Near-optimal demand side management for retail electricity markets with strategic users and coupling constraints

Transactive Energy for Flexible Prosumers Using Algorithmic Game Theory

“…In [26], distributed mechanism design is used to achieve the same incentive alignment in a peak-shaving use case, while also exhibiting attractive scalability properties, in contrast to the standard VCG mechanism. 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.…”

“…Incentives & Non-convex ility constraint satisfaction strategic behavior models [5], [13] [14], [15], [16], [17] [18] - [20] [23] - [25] [26] - [28] This work convex community energy management problem with (global) resource constraints. We test the proposed method on a particular case study and conduct simulations to assess its performance with respect to all four of the above requirements.…”

“…With respect to the functions s n (•), their choice has been subject to discussion in recent literature on energy communities (e.g. [14], [32], [28]) and relates to how the costs of energy are shared among the community members. Nevertheless, it is easy for the community manager to make sure that the payments received add up to the energy costs, i.e., Algorithm 1 Combinatorial auction for transactive energy 1: Initialize b n for every bidder n 2: Initialize x t n = x max n , ∀t for every bidder n…”

Transactive Energy for Flexible Prosumers Using Algorithmic Game Theory

“…In this paper, we draw on concepts of algorithmic game theory and propose an auction-theoretic solution for a non- [5], [13] [14], [15], [16], [17] [18] - [20] [23] - [25] [26] - [28] This work convex community energy management problem with (global) resource constraints. We test the proposed method on a particular case study and conduct simulations to assess its performance with respect to all four of the above requirements.…”

“…With respect to the functions s n (•), their choice has been subject to discussion in recent literature on energy communities (e.g. [14], [32], [28]) and relates to how the costs of energy are shared among the community members. Nevertheless, it is easy for the community manager to make sure that the payments received add up to the energy costs, i.e., Algorithm 1 Combinatorial auction for transactive energy 1: Initialize b n for every bidder n 2: Initialize x t n = x max n , ∀t for every bidder n 3: Initialize k = 1…”