Multi-objective Optimization Based Bidding Strategy for DER Aggregators in Ancillary Services Market Considering Environmental Benefits

Considering the features of distributed energy resources (DER) (i.e., small capacity, scatter, and uncertainty), DERs are supposed to be managed by distributed energy resources aggregators (DERA). On the basis of the complementarity of the various DERs, DERA can dig out the flexibility of the DER to support the power system operation via bidding in the ancillary services market. It’s essential for DERA to get maximum benefit through making optimal bidding strategies. To this end, this paper proposes an optimal bidding strategy based on a multi-objective planning approach. Firstly, DERA aggregates multiple resources (distributed generation, demand response, and energy storage systems) to participate in the ancillary services market in order to maximize benefits. In addition, the impact of the energy market on its bidding program is considered. Secondly, the environmental benefits (consumption of renewable energy and pollutant emissions) of DER are considered. The multi-objective bidding strategy is solved using an improved Artificial Hummingbird Algorithm (MOAHA). In MOAHA, a dynamic elimination-based crowding distance (DECD) method is developed to refine Pareto optimal solutions for improving the convergence of the algorithm. Case studies verified the effectiveness of the proposed model using the real-world data of the PJM market.