State or Market : Investments in New Nuclear Power Plants in France and Their Domestic and Cross-border Effects

France wants to become carbon-neutral by 2050. Renewable energies and nuclear power are expected to make the main contribution to this goal. However, the average age of nuclear power plants is approaching 37 years of operation in 2022, which is likely to lead to increased outages and expensive maintenance. In addition, newer nuclear power plants are flexible to operate and thus compatible with high volatile feed-in from renewables. Nevertheless, it is controversially discussed whether nuclear power plants can still be operated competitively and whether new investments will be made in this technology. Using an agent-based simulation model of the European electricity market, the market impacts of possible nuclear investments are investigated based on two scenarios: a scenario with state-based investments and a scenario with market-based investments. The results of this investigation show that under our assumptions, even with state-based investments, carbon neutrality would not be achieved with the estimated nuclear power plant capacity. Under purely market-based assumptions, large amounts of gas-fired power plants would be installed, which would lead to an increase in France\u27s carbon emissions. State-based investments in nuclear power plants, however, would have a dampening effect on neighboring spot market prices of up to 4.5 % on average

Evaluation of photovoltaic storage systems on energy markets under uncertainty using stochastic dynamic programming

The rising share of intermittent renewable energy production in energy systems increasingly poses a threat to system stability and the price level in energy markets. However, the effects of renewable energy production onto electricity markets also give rise to new business opportunities. The expected increase in price differences increases the market potential for storage applications and combinations with renewable energy production. The value of storage depends critically on the operation of the storage system. In this study, we evaluate large-scale photovoltaic (PV) storage systems under uncertainty, as renewable energy production and electricity prices are fundamentally uncertain. In comparison to households who largely consume the stored energy themselves, the major business case for large-scale PV and storage systems is arbitrage trading on the electricity markets. The operation problem is formulated as a Markov decision process (MDP). Uncertainties of renewable energy production are integrated into an electricity price model using ARIMA-type approaches and regime switching. Due to non-stationarity and heteroskedasticity of the underlying processes, an appropriate stochastic modeling procedure is developed. The MDP is solved using stochastic dynamic programming (SDP) and recombining trees (RT) to reduce complexity taking into account the different time scales in which decisions have to be taken. We evaluate the solution of the SDP problem against Monte Carlo simulations with perfect foresight and against a storage dispatch heuristic. The program is applied to the German electricity and reserve power market to show the potential increase in storage value with higher price spreads, and evaluate a possible imposition of the feed-in levy onto energy directly stored from the common grid

Uncertainties in energy markets and their consideration in energy storage evaluation

This book successfully illustrates the modeling of electricity prices with the help of stochastic processes. The relatively new phenomenon of negative prices is also integrated into the models. The integration of feed-in from wind power plants in energy models is also very innovative. This approach helps to simulate electricity prices in order to take into account the "merit-order effect of renewable energy". Finally, the models are used for the techno-economic evaluation of energy storages

Evaluation of photovoltaic storage systems on energy markets under uncertainty using stochastic dynamic programming

The rising share of intermittent renewable energy production in energy systems increasingly poses a threat to system stability and the price level in energy markets. However, the effects of renewable energy production onto electricity markets also give rise to new business opportunities. The expected increase in price differences increases the market potential for storage applications and combinations with renewable energy production. The value of storage depends critically on the operation of the storage system. In this study, we evaluate large-scale photovoltaic (PV) storage systems under uncertainty, as renewable energy production and electricity prices are fundamentally uncertain. In comparison to households who largely consume the stored energy themselves, the major business case for large-scale PV and storage systems is arbitrage trading on the electricity markets. The operation problem is formulated as a Markov decision process (MDP). Uncertainties of renewable energy production are integrated into an electricity price model using ARIMA-type approaches and regime switching. Due to non-stationarity and heteroskedasticity of the underlying processes, an appropriate stochastic modeling procedure is developed. The MDP is solved using stochastic dynamic programming (SDP) and recombining trees (RT) to reduce complexity taking into account the different time scales in which decisions have to be taken. We evaluate the solution of the SDP problem against Monte Carlo simulations with perfect foresight and against a storage dispatch heuristic. The program is applied to the German electricity and reserve power market to show the potential increase in storage value with higher price spreads, and evaluate a possible imposition of the feed-in levy onto energy directly stored from the common grid

On the Role of Electricity Storage in Capacity Remuneration Mechanisms

In electricity markets around the world, the substantial increase of intermittent renewable electricity generation has intensified concerns about generation adequacy, ultimately driving the implementation of capacity remuneration mechanisms. Although formally technology-neutral, substantial barriers often exist in these mechanisms for non-conventional capacity such as electricity storage. In this article, we provide a rigorous theoretical discussion on design parameters and show that the concrete design of a capacity remuneration mechanism always creates a bias towards one technology or the other. In particular, we can identify the bundling of capacity auctions with call options and the definition of the storage capacity credit as essential drivers affecting the future technology mix as well as generation adequacy. In order to illustrate and confirm our theoretical findings, we apply an agent-based electricity market model and run a number of simulations. Our results show that electricity storage has a capacity value and should therefore be allowed to participate in any capacity remuneration mechanism. Moreover, we find the implementation of a capacity remuneration mechanism with call options and a strike price to increase the competitiveness of storages against conventional power plants. However, determining the amount of firm capacity an electricity storage unit can provide remains a challenging task

On the role of electricity storage in capacity remuneration mechanisms

In electricity markets around the world, the substantial increase of intermittent renewable electricity generation has intensified concerns about generation adequacy, ultimately driving the implementation of capacity remuneration mechanisms. Although formally technology-neutral, substantial barriers often exist in these mechanisms for non-conventional capacity such as electricity storage. In this article, we provide a rigorous theoretical discussion on design parameters and show that the concrete design of a capacity remuneration mechanism always creates a bias towards one technology or the other. In particular, we can identify the bundling of capacity auctions with call options and the definition of the storage capacity credit as essential drivers affecting the future technology mix as well as generation adequacy. In order to illustrate and confirm our theoretical findings, we apply an agent-based electricity market model and run a number of simulations. Our results show that electricity storage has a capacity value and should therefore be allowed to participate in any capacity remuneration mechanism. Moreover, we find the implementation of a capacity remuneration mechanism with call options and a strike price to increase the competitiveness of storages against conventional power plants. However, determining the amount of firm capacity an electricity storage unit can provide remains a challenging task

Diffusion and system impact of residential battery storage under different regulatory settings

Cost reductions of rooftop photovoltaics and battery storage, increasing retail electricity prices as well as falling feed-in remuneration provide strong incentives for many German households to engage in self-consumption. These developments may also affect the electricity system as a whole. Against this background, we jointly apply a prosumer simulation and an agent-based electricity market simulation in order to investigate the long-term impacts of a residential battery storage diffusion on the electricity market. We analyze different regulatory frameworks and find significant effects on the household level, yet only moderate system impacts. In the long run, the diffusion of residential battery storage seems difficult to govern, even under a restrictive regulation. In contrast, the way the batteries are operated may be easier to regulate. Policymakers and regulators should focus on this aspect, since a system-friendly battery operation supports the system integration of residential photovoltaics while having little impact on the households’ self-sufficiency

Diffusion and System Impact of Residential Battery Storage under Different Regulatory Settings

Cost reductions of rooftop photovoltaics and battery storage, increasing retail electricity prices as well as falling feed-in remuneration provide strong incentives for many German households to engage in self-consumption. These developments may also affect the electricity system as a whole. Against this background, we jointly apply a prosumer simulation and an agent-based electricity market simulation in order to investigate the long-term impacts of a residential battery storage diffusion on the electricity market. We analyze different regulatory frameworks and find significant effects on the household level, yet only moderate system impacts. In the long run, the diffusion of residential battery storage seems difficult to govern, even under a restrictive regulation. In contrast, the way the batteries are operated may be easier to regulate. Policymakers and regulators should focus on this aspect, since a system-friendly battery operation supports the system integration of residential photovoltaics while having little impact on the households’ selfsufficiency