To decarbonize the heating sector, residential consumers may install heat pumps. Coupled with heating loads with high thermal inertia, these thermostatically controlled loads may provide a significant source of demand side flexibility. Since the capacity of residential consumers is typically insufficient to take part in the day-ahead electricity market (DAM), aggregators act as mediators that monetize the flexibility of these loads through demand response (DR). In this paper, we study the strategic interactions between an aggregator, its consumers and the DAM using a bilevel optimization framework. The aggregator-consumer interaction is captured either as a Stackelberg or a Nash Bargaining Game, leveraging chance-constrained programming to model limited controllability of residential DR loads. The aggregator takes strategic positions in the DAM, considering the uncertainty on the market outcome, represented as a stochastic Stackelberg Game. Results show that the DR provider-aggregator cooperation may yield significant monetary benefits. The aggregator cost-effectively manages the uncertainty on the DAM outcome and the limited controllability of its consumers. The presented methodology may be used to assess the value of DR in a deregulated power system or may be directly integrated in the daily routine of DR aggregators.

Matching markets, Preferences, Nash equilibrium, Privacy, Consensus ADMM

This paper deals with a generic problem of matching agents with underlying preferenceswhile guaranteeing a certain level of privacy is met. As a general framework, we considerconsumers and prosumers who trade energy on a platform. Consumers buy energy to theplatform to maximize their usage benefit while minimizing the cost paid to the platform.Prosumers, who have the possibility to generate energy, self-consume part of it to maximizetheir usage benefit and sell the rest to the platform to maximize their revenue. Inspired bya variant of the Hotelling model, product differentiation is introduced and consumers canspecify preferences regarding locality and green origin of their supply. The consumers andprosumers problems being coupled through a matching probability, we provide analyticalcharacterization of the resulting Nash equilibrium, and conditions for existence and unique-ness. Assuming supply shortages occur on the platform, we reformulate the local marketclearing problem as a consensus problem that we solve using Consensus Alternating Direc-tion Method of Multipliers (C-ADMM), enabling minimal information exchanges betweenprosumers and consumers. C-ADMM complexity is recalled and strategyproofness is anal-ysed. The algorithm is then run on a case study made of 300 prosumers from New SouthWales in Australia, equipped with solar panels. We consider privacy-preservation outputagainst a centralized benchmark approach, and evaluate C-ADMM computational time un-der three scenarios with an increasing number of agents. Regarding economic analysis, weobserve that it is more profitable for prosumers than for consumers to be flexible withina local energy community, and that belonging to a local energy community incentivizesthem to reduce their demands by comparison with their initial targets. Furthermore, theexpectation to make a substantial profit is a main driver for prosumers' engagement withina community; whereas for consumers, the green origin of the supply is determinant.

There is an intrinsic value in higher integration of multi-carrier energy systems (especially gas and electricity), to increase operational flexibility in the electricity system and to improve allocation of resources in gas and electricity networks. The integration of different energy carrier markets is challenging due to the existence of physical and economic dependencies between the different energy carriers. We propose in this paper an integrated day-ahead multi-carrier gas, electricity and heat market clearing which includes new types of orders and constraints on these orders to represent techno-economic constraints of conversion and storage technologies. We prove that the proposed market clearing gives rise to competitive equilibria. In addition, we propose two decentralised clearing algorithms which differ in how the decomposition of the underlying centralised clearing optimisation problem is performed. This has implications in terms of the involved agents and their mutual information exchange. It is proven that they yield solutions equivalent to the centralised market clearing under a mild assumption of sufficient number of iterations. We argue that such an integrated multi-carrier energy market mitigates (spot) market risks faced by market participants and enables better spot pricing of the different energy carriers. The results show that conversion/storage technology owners would suffer from losses and/or opportunity costs, if they were obliged to only use elementary orders. For the test cases considered in this article, sum of losses and opportunity costs could reach up to 13,000 EUR/day and 9,000 EUR/day respectively, compared with the case where conversion and storage orders are used.