Advancing Transactive Energy Market Management using Community Microgrid Emulator that Supports OpenADR and Q-learning Based Auction Model

This paper delves into the practical application of transactive energy markets within community microgrids, making use of an innovative openADR-based microgrid emulator for thorough testing and validation. The emulator serves as a crucial conduit between theoretical concepts and real-world deployment, faithfully emulating the intricate behaviors of microgrids. Four distinct scenarios were meticulously examined: "Surplus Energy Selling," involving the return of excess energy to the grid; "Prioritized Community Sharing," which prioritizes local energy consumption within the community; a "Demand Response (DR)" scenario with a 10% load reduction to explore demand-side management potential; and "Energy Auction model using Q-learning," introducing Q-learning models to optimize energy bidding strategies for efficient distribution within the community. Through rigorous testing in the community microgrid emulator, these scenarios were effectively verified, underscoring the emulator’s adaptability and versatility. This unique combination of advanced simulation and hardware-in-loop testing facilitates the exploration of a wide array of energy market strategies and demand-side management techniques, affirming the practical significance and robustness of community microgrid applications in real-world contexts.