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.

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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.

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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.@en

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.@en

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.@en

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.

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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.@en

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.

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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.

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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. @en

The adoption of distributed energy resources such as PV cells, electric vehicles and batteries in electric grids is increasing steadily. This brings new challenges for distribution networks. The current network tariffs were not designed for these types of usage and, in many cases, they are not adequate anymore. Thus, many new tariff frameworks have been proposed. In this paper, we focus on the question of how to assess whether a given tariff framework fulfills its objectives. We propose to use quantitative indicators for performance assessment. We give examples of indicators for common objectives and demonstrate how they can be derived from a cost-accounting methodology for distribution networks.@en

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.@en

Household electricity use has an increasing impact on the overall energy system. Numerous proposals have been made to support households to consume electricity in a system-friendlier manner. By breaking these proposals down into functions and how they are performed, this paper identifies four distinctive governance designs: energy communities, variable electricity tariffs, local energy markets and virtual power plants. None covers all the functions required and each addresses different trade-offs that households face. Energy communities focus on investing in energy assets, while the others target the operation of households’ assets, including demand response. Virtual power plants attract profit-oriented consumers, while the others primarily target normative consumers.@en

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.@en

DC distribution systems are a promising alternative to existing AC distribution systems. They connect customers to local energy sources without conversion, thus reducing power losses. However, the unique features of DC impose strict requirements for system operation compared to AC. Within the context of a liberalized energy market, this article demonstrates three promising market designs—an outcome of a comprehensive engineering design framework—that meet those DC requirements. They are an integrated market design, which incorporates all system costs into energy prices; a market design that passes wholesale energy prices directly to prosumers; and a locational energy market design that relieves congestion with nodal prices. An optimization model estimates the three market designs’ performance by simulating a realistic DC distribution system, featuring a high share of electric vehicles. Results indicate that the integrated market design is optimal in theory but computationally infeasible in practice. The wholesale energy price design aiming at constraint-free energy trading requires substantial investments in flexibility. The locational energy market design yields nearly optimal operation in urban networks and is considered the best feasible market design for DC distribution systems.

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Electricity balancing is one of the main demanders of short-term flexibility. To improve its integration, the recent regulation of the European Union introduces a common standalone balancing energy market. It allows actors that have not participated or not been awarded in the preceding balancing capacity market to participate as voluntary bidders or ‘second-chance’ bidders. We investigate the effect of these changes on balancing market efficiency and on strategic behavior in particular, using a combination of agent-based modelling and reinforcement learning. This paper is the first to model agents' interdependent bidding strategies in the balancing capacity and energy markets with the help of two collaborative reinforcement learning algorithms. Results reveal considerable efficiency gains in the balancing energy market from the introduction of voluntary bids even in highly concentrated markets while offering a new value stream to providers of short-term flexibility. ‘Second-chance’ bidders further drive competition, reducing balancing energy costs. However, we warn that this design change is likely to shift some of the activation costs to the balancing capacity market where agents are prompted to bid more strategically in the view of lower profits from balancing energy. As it is unlikely that the balancing capacity market can be removed altogether, we recommend integrating European balancing capacity markets on par with balancing energy markets and easing prequalification requirements to ensure sufficient competition.

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Market-based procurement of balancing services in Europe is prone to strategic bidding due to the relatively small market size and a limited number of providers. In the European Union, balancing markets are undergoing substantial regulatory changes driven the efforts to harmonize the market design and better align it with the goals of the energy transition. It is proposed to decouple the balancing energy (real-time) market from the (forward) balancing capacity market and the price of balancing energy will be based on the marginal bid. In this paper, the potential effects of these changes on market participants’ strategies are analyzed using an agent-based model. This model compares the effects of a standalone balancing energy market with different pricing rules on economic efficiency with agents that apply naïve, rule-based and reinforcement-learning strategies. The results indicate that the introduction of a standalone balancing energy market reduces the cost of balancing, even in a concentrated market with strategic bidders. Marginal pricing consistently leads to lower weighted average prices than pay-as-bid pricing, regardless of the level of competition. Nevertheless, in an oligopoly with actors bidding strategically, prices can deviate from the competitive benchmark by a factor of 4–5. This implies that the introduction of a standalone balancing energy market does not entirely solve the issue of strategic bidding, but helps dampen the prices, as compared to the balancing market prior to the design change.

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The zonal electricity market design in the Central Western European electricity market relies on redispatching generation units after market closure to manage congestion within bidding zones, while congestion between the zones is handled using flow-based market coupling. The combination of internal congestion in the meshed European network with a growing share of renewables increases the frequency and magnitude of congestion events and limits cross-border trade. The growing costs of redispatching and the divergence between grid physics and zonal markets lead to welfare losses. This paper is the first to propose an approach to improve the combined efficiency of flow-based market coupling and redispatching. We develop a novel methodology for congestion management in a zonal market with flow-based market coupling in order to increase cross-border exchanges by integrating preventive redispatch into the day-ahead market. In this approach, a set of integrated redispatch units is selected based on their high potential to reduce congestion and, as a result, free up grid capacity for cross-border exchange. We use three multi-step optimization models to demonstrate the benefits of the enhanced zonal market with integrated redispatch by comparing it to the nodal market model and a zonal market model with flow-based market coupling. The case study demonstrates the potential of the proposed methodology to significantly increase cross-border capacity and reduce the need for costly ex post redispatch. The approach is shown to be a feasible option for improving European market integration and thereby to achieve overall welfare gains.

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