Distributed Multiobjective Optimization Scheme for Load Aggregators in Incentive-Based Demand Response Programs

Abstract

Demand response (DR) programs are an effective means of mitigating power shortages. This article proposes a novel incentive-based DR program for peak shaving situations. The system operator (SO) determines the response power amount and provides tiered subsidy policies. The load aggregators (LAs) are rational decision-makers and formulate multiobjective optimization problems (MOPs) to make compromises for incentive income, user comfort, and environmental contribution. The innovation lies in the proposed distributed event-triggered solution methodology, including a distance-minimization algorithm to find the decision closest to the ideal point from the Pareto front, and a weighting coefficients optimization algorithm to allocate the importance of each objective at a predefined time. The distributed solution framework maintains the autonomy of each LA, and the event-triggered communication mechanism saves communication resources. Numerical simulations validate the effectiveness of the proposed solution methodology, including the realization of the response power target given by the SO, the optimal compromise of the MOP, and the saving of communication resources in the solution process.