A two-stage stochastic p-robust optimal energy trading management in microgrid operation considering uncertainty with hybrid demand response

This study proposes a two-stage stochastic p-robust optimal energy trading management for microgrid, including photovoltaic, wind turbine, diesel engine, and micro turbine. To achieve optimal energy management for an microgrid, a hybrid demand response, which combines improved incentive-based and price-based demand responses, is incorporated to reduce peak period load while ensuring the reliability of the microgrid. A multiscenario tree method is used to generate scenarios for uncertain parameters such as wind turbine, photovoltaic, loads, and market-clearing prices, where each probability density function has been discretized by certain intervals. Then, using a scenario reduction technique, a differential evolution clustering, a set of reduced scenarios can be obtained. The proposed energy management combines a Gaussian-based regularized particle swarm optimization with a fuzzy clustering technique to solve the optimization problem and determine the best compromise solution according to cog-effectiveness and reliability. The effectiveness of the proposed approach has been analyzed for a typical microgrid test system, and then the results demonstrate that the robustness can be improved substantially while guaranteeing the economical operation of microgrid. Therefore, the proposed energy trading management determines the most reasonable solution in terms of economic and reliability issues for the microgrid operator.