Mechanism Design for Fair and Efficient DSO Flexibility Markets

Abstract

The proliferation of distributed energy assets necessitates the provision of flexibility to efficiently operate modern distribution systems. In this paper, we propose a flexibility market through which the DSO may acquire flexibility services from asset aggregators in order to maintain network voltages and currents within safe limits. A max-min fair formulation is proposed for the allocation of flexibility. Since the DSO is not aware of each aggregator's local flexibility costs, we show that strategic misreporting can lead to severe loss of efficiency. Using mechanism design theory, we provide a mechanism that makes it a payoff-maximizing strategy for each aggregator to make truthful bids to the flexibility market. While typical truthful mechanisms only work when the objective is the maximization of Social Welfare, the proposed mechanism lets the DSO achieve incentive compatibility and optimality for the the max-min fairness objective.

Transactive Energy for Flexible Prosumers Using Algorithmic Game Theory

“…A decomposition method for accommodating coupling constraints in a game-theoretic framework is shown in [27], while in [28] a novel mechanism is proposed for satisfying resource constraints in a setting with strategic, price-anticipating prosumers. Finally, study [29] proposes a generalization of the payment rule used by VCG, in order to maintain truthful participation also in the case that the DSO opts for a max-min fair allocation for the distribution network resources, and not only for an allocation that maximizes the social welfare. Nevertheless, all the studies of this paragraph again assume convexity of prosumer models.…”

Local Electricity and Flexibility Markets: SWOT Analysis and Recommendations

“…DSO -Aggregator (1): Hesitation to disclose sensitive data: Information sharing is not DSOs' primary focus, in comparison with data collection that is necessary. When it is required to transmit the data to the aggregator, the DSO may be reluctant or unable to do so because of privacy and data ownership issues [18,19].…”

Managing Distributed Flexibility Under Uncertainty by Combining Deep Learning With Duality

“…Thus, problem (11) cannot be tackled by traditional dynamic programming algorithms. Moreover, constraints (7) depend on the whole State-Action trajectory and not only on the current Action. This makes it very difficult to guarantee their satisfaction.…”

“…The dispatch of the EVs (or, more generally, the DERs) of an EC can be realised via direct control, or via an intra-community market. Such markets have been proposed for various use cases, including prioritizing EV charging in a charging station [6], flexibility markets of distribution networks [7], [8], and allocation of load curtailments by a demand response aggregator [9].…”