A Comparative Analysis of Market-Clearing Methods in Community-Based Transactive Energy Microgrid

This paper presents a comparative analysis of market-clearing methods in a community-driven transactive energy micro-grid aimed at enhancing community self-sufficiency, boosting self-consumption, and reducing costs for prosumers. The study explores various market-clearing approaches, including bill sharing, supply-to-demand ratio, and mid-market rate, to establish internal market prices for peer-to-peer energy transactions within the community microgrid. A deep learning algorithm is applied to forecast the participants' day-ahead demand and generation profiles. Simulation results demonstrate a marked improvement in self-sufficiency, higher self-consumption, and lower energy costs for participants across the community.