The problem of network flow congestion occurring in power networks is increasing in severity. Especially in low-voltage networks this is a novel development. The congestion is caused for a large part by distributed and renewable energy sources introducing a complex blend of prosumers to the network. Since congestion management solutions may require individual prosumers to alter their prosumption, the concept of fairness has become a crucial topic of attention. This paper presents a concept of fairness for low-voltage networks that prioritizes local, outer matching and allocates grid access through fair division of available capacity. Specifically, this paper discusses three distinct principal notions of fair division; proportional, egalitarian, and nondiscriminatory division. In addition, this paper devises an efficient algorithmic mechanism that computes such fair allocations in limited computational time, and proves that only egalitarian division results in incentive compatibility of the mechanism.
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.
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.
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