LAUSR

Abstract Flexibility provisioning through demand response (DR) programs has emerged as an efficient tool for the economic and reliable management of electricity grids. In this work, an incentive-based DR model of a community microgrid (CMG) is considered, where an aggregator provides flexibility to the CMG. The objective of the aggregator is to minimize the cost of flexibility management that comprises the incentives paid to the residential users for shifting demands and penalty payments to the CMG operator for violating contractual commitments. Instead of attempting to minimize the total cost as single objective minimization, we adopt a two-stage optimization approach, wherein a bi-objective formulation is used in the first stage and a single objective formulation is used in the next. The bi-objective formulation enhances diversity and preserves promising solutions during the course of the search. An evolutionary algorithm is used to solve the bi-objective formulation and the obtained solution is improved in the next stage using local search conforming to a memetic algorithm paradigm. The performance of the proposed algorithm is investigated through statistical analysis and comparison with existing algorithms. The proposed algorithm reduces the peak-to-average ratio of CMG load by 3.97% with at least 27.94% cost saving compared to the state-of-the-art algorithms.