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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We consider energy systems in the built environment. With the transition to a more sustainable, distributed, and 'smart' energy system, such local grids are undergoing significant changes. Among other developments, the new role of end-users as 'prosumers' - users that can either produce or consume power depending on the situation - is turning energy systems in the built environment into autonomous microgrids with complex internal interactions.

One of the primary challenges for these local grids is maintaining grid stability, which requires constant balancing of supply and demand. Because local grids were not designed for distributed energy generation and large loads such as electric vehicle charging, their limited capacity is now leading to congestion. Since the responsibility for resolving congestion falls increasingly on the individual prosumers and their flexibility, the concept of fairness must take a central role in congestion management.

In this dissertation we present our research on supply-demand matching mechanisms for fair congestion management. The local networks populated by users can be represented by radial multi-agent commodity flow systems. For the resource allocation problems in this setting we draw on the fields of mechanism design and fair division to design provably fair congestion management mechanisms. We evaluate the merit of different notions of fairness and present algorithmic mechanisms that align agent incentives with fair allocations.

We find that notions of fairness regarding congested commodity flow networks can either focus on local or global fairness. Agents can have differing opinions on the two, depending on how wide they draw the circle of peers that they compare themselves to. We find that the mix of producers and consumers requires slight adaptation of notions of fairness, with agents envying one group while welcoming the other. Furthermore, we find that it is possible to combine notions of fairness with welfare optimization by letting individual agents decide which of the two is more important, and protecting their fair shares.

We are able to use the radial structure prevalent in energy systems in the built environment to design algorithmic mechanisms of consistently low computational complexity. The congestion solutions of these mechanisms satisfy different local and global fairness criteria, for which we provide rigorous proofs. We prove that our mechanisms are individually rational and, for variations of egalitarian fairness, also incentive compatible. Finally, we introduce a congestion aftermarket where agents compensate their peers for flexibility.@en

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.@en

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.

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