An Energy Sharing Game with Generalized Demand Bidding: Model and Properties

This paper proposes a novel energy sharing mechanism for prosumers who can produce and consume. Different from most existing works, the role of individual prosumer as a seller or buyer in our model is endogenously determined. Several desirable properties of the proposed mechanism are proved based on a generalized game-theoretic model. We show that the Nash equilibrium exists and is the unique solution of an equivalent convex optimization problem. The sharing price at the Nash equilibrium equals to the average marginal disutility of all prosumers. We also prove that every prosumer has the incentive to participate in the sharing market, and prosumers' total cost decreases with increasing absolute value of price sensitivity. Furthermore, the Nash equilibrium approaches the social optimal as the number of prosumers grows, and competition can improve social welfare.Comment: 16 pages, 7 figure

Approaching Prosumer Social Optimum via Energy Sharing with Proof of Convergence

With the advent of prosumers, the traditional centralized operation may become impracticable due to computational burden, privacy concerns, and conflicting interests. In this paper, an energy sharing mechanism is proposed to accommodate prosumers’ strategic decision-making on their self-production and demand in the presence of capacity constraints. Under this setting, prosumers play a generalized Nash game. We prove main properties of the game: an equilibrium exists and is partially unique; no prosumer is worse off by energy sharing and the price-of-anarchy is 1-O(1/I) where I is the number of prosumers. In particular, the PoA tends to 1 with a growing number of prosumers, meaning that the resulting total cost under the proposed energy sharing approaches social optimum. We prove that the corresponding prosumers’ strategies converge to the social optimal solution as well. Finally we propose a bidding process and prove that it converges to the energy sharing equilibrium under mild conditions. Illustrative examples are provided to validate the results

Review on Control of DC Microgrids and Multiple Microgrid Clusters

This paper performs an extensive review on control schemes and architectures applied to dc microgrids (MGs). It covers multilayer hierarchical control schemes, coordinated control strategies, plug-and-play operations, stability and active damping aspects, as well as nonlinear control algorithms. Islanding detection, protection, and MG clusters control are also briefly summarized. All the mentioned issues are discussed with the goal of providing control design guidelines for dc MGs. The future research challenges, from the authors' point of view, are also provided in the final concluding part

A Distributed Energy Management Strategy for Renewable Powered Communication Microgrid using Game Theory and Reinforcement Learning

This dissertation explores distributed energy management strategies for base stations in a communication microgrid, which is intended to be operating in island mode powered exclusively by renewable power sources. The energy management strategy aims at searching for an optimal energy consumption plan considering both communication quality and energy availability. In this dissertation, the objective is to accomplish such energy management using distributed control architecture, because such architecture is more durable and robust compared to a central controller. Three approaches have been proposed: multi-player game, reinforcement learning, and a hierarchical load-ratio updating algorithm. The modelings, mechanisms, performance, and applicable conditions of the three algorithms are discussed and compared. Numerical simulation results of communication microgrids in multiple cases implemented with the three algorithms were conducted. As the numerical results show, the hierarchical game-learning algorithm has a better performance compared to the multi-player game approach in terms of computation complexity and faster-converging speed compared to that of the reinforcement learning approach