Optimization of social welfare in P2P community microgrid with efficient decentralized energy management and communication-efficient power trading

The primary objective of implementing a demand response (DR) program is to actively involve participants in maximizing social welfare by effectively reducing electricity demand, optimizing cost savings, and facilitating peer-to-peer (P2P) trading of surplus power between prosumers and consumers residing within a community. In this paper, a novel approach is proposed to optimize the scheduling of home-controlled appliances, minimize overall electricity costs of the community, and prioritize participants' privacy by integrating non-cooperative energy game theory under time-of-use (TOU) tariffs and incentive schemes. The proposed Segmentation Based Distributed Alternating Direction Method of Multipliers (SBD-ADMM) is a novel algorithm that efficiently addresses the social welfare and participants' privacy problem in a decentralized distribution of power. It introduces a communication-efficient approach to optimize power trading while maximizing collective welfare, without relying on a central hub or authority. Node coloring is used in the formulation of the algorithm, in which every node just needs to trade with its neighbors in order to attain the entire optimum solutions. The suggested P2P trading algorithm is assessed on a broad range of scenarios, demonstrating its efficiency as well as fast convergence. The P2P trading algorithm provides similar optimum results with considerably a lesser number of communications and iterations as compared to other contemporary decentralized techniques. The suggested strategies for DR program and P2P trading are more feasible and privacy-preserving compared to other modern strategies. Finally, numerical results demonstrate that demand response and P2P trading can reduce overall costs for consumers by up to 30.23 % and for prosumers by up to 44.34 %.