Smart grids. Socioeconomic value and optimal flexibility portfolios JUNE RTE s contribution ABRIDGED REPORT

Transcription

1 Smart grids Socioeconomic value and optimal flexibility portfolios JUNE 217 RTE s contribution ABRIDGED REPORT

2 RTE, France s Transmission System Operator, shall not be liable for damages of any nature, direct or indirect, arising from the use or exploitation of the data and information contained in this document, including any operational, financial or commercial losses.

3 Smart grids Socioeconomic value and optimal flexibility portfolios JUNE 217 RTE s contribution ABRIDGED REPORT

4 Contents Section 1 Background and aims 6 Section 2 Perimeter of studies 8 Section 3 Methodological framework Overview of the methodological framework defined in the July 215 report Presentation of aspects of the methodology provided since Representation of scaling up and modelling of competition effects between smart grid functions Representation of the transmission network Representation of the diversity of possible local configurations Representation of the short-term operation of supply-demand balance Positioning of the new results compared with work published in France and in Europe 17 Section 4 Assumptions Context scenarios for the mix Networks Technical characteristics and costs of various technical solutions 22 Section 5 Main findings Helping to manage peaks in national electricity demand is the main economic reason for having smart grid flexibility solutions Services for short-term balancing of the power system Services for the transmission network Contribution to capacity adequacy on the security of supply criterion The economic relevance of smart grid flexibility solutions is rising as capacity requirements emerge to secure the power supply 25 Smart grids: Socioeconomic value and optimal flexibility portfolios - 4

5 / Contents 5.3 This need could be covered by a combination of smart grid solutions By 23, the net benefits in deploying the smart grid solutions studied will be worth around 4 million/year By 23, deploying smart grids could reduce CO 2 emissions by around.8mtco 2 /year, factoring in the life cycles of the solutions deployed 29 Section 6 Detailed analysis Storage Economic analysis Additional details to do with value for the transmission network Environmental analysis Residential demand-side response Economic analysis Environmental analysis Demand-side response in industry (and in the major tertiary sector) Economic analysis Environmental analysis Wind power controllability Economic analysis for the transmission network Environmental analysis for the transmission network Economic analysis for short-term supply-demand balancing services Environmental analysis for short-term supply-demand balancing services 39 Section 7 Conclusions and outlook 4 Bibliography Smart grids: Socioeconomic value and optimal flexibility portfolios

6 Section 1 BACKGROUND AND AIMS For several years now, France and many other European countries have been committed to an ambitious energy transition. In France, this commitment has been embodied by the Energy Transition for Green Growth Act which came into force in August 215. This defines the country s aims in terms of changes to the power generation mix, managing consumption and switching to electricity. In particular, increasing the use of electricity generated using renewable, variable and decentralised sources and changing the way in which we consume power such as by developing electric mobility solutions represent numerous challenges for all of the stakeholders involved in the power system. The innovations made possible by information and communications technologies include so-called smart grids for helping to take up these challenges. Many of these innovations have already been technically validated in a number of different frameworks (smart grid, demonstrators, involvement in market mechanisms, etc.) Their deployment is a potential lever for economically optimising the whole power system, reducing its environmental footprint and consolidating a competitive industrial sector that can create new jobs. To support the rational deployment of these various smart grid solutions, the public authorities needed public quantitative analyses of their socio-economic value (economic and environmental utility, and an assessment of their benefits for employment). The aim was to supplement the information provided by the various demonstrators. This had been used to make an initial selection of promising smart grid solutions, but their analyses had been highly heterogeneous in terms of method, assessment scope and underlying assumptions, making using them difficult as a means of supporting the deployment of smart grids. Within this context, RTE set up a working group made up of all of the stakeholders involved in the electricity sector, including with manufacturers working on developing smart grid solutions, academics and representatives of institutional bodies. Then in July 215, it submitted a socio-economic assessment of smart grids to France s ministers in charge of the economy and energy. This report was used to produce a reference methodological framework for assessing smart grids. This framework was shared with stakeholders in the sector and was then used to identify the smart grid solutions which seemed promising from an economic and environmental perspective, as well as in terms of their ability to create jobs if deployed on a large scale. It therefore served as an initial decision-making tool, informing discussions between manufacturers and public stakeholders tasked with making choices on implementing certain smart grid solutions so they could focus on developing those functions which had the most value for the power system, the environment and the job market. Following submission of this study, France s ministers in charge of the economy and energy tasked the ADEME (The French Environment and Energy Management Agency), the ADEeF (Association of the Distributors of Electricity in France), Enedis and RTE with continuing these analyses, supported by a working group expanded to include all stakeholders involved in the power system and under the supervision of the ADEME and RTE. The purpose of these assessments is to provide the various stakeholders concerned with more information in their decision-making processes: (i) the public authorities so they can define a useful regulatory framework and promote if needed the deployment of the solutions through various forms of support, (ii) the manufacturers of these solutions, so they can focus their innovation strategy on solutions which have high added value from the collective perspective, (iii) the transmission system operator and the distribution system operators, so they can define their strategies (either through direct investment or through recourse to services supplied by third parties) such that they can meet requirements in terms of network management and, specifically for the TSO, system management so they can keep costs down and improve the service delivered to users and (iv) the market parties (particularly flexibility operators) so they can effectively focus their development strategies. Smart grids: Socioeconomic value and optimal flexibility portfolios - 6

7 Background and aims / Section 1 This report is RTE s contribution to the public authorities request. It provides a summary of the analyses carried out, in consultation with the stakeholders, and supplements the information contained in the July 215 report on the socioeconomic value of smart grid solutions, in particular: w by refining and updating a number of assumptions (characteristics of the services provided by smart grid solutions, costs, etc.) using feedback that has been available since July 215 and contributions from the sector s stakeholders, and by assessing the sensitivity of the results to those assumptions which are the most hotly debated and about which there is the most uncertainty; w by expanding the scope of the smart grid solutions studied to include power storage and various demandside response solutions in particular, and factoring in for residential demand the types of usage management made possible by smart meters which are in the process of being deployed; w by transitioning from analyses focusing on limitedscale solution by solution deployments (on the margins of the system) to ones which take more of the flexibility potential into account, as well as the effects of deployment levels on economic value and the effects of competition between various smart grid solution, ultimately recommending economic efficient multi-solutions deployment scenarios. w by supplementing the analyses conducted in relation to future contexts (the situation in 23) with assessments of the current situation. This way, the time frames for which the deployment of smart grid solutions is appropriate can be identified; w by factoring in life-cycle analysis into environmental assessments, in order for example to assess the influence of place of manufacture. 7 - Smart grids: Socioeconomic value and optimal flexibility portfolios

8 Section 2 PERIMETER OF STUDIES Smart grids are made possible by innovations in software, hardware and the way in which the power system is structured. The result is new services and optimised operation resulting in savings from an economic, environmental and technical perspective. The sheer diversity of smart grid solutions (in terms of the services they provide for the power system, the maturity of the technology involved, etc.) means that decisions have to be made about the perimeter of the solutions to be studied first. A hierarchy of priority was drawn up based on the following criteria: w Public debate w Existence of economically significant potential w Technological maturity Coherent criteria should be used for determining the perimeter of the solutions studied. This is because the various smart grid solutions available can either provide the power system with complementary services, or, on the contrary, similar services. In the latter case, there can be competition between the solutions available. The value of a smart grid solution and its efficient level of deployement are therefore likely to depend on whether or not other advanced functions of smart grid have been deployed. In the reflections and analyses undertaken by public or private stakeholders, it is therefore particularly useful to have analyses which provide an understanding of the main potential effects of discarding a particular solution or of the effects of their complementing one another. This will obviously have an impact on the perimeter of the functions to be studied. Based on these criteria and in consultation with the various stakeholders, changes were therefore made to the perimeter of the smart grid functions studied compared with the one used for the July 215 report: w Some functions have not been kept within the perimeter of the analysis, as new analyses would not have led to the results being significantly reviewed. This applies to automatic fault detection, which RTE has now deployed over most of the transmission network, and to observability of renewable energy generation, for which the regulatory framework has now been defined 1. The July 215 analyses have helped inform these decisions and policies. It also applies to dynamic line rating systems for the transmission network, a function for which the economic utility presented in the July 215 report is still generically valid, but for which conducting more detailed analyses will involve a technical characterisation (in terms of savings on transmission capacities) which is highly specific to each local situation. Preference will therefore need to be given to a technical characterisation on a case-by-case basis, within the framework of operating and developing the transmission network. w The functions that provide a flexibility service, partly studied in the July 215 report, have been kept and expanded. Additional analyses have been conducted of them in order to determine efficient levels of deployment. These analyses also include details of the effects of the solutions being scaled up, factoring in the effects of one of them being discarded or of the functions complementing one another, and based on the most realistic hypotheses that can be made in relation to the costs and technical characteristics of the services that can be provided. 1. European Requirement for Generators network code, published in the European Union s Official Journal. Smart grids: Socioeconomic value and optimal flexibility portfolios - 8

9 Perimeter of studies / Section 2 The analyses conducted seek to determine the efficient levels of deployment for these various solutions. They factor in the effects of (i) increases in costs as the capacities deployed/mobilised are increased (this applies to demand response /load modulation in particular) and (ii) decreases in benefits based on each solution s level of deployment and the deployment of potentially competing solutions. The effects of benefits decreasing involves both the values associated with the services for supply-demand balance and local services for the transmission network. Several hypotheses regarding the costs of the solutions studied have been used when conducting these assessments if they are subject to significant changes (battery storage, usage management solutions in the residential sector, etc.). The analyses assess the overall economic and environmental contribution (in terms of greenhouse gas emissions avoided) that these solutions make to the power system. Power storage There is significant variation in demand on the French power system. The rise in renewable energies means that the power system is faced with a greater need to be able to tackle this variability. For these reasons, energy storage seems to be an ideal technical solution: energy consumed can be more equally distributed over time and power can be smoothed, so the electricity generating mix and networks can be more appropriately sized. Power storage solutions have already been incorporated into the power system on a relatively large scale. Examples include hydraulic gravitational storage (lakes, pumpedstorage hydroelectric generation units) the development potential of which would appear to be limited and certain usages which involve electricity being stored (e.g. electrically-heated domestic hot water) 2. A great deal of the work being done on the technical, economic and institutional aspects of the issue is currently focused on other storage technologies including batteries, the performance and costs of which are continuously being improved. Their incorporation on massive scale into the power system must be considered. The perimeter studied in this new report focuses on battery storage and storage using new pumped-storage hydroelectric stations. The perimeter of the services studied has been extended beyond what was covered in the July 215 report. It now factors in various services that can be provided by storage solutions including batteries, the potential and location of which are relatively unconstrained: congestion management and deferment of investments in the transmission network, contribution to generation portfolio adequacy in order to comply with the security of supply criterion, trade-offs on the energy markets, short-term balancing services (contribution to reserves and balancing). 2. The solutions are looked at in the section on demand-side response. 9 - Smart grids: Socioeconomic value and optimal flexibility portfolios

10 Demand-side response (i.e. demand response/load modulation) Demand-side response (or demand response/load modulation) is the ability that consumers have to temporarily adapt and in a specific way their power consumption to the power system s needs in response to an external signal. This response can be the result of a price incentive, or of certain power usages being managed by an independent operator referred to as a demand response operator which harnesses this flexibility by offering it to the power system (via various electricity markets). Consumption demand response/modulation solutions are different from energy efficiency solutions, even though in some cases, demand response/modulation can reduce energy demand. Consumption demand response/modulation solutions can deliver various services to the power system: congestion management and deferment of investments in the transmission network, contribution to the generation portfolio adequacy in order to comply with the security of supply criterion, trade-offs on the energy markets, short-term balancing services (contribution to reserves and balancing). The study perimeter selected here targets the consumers and power usages which are considered the most relevant, - in the residential sector (electric heating, electric production of domestic hot water, electric vehicle charging); - i n the industrial and major tertiary sectors (factoring in generating facilities generators available at certain industrial or tertiary sites). Wind power generation curtailment Variable renewable energies (electricity generation which depends on a natural variable phenomenon, such as wind or sunshine) are often described as unavoidable energy. However, these generating facilities can be controllable and their active power can be modulated downwards (a practice referred to as curtailment) at extremely short notice (just a few seconds). This flexibility can be of economic interest to the power system for two reasons: (i) contribution to supply-demand balance requirements in real time and (ii) management of the constraints affecting networks due to the incorporation of renewable generation, which means that network infrastructure can be kept to a minimum size. Smart grids: Socioeconomic value and optimal flexibility portfolios - 1

11 Methodological framework / Section 3 Section 3 METHODOLOGICAL FRAMEWORK The creation of a methodological framework shared across all the power system s stakeholders and all actors operating on the smart grid is a significant accomplishment that has resulted from the work published in July Academic references and the expertise of various partners who wanted to play their part have helped in developing this framework. Its added value is the fact that it is not specific and targeted at certain functions. Indeed, it can be applied to a wide perimeter of smart grid solutions. This way, it can be used within the framework of this report, and to carry out coherent and objective quantitative analyses of the socio-economic consequences of deploying various smart grid solutions over the power system. 3.1 OVERVIEW OF THE METHODOLOGICAL FRAMEWORK DEFINED IN THE JULY 215 REPORT The methodological framework is based on a representation of the way in which the power system operates within the perimeter of generation-consumption, together with a representation of the transmission network. This means that it represents: (i) balance between supply and demand requirements over different time frames (when investments are made in generating capacities, D-1 scheduling, infra-daily markets and balancing); (ii) the characteristics and constraints of the transmission network. The analyses summarised in this report quantify the collective economic value of smart grid solutions, without looking at the way in which this value is split across the various stakeholders operating on the power system. Under no circumstances should the benefits identified in this report be seen as revenue that stakeholders investing in smart grid solutions can expect to receive. This methodological framework is embodied in a modelling solution called FlexiS, developed by RTE. In practice, it: w simulates central decisions taken at different stages, by the power system s stakeholders: investments in new generation portfolios and downgrading of existing generating facility infrastructure and across the high and extra-high voltage network in multi-year cycles, scheduling of generation and reserves/margins on an intraday basis, balancing decisions in real time at different facilities, etc.; 3. ADEME, ANCRE, General Electric, French Energy Regulatory Commission, DG Enterprises within the Ministry of the Economy, Industry and the Digital Sector, EDF, Engie, Enedis, G2Elab, Schneider Electric, URM Smart grids: Socioeconomic value and optimal flexibility portfolios

12 w considers that decisions taken by stakeholders (producers, flexibility operators, suppliers, consumers, transmission system operators, etc.) are economically rational and do not exploit the existence of any market powers (assuming pure and perfect competition); w factors in the characteristics and technical (and acceptability 4 ) constraints of different assets (generating facilities, transmission network infrastructure, smart grid flexibility solutions, etc.); w represents the uncertainties affecting the power system (uncertainties affecting demand, generation based on renewables, availability of generating facilities, hydraulic contributions, availability of the transmission network, etc.). The modelling approaches have been developed so as to precisely assess the contribution of smart grid solutions to the power system. One of the key factors behind the utility of smart grid solutions at least in qualitative terms is the short-term flexibility that they provide, or their ability to reduce uncertainties (by measuring and processing information so as to improve forecasts). For the value to be quantified, the modelling tool used has to focus in a detailed way on the short-term operation of the power system. To this end, the FlexiS modelling tool represents the short-term uncertainties facing the power system s stakeholders (for different time frames), the margin and reserve requirements to handle these uncertainties and the flexibility constraints affecting the system (constraints on generating facilities, etc.). These specific features are what set FlexiS apart from other commercially available tools. It provides a set of objective bases in relation to the flexibility value. 3.2 PRESENTATION OF ASPECTS OF THE METHODOLOGY PROVIDED SINCE Representation of scaling up and modelling of competition effects between smart grid functions The new report is a major breakthrough from a methodological point of view: instead of quantifying solutions on a function by function basis (the approach so far used in all studies on the deployment of smart grid solutions), it looks into how multi-function deployments can be scaled up. This method can be used to identify suitable levels for various smart grid functions, factoring in the effects of some of them being discarded if there is a degree of overlap between the services that they provide such that they end up competing against one another for accessing areas of potential value. The 215 report quantified values on a flexibility by flexibility basis for marginal deployments. This type of analysis is useful. Consequently, most of the methods and results discussed in the literature and in public reports in particular are based on such an approach (see table 1, page 18). Yet such analyses do not suffice for identifying the right levels for developing various smart grid solutions. In order to do so, the effects of decreasing benefits (as well as the effects of potential cost increases 5 ) have to be factored in depending on deployment levels per scale effect on the one hand, and on the other hand, the effects of competition between flexibility solutions for accessing potential value need to be taken into account (value for the various supply-demand balance timescales contribution to the security of power 4. This is a key factor in implementing demand response solutions. 5. Particularly for demand-side response (residential, industrial and tertiary). Smart grids: Socioeconomic value and optimal flexibility portfolios - 12

13 Methodological framework / Section 3 Context Electricity demand Fuel and CO 2 costs Existing generating capacities & existing network capacities Capacities, technical characteristics and operating costs of existing generating facilities studied which will be technically viable by the date in question: combined cycle gas turbine, coal, Open cycle gas turbine, hydraulic, etc. Existing transmission network infrastructure Public policy on changes to the générating mix Nuclear capacity and renewable energies in the power mix Level of security of supply Candidates for investment solutions Technical characteristics, investment costs and accessible potential: New generating capacities (combined cycle gas turbine, Open cycle gas turbine) New transmission network infrastructure Smart grid solutions FlexiS model Stochastic simulation of optimum decision in an uncertain future Investment horizon in generating facilities, networks and smart grid solutions Investments and downgrading of generating facilities Investments/strengthening of the transmission network Investments in smart grid solutions Usage/dispatch horizon of generating facilities, networks and smart grid solutions Scheduling/D-1 Scheduling D-1 of generating facilities and smart grid levers on the basis of D-1 forecasts Setting up of reserves Infra-daily and balancing Use of generating facilities and smart grid levers for balancing Congestion management Redispatching of generating facilities and smart grid levers (including renewable curtailment) Loss of load D-1 uncertainties and uncertainties dynamics between D-1 and real time due to forecast errors on the demand, water intake, renewable energy production and availability of generating facilities and transmission infrastructure Optimum generation mix, network and smart grid deployment Installed capacities of generating units and smart grid solutions and network reinforcement Generation and smart grid lever dispatch Generation and smart grid lever dispatch Redispatching for congestion on the transmission network Loss of load (either caused by uncertainties of supply and demand or by network congestion) CO 2 Figure 1 / How the FlexiS modelling tool works 13 - Smart grids: Socioeconomic value and optimal flexibility portfolios

14 supply, trade-offs on the energy markets, contribution to balancing reserves and value for congestion management, etc.). So the main advantage of the exercise was the ability to use a single model to compare various flexibilities. Scaling up and a multi-function analysis can provide crucial insight as far as the outlook is concerned. This way, the volumes that can be deployed for whichever changes to the power mix and energy context can be identified. These analyses can then be used to inform public strategies for supporting the growth of certain sectors. This is what RTE wanted to do in refining the modelling tool that was used for the analyses presented in this report. The new FlexiS modelling tool can now be used to globally optimise the deployment of smart grid solutions, as well as optimising decisions about investments in generating facilities and downgrading them, and decisions about investments in the transmission network. This modelling takes the following into account: (i) the constraints affecting potential; (ii) the dependency of costs on level of deployment for a number of flexibility solutions. This applies in particular to demand response solutions in the industrial, tertiary and residential sectors Representation of the transmission network The work done since 215 has resulted in a more realistic representation of the transmission network, together with the constraints affecting it and the associated costs. This major work which focuses in particular on more effectively factoring in the density of the network and power losses has led to more accurate analyses. In order to assess the possible benefits of smart grid solutions for the transmission network (reduction in congestion costs and power losses and lower investments needed to reinforce it), decisions about developing the transmission network are simulated by applying the technical-economic principles which govern RTE s decisions. These principles are based on economic trade-offs between the costs avoided by reinforcing the network (reducing the congestion costs, the costs of loss of load expected and reducing any power losses) and the costs involved in reinforcing the network (investment costs and the costs involved in maintaining new infrastructure). The modelling tool used for the July 215 report is based on these principles and so is still valid. But it was based on the way in which physical phenomena on the networks are represented as simplified in a number of ways compared with RTE s operational tools. For this study, a number of major improvements were made to this modelling tool so as to more faithfully represent the physical nature of the networks (flows, power losses, etc.) and to end up with a more accurate economic quantification. A representation of meshed networks that is more representative of reality The representation of the sub-transmission network used for the first report was based on radial networks. The studies presented in this report are based on a representation of the way in which the various configurations of the transmission network are actually meshed together (the sub-transmission network section). Factoring in power losses on the network, which represent a significant component of investment decisions and is important for the environmental assessment Power losses on the network were not represented in the model used for the July 215 report. By factoring them in, network development decisions (the effects of 6. The cost of industrial demand response capacity depends very much on the level of deployment, since consumers have differentiated process characteristics. This is also true for residential consumers, since households have different energy consumption profiles (particularly in terms of the energy they consume on an annual basis). Smart grids: Socioeconomic value and optimal flexibility portfolios - 14

15 Methodological framework / Section 3 reinforcing the network on power losses is frequently a significant component of the value inherent in development projects) can be more accurately simulated and the benefits of the smart grid solutions studied are not overestimated. When these solutions can be used to defer or cancel network reinforcement projects, the resulting increase in power losses can now be taken into account. This change also has a bearing on the environmental assessment Representation of the diversity of possible local configurations The representations of the subtransmission network are based on 28 typical cases (as opposed to 2 in the July 215 report). This way, the sheer diversity of local situations can be more faithfully reflected and the economic issues associated with developing smart grid solutions can be more representatively assessed at national level The value that smart grid solutions have for the transmission network depends on the network s specific local features: topology of existing networks, network saturation, local increase in consumption or of the Figure 2 / Representation of the diversity of network situations shown as representative typical cases Characteristics differentiating the typical cases Nature of constraints Power supply Evacuation of renewable energy generation Rate of multi-year change in local renewable energy consumption or generation =/ Local consumption and generation load curves Network topology/density Cost of the network reinforcement project 15 - Smart grids: Socioeconomic value and optimal flexibility portfolios

16 capacities of renewable energy generating facilities connected up to them, costs of reinforcement projects, etc. The assessments are only meaningful if all of the specific features are taken into account Representation of the short-term operation of supply-demand balance However, the aim of the analyses is not simply to assess the value that smart grid solutions can provide for each portion of the network. On the contrary, the purpose lies in providing overall information, presented in an understandable way for France as a whole, factoring in the diversity of all local situations. Representing all these local situations is a significant challenge. This representation is based on typical cases 7 actual situations which are representative of real circumstances and which can be used to learn quantitative lessons about the national picture, and which are useful to all stakeholders. In the July 215 report, the number of typical cases studied was very low. This limited the utility of the report and the way in which its information could be extrapolated and applied to France as a whole. The work undertaken since its publication has involved developing a wider basis that is more representative of local situations, the aim being to reflect all situations found in mainland France. In practice, 28 typical cases have been produced. They are differentiated by: w network topology; w nature of constraints (feeding consumption or evacuating renewable energy generation); w the profiles of local extraction and injection load curves; w the levels of associated constraints and the way in which they will change in the long term; w the cost of the reinforcement project. The methodology developed in 215 is based on the modelling of the power system at all timescales. The way in which the modules describing short-term operation are represented has been improved. One of the important focuses of the modelling tool is the short-term representation of the power system s balancing: representation of uncertainties and of the way in which they change in close-to-real time (between D-1 and real-time), constraints on various balancing assets (generating units, smart grid solutions). The purpose of this modelling exercise is to gauge the specific value of smart grid solutions that results from their short-term flexibility which differs from that of generating facilities. The modelling was improved compared with the methods used for the report submitted in July 215 thanks to (i) a more complete representation of uncertainties affecting balancing requirements (more scenarios) and (ii) a more detailed representation of the way in which these uncertainties evolve. These developments can be used to refine the model without calling the fundamental aspects of it into question and so to consolidate the analyses conducted for the July 215 report. Each typical case is then weighted by a level of representativeness, the aim being to create a comprehensive overview that reflects the national subtransmission network in a simplified manner. 7. This representation based on typical cases is frequent in research that involves modelling the power system. By definition, it cannot claim to represent all possible situations which might exist on the transmission network. In very specific situations where (i) either reinforcing the network would be particularly expensive (geographical configuration, local acceptability) or (ii) the characteristics of the flexibility solutions would be particularly well-adapted to the constraints (form, temporary nature, non-long-term nature) the values of flexibility solutions for the network can, theoretically, be significantly higher than those presented in the report. These potential situations must be seen as fat tails where opportunities for value fall within niche opportunities. Smart grids: Socioeconomic value and optimal flexibility portfolios - 16

17 Methodological framework / Section POSITIONING OF NEW RESULTS COMPARED WITH WORK PUBLISHED IN FRANCE AND IN EUROPE There is now a significant amount of literature about the value of smart grid flexibility solutions. A number of findings emerge from these analyses: w In the studies referenced, quantifying the value that smart grid flexibility solutions can provide does not involve all of the services that they can offer Almost all of the studies published in France and Europe about the economic value of flexibility solutions focus on quantifying (i) the capacity value for the contribution they make to the generation portfolio adequacy in order to comply with the security of supply criterion and (ii) the trade-off value on the energy markets. A few studies quantify the value associated with contributing to reserves for supply-demand balance. No study assesses the value generated by the effective use of smart grid flexibility assets in the short-term balancing process. Only one study, which focuses on storage, assesses some of the value for the transmission network in a constrained network configuration, based on normative costs. w Existing analyses of the value of flexibility solutions focus on storage The economic issues to do with demand response and curtailment solutions are relatively unexplored and only look into some of the available potential (generally speaking, controlling domestic hot water heated using electricity and recharging electric vehicles). The issues associated with generation curtailment have not been looked into. w Existing studies do not factor in the effects of competition between the various smart grid flexibility solutions The analyses that have so far been published are solution by solution analyses. They assess the marginal value or the value of a wider-scale deployment, but without assessing the efficient level of economic penetration. The effects of competition between smart grid flexibility solutions or of one or several of them being discarded have not been studied. There are, however, two exceptions. The 215 ADEME study and the 216 CNRS study. These look at the effect of competition between solutions, but limit their analyses to different storage technologies. This study is therefore a significant asset at French and European level, supplying more information for the debate by making it possible to: (i) assess the value of flexibility solutions for all the possible services with which the power system can be provided (except needs for the distribution networks), factoring in the conflicts of potential usages and accessing the various sources of flexibility (ii) identifying the economically efficient levels of deployment for the various flexibility solutions, factoring in sources of mobilisable potential, their costs and the effects of scaling them up and the effects of competition in accessing sources of value (congestion management, generating capacity requirements, shortterm flexibility requirements, etc.). The analyses presented in this study are underpinned by modelling tools and hypotheses which characterise flexibility sources which are at least as refined as all of the other published studies (modelling of the shortterm operation of the power system, representation of the transmission network, characteristics of flexibility solutions, etc.). Analyses of the solutions sensitivities to the most frequently discussed/uncertain hypotheses (particularly in relation to demand response/modulation of residential consumption) provide an overview of the issues weighing on a number of uncertain parameters Smart grids: Socioeconomic value and optimal flexibility portfolios

18 Table 1 / Positioning of new study compared with reports published in France and in Europe about the value of smart grid flexibility solutions for the power system (RTE, 217) (ADEME, 213) (IEA 214) (ADEME, 215) (EDF, 215) (RTE, 215) (Strbac et al., 215) (Brouwer et al., 216) (Commission européenne, 216) (SystemX, 216) (Després, et al., 217) Estimated value Social surplus Social surplus Social surplus Social surplus Social surplus Battery storage Social surplus 3 Specific service Social surplus Social surplus Social surplus Social surplus Social surplus Smart grid flexibility solutions studied Demand response/ modulation of industrial and tertiary sector consumption Demand response/ modulation of residential consumption DHW and EV recharging REV and V2G recharging 3 VE recharging Renewable generation curtailment Contribution to capacity adequacy on the security of supply criterion Trade-off on D-1 markets Contribution to reserves Services studied Contribution to balancing Transmission congestion management 3 Distribution congestion management 7 3 In a special case 3 In a special case Factoring in of competition between services Marginal Incremental (with significant volume) Deployments considered Optimised Factoring in of the effects of competition between flexibility solutions and of one or several being discarded Representations of investment decisions across generating facilities Peaking facilities Representations of D-1 dispatch Representation of reserve needs and their constitution Modelling approach Representation of shortterm balancing Representation of investment decisions in France's transmission network Representation of the constraints on France's transmission network Representation of interconnections 3 Static 3 Static Static 3 Null annual assessment constraint Smart grids: Socioeconomic value and optimal flexibility portfolios - 18

19 Assumptions / Section 4 SECTION 4 ASSUMPTIONS 4.1 CONTEXT SCENARIOS FOR THE MIX The value of a given solution being deployed depends to a large extent on the context in which it is deployed. It is therefore useful to conduct analyses on several context scenarios so as to determine how robust the results are. The analyses presented in this report have been carried out based on two context scenarios: w A representation of the current system ( current scenario ); w A representation of the way the system could be in 23 that is consistent with the aims of the energy transition and supported by a public scenario ( new mix scenario). The general framework of the current context scenario is for the high thermal scenario predicted for winter in the 216 edition of the RTE s Generation Adequacy Report. It reflects the demand levels currently seen, existing generating facilities and the fuel and CO 2 prices seen on the markets. For 23, the new mix 23 scenario from the 214 edition of the RTE s Generation Adequacy Report is used. This scenario is a forecast of how the power mix could be in 23, consistent with the provisions of the Energy Transition for Green Growth Act which provides in particular for a reduction in the share of energy generated using nuclear means (5% of energy produced) and significant emphasis placed on developing renewable energies (4% of energy used). It is characterised by the need for new capacity (in addition to developing renewables) in order to secure the energy supply in a context in which electricity consumption is stagnating compared with current levels. Indeed, the addition of the existing generating facilities which will still exist and be viable by this time (factoring in the necessary downgrading of nuclear facilities for compliance with public policy) and forecasts for the development of renewables are not enough to comply with the security of supply criterion. It is also characterised by the high fuel and carbon prices ( 95/t for CO 2, for example). It should be noted that the generating facilities considered in the following analyses are simulated 8, taking into account (i) existing generating facilities (i.e., the generating facilities which may still be in operation by the year in question), (ii) public policy regarding changes to the generating facilities (share of nuclear power, renewables, the role played by coal), (iii) generating costs (variable costs, operating and maintenance costs, investment costs) for the various facilities and (iv) compliance with the public security of supply criterion. 8. Simulating investment decisions and decisions to do with downgrading generating facilities serves as a means of quantifying the capacity value of smart grid solutions Smart grids: Socioeconomic value and optimal flexibility portfolios

20 Installed capacity (GW) Current scenario 5.6 Existing facilities which can be downgraded Section of facilities resulting from public choices New mix 23 scenario Installed capacity needed to secure the supply Open cycle gas turbine (to be built) Combined cycle gas turbine (to be built) Peaking heavy fuel oil power plants (heavy fuel oil generators, heavy fuel oil combustion turbines) Open cycle gas turbine Combined cycle gas turbine Coal Other renewables and distributed power generation (including CHP) Solar Wind Nuclear Hydraulic Figure 3 / Power mix without smart grid solutions, adjusted based on the security of supply criterion, in the current context and new mix 23 scenarios Current context scenario New mix 23 scenario Annual consumption (TWh) Peak demand at a one in 1 chance (GW) Share of nuclear used in generation 75% 5% Share of renewables used in generation 19% 37% 9 Interconnection capacity for imports (GW) CO 2 and fuel prices CO 2 ( /t) 7 95 Gas ($/Mbtu) Fuel oil ($/barrel) Coal ($/tonne) Lignite ($/tonne) 1 1 Exchange rate ($/ ).8.8 Table 2 / Main context assumptions 9. Represents 4% of consumption. Smart grids: Socioeconomic value and optimal flexibility portfolios - 2

21 Assumptions / Section NETWORKS In terms of demand and power generation mix at national level, these scenarios are applied locally, to the transmission network nodes. This way, changes to the characterisation of constraints and the network s development requirements can be assessed. The benefits that various smart grid solutions bring to the transmission network can then be quantified on this basis, depending on their location. Number of transmission network substations < -.5% [-.5% > -.4%] [-.4% > -.3%] [-.3% > -.2%] [-.2% > -.1%] [-.1% > %] [% >.1%] [.1% >.2%] [.2% >.3%] [.3% >.4%] [.4% >.5%] >.5% Growth in local consumption (in %/year) Figure 4 / Local forecasts for annual increases/ decreases in consumption on the transmission network Number of transmission network substations 1,8 1,6 1,4 1,2 1, [>1 MW/year] [1>2 MW/year] [2>3 MW/year] [3>4 MW/year] [4>5 MW/year] Increase of RES local generation capabilities (in MW/year) > 5 MW/year Figure 5 / Local forecasts for annual increases/decreases in renewable generation capacity on the transmission network (all voltage levels) 21 - Smart grids: Socioeconomic value and optimal flexibility portfolios

22 4.3 TECHNICAL CHARACTERISTICS AND COSTS OF VARIOUS TECHNICAL SOLUTIONS The economic and environmental value of smart grid solutions and the role that they can play on the power system depend on their characteristics and on those of the classic solutions which they will be replacing (network infrastructure, generating facilities, etc.). The assumptions that should be considered for the various technological solutions (smart grid solutions, generating sectors, network infrastructure) concern: w technical characteristics: availability rates, stock constraints, minimum or maximum running times, Table 3 / Main technical-economic assumptions (for smart grid and conventional solutions) Technological solutions Batteries New pumped-storage hydroelectric stations Main characteristics Discharge time: optimised Technical hypotheses Contribution to reserves System services 15' reserve 3' reserve Stock, number and/or maximum activation duration contraint Minimum on/off duration constraint Minimum power 9% efficiency Discharge time: 24h 8% efficiency Smart grid flexibility solutions Conventional solutions Demand response/ modulation of industrial and major tertiary sector consumption Demand response/ modulation of residential consumption Long modulations Short modulations Duration constraint: none 1% transfer Duration constraint: 2h 1% transfer Existing generator units Duration constraint: none Managment via smart meters Real-time dynamic managment via dedicated boxes Supervised usages: - Heating (demand response with 85% transfer) - Domestic hot water (modulation) - Electric vehicle recharging (modulation of extraction only) Static managment (static monthly profile) Real-time dynamic managment Heating: 4 activations of 1h/day Domestic Hot Water: infra-daily modulation under DHW requirement constraint Electric vehicle: infra-daily/weekly modulation under travel requirement constraint 7 7 Wind power curtailment No constraint Nuclear - Minimum powers Combined cycle gas turbine - Minimum on and off durations Coal - Start-up costs Open cycle gas turbine Hydraulic (including pumpedstorage hydroelectric stations) Existing pumped-storage hydroelectric stations Transmission network 1. Downwards only Smart grids: Socioeconomic value and optimal flexibility portfolios - 22

23 Assumptions / Section 4 full activation times, available power for various reserves, etc.; w accessible sources; w costs: deployment costs (potentially dependent on the type of deployment considered) and usage costs; w environmental impacts: greenhouse gas emissions resulting from the life cycles of the various solutions and their usage. Table 3 shows the main assumptions investigated in relation to the various technical solutions. Start-up costs Fixed/optimised capacity Potential 7 Optimised Annualised complete fixed cost Costs 2k/MW/an + 25k/MWh storable /year Variable cost Life-cycle greenhouse gas emissions (excluding usage) annualised over the service life GHG emissions Greenhouse gas emissions to do with usage 9 tco 2 /MWh storable /year 7 Optimised 2.5 GW 8k/MW/year 7 Optimised 7 Optimised Supply curve (see figure 19) Supply curve (see figure 19) 7 Optimised 1 GW 15 k/mw/year 7 Optimised 25% of households Different household categories Cost of meters and DHW and EV servo-control outside the scope Heating servo-control: 15/household/year 3/MWh (includes the costs of transferred energy) (low impact process: process with inertia) 3/MWh (includes the costs of transferred energy) (low comfort impact) o (existing units) (meters outside of scope) 1.3 tco 2 /MWh 7 Optimised 25% of households Different household categories 72/household/year (low comfort impact) 23 tco 2 / household/year 7 Optimised All facilities 3 Fixed 1/MWh 3 Optimised 74k/MW/year 82/MWh 1.5 ktco 2 /MW/year.35 tco 2 /MWh 3 Fixed 115/MWh.86 tco 2 /MWh 3 Optimised 6k/MW/year 131/MWh 1 ktco 2 /MW/year.52 tco 2 /MWh 7 Fixed 7 Fixed Optimised.2 à.5 k/km.mw/year.2 à.5 tco 2 / km.mw/year (factoring in of effect on losses) 23 - Smart grids: Socioeconomic value and optimal flexibility portfolios