Smart grids. Socioeconomic value and optimal flexibility portfolios JUNE 2017 RTE SUMMARY

Transcription

1 Smart grids Socioeconomic value and optimal flexibility portfolios JUNE 2017 RTE SUMMARY

2 BACKGROUND To succeed in the energy transition, i.e. increase the share of renewable energy sources in the generation mix, handle changing electricity flows and consumption profiles, the power system must become more flexible. New technical solutions smart grids can help take up these challenges. These solutions improve the forecast of short-term evolutions which impact the electrical system or adapt the load, the generation (particularly wind or solar power) and the state of the network, in real time. These solutions are generally based on the connection (through a telecom/internet network) of various components of the power system (a high-voltage power cable, wind turbine, solar panel, electric vehicle, electric heater or water heater) to an IT system enabling for a flexibility aggregator, a supplier or a system operator to access it in a matter of seconds. Many companies in France develop such solutions. Since 2013, the French public authorities have undertaken a work to structure this industrial sector. This initiative has led to the creation of the association Think Smart Grids. Smart grid solutions could reduce the cost and the environmental footprint of electricity. Public support should be given to the most promising solutions. The socio-economic assessment of smart grids published in 2015 In July 2015, a first report of the socio-economic assessment of smart grids was published by RTE and its partners (ADEME, ANCRE, CRE, DGE, EDF, Engie, Enedis, GE, G2ELab, Schneider Electric and URM). This report set out a methodological framework for assessing the contribution of smart grid solutions to the economic performance of the power system, its environmental footprint and the job creation. Different smart grid solutions have been considered as promising given their proven potential and the benefits expected for the community (social welfare). SOCIOECONOMIC ASSESSMENT of smart grids Summary In November 2015, France s ministers for energy and economy tasked a group made up of RTE, the ADEME (The French environment and energy management agency), the ADeEF (Association of the Distributors of Electricity in France) and ENEDIS (EDF subsidiary tasked with managing 95% of the electricity distribution system in France) with continuing with these analyses. They entrusted ADEME and RTE with supervising this work. JULY 2015 In concertation with all the stakeholders involved in the power system, RTE performed the socio- economic assessments related to the whole power system, except distribution networks issues. Socioeconomic value and optimal flexibility portfolios - 2

3 / BACKGROUND WHAT DOES RTE S NEW STUDY BRING? MAIN ASSUMPTIONS For the first time, analyses were carried out in order to determine how all smart grid solutions could be efficiently implemented. The types of solutions analysed included storage (batteries and pumped-storage hydroelectric power stations), demand response (in the industrial and residential sectors) and modulation of the power generated by wind turbines (generation curtailment). This is a major improvement compared with all other studies published so far on smart grids. Previous studies are based on a solution by solution approach: Each smart grid solution is analysed independently without considering the effect of the implementation of any the other solutions. The new analyses provide insights on the economic issues related to the cohabitation of different flexibility solutions: w they take into account the effects of competition between the different solutions (between demand response and storage, between batteries and hydropower solutions); w they take into account the effect of the scale of deployment of flexibility solutions on their added value. This way, it is possible to differentiate between flexibility services which have very high value but only for very low potential and other flexibility services which can be deployed on a larger scale but with lower value. Based on the 2030 new mix scenario outlined in RTE s 2014 Generation Adequacy Report, the economic potential for smart grids could reach about 9 GW by The flexibility solutions studied should bring a significant contribution to the success of the energy transition (increasing the share of renewable energies and reducing the share of nuclear power in the energy mix) by keeping costs under control. These results are not forecasts. Instead, they should be seen as a decision-support tool, providing information about possible configurations, as well as their consequences on the system s economy and greenhouse gas emissions. The economic analysis focuses on the one hand on the supply-demand balance value (value for the capacity adequacy, participation in the energy markets and short-term balancing) that each solution brings, and the value for the transmission network. Analysing the global value of flexibilities for the transmission network at a national level is a challenge as local situations are heterogeneous (deployment within a very constrained area holds more interest and thus more value than in another less constrained area). RTE has therefore developed a method based on representative situations (28 different situations) representing the range of local configurations on the transmission network. On this basis, lessons can be learned about the value for the transmission network of a large-scale, well-located, implementation of flexibility solutions. For the various flexibility solutions, the analyses take into account: w the constraints on available sources of flexibility (particularly for demand response), including location constraints; w the increase of marginal costs of flexibility sources as their deployment increases; w the decrease of benefits as the deployment of flexibility sources increases, and w the crowding-out effect and effect of the competition between flexibility solutions. The environmental analysis now includes the life cycles of equipment. It provides a sensitivity analysis of the environmental impact assessment towards the location of the manufacturing process. This type of analysis provides a response for frequent questions, particularly concerning batteries. As the carbon content of the energy may vary across the countries, the location of the manufacturing of smart grid assets (such as batteries) impacts significantly the GHG emissions. Sensitivity analyses were carried out on various key parameters of the analysis (cost of batteries, cost of equipment for managing demand, etc.). The results have therefore been presented for two states of the energy transition: a representation of the current state of the system ( current context ), and a projection for how the system will be in 2030 ( new mix scenario, characterised by a 50% share of nuclear power and a 40% share of renewable energies in the energy mix). 3 - Socioeconomic value and optimal flexibility portfolios

4 RESULTS OPTIMAL PORTFOLIOS OF SMART GRIDS 1. Even by 2030, the management of electricity consumption peaks will remain the main economic value for smart grid flexibility solutions in France By 2030, new thermal power stations (combined-cycle turbines, combustion turbines) will need to be built to compensate for the reduction in the share of nuclear power in the energy mix, despite a significant increase in the share of renewable energies. These power stations are mainly used during peak demand periods. Smart grids flexibility solutions can reduce this need for investment and therefore have a strong economic value. For example, demand response is particularly well suited to manage peaks in electricity demand. It is also true for battery storage (batteries are charged during the night and in the middle of the day, and inject the energy onto the network in the morning or in the evening). The resulting benefits are of importance equal to the costs of new power stations avoided. By 2030, this value related to the new power stations avoided prevails in the global value of smart grids flexibility solutions. Such a result is not intuitive. According to the most widely accepted discourse, smart grids are associated with managing the daily intermittent nature of renewable energies, or with the management of to the grid in real time. Such needs do indeed exist, and studies have quantified the economic value associated with these services. Nevertheless, most of the value of smart grids flexibility solutions lies in the management of peak demand periods. There is also a very significant niche value for smart grid flexibility solutions which can contribute to frequency automatic reserves (frequency containment reserve and automatic frequency restoration reserve) a highly demanding technical service. But the needs for such services are limited: the high value of these services cannot be extrapolated up to a significant volume. 2. The economic potential of smart grid flexibility solutions increases with the rising needs for new capacities to ensure the security of supply The economic value of smart grids is highly dependent on other energy policy decisions. In the prospective scenario considered for 2030, batteries and demand response solutions avoid the need to build peak power stations. The higher the need for such power plants, the higher the economic value of smart grid solutions. The need for new capacities appears as soon as the decrease of the nuclear capacity is greater than the contribution of renewables to the management of consumption peaks. The economic value provided by smart grid solutions would be different in other energy contexts. For example, in the current context scenario, the value generated by smart grid solutions is limited to a niche market corresponding to frequency automatic reserves. Socioeconomic value and optimal flexibility portfolios - 4

5 / RESULTS 3. This need could be covered by a mix of different smart grid solutions (battery storage, pumped hydroelectric power stations, demand response by industrial or residential consumers) No pre-existing studies have taken into account the potential competition between smart grid solutions. Theoretically, it is considered that these solutions could compete with each other as they provide similar services to the power system. A technological breakthrough in one of these solutions battery storage, for example was seen as something which might lead to push the other solutions out of the market. On the contrary, our analyses show that a mix of different functions could be an optimum solution. This is a strong result based on analysis, which can be broken down in the following way: w The optimal deployment analysis compared with the function by function analysis shows a 2 GW difference in the volume of smart grid solutions deployed in the 2030 scenario. This confirms the relevance of a global approach. w There is no cannibalisation from any of the smart grid solutions: in the scenario studied, 1.3 GW of batteries can be deployed together with 1.7 GW of new pumped-storage hydroelectric power station projects, 700,000 households with static demand response for their heating via smart meters and nearly 300,000 households with dynamic (i.e. real-time) demand response via specific DR boxes (i.e. technical solution dedicated to the management of demand response). w In practice, the competition effect exists mainly between smart grid flexibility solutions and new thermal power stations whose existence would be justified by the security of supply. w Furthermore, the more the cost of technologies such as storage falls, the greater the substitution effect at the expense of new thermal power stations is. So, in the first instance, this competition effect impacts thermal power stations and not other smart grid solutions. Level of deployment (GW) ~440,000 households Function by function approach Competition effect between smart grid solutions ~ -1 GW Multi-function approach optimised deployment of different functions ~290,000 households ~1 million households Function by function approach Competition effect between smart grid solutions ~ -2 GW Multi-function approach optimised deployment of different functions ~270,000 households ~710,000 households Demand response/modulation of residential consumption (real-time management of all usages via dedicated DR boxes) Demand response/modulation of residential consumption (management of heating by smart meters) Demand response/modulation of industrial and tertiary sector consumption Batteries New pumped-storage hydroelectric stations Current scenario 2030 scenario Figure 1 / Economically relevant deployment levels of the different smart grid flexibility solutions 5 - Socioeconomic value and optimal flexibility portfolios

6 4. Flexibility solutions, even connected to the distribution network, can moderate reinforcements of the transmission network Flexibility solutions, appropriately located on the network, can help manage congestion. In such cases, they reduce the need for reinforcement of the electricity network. The benefits for the transmission network of the various flexibility solutions depend on the specific characteristics of each solution: there is no general rule. In particular, the option to curtail renewable generation to manage congestion on the network is extremely useful to contain reinforcement costs in areas with substantial development of wind energy. The associated benefits for the whole transmission network are estimated to be around 25Myear. The energy volumes curtailed required to limit reinforcement are very limited. This means that the use of this flexibility is very useful for the grid, but has no effect on the share of renewable energy sources in the energy mix. For other flexibility solutions examined, there is a value for the transmission grid, but it is significantly lower than the benefits related to the services for supply-demand balance. It highlights the need for coordinated mechanisms to manage the balancing requirements and grid management needs to ensure that flexibilities are employed in priority where they are most profitable. 5. The economic benefits are significant: smart grid solutions can be deployed for the benefit of consumers and can support the energy transition If smart grid solutions are deployed optimally, they can generate significant savings. These could be around 400 million per year by 2030 for the solutions studied. The analyses carried out by RTE show the contribution of the different smart grid solutions to this result. This figure mainly corresponds to the investments avoided in new conventional production capacities. In absolute terms, this is a significant value but it represents approximately 1% of the power system s total costs. These benefits should not be understood as a future reduction in consumers electricity bills: they correspond to the benefits related to the development of smart grid solutions, all else being equal. Some smart grid solutions have already been partly deployed over the power system. This is specifically the case for a part of the demand response potential. 6. The implementation of smart grid solutions in France can moderately reduce the GHG emissions of the French power system, even when the life cycles of equipment are taken into account in the analysis The deployment of all smart grid flexibility solutions has an overall effect of reducing emissions (0.8 MtCO 2 /year avoided, which is equivalent to 3% of the French power system s annual emissions). A significant share of these environmental benefits comes from the flexibility solutions (storage and demand response) that are able to provide frequency automatic reserves. As these flexibility solutions contribute to the automatic reserves, they reduce the contribution of the nuclear power plants to these reserves and allow them to generate more energy and thus reduce fossil fuelbased power stations. The new studies take into account the life cycle of batteries: this is a major change compared with previous studies. Although it does not have a major impact on the results, taking into account the life cycle analysis of batteries offers insights on the environmental issues related to the geographical location of the manufacturing of components. Socioeconomic value and optimal flexibility portfolios - 6

7 / RESULTS Figure 2 / Economic assessment of the optimal flexibility portfolio by 2030 (in M/year) Reference assumption about solution costs Wind power generation controllability Demand response Storage For transmission network congestion For short-term balancing Industrial and major tertiary sector Static supervision via smart meters Residential Real-time supervision via DR boxes Batteries New pumpedstorage hydroelectric stations Competition effects between smart grid solutions for accessing sources of value Total for all solutions Annual costs and benefits (in M/year) ~2.5 GW ~4 GW ~5.1 GW ~7,000,000 households for water heating supervision ~4,800,000 households for EV supervision ~710,000 households for heating supervision ~270,000 households for all usages ~0.35 GW (30 stock) + ~1 GW (2h stock) ~1.7 GW (24h stock) Fixed costs of smart grid solutions Costs of using smart grid solutions Generating capacity costs avoided Fuel and CO 2 costs avoided Transmission network costs avoided Costs Benefits Net profits 7 - Socioeconomic value and optimal flexibility portfolios

8 Figure 3 / Environmental assessment of the optimal flexibility portfolio by 2030 (in ktco 2 /year) Wind power generation controllability Demand response Storage For transmission network congestion For short-term balancing Industrial and major tertiary sector Static supervision via smart meters Residential Real-time supervision via DR boxes Batteries New pumpedstorage hydroelectric stations Competition effects between smart grid solutions for accessing sources of value Total for all solutions Annual GHG emission avoided (in ktco 2 /year) 1,400 1,300 1,200 1,100 1, ~2.5 GW 3-21 ~4 GW ~5.1 GW ~7,000,000 households for water heating supervision ~4,800,000 households for EV supervision ~710,000 households for heating supervision ~270,000 households for all usages ~0.35 GW (30 stock) + ~1 GW (2h stock) ~1.7 GW (24h stock) Life cycles of smart grid solutions (and specific electric consumption) Emissions from the use of smart grid solutions (i.e. fuel for power generators) Life cycles of avoided generating capacities Emissions linked to combustion at the thermal power stations Life cycle of the transmission network Additional GHG emissions Avoided GHG emissions Net impact on GHG emissions Socioeconomic value and optimal flexibility portfolios - 8

9 / RESULTS DETAILED RESULTS 1. Electrochemical energy storage: in the next years, a solution that will no longer be limited to a niche market and can take the place of peak thermal solutions Figure 4 / Economic optimal portfolio of smart grid solutions and thermal units, according to the cost of Li-Ion batteries, in the 2030 scenario Under current conditions, a niche development of battery storage would seem appropriate. These batteries would be used to supply frequency automatic reserves Current cost assumption of Li-Ion batteries (2017) Reference cost assumption of Li-Ion batteries for 2030 By 2030, the cost reduction of batteries will lead to an increase in the economic potential. A large penetration of batteries will become appropriate. These batteries will contain the need for new peaking power stations. As changes in battery costs is a frequently-debated issue (for example, through the likely possibility of using second-life batteries), the sensitivity of the results to assumptions about battery costs was studied. This analysis reveals the existence of a turning point in the values generally accepted as credible targets for A heavier drop of the cost of batteries could lead to a massive development of batteries in the power system. Installed capacity (GW) Cost assumption for Li-ion batteries ( k/mwh storable ) This analysis provides an understanding of the nature and dynamics of substitution effects: w the increase in the role played by batteries would first and foremost negatively impact the construction of new thermal power stations and also the construction of new pumped-storage hydroelectric power stations; w the development of demand response solutions would only be slightly affected by increases in battery potential. Indeed, demand response deployment would be focused on the potential which remain competitive in all cases (very large consumption sites, industrial processes that are well-suited to demand response solutions, etc.). The environmental assessment, taking into account the battery life cycles, is positive, but quite limited to the effects resulting from the contribution of batteries to frequency automatic reserves. Finally, the environmental assessment is deteriorated if the batteries are manufactured in countries in which power is mainly generated using coal-based means (such as China, for example), since the manufacturing process is highly electricity-intensive. Demand response/modulation of residential consumption (real-time management of all usages using dedicated DR boxes) Demand response/modulation of residential consumption (management of heating using smart meters) Industrial & tertiary sector consumption demand response/modulation Batteries New pumped-storage hydroelectric power stations Thermal power stations (coal, CCGT, combustion turbine) 2. Residential demand response: a deployment reflecting the heterogeneity of consumers and the different development stages of demand response solutions With the development of variable renewable energy sources, the active participation of residential consumers to the needs of the power system is a central issue for France s and Europe s public authorities. Residential demand response can be based on different uses of electricity: domestic hot water, electric heating and recharging electric (or rechargeable hybrid) vehicles are considered as having the highest potential for flexibility with a limited impact, if well managed, on consumers welfare. 9 - Socioeconomic value and optimal flexibility portfolios

10 The analysis differentiates three development stages for these flexibility solutions: w demand-side response systems in the residential sector which are already in use (particularly through the peak/off-peak management) can provide most of the potential value of the flexibility of the residential consumers; w the features and functions of smart meters which are currently being deployed in France will bring improvements in the way to manage the flexibility of residential consumers. The associated cost is deemed to be zero or very low, since the decision has already been made to deploy these smart meters; w the deployment of dedicated systems ( DR boxes ) for the close to real time management of residential usage is of economic interest for a limited potential, corresponding to the heaviest residential consumers. These heavy consumers are estimated to number 300,000, taking into account eligibility criteria for this approach. A sensitivity analysis was carried out on the overall cost of these DR boxes (excluding smart meters): w if a technological breakthrough or the emergence of business models for pooling the equipment or installation costs with other services to the consumers, could significantly reduce these costs, a deployment of nearly 3 million households might be economically appropriate; w under such an assumption, the deployment of these DR boxes would mainly take the place of battery storage solutions. The environmental assessment is positive. It takes into account the life cycles of equipment and their electricity consumption. The scope of this assessment is highly dependent on the assumption on the energy savings resulting from demand response on heating (i.e. the energy consumption postponed after a load reduction on heating). 3. Demand response in the industry or tertiary sector: a no regret option for the deployment of smart grids Demand response of industrial sites and big tertiary sites are of significant benefit for the power system, mainly through the contribution to the security of supply. The economically efficient level of development for the various forms of industrial and tertiary demand response is around 3 GW in the current context and should be around 5 GW by This level of development is not very sensitive to possible changes in the costs of other smart grid solutions: a significant share of demand response potential in the industry and tertiary sector is competitive in all scenarios. The environmental assessment of industrial demand response depends on the detailed characteristics (availability, maximal load reduction duration, etc.). The impact is positive for all kinds of industrial demand response. Analysis reveals the environmental impact of the various forms of demand response. 4. Wind power controllability: a no regret option to moderate investments in the network. The controllability of wind power appears to be a key solution to support the growth of renewable energies. It is justified by an economic trade-off between the value of the energy curtailed (which must be compensated by the generation of other power stations) and the costs of reinforcing the network. RTE has already started to integrate this approach into its investment decisions. The benefits result from investment avoided, reduced by (i) the cost to compensate the energy curtailed and (ii) the cost of the additional losses on the grid, due to its lesser development. They will be approximately 25 million per year by The volumes of curtailed energy are extremely low. They have no impact on the share of renewables in the electricity mix. The environmental impact is very slightly negative, but not enough to warrant giving up on this solution, unless a value of more than 1000 per tonne of CO 2 is set. Furthermore, the contribution of wind power generation to downward balancing reserves has an economic value because of the constraints affecting the other units (times, minimum durations, etc.). By 2030, the economic benefit for the power system is expected to be around 12 million/year. Socioeconomic value and optimal flexibility portfolios - 10

11 / NEXT STEPS NEXT STEPS The socio-economic assessment of smart grid solutions summarised in this report provides new information about the issues associated with the development of smart grid flexibilities in the French power system. It can now be used to assess the most efficient level of development of various smart grid solutions, taking into account the effects of competition between the different solutions in accessing the sources of value. These results are not definitive. In order to remain a useful decision-support tool over time, the results will need to be updated with the changes on the prospective scenarios. This methodological framework will now be incorporated into RTE s Generation Adequacy Report, published every year according to French law. In concrete terms, this means that the smart grid solutions investigated (storage, various forms of demand response) will form an integral part of the development of the future longterm scenarios. These will be established on the basis of economic feedback which will evaluate the economically relevant deployments in the various potential contexts The next Generation Adequacy Report will be published in autumn 2017 and will provide a wider selection of possible changes in the power system. An analysis of the effects of deploying smart grid solutions on the distribution networks was published by the ADEeF (Association des distributeurs d énergie en France) and ENEDIS in June An analysis of the effects of the development of the identified smart grid solutions on employment will be performed by ADEME (The French environment and energy agency), based on these results and on the results published by the distribution system operators. This analysis is planned for autumn 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 Socioeconomic value and optimal flexibility portfolios

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