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We develop a Nash equilibrium problem representing a perfectly competitive market wherein all players are subject to the same source of uncertainty with an unknown probability distribution. Each player—depending on her individual access to and confidence over empirical data—builds an ambiguity set containing a family of potential probability distributions describing the uncertain event. The ambiguity set of different players is not necessarily identical, yielding a market with potentially heterogeneous ambiguity aversion. Built upon recent developments in the field of Wasserstein distributionally robust chance-constrained optimization, each ambiguity-averse player maximizes her own expected payoff under the worst-case probability distribution within her ambiguity set. Using an affine policy and a conditional value-at-risk approximation of chance constraints, we define a tractable Nash game. We prove that under certain conditions a unique Nash equilibrium point exists, which coincides with the solution of a single optimization problem. Numerical results indicate that players with comparatively lower consumption utility are highly exposed to rival ambiguity aversion.

We address the market design issue for a local energy community, comprising prosumers, consumers, photovoltaic, and energy storage systems, all connected as a community to a distribution grid. Our work explores different market design options based on cooperative and non-cooperative game-theoretic models that enable an economic access to the benefits of energy storage for prosumers without a direct ownership of a storage system. We compare market outcomes in terms of the community cost as well as the individual cost. We pay special attention to potential uncertainties, and investigate financial instruments that allow storage systems to be utilized by multiple prosumers. In particular, we explore a case where a prosumer that owns a storage system provides rights, either physical or financial, rather than participating in the local market as an arbitrageur. Moreover, we consider a cooperative market design where energy community members agree on the Shapley value or the nucleolus as a community cost allocation rule. Our results show that an access economy for energy storage systems enhances energy communities by reducing the cost volatility for most prosumers, while the expected operational cost of the community as a whole remains unchanged.

Local energy communities are proposed as a regulatory framework to enable the market participation of end-consumers. However, volatile local market-clearing prices, and consequently, volatile cost give rise to local market participants with generally heterogeneous risk attitudes. To prevent the increased operational cost of communities due to conservative trading decisions in the forward stage, e.g., a day-ahead market, we propose risk trading in energy communities via financial hedging products, the so-called Arrow-Debreu securities. The conditional value-at-risk serves as our risk measure for players to study different degrees of market completeness for risk. We define a risk-averse Nash game with risk trading and solve the Nash equilibrium problem for an incomplete market for risk as a mixed complementarity problem. We show that such a Nash equilibrium problem reduces to a single optimization problem if the market is complete for risk. Numerical findings indicate that a significant community cost saving can be realized when players engage in risk trading and sufficient financial hedging products are available. Moreover, risk trading efficiently protects less risk-averse players from highly risk-averse decision-making of rival players.

The increasing amount of self-produced energy reduces the customer base of network utilities. Assuming constant grid costs, network charges have to be increased in systems applying volumetric network tariffs. In order to understand the cost recovery problem of utilities, it is crucial to analyze consumers' PV investment and operation decisions as sources of self-produced energy. This work proposes a mathematical framework that determines PV investment by consumers subject to the day-ahead market. Volumetric and capacity network tariffs are considered, which are altered by consumers' day-ahead market demand. The optimal PV investment from a central planner's perspective serves as a benchmark. The results show that a volumetric network tariff incentivizes inefficient investments in distributed PV systems, which causes all consumers' energy costs to increase. In contrast, a capacity network tariff reduces these incentives as consumers cannot offset their expected burden of network costs by installing PV systems.

In order to enable large-scale penetration of renew-abies with variable generation, new sources of flexibility have to be exploited in the power systems. Allowing asymmetric block offers (including response and rebound blocks) in balancing markets can facilitate the participation of flexibility aggregators and unlock load-shifting flexibility from, e.g., thermostatic loads. In this paper, we formulate an optimal offering strategy for a risk-averse flexibility aggregator participating in such a market. Using a price-taker approach, load flexibility characteristics and balancing market price forecast scenarios are used to find optimal load-shifting offers under uncertainty. The problem is formulated as a stochastic mixed-integer linear program and can be solved with reasonable computational time. This work is taking place in the framework of the real-life demonstration project EcoGrid 2.0, which includes the operation of a balancing market on the island of Bornholm, Denmark. In this context, aggregators will participate in the market by applying the offering strategy optimization tool presented in this paper.

Energy storage systems (ESS) are considered as a promising solution to improve power system flexibility and facilitate the integration of renewables in electricity markets. This paper investigates the impact of strategic offering by an ESS operator in the day-ahead and balancing market. The offering strategy of a price-maker ESS operator is formulated as a bilevel model, where the upper-level problem represents the profit maximization of the ESS operator and the lower-level problem simulates the market-clearing outcome. This methodological framework can be used either to assess market efficiency distortion or as a trading strategy from the perspective of the ESS operator. Our analysis shows that adopting strategic behavior may improve ESS expected profit but reduces social welfare, especially for high ESS energy-to-power ratios.