Motivated by generation system adequacy concerns, many European countries have introduced capacity remuneration mechanisms (CRMs) to ensure sufficient investments in power generation. However, it is uncertain whether the existing CRMs will promote sufficient adequacy and flexibility in a decarbonized power system, where supply and demand will become more weather-dependent. We assess the effectiveness of a centralized capacity market, a strategic reserve, and a decentralized capacity market via capacity subscriptions in a climate-neutral, weather-driven power system. We develop a co-simulation of two agent-based models simulating myopia in both operational and investment decisions. We simulate weather uncertainty by running the model with 40 different weather years. Our results from a case study based on the Netherlands indicate that a strategic reserve may increase electricity price volatility in the long-term. A centralized capacity market is more cost-effective than a strategic reserve, but administratively setting its parameters is prone to over- or underprocurement. Capacity subscription allows consumers to select their desired level of reliability. Results indicate that these decentralized capacity markets may yield a clearer signal for the needed dispatchable capacity and promote demand-side response, but it may be challenging to provide long-term certainty for investors.

The rapid decarbonisation of the power sector is challenging the traditional resource adequacy framework. Variable and energy-limited resources are driving the emergence of new correlations that, together with extreme weather events, are rapidly changing the expected scarcity conditions in the electricity system. Traditional resource adequacy metrics are showing their limitations under these new conditions, and many regulators have already started to reform them. This article presents the first model-based comparative analysis of two different approaches that have been proposed to overcome these limitations, i.e., multi-metric standards (imposing a set of different resource adequacy constraints) and composite-metric standards (combining different resource adequacy metrics through weighting factors to build a single reliability standard). These two approaches are quantitatively evaluated in this article through case studies obtained from a simulation model, focusing not only on the impact of the reliability standard on the resource mix, but also on the design of the reliability product to be traded in a capacity mechanism to guide the system towards that mix.

The growing share of renewable energy in shortterm European electricity markets has significantly increased congestion management costs and demands. Therefore, current market design is not optional to keep congestion costs low. A proper market would incentivize the integration of flexibilities to boost competition and lower costs, while mitigating risks of manipulation. However, assessing behavioral impacts is challenging due to increasingly interconnected market structures. Studies modeling more than two markets often overlook the strategic opportunities that emerge from these interactions, focusing instead on large-scale dynamics. To capture the detailed impact of bidding strategies, we use reinforcement learning to explore multi-market strategies. By progressively training a Deep Reinforcement Learning (DRL) agent as a market participant - from replicating established behaviors to mastering intricate multimarket interactions - we employ Domain-Informed Curriculum Learning (DomCL), a structured approach that systematically guides learning through staged complexity. We validate our approach against established two-market studies, then evaluate it in two progressively complex four-market case studies spanning a 6-bus network, including historical data. Results show that our DRL-based method improves performance while uncovering challenges that arise as strategic opportunities expand, offering a structured approach for multi-market design analysis.

Future energy markets for low voltage AC and DC distribution systems will facilitate prosumer participation in the market. To comply with market regulations and grid constraints, a tailored market design reflecting (DC) operational requirements is needed. Our previous work identified a locational energy market design. However, its real-life implementation faces challenges due to uncertainties in system operation, prosumer preferences, and bidding strategies. This article tests the market design under uncertain scenarios. To this end, we develop an agent-based model that simulates typical electric vehicle user preferences and bidding strategies, influenced by varying degrees of range anxiety. The market design is tested in challenging scenarios with a high share of solar panels and electric vehicles, modelled using the high-resolution Pecan Street database. Simulations indicate that the proposed market design maintains both economic efficiency and system reliability under real-life uncertainties. This in turn indicates the practical feasibility of locational energy markets in helping to integrate renewable generation sources and bidirectional power flows.

Hydrogen and derived fuels may act as long-term energy storage in climate-neutral energy systems. However, riskaverse investors will not invest in sufficient renewable electricity, back-up, electrolyzer and storage capacity if they are only remunerated for the hydrogen or electricity produced and markets for risk are missing. We develop a stochastic equilibrium model to study whether capacity markets can limit costs to consumers by restoring investments risk-neutral levels. Our results show that the efficacy of capacity markets depends on complementary instruments to ensure the availability of renewables. If riskaversion and missing markets for risk reduce renewable buildout, capacity markets in the electricity and hydrogen sectors are needed to restore the overall capacity mix and limit consumer costs. If complementary instruments lift investments in renewables, a capacity market in the electricity sector suffices. In this situation, an additional capacity market in the hydrogen sector triggers a bias toward hydrogen-fired backup capacity. This illustrates that an integrated systems perspective is required to design future energy markets.

Future power systems, in which generation will come almost entirely from variable Renewable Energy Sources (vRES), will be characterized by weather-driven supply and flexible demand. In a simulation of the future Dutch power system, we analyze whether there are sufficient incentives for market-driven investors to provide a sufficient level of security of supply, considering the profit-seeking and myopic behavior of investors. We co-simulate two agent-based models (ABM), one for generation expansion and one for the operational time scale. The results suggest that in a system with a high share of vRES and flexibility, prices will be set predominantly by the demand’s willingness to pay, particularly by the opportunity cost of flexible hydrogen electrolyzers. The demand for electric heating could double the price of electricity in winter, compared to summer, and in years with low vRES could cause shortages. Simulations with stochastic weather profiles increase the year-to-year variability of cost recovery by more than threefold and the year-to-year price variability by more than tenfold compared to a scenario with no weather uncertainty. Dispatchable technologies have the most volatile annual returns due to high scarcity rents during years of low vRES production and diminished returns during years with high vRES production. We conclude that in a highly renewable EOM, investors would not have sufficient incentives to ensure the reliability of the system. If they invested in such a way to ensure that demand could be met in a year with the lowest vRES yield, they would not recover their fixed costs in the majority of years.

The design of electricity markets may be facilitated by simulating actors’ behaviors. Recent studies model human decision-makers within markets as agents which learn strategies that maximize expected profits. This work investigates the problem of ‘non-stationarity’ in the context of market simulations, a problem with the learning-algorithms used by such studies which results in agents behaving irrationally, thus limiting the studies’ applicability to real-world strategic behavior. Isolating the source of the problem for a day-ahead electricity market, this paper proposes methods which meliorate this problem in simple test-cases, and proves requirements under which ‘centralized-training, decentralized-execution’ value-learning methods will converge to correct behavior in general. Subsequently, this paper proposes a framework for ‘adversarial market design’ that includes the market-designer as an agent. This allows the optimization of market-designs subject to possibly strategic behavior of participating firms — in turn enabling the automated selection of the optimal market from any set of markets.

Coordinating the interactions between increasingly interconnected energy sectors and carriers can lead to an efficient integration of variable renewable energy (VRE) resources, and a more cost-efficient energy transition. This paper proposes a model coupling approach that uses a market-based mechanism to efficiently coordinate the interactions among electricity, heat, and (hydrogen) gas systems, and (near) optimally schedule flexibility to maximize social welfare. The proposed approach is benchmarked against traditional co-optimization, and is shown to achieve comparable results with a moderate "optimality gap"in terms of reduction in system costs, peak load, and VRE curtailment. Its added value is the ability to enable each system to interact in an integrated energy system and locally optimize their decisions without sharing confidential information. The practical implication of this new approach is to provide a modeling environment where system operators and flexibility aggregators can obtain insights into the impacts of decarbonization of other parties on their systems - thereby avoiding myopic operational or investment decisions.

Electrification of energy end-uses brings an increasing load on electric distribution grids with load peaks that can cause network congestion. However, many new end-uses like electric vehicles, heat pumps, and electrified industrial processes have some flexibility to move their power consumption away from peak times. Congestion management mechanisms can harness this flexibility. This paper investigates congestion management mechanisms based on limited available network capacity for flexible loads during peak times. A case study discusses and investigates real-world examples of such mechanisms from proposals in Germany and the Netherlands. They differ concerning the lead time at which the capacity limitation is announced, with options from near real-time and day-ahead to long-term. These mechanisms are suited to remove network congestion, but there are significant trade-offs concerning the lead time. A shorter lead time leaves more room for using the network during non-congested times but creates a risk of curtailment for end-users, which may come with associated balancing and re-procurement costs. Longer lead times give more certainty on network access conditions but often restrict network usage even when there is no network congestion.

Aligning prosumers' electricity consumption to the availability of self-generated electricity decreases CO₂ emissions and costs. Nudges are proposed as one behavioral intervention to orchestrate such changes. At the same time, fragmented findings in the literature make it challenging to identify suitable behavioral interventions for specific households and contexts - specifically for optimizing self-consumption. We test three sequentially applied interventions (feedback, benchmark, and default) delivered by digital tools in a field experiment with 111 German households with rooftop-photovoltaics. The experiment design with a control-group, baseline measurements, and high-frequency smart-meter-data allows us to examine the causal effects of each intervention for increasing self-consumption. While feedback and benchmark deliver small self-consumption increases (3–4 percent), the smart changing default leads to a 16 percent increase for active participants. In general, households with controllable electric vehicles show stronger effects than those without. For upscaling behavioral interventions for other prosumers, we recommend interventions that require little interaction and energy literacy because even the self-selected, motivated sample rarely interacted with the digital tools.

Households equipped with flexible technologies, such as electric vehicles, can support the energy transition by shifting electricity consumption to times of high renewable supply and by preventing consumption peaks that cannot be covered by existing grid and generation infrastructure. Demand response services support households in performing these consumption shifts. Households ask for specifications of services that stand partly in contrast to each other. For instance, while electric vehicle owners tend to insist on retaining control over their charging, others prefer data-driven automation to minimize their active involvement. Recent studies exploring the acceptance of demand response services focused either solely on specific household groups (e.g. electric vehicle users) or on a broad representative sample without further differentiation. Complementarily to fill this gap, we examine differences in preferences for contrasting service designs between household groups. Specifically, we consider: (i) the type of flexible technology to which demand response is applied, and (ii) the adoption level, i.e., whether the households plan to, or currently own, a flexible technology. In a vignette survey, we examine the preferences towards four contrasting service designs with German households that either own or have expressed interest in acquiring a flexible technology (n = 962). Our results show that the preferences do not fundamentally differ between the kind of flexible technology and adoption level. Generally, participants prefer automated demand response services with data sharing. The added value of realizing energy cost savings effectively and efficiently stands out as the main driver for the diffusion of demand response services, outweighing data privacy concerns. Contrary to our expectations, electric vehicle owners did not show a special need for control and households not yet owning flexible technologies did not express a need for little effort. We discuss the implications of our findings for demand response service providers and outline pathways of future research in this domain.

Electric vehicle (EV) users who aim to become flexibility providers face a tradeoff between staying in control of charging and minimizing their electricity costs. The common practice is to charge immediately after plugging in and use more electricity than necessary. Changing this can increase the EV’s flexibility potential and reduce electricity costs. Our extended electricity cost optimization model systematically examines how different changes to this practice influence electricity costs. Based on the Prospect Theory and substantiated by empirical data, it captures EV users’ tradeoff between relinquishing control and reducing charging costs. Lowering the need to control charging results in disproportionally large savings in electricity costs. This finding incentivizes EV-users to relinquish even more control of charging. We analyzed changes to two charging settings that express the need for control. We found that changing only one setting offsets the other and reduces its positive effect on cost savings. Behavioral aspects, such as rebound effects and inertia that are widely documented in the literature, support this finding and underline the fit of our model extension to capture different charging behaviors. Our findings suggest that service providers should convince EV-users to relinquish control of both settings.

Decarbonisation of the electricity sector has led to the adoption and deployment of a large number of consumer-sited flexible assets. Simultaneously, consumers are becoming increasingly aware of their consumption patterns and are eager to reduce their energy expenses making demand response a significant source of flexibility in energy markets. In this paper, we discuss the policy measures that influence a consumer's ability to respond to price signals and offer flexibility in the day-ahead market. We propose two methods to quantitatively analyse these policy instruments through their inclusion in market clearing models for the Dutch day-ahead power market. A single-level optimisation model with social welfare maximisation objective can be used to perform a simplified assessment of changes in demand bids due to policy-based financial influences. This model is suitable for studying simple policies such as time-independent taxes but unsuitable for complex policies such as network tariffs and subsidies. A bi-level optimisation model with consumer surplus maximisation on the upper level and social welfare maximisation on the lower level allows more sophisticated modelling of policies but is limited by its scalability and computational complexity. The two methods can be compared on the basis of their ability to incorporate different policy instruments and market design choices, model consumer bidding behaviour, their computational complexity and challenges to implementation.

Increasing peaks from high-power loads such as EVs and heat pumps lead to congestion of electric distribution grids. The inherent flexibility of these loads could be used to resolve congestion events. Possible options for this are smart network tariffs, market-based approaches, and direct control of flexible loads by the network operator. In most instances, these approaches are looked at in isolation, without considering potential connections and trade-offs between them. In this contribution, we aim to bridge this gap by presenting an overarching design framework for congestion management mechanisms. We classify proposals based on design choices and qualitatively discuss their benefits and risks based on an extensive literature analysis. As there is no one-size-fits-all solution, we map possible risks and discuss the pros and cons of different mechanisms for various problem types. We caution against using market-based mechanisms for local congestion, as they can be susceptible to undesired strategic behavior of market actors.

Developing innovative electricity market designs to facilitate a sustainable transition to (near) 100% renewable power systems while meeting societal needs is a crucial and actual topic of research. This article presents preliminary key findings from the H2020 European project TradeRES, addressing this critical topic. The project uses agent-based and optimization models to effectively capture the behaviour of different market players, and to analyse the current and future power system energy mixes of selected European case studies with different physical and spatial scales from: i) local energy communities and local energy markets (LEMs); ii) national/regional - the Netherlands, Germany, and Iberia (Portugal and Spain); and iii) pan-European energy markets. The first results on LEMs indicate a substantial economic benefit for participants and enhanced revenue streams for distributed energy resources, able to i) incentivise further decentralised investments; ii) promote the growth of variable renewable energy systems (vRES) and iii) increase flexibility at the local level. The outcomes are sensitive to the tariffs’ structure, while the retail sector competitiveness was identified as a critical parameter affecting its efficiency. For the pan-European and national/regional case studies, the first set of simulations had consistent outcomes, namely, by pointing out current design of energy-only markets to be insufficient to incentivize the high levels of vRES foreseen in Europe. Different support schemes (e.g., fixed market premia, contract for differences) were tested and results suggest they may play a relevant role in effectively covering the cost of vRES in a market environment.

While the traditional perspective on regional power pools is that of regional electricity markets, regional power pools in Africa are development tools with unrepresentative and under-researched dynamics in their evolution and market design. Five African power pools are under development to bring the continent into a single electricity market. This paper analyses the evolution of African power pools by examining their design factors and dynamics. It compares the three most advanced power pools (SAPP, WAPP and EAPP) to identify barriers, forces, and development approaches. The paper devises a multilayer analytical framework to organise the various elements in the institutional context of African power pools and structure the analysis of the physical, economic, institutional and political factors. The analysis shows that the unequal distribution of natural energy endowments across the regions stimulated the historical development of interconnections and trade between countries. The characteristics of regional integration were vital in shaping the power pool's institutions and development approaches. We have identified three motors of change that have driven the development; regional economic communities, members’ strategies, and international institutions. It is crucial for progress that the pool staff can mitigate non-cooperative strategies, address members’ concerns, and incentivise a shift in national agendas to build confidence in regional trade.

Prosumers with photovoltaic systems can reduce their electricity expenses by increasing their consumption of self-generated electricity. This makes them more resilient to price shocks, like the 2022 European energy crisis. We evaluate how prosumers adapt their consumption behavior in response to such political uncertainty and increasing electricity prices. The collected survey and smart meter data allow us to evaluate the perceived self-reported and measured impact on self-consumption.Saving intentions due to the energy crisis are more clearly displayed by the survey than by the measured self-consumption. While solar radiation predominantly explains self-consumption changes, Google searches on electricity-related topics have limited explanatory power. However, considering time lags and the interaction with solar radiation leads to more nuanced insights on the effect of Google searches. Depending on the level of solar radiation, the effect of Google searches ranges from decreasing the daily self-consumption by 26.45 Wh to increasing it by 69.45 Wh.

The steady uptake of PV cells and high-power flexible loads such as electric vehicles (EVs) and heat pumps can lead to localized network congestion, if their power consumption or feed-in is not controlled well. One potential way that has been proposed to manage this congestion, are so-called Local Flexibility Markets. It is often argued, that these proposals are theoretically efficient, as they are market-based. However, some of these proposals may suffer from design flaws that allow market participants to obtain undue profits at the expense of the network operator. In this contribution, we discuss which kinds of market failures can occur based on theoretical reasoning and demonstrate them in a toy model. Based on this, we argue for a more careful consideration of congestion management options.

Existing indicators of electricity system adequacy need to be supplemented with economic performance indicators. As power systems are decarbonized, energy storage technologies are being developed and demand is becoming more flexible. Reliability standards need to reflect the price elasticity of these sources of flexibility. During scarcity situations, this increased demand flexibility may prevent outages, but still lead to high electricity prices. If the average electricity price is well above the average cost of power supply, this can be an indication that the system is not adequate, even if the outage rate does not exceed the current reliability standards.