The adoption of new market mechanisms - vital to the better integration of flexible assets - depends on the fairness and nondiscrimination of the pricing rules. We consider a market setting with time-flexible unit energy buyers and sellers, that additionally submit their availability in time. The time-flexibility of the agents allows for different schedules to be equivalent with regard to social welfare, which can lead to arbitrary price differences, i.e. price discrimination. In this work, we demonstrate that non-discriminatory prices are not trivially defined in time-flexible settings, provide a definition of non-discrimination as consistent over equivalent outcomes, show that this concept does not conflict with individual rationality and, finally, compare our work to broader concepts of fairness from economic psychology.

We consider network flow congestion management modelled after electricity distribution networks. The desired consumption or production of the agents that populate such networks are determined by a higher-level (e.g. national) market mechanism, but this can lead to congestion locally. We first consider congestion solutions in the form of curtailment independent of the price set by the higher-level market. Congestion solutions of this type that satisfy properties of fairness are described in the literature. We contrast these fair solutions with curtailment solutions that maximize total welfare, and we present an algorithmic mechanism that computes such maximal welfare solutions. We then combine the two approaches to compute hybrid congestion solutions where agents can choose to either claim their fair share or to participate in a welfare-maximizing aftermarket. We incentivize aftermarket participation with an individually rational pricing scheme, while offering agents' fair shares at the higher-level price. Our aftermarket solution provides a budget balanced alternative to locational marginal pricing that gives agents the choice to claim their fair share at a fair price.

Increased uptake of variable renewable generation and further electrification of energy demand necessitate efficient coordination of flexible demand resources to make most efficient use of power system assets. Flexible electrical loads are typically small, numerous, heterogeneous and owned by self-interested agents. Considering the multi-temporal nature of flexibility and the uncertainty involved, scheduling them is a complex task. This paper proposes a forecast-mediated real-time market-based control approach (F-MBC) for cost minimizing coordination of uninterruptible time-shiftable (i.e. deferrable) loads. F-MBC is scalable, privacy preserving, and useable by device agents with small computational power. Moreover, F-MBC is proven to overcome the challenge of mutually conflicting decisions from equivalent devices. Simulations in a simplified but challenging case study show that F-MBC produces near-optimal behaviour over multiple time-steps.

With the energy transition, grid congestion is increasingly becoming a problem. This paper proposes the implementation of fairness in congestion management by presenting quantitative fair optimization goals and fairness measuring tools. Furthermore, this paper presents a congestion management solution in the form of an egalitarian allocation mechanism. Finally, this paper proves that this mechanism is truthful, pareto efficient, and maximizes a fair optimization goal.

In order to reduce CO2 emissions, energy systems using different energy carriers (e.g., heat and power) are becoming more intertwined and integrated. However, coordination between non-cooperative participants of these systems in the combined heat and power domain has been limited to single-sided auctions with one centralised seller. In this paper, we present a double-sided auction mechanism in which prosumers as well as consumers and producers of heat and power can participate. By showing that our mechanism is Incentive Compatible and Individually Rational, we ensure that truthful bidding is the optimal strategy, simplifying the bidding process and thus accommodating agents with limited computational resources. Finally, we show that our mechanism is fiscally sustainable, i.e., Weakly Budget Balanced.

The imperfect decision-making of human buyers participating in retail markets varies from fundamental models that assume rational economic choices: even in markets with identical items human buyers are not rational, i.e., buyers do not always choose the cheapest option. Recent developments in artificial intelligence and e-commerce enable market participation by software agents that are (almost) perfectly rational due to their computational capacity. However, the increasing degree of buyers’ rationality might have unfavorable effects on retail markets with regards to the competition between sellers and the resulting prices. In this paper, we study the effects of varying degrees of buyers’ rationality on the competition and the prices buyers face in retail markets with identical items. We use the multinomial logit function to model different degrees of buyers’ rationality. We further model the competition between sellers using k-level reasoning: each seller computes the price to offer (best response strategy) with regards to its belief for the competition. First, we derive an analytical best response strategy (price) of a seller given the competing prices and the degree of buyers’ rationality, and show that there exists an optimal degree of buyers’ rationality that minimizes the price. Last, we use evolutionary game theory to show that perfect rationality leads to unstable competition dynamics increasing the overall cost for buyers. In contrast, bounded rationality leads to smoother dynamics and lower cost for buyers. Our insights raise the need to revisit design objectives for software agents in retail markets in light of their wider systematic impact.

Optimal power flow is usually a non-convex problem of central coordination because of the nonlinear relations between nodal voltages and power supplied/withdrawn. In this paper, we propose a sequential distributed algorithm to address the problem of cost minimization power flow in distribution systems. Each node is considered as an agent, which solves its local optimization problem with the local knowledge of the network and communicated state variables of its neighbors. The power generation is optimized by the decomposed sub-problem at each node in backward sequence and the voltages are calculated in forward sequence. Our approach does not require any form of central coordination or regional control. The proposed algorithm has a fast convergence which is illustrated on various distribution test systems.

We study mechanisms to incentivize demand response in smart energy systems. We assume agents that can respond (reduce their demand) with some probability if they prepare prior to the real-ization of the demand. Both preparation and response incur costs to agents. Previous work studies truthful mechanisms that select a minimal set of agents to prepare and respond such that a fixed demand reduction target is achieved with high probability. In this work we additionally consider the balancing responsibility of a retailer under a given demand forecast and imbalance price: The retailer is responsible to purchase additional reserve capacity at a high imbalance price to cover any excess in the demand. In this extended setting we study mechanisms that request only a subset of prepared agents to respond since the reduction target depends on the realization of the demand: We propose: (i) a sequential mechanism that in each round embeds a second-price auction and is truthful under some mild assumptions for the setting, and (ii) a truthful combinatorial mechanism that runs in polynomial time and uses VCG payments. We show that both mechanisms guarantee non-negative utility in expectation for both agents and the retailer (mechanism), and can further be used for simultaneous downward and upward flexibility. Last, we verify our theoretical findings in an empirical evaluation over a wide range of mechanism parameters.

The Multi-objective Gene-pool Optimal Mixing Evolutionary Algorithm (MO-GOMEA) has been shown to be a promising solver for multi-objective combinatorial optimization problems, obtaining an excellent scalability on both standard benchmarks and real-world applications. To attain optimal performance, MO-GOMEA requires its two parameters, namely the population size and the number of clusters, to be set properly with respect to the problem instance at hand, which is a non-trivial task for any EA practitioner. In this article, we present a new version of MO-GOMEA in combination with the so-called Interleaved Multi-start Scheme (IMS) for the multi-objective domain that eliminates the manual setting of these two parameters. The new MO-GOMEA is then evaluated on multiple benchmark problems in comparison with two well-known multi-objective evolutionary algorithms (MOEAs): Non-dominated Sorting Genetic Algorithm II (NSGA-II) and Multi-objective Evolutionary Algorithm Based on Decomposition (MOEA/D). Experiments suggest that MO-GOMEA with the IMS is an easy-to-use MOEA that retains the excellent performance of the original MO-GOMEA.

The Power TAC is a competition-based simulation of an electricity market. The goal of the competition is to test retailer (broker) strategies in a competitive environment. Participants create broker agents that trade electricity. In this paper we describe our broker, which we created as a participant of the 2014 Power TAC competition. We describe the strategies for two main components of the game: the tariff market and the wholesale market. We also discuss the performance of our broker in the competition, where we were second in the final ranking.

Motivated by the energy domain, we examine a risk-averse buyer that has to purchase a fixed quantity of a continuous good. The buyer has two opportunities to buy: now or later. The buyer can spread the quantity over the two timeslots in any way, as long as the total quantity remains the same. The current price is known, but the future price is not. It is well known that risk neutral buyers purchase in whichever timeslot they expect to be the cheapest, regardless of the uncertainty of the future price. Research suggests, however, that most people may in fact be risk-averse. If the future price is expected to be lower than the current price, but very uncertain, then they may prefer to purchase in the present, or spread the quantity over both timeslots. We describe a formal model with a uniform price distribution and a piecewise linear risk aversion function.We provide a theorem that states the optimal behavior as a closed-form expression, and we give a proof of this theorem.