the French storage demonstration is the aggregation of several services for different stakeholders (TSO, DSO and wind farm operator). An analysis has been made identifying the sort of the services that can be provided by the storage system and the potential stakeholder (TSO, DSO, DG/RES, End-Users and the Storage owner): the applicability or adequacy of each service within the solution distinguishing 4 categories: - Not feasible in VENTEEA - Not considered in VENTEEA - Dedicated feeder mainly - Feeder with customer mainly The image below serves to illustrate the services, to be tested: Also it is analyzing the feasibility to provide several services at the same time (it is priority of de demo to identify potential uses and combinations). Simulations have offered a good result but must be confirmed with real tests. In the figure below it is presented the system architecture of the storage system. From the remote control system a schedule is submitted periodically from a day-ahead or hour-ahead planning based on production and load forecasts (there are not real time set points sent to the storage system). The local management control is in charge of executing the schedule. The system receives local 56/88

57 measurements (every second) from the wind farm injection and modulates the battery output to follow the schedule. MV Centralised (SE) Voltage Control with Storage The purpose solution is to enable an increase in DG s connection on existing distribution circuits instead of building dedicated MV feeders or reinforcing the existing MV network. It aims to use the storage to provide/storage energy (active or reactive) in case of voltage violation:: 1) The DSE obtains the state of the MV network in real time from the data gathered (voltages, tap changer position, DER voltage regulator, etc...). 2) In case of voltage threshold violation, DSE sends the voltage profiles along the MV feeders to the storage optimization process and to the Controller. The storage optimization process will be able to calculate at each moment the active/reactive power flexibilities available in the storage (maximizing the earnings by optimizing charge and discharge cycles considering all, the actual and future market opportunities). Also it decides if the problem is going to be solved applying the P and Q set-points to the storage system. MV Monitoring with DSE From the P, Q, V, I measurements at the primary substation level and at the DER injection point and other measurements in the MV network, the estimation state function calculates the voltage profile of each MV feeder taking into account the network topology and characteristics (impedance). It is implemented in the DMS system. A summary of the information process can be summarized as follow: 57/88

58 5.2.4 Carpinone 1) Sensors measure active and reactive power 2) Sensors installed in the primary substations send the measures to the SCADA 3) Sensors installed in aerial feeder sends directly or through a RTU the measures to the SCADA (communication architecture not defined yet) 4) RTU recuperates the measures from the sensors installed in the secondary substations 5) RTU sends the measurements to the SCADA 6) Sensors (through metering system) measures at the injection point level and sends this measures to the DEIE (device installed at the DG level). 7) DEIE sends the measures to the SCADA 8) SCADA updates the network parameters 9) SCADA updates the actual network topology 10) DSE function runs and calculates the loads in the MV network and at the secondary substation level 11) DSE function calculates at the same time the voltage profiles along the MV feeders Table 12 (French Solutions) The Italian (ISERNIA) demonstration project focuses on the refurbishment of a specific electric grid, with a focus on standardization, unification of technologies and cost reduction. Solution to test MV Supervised Voltage Control Demo plan The scope of this solution is to keep the voltage profile on a MV grid with DERs in the range defined by the norm EN minimizing operating costs. The voltage control algorithm resides into DMS. The Operation Control System receives violation signals from the devices where the generators are connected to. Also economic evaluations and load/generation forecasts are additional inputs to the algorithm in charge of selecting the appropriate control actions. The control action could be: 58/88

59 - Send a command to the generators along the MV line to work in reactive way (with the objective to involve the lower number of generators) - Act on OLTC to decrease/increase the voltage on the whole grid portion - Restrict the active injections of generators, keeping the same production of reactive power according the technical limit of the generator, until in the worst case, suppress the active injection of the generator The sequence of operations followed in the solution is as follows: 1) In the HV/MV substation, at the MV busbar, calculating the voltage set-point that is going to be applied to the OLTC in the HV/MV transformer (by the DV7500 device). The calculation is made using global information in DMS every 10 minutes. The central system receives from strategic network nodes voltage measurements. 2) Locally, at DG level (along the MV grid), performing a local voltage regulation: in case of high/low voltage detection in the controlled point, the controller will start to modulate the reactive power. There are two possible local controls, mutually exclusive. One with a continuous Q=f(U) and one with the cos(phi) = f(u). The second approach is cleaner as the regulation is proportional to the power injection. 3) Locally, at DG level (along the MV grid), performing the socalled centralized voltage control: this regulation is actuated if the local voltage regulation is not enough to solve the over/under voltage problem. The calculation is made by obtaining online a sensibility matrix in order to identify which generator will have the largest impact as to eliminate the alarm. 4) In extreme situations the control center could ask the generator that causes the overvoltage to curtail the active power generation. 59/88

60 The performances of the above described operation has been tested and show the differences of each approach: MV FLIR (Anti-islanding Relay) The scope of this function is to avoid unintentional islanding operation in MV grid with DERs; in case of local balance between generation and load, LOM on a section of MV grid with DERs could not be detected by DERs local protection. Generators remain then connected to the MV grid creating an unintentional island. In order to avoid this situation, the SCS (Substation Control System) sends a message to interested DERs for remote tripping of DER protections. The communication channel is based on LTE or fiber because the response time requirements (trip in less than 500ms) The SCS can locally monitor the state of the primary substation circuit breaker, while the status of circuit breakers located in secondary substations can be obtained through the MV Control Systems, which generate an event message whenever a monitored circuit breaker trips. The SCS hosts the state estimator (with topology processor), the power flow and voltage regulation algorithms, and other supervisory, control and protection devices. When SCS detects a trip along the line sends an explicit command to the CCS in order to trip the DER local circuit breaker. Additionally the anti-islanding control system could be complemented with distributed local controllers near DER devices. To test this device the following action has been made: 60/88

61 1) Installation of device in a real grid. 2) Installation of provisional DRES in the same feeder that device. 3) Open the breaker where the device and DRES are installed to produce an isle. 4) Testing the correct detection of Islanding. It is not intended to substitute the min/max frequency or the min/max voltage relays, but overcome the problems related to their NDZs (no detection zones). MV Power Flow & Ancillary Services with Storage The local control system will use the ESS to optimize both the active and reactive power exchanges between the node and the feeder; alongside the mitigation of the PV emission and EV recharging impact on the network, a real optimization of both local and global parameters will be taken into account. The ESS has the following power and energy characteristics: - 1 MVA of max power (rated power in normal operation is 500 kva); kwh of energy capacity; The typical objective of the storage is smoothing the load profile, voltage control and load levelling. The Electric Storage System is located in the MV network in correspondence of a MV/LV substation. The functions that are testing refer to: 1) Optimization of the complex node taking into account the PV power plant, the recharging station and the load of some supplied customers; 2) Voltage regulation using active and reactive power exchanges between the node and the feeder; 3) Black start of a portion of the MV network isolated. This is a function that allows energizing a limited portion of the MV network in case of a lack of the main. When the main returns the synchronization between the two islands (storage and the mains) is done with a normal synchrocheck relay. 61/88

62 The installation is consists of 3 blocks: battery container, transformers box and control box: LV Customer Engagement The scope of the solution is to demonstrate whether giving to end users a feedback on their energy consumption can address more efficient energy behaviors. It allows to end users to have certified information on electricity data managed by their electronic smart meter at their fingertips. The consumers participating to the project thus receive an energy monitoring kit and dedicated interfaces. The identified tested features, which users valuate as of outmost importance, are: 1) Easy user interface to be accepted by all trial participants (having different profiles: age, education, etc.). 2) Instantaneous power. 3) Receiving alarms. Alarms can be set by the user to receive 62/88

63 an alert when the energy usage gets to modifiable thresholds. 4) Establish continuous communications (feedback). Consumers can reach their goals of personal efficiency while having a key role in supporting the aforementioned continuous improvement process. The data gathered includes also answers to three waves of sociological surveys: 1) Completed before the massive distribution of monitoring kits to the trial participants. It was aimed at defining a representative model of the consumers. The 67% of the interviewees showed their interest in the project. 2) Two months after the distribution of monitoring kits to the trial participants. It was aimed at collecting the early on consumers awareness, understanding and attitude towards energy and its use and the first impressions: o o o o o o 94% of the participants declared they acquire a better understanding of their consumption. 9% of the participants discovered an unexpected consumption due to some of their appliances and decided to replace them with efficient ones. 59% declared they modified their habits in the usage of their appliances. 79% of the participants declared the kit is a good means for verifying whether their supply contract is appropriate. 96% of the participants gave a positive judgment of the kit in terms of usefulness. 77% of the experimenters use the display at least two or three times a week. 3) At the project completion. It is aimed at verifying whether the consumers awareness, understanding and attitude towards energy and its use have changed during the project. 63/88

64 MV FLIR (Grid Automation) The test made on this solution intend to demonstrate that automation equipment of the MV grid together with grid operator of superior SCADA supported will resolve failures on MV grid, minimize impact of the failure providing the alternative feed, and based on proposal of regional SCADA will return operation of MV grid to standard conditions after recovery from the failure. The functionality of fault identification on MV line is performed through logical selectivity, which means that fault detectors send/receive the inhibition/block signal from the other substation along the line. Once a fault happens on MV line, the fault detector of the substation feeding the fault sends a block message towards the upstream substations, while it sends a trip message towards the downstream substations. 1) Fault detection and clearance The protection devices in the grid detect the fault and issue suitable breaker tripping. 2) Fault localization Identify the physical location of the fault by analyzing the telemetered alarms received from protection devices in the grid. 3) Fault isolation Determine switching actions which will isolate the faulty equipment(s) from the rest of the grid. 4) System restoration Resupply those healthy parts of the grid, which are de-energized during the fault clearing, but considering safety issues. Table 13 (Italian Solutions) Lungau, Köstendorf, Eberstalzell and Littring The Austrian prosed solutions focus on the effective integration of distributed renewable energy sources by exploring the applicability and results of different alternative solutions under different scenarios. Solution to test MV Centralised (SE & OPF) Voltage Control Demo plan The solution consists on a central optimization of the set points for the OLTC transformer and the generators (reactive power) via applications in the Distribution Management System (DMS). A Distribution system state estimator uses a network model and the 64/88

65 primary voltage of the 110/30 kv transformer for an optimized online calculation of the network situation. Based on the state estimation and the target function selected the Voltage/ VAR Controller (VVC) defines the set points. The test made in the 30 kv grid in Lungau showed that an additional increase in generation capacity of around 20% in critical network sections / branches is realistic. MV Centralised (SE & OPF) Congestion Management This is almost the same solution that above. The only change is that the OPF optimization function is changed. So all the characteristics described for the previous solution are valid for this, the basic difference is the objective function of the OPF, that is used to relieve network overloading constraints by means of a voltage reduction (some loads modify the consumed energy depending on the supplied voltage). MV Supervised Voltage Control with field measurements The solution explores a decentralized MV Voltage VAr Control (VVC) scheme in comparison to the centralized approach of the previous solutions. One difference is that the optimal solution calculated by the OPF is here replaced by a sub-optimal (but less complex and very robust) two step algorithm issuing commands for the OLTC ( Level Control ) and to the generators ( Range Control ); other main difference is that, instead of a distribution state estimator, some measurements from critical nodes are used to create a network 65/88

66 snapshot suitable for the algorithms. As a consequence, no historical load profiles are necessary, because actual voltage measurements are used. The control strategy is based on two separate objectives: One keeps the range (spreading width) within the allowed voltage band range and the other is to bring this range to the right voltage level, so that neither upper limit nor lower limit is violated. In the test phase at the end of the field trials, the performance of the developed voltage control concepts (distributed and coordinated control scenario) was compared with the conventionally controlled grid (reference control scenario). Therefore the control strategy that operates the grid was switched in a daily cycle over a period of several months in winter/spring 2013 to record highly comparable data from each control strategy. In Lungau grid the highest voltage drop occurs in winter (high loads), and the highest voltage rise occurs in spring (high generation due to snow melting), so in the inspected time period both extremes are contained. LV Distributed Voltage Control In this solution, local actors, like PV-inverters, transformers with onload-tap-changer and e-vehicle charging stations have to ensure their local voltage limits according EN There is no communication between the grid controller and the mentioned actors of the system. Each local actor works on his own, taking into account local available measurement values of the voltage and try to stabilize it with the help of a locally acting controller. In the test phase at the end of the field trials, the performance of the developed voltage control concepts (distributed and coordinated control scenario) was compared with the uncontrolled grid (business as usual scenario). Therefore the control strategy that operates the grid was switched in a daily cycle over a period of several months in spring/summer 2014 to record highly comparable data from each control strategy. The main focus of the evaluation was the comparison of the voltage 66/88

67 band the different control concepts required. In the figures above it show the Voltage band allocation in Littring and Köstendorf for the different control strategies. Percent values on top of the bars show the voltage band referring to nominal (230 V). Littring voltageband % Littring 40 Voltage Band [Volt] reference 14.5% local 13.5% distributed 12.7% coordinated Köstendorf voltageband Köstendorf Voltage Band [Volt] % reference 8.3% local 8.0% distributed 7.4% coordinated 5 0 LV Distributed Voltage Control with AMI This solution applies remote voltage measurements from a set of smart meters to calculate the optimal transformer tap. The logic uses unidirectional communications from the meters at the MV/LV OLTC while the PV inverters remain in LV Local control mode. The final objective of this solution is the same that the previous, the difference is the method that the solution uses to get the voltage measurements in the network in a simple control logic for transformer tapping. The communication infrastructure is used to get information of the actual voltage band violations to control the voltage level in the system. The other actors like PV-inverters and e-mobility charging units are still in droop control mode as well as in previous solution. 67/88

68 There is only a unidirectional communication from the components of the grid (especially smart meters) to the LV grid controller. One limitation in this stage is that, due to the limited bandwidth of the communication, not all smart meters can be used as measuring points. Therefore, a previous selection of for the control relevant measuring points has to be performed. This selection can be done by off-line analyses of the low voltage grid and program the LV grid controller with the determined points. LV Supervised Voltage Control with AMI In this solution additionally to the previous one, it is possible to give a system wide strategy resulting in new set points for the locally acting controllers of the actors (e.g. dependence of the reactive power to the voltage for PV inverters) via broadcasts by the LV Grid Controller. This approach enables that all PV inverters are able to feed in a higher amount of active power Sperchiada There is a simple bidirectional communication now, but only a suboptimal solution for the goal of maximizing the feeding in of active power. The global set points for the actors broadcasted can be substituted by individual set points for all active network components. The individual set points will be generated by the optimization algorithm of the LV grid controller. In this case much more information about the low voltage grid and its actors is needed by the LV grid controller to address the actors properly. Also a selection of important measurement points is again needed. Table 14 (Austrian Solutions) The solutions implemented by HEDNO in cooperation with ICCS consisting on testing, demonstration and evaluation of advanced management tools and monitoring applications. Solution to test Load/RES Forecasting and Probabilistic Load Flow Demo plan This solution is built over the advanced metering infrastructure deployed into the MV network of pilot site. Each MV node with either generation or load is equipped with a smart meter communicated through GPRS. The main server running the application software 68/88

69 hosts the database, the modules (Forecasting and Probabilistic Load Flow) and the Web server, that provides the Human Machine Interface (HMI). Next to the data coming from the HV/MV substation and the telemetry center, meteorological forecasts are provided every morning. The modules of Load & PV Forecasting and the Probabilistic Load Flow are executed periodically and the results are also stored in the database. Finally, the web server hosts a web page that is used for the display of the results to the authorized users. DMS Telemetry Center Meteo Forecast DB Load Forecast Probabilistic Load Flow PV Forecast Web Server The PV forecasting model runs once per day providing predictions for the next three days. The basic process that has been tested could be summarized in the following steps: 1) The electronic meter data is daily transmitted via GPRS. 2) Measurements of voltage and current at the beginning of the feeders are available by the SCADA DMS systems, in realtime. 3) The combination of these real-time measurements and the forecasted load and PV generation values obtained by the forecasting algorithm from the electronic measurements of the previous day, provides information for the analysis and prediction of undesirable situations 4) To take preventive actions based on probabilities of violating operational constraints The aim of the test is to demonstrate that the above process allowing 69/88

70 reduce the violating operational constraints rather than based on static evaluations. A number of further functionalities have been also developed: - Estimation of the RES Hosting Capacity of MV grids. - Congestion and Voltage Limits Violations Management. - Planning of PV control strategies (set-points for DERs) or PV Production Curtailment. Table 15 (Greek Solutions) 5.3 SuSTAINABLE process to test the solution evaluated Sustainable has proposed different functionalities, which were developed inside the project. These functionalities were tested at different levels: simulation, proof of concept (using real data) and field activities. This section focus on the field activities to validate those functionalities that will have a final field validation. Figure 10 (SuSTAINABLE Functionalities) 70/88

71 Solution to test Functionality SF1 Load Forecast Demo plan Accurate prediction of the load plays an indispensable role in power system planning and electricity market analysis. This work presents comparative analysis of the state of the art of some most widely used load forecasting methodologies and applies them to real power networks. It is confirmed that any of the approaches can be effective in load forecasting and that they have comparable performance if relevant parameters are set appropriately. This work also presents a pioneering ANN based approach to estimate percentage of different load categories and distinguish controllable load from the total demand at bulk supply point at any given time based on RMS voltage, real and reactive power measurements at the substation. Load compositions obtained by the AI tool are compared with the validation data and used for load characteristics estimation and validation. The estimated controllable and uncontrollable load percentages are also compared with the targets in the validation process. Moreover, the probability distribution and the confidence levels of load participation estimation errors are obtained. The robustness of the methodology with respect to missing input data is also evaluated. It demonstrates that the absence of an input, especially the absence of the reactive power, can reduce the confidence level of estimation with the same estimation error. This module could be integrated with the total demand forecasting tool, which will provide prediction of load compositions and their controllability and eventually facilitate effective demand side management (DSM). Furthermore, the work presents an approach for prediction of DRD at distribution network buses by taking into account daily variation in demand composition and generic dynamic responses of different types of load. Dynamic load models for different load categories obtained from field or laboratory measurements or through appropriate mathematical modelling are used in combination with the derived or predicted hourly load compositions at the given bus. According to the result of load composition prediction, uncertainties in both dynamic load model/responses of individual loads and load compositions at different times of the day are modelled probabilistically. With established dynamic signatures of different load categories and load compositions 71/88

72 at different times of the day, Monte Carlo Simulation is used to predict probabilistic real and reactive power responses, including ranges of variation of these responses at given time of the day. Finally, a DSM example is shown to illustrate the change to DRD when control action is taken. Functionality SF2 RES Forecast The penetration of renewable energy sources for electricity (RES-E) is reaching non-marginal levels in numerous power systems. For example, the installed solar power in Germany, Italy and Greece is around 37 GW, 17 GW and 2.5 GW respectively. In terms of wind power, the installed capacity in Portugal is around 4.8 GW (for an annual peak load of 8 GW), 1.8 GW in Greece and 34.6 GW in Germany. In terms of cost, PV is reaching grid parity in many countries, meaning that it can generate electricity at a levelized cost less than or equal to the electricity retailing tariffs. In this context, the deployment of solar PV will likely continue even if financial subsidies are withdrawn. The same is happening with wind power, even with a reduction in the feed-in tariffs. For instance, in Spain wind farms participate directly in the market and the feed-in tariff was reduced to a variable premium component. The majority of wind power is connected to EHV and HV, but small installations up to 2-5 MW are connected to MV and urban turbines of around a few kw are connected to the LV level. The majority of the installed solar power is connected to the MV and LV distribution grids. The roll-out of the smart grid infrastructure provides additional capabilities for monitoring and controlling assets at the distribution grid level, and fosters demand-side management and renewable energy integration. This creates conditions to develop a new generation of 72/88

73 management tools that maximize the integration of distributed generation at the MV and LV levels [3], such as boosted voltage control, state estimation algorithms and energy management systems (including an optimal power flow and machine learning algorithms). Furthermore, a massive deployment of small-scale storage at the residential level (e.g., thermal storage, batteries) might occur if governments create incentives for such goal. For instance, Germany has created financial incentives for owners of solar systems with batteries. At the building and micro-grid level, energy from solar PV and urban wind turbines can be combined with storage (e.g., supercapacitors) and nonrenewable energy micro-turbines (e.g., gas) using centralized and local energy management functions. Moreover, for operating large-scale storage (e.g., hydro pump storage) it is also essential to have accurate wind and/or solar forecasts. The new management tools, and the joint coordination of PV/wind generation and storage at the building and grid levels, require the use of power forecasts for several hours ahead. The time-horizon of interest for power system operations and electricity markets can be divided into two classes: (a) very short-term (up to six- hours-ahead); (b) short-term (up to three days ahead). This functionality describes different statistical forecasting models that produce wind and solar forecasts covering both time horizons and exploring information from the smart grid infrastructure. Functionality SF3 State Estimation The advanced local distribution grid monitoring / state estimation will be based at the top level of the system architecture (central systems) and also at the HV/MV substation having as input information gathered from lower levels of the architecture. 73/88

74 Hence, equipment deployed must not only have a great deal of processing capability but also be able to collect data from local sensors. Also, an adequate and flexible communication link must be guaranteed. For this functionality, several data acquisition points are required so that enough redundancy is assured to make the state estimation function converge and have accurate results. The main objective of the state estimation (SE) functionality is to find the values for a set of variables (states) that adjust in a more adequate way to a set of network values (measurements) that is available in real-time. The state variables are such that all the other network variables can be evaluated from them, and the operation state is obtained. The calculation of the state variables considers the physical laws directing the operation of electrical networks and is typically done adopting some criteria. The Distribution State Estimation (DSE) is implemented at the functional level of the HV/MV primary substation and only the MV level state variables are calculated. It is assumed that the state estimation functionality will be installed at the central management level, i.e., at the SCADA/DMS. The following issues should be considered in distribution networks: Instrumentation: no (or only a few) sensors in the distribution networks. Algorithmic: long radial feeders with heterogeneous lines and cables may result in ill conditioned matrices. Large number of nodes: long calculation times. Active and reactive power cannot be decoupled: decoupled 74/88

75 transmission state estimation algorithms cannot be applied. Functionality SF4 Advanced Voltage Control Large-scale integration of Distributed Energy Resources (DER), and especially variable RES, brings significant challenges to grid operation that require new approaches and tools for distribution system management with voltage control being one of the most demanding tasks. Within the SuSTAINABLE concept, advanced voltage control involves a coordinated management of the several DER connected at the MV and LV levels in order to ensure a smooth and efficient operation of the distribution system as a whole. The proposed approach is developed in accordance to the technical reference architecture defined, where the main focus is put at the MV network level. Furthermore, the database specification and data communication requirements for both the MV and LV control are identified. Therefore, two innovative approaches for voltage control at the MV level are proposed. These approaches are based on a preventive dayahead analysis using data from forecasting tools for load and RES to establish a plan for operation for the different DER for the next day and a corrective intraday analysis aimed at minimizing the deviations from the day-ahead plan. INESC developed an approach for advanced voltage control using a multi-temporal Optimal Power Flow (OPF) solved by a meta-heuristic in order to tackle large dimension systems. The performance of the algorithm is tested in a real, large dimension test network with good results. This algorithm will also be tested through simulation for a part of the Évora network using real forecasting data. ICCS has also developed an algorithm for advanced voltage control in MV networks using multi-objective optimization. A small scale demo network is used to show indicative results obtained from the application of the algorithm aimed at highlighting the variety of problems that can be addressed, as well as the potential solutions and the effectiveness of the algorithm. The resulting algorithm is going to be implemented on the real study-case network of Rhodes. 75/88

76 At the LV level, two distinct yet complementary approaches are proposed and developed by INESC. First, a local control scheme based on active power / voltage droop functions installed at the inverter levels of DER is presented. This action is able to quickly mitigate eventual voltage problems that may arise in grid operation resulting from the variability of RES. Validation of the proposed methodology was achieved through some preliminary tests in a laboratory environment. Another level of control is proposed, where a centralized scheme is able to ensure a more optimized operation of the LV grid. In this case, a set of rules embedded in the DTC implements a merit order aimed at mobilizing the most adequate resources to correct a specific voltage 76/88

77 problem while trying to maximize the integration of energy from RES. This approach has been tested for a real LV test network with largescale integration of DER. Both these approaches will be tested in a laboratory environment of INESC. The centralized control scheme will also be evaluated in real field tests to be conducted in the main site of Évora. Moreover, the impact of the communication infrastructure on the performance of the algorithm is evaluated, which allows assessing the minimum requirements for the performance of the chosen communication system. Functionality SF5 TVPP The design and coordinated operation of a virtual power plant as a cluster of distributed generation units, storage and flexible loads with the network operators are introduced and discussed. An important focus is placed on the TVPP, the part of the virtual power plant that is responsible for providing technical services to the system, as a new tool for smart distribution networks. Firstly, the modelling suites that were considered and include the VPP operation are presented. These cover multiple time scales, from dayahead to intra-day and close to real time operation. The TVPP functions for the different time steps are described and pre-prototyped as tools. The close coordination between the TVPP and the DSO and TSO is emphasized for the provision of each service. Moreover, the case of a virtual power plant with RES and a hybrid station in an island environment is described. In particular, the market environment on the selected use-case of the Greek islands is described and the day-ahead operation of such a VPP is described by also showing a demonstration tool that was developed. An alternative approach to the TVPP for supporting the DSO-TSO coordination is also described which aims at reducing the impacts of the RES variability on the transmission network. The formulation of the proposed approach is described however the algorithm was not developed following the discussion on the technical architecture. Table 16 (SuSTAINABLE Solutions) 77/88

78 6 Common Activities 6.1 Meeting with External Advisors One of the most important activities of these projects is communicating to other technical, commercial, scientific, policy and regulatory communities the results of the analyses performed during the project. Some of these results will be based on scenarios and assumptions provided by these third parties. In order to draw the attention on the analysis carried out during the Project and obtain feedback from reputed professional in distribution grid management and DRES, world-class professionals (from energy sectors, equipment manufacturers, engineering companies, owners/operator of energy facilities, policy makers, R&D institutions, ) are contacted by projects in order to collaborate in the following activities: - Analyse and evaluate current situation, identifying the problems and obstacles presently restricting the large-scale integration of DRES in low and medium voltage grids. - Propose solutions that could be tested during the project. - Provide feedback (according technical, regulatory and economic criteria) about inputs that could be taken into consideration and intermediate outputs. - Carry out recommendations regarding project outputs. These third parties collaborators will be organized in committees in each project ( Stakeholders Committee / Advisory board Committees ), the members of these committees will be able to take part in internal work teams meetings, and periodically are called to participate in meetings. In this section is summarizes the main output of these meetings IGREENGrid Stakeholder Committee Meetings During this period (from January 2013 until June 2014) two Stakeholders Committee meetings have been held. The main objectives of these meetings were: - First Meeting (September 2013, Paris): o o o o Project general presentation and objectives description. To share the expectations/objectives in order to adapt the stakeholders committee meetings. To know if the project objectives are aligned with the stakeholders expectative. To share with the Stakeholder committee members their opinion about the project 78/88

79 approach and best way to achieve the project goals. o Pilot s demonstrator presentations. - Second Meeting (February 2014, Web-conference): o o o o To present the first year main result. To share with the Stakeholder committee members their opinion about first results. To share about the next steps, methodology, others... To collect recommendations regarding Project outputs. The main outputs of these meeting were the feedbacks from stakeholders First Meeting Feedbacks Joint with the presentations made by project members the Stakeholders showed their expectations about the project. These expectations are summarized below: - Establish the responsibilities of TSO and DSO and propose regulation regarding the integration of DRES. - Define processes to evaluate the benefits for the DRES projects. - Evaluate the cost and benefits of DRES and propose business models for DRES - Propose solutions to increase the DRES hosting capacity Second Meeting Feedbacks In this meeting the first main results of the project were presented: - Barriers for connection of DRES in distribution grids. The main conclusions agreed with the Stakeholders after the barriers explanation were: o o o More systems imply additional costs DSO needs to recovers these costs. Regulation must been adapted in order to overcome these barriers. More of standards are needed in order to reduce the costs. - Indicative IGREENGrid Key Performance Indicators (KPI). After the KPI presentation the Stakeholders agreed the KPI methodology defined, but they also agreed that it not possible define a more detailed methodology due the variety of solutions to evaluate. - Data Gathering Methodology. The stakeholders conclude that the tool that will be implemented to support the methodology shall: o Organize the data. 79/88

80 o Perform automatic KPI calculations SiNGULAR Advisory Board Committee Meetings One Advisory Board Meeting has been held in the SiNGULAR project. This meeting was celebrated at ITC facilities in the Canary Islands, in December 2013 (month 12 of the project). The main objectives of this meeting were: - Share the general information of the SiNGULAR project and present the work developed in the different Work Packages. A public workshop was held before the private meeting with the main stakeholders and the family project s representatives. The objective of that workshop was to disseminate the achievements and work developed. - Try to involve the DSO and TSO in Canary Islands, ENDESA and REE respectively, in the pilot demonstrations in the Canary Islands. - To know if the project objectives are aligned with the REE and ENDESA necessities. - To know and discuss with the Committee the project approach and the way to achieve the objectives Meeting Feedbacks The Committee comprised representatives from the SiNGULAR, SUSTAINABLE and IGREENGrid projects, electrical companies in the Canary Islands and a representation of the market regulator in Spain. From the Family Projects: - Pablo Frias, IIT-Comillas (Sustainable Project) - María Sebastián, ERDF (IGREENGRID Project) - David Rubio, Iberdrola (IGREENGRID Project) - Joao Catalao, Universidad Beira Interior (SiNGULAR Project) - Anastasios Bakirtzis, Aristotelio Panepistimio Thessalonikis (SiNGULAR Project) - Gianfranco Chicco, Politecnico di Torino (SiNGULAR Project) - Claudio Monteiro, SMARTWATT (SiNGULAR Project) - Javier Contreras, Universidad Castilla La Mancha (SiNGULAR Project) - Salvador Suáez, ITC (SiNGULAR Project) - Daniel Henríquez, ITC (SiNGULAR Project) From REE (TSO and System Operator in Canary Islands): 80/88

81 - Jesús Rupérez (Operation Department) - Alfredo Rodriguez (Operation Department) From ENDESA (DSO and main Generator in Canary Islands) - José Manuel Valle From CNMC (Energy market regulator in Spain): - Gabriella Nemeth From other companies: - Thomas Walter (WIRSOL) Having heard the presentations by the SiNGULAR partners, the external members of the Committee gave their feedback and expressed their expectations about the project, summarized in the following key points: - From the Regulator point of view, the project addressees key issues that have to be regulated, not only in the electrical insular systems where the impact of high levels of RES penetration will be visible long before than in continental electrical systems. CNMC is highly interested in WP6, WP10 and WP11 of the SINGULAR project. During discussion round, emphasis was put on storage system regulation and its role in the energy market. - Who owns Energy Storage is a key aspect discussed by all participants, and must be analysed. - TSOs and DSOs highlighted the need of a thorough and deep cost-benefit analysis of the tools developed and the need to ensure power quality as a criteria when applying them: service must be guaranteed in any given scenario of RES penetration. They underline also that conventional flexible generation should be highly considered. In general, SO in islands, needs to dispose of tools to handle the uncertainties in high RES penetration scenarios. 81/88

82 6.1.3 SuSTAINABLE Advisory Board Committee Meetings Sustainable will have 2 major Advisory Board (ADB) Committee meetings according to the plan. The first ADB Meeting was held in Porto (Portugal) in the 10 th of December 2013, with the presence of several stakeholders, and in particular the coordinators from IGREENGrid and Singular. Following a brief presentation of the project status, after the first year of work, the ADB Members were invited to take part in an interactive discussion with the project partners on the different topics and challenges that are addressed at project level. In particular the discussion was centred on the following topics: - Main characteristics of Grids in the future coordinated by Prof. Peças Lopes - Design and Operation of Distribution grids in the future coordinated by Dr. Nuno Silva - Main DSO System Services impacted by SG coordinated by Eng. Kostas Kaousias - New business models most effective to deal with DER coordinated by Eng. Aires Messias The following questions were discussed: 1. What are the main characteristics of Grids in the future? Management and control architectures? Main tools for Grid Management? What are the opportunities to ensure resilience of the grid? What main iterations shall we have between DSO and TSO? How should the TVPP be defined? 2. What are main DSO System Services impacted by SG? What are the opportunities to drive value/efficiency in major programs? How to exploit smart metering infrastructure, new Grid sensors, big data analytics, and power quality management? 3. Design and Operation of Distribution grids in the future? What are the opportunities to drive value/efficiency in the day to day operations? Ex) Electric Mobility, asset management, field 82/88

83 force and supply chain, new protection schemes. 4. What are the new business models most effective to deal with DER (Storage, EV ) in next 5 years? What are key areas for collaboration & partnership? Ex) Partners, suppliers, alliances, types of activities. The conclusions of the discussions were very fruitful and the Advisory Boards feedback in certain topics was very important to confirm the alignment of the project developments with major EU ongoing initiatives, e.g., other projects. Below a summary of the main conclusions: Main characteristics of Grids in the Future 1. Ownership and cost of storage is not clear (whose responsibility, CBA for DSO owning ); 2. Grid reinforcement is less expensive than storage; 3. Second life of EV batteries could be used; 4. New architectures and structures must be developed to cope with this future challenges; 5. Sustainable is aligned with the previous conclusion, and is proposing a new architecture; 6. Increase DSO/TSO interactions + Improve communication infrastructure; 7. Solutions capable to manage deviations from DSO by layers (voltage levels); 8. Balancing presently done at system level (single bus approach), for future must be managed in a distributed layers with DER; 9. The TVPP could be an independent operator, but it must include the DSO (who can either own it or operate it); 10. Why should DSOs pass benefits to TSOs? (should be shared?) Otherwise investment will not happen; 11. Distributed intelligence coexisting with hierarchical control schemes; 12. Allow islanding operation and automatic restoration to increase resilience (part of the grid surviving following nature disasters); 13. GOOSE communications solutions for MV protections automation and islands operations. Design and Operation of Distribution Grids in the Future 1. Aggregated view of DSO grid and connect DG; 2. Reactive power compensation aggregated at the primary substation; 3. Participation on markets dynamic markets; 4. Balancing; 83/88

84 5. Compliance with the European grid codes; 6. Control of DG according to operational parameters (frequency); 7. Participation of certain DG on services for the grid; 8. Standardization of smaller unites; 9. Controllable large DG; 10. Real-time information about specific parameters and levels of the grid; 11. Sustainable architecture adequate. Main DSO System Services impacted by SG 1. Increase the degree of automation based on available data; 2. Deploy automation on the LV grid ; 3. Common standards for metering (meters and also data formats); 4. Reliability of SG assets such as meters; 5. Increasing intelligence in the grid, changing the role of the grid operator; 6. Increase efficiency and improve reliability through wider control given to the operator; 7. New functionalities at the control center base on new data available; 8. Big data analysis; 9. Private vs public system; 10. Availability of data and ownership; 11. Smart Meter restricted / filtered recorded data and correspondent impact; 12. Security of the communications and privacy of data. New business models most effective to deal with DER 1. DSM in the market; 2. Storage to participate in the reserve market; 3. Aggregators bidding in the market aggregating small units; 4. Car sharing with car charging model; 5. Second life for batteries (leasing process); 6. Ancillary services from independent operators (e.g. aggregators); 7. Use thermal power for storage (aggregators or retailers); 84/88

85 8. Aggregators working on the market with other retailers (service providers for forecast); 9. ESCO managing the home, microgrid operators; 10. EV batteries for storage (automatic) Nissan in doing it in Japan connected with DSO; 11. Mixed aggregated storage, EVs, loads specialized aggregate or more broad scope; 12. Storage owned by DSO/TSO can be possible (opportunity); 13. Contracts with Generators available for TSO/DSO control during emergency situations; 14. Mixed market regulation (Marketing does not solve everything!). The meeting ended with a visit to the laboratory of INESCP, where the participants had the opportunity to take a closer-look to the setup for one of the use-cases. 6.2 Web Presence and Interaction Sustainable has its website running since March 2013, and a Facebook page, which are connected to IGREENGrid and Singular projects, reinforcing the online cooperation of the projects. Website: Facebook: IGREENGrid has its website running since July 2013, and also have groups/profiles created in social media: Website: LinkedIn: Facebook: Twitter: twitter.com/igreengrid 85/88