A Grid-Friendly Multi-Objective Approach for Energy Scheduling Optimization in Microgrids

Abstract

This paper proposes a novel grid-friendly multi-objective approach to optimize energy management in an integrated source-grid-load-storage microgrid (MG). To enhance the MG's grid integration potential and cost-effectiveness, this approach develops a grid-friendly multi-timescale energy scheduling optimization (Gf-MtESO) strategy and a new evaluation metric (${\omega }_{\mathrm{Gf}}$). Gf-MtESO first establishes electricity market coordination by pre-submitting energy demand as subsequent scheduling constraints, effectively mitigating power exchange fluctuations between MGs and the main grid. Additionally, ${\omega }_{\mathrm{Gf}}$, by holistically evaluating dependency and volatility, facilitates comprehensive assessment of MGs' grid integration potential. To resolve conflicting objectives and multi-constraints challenges in developing the Gf-MtESO strategy, this approach applies an improved elitist non-dominated sorting genetic algorithm based on stepwise-solving and rotating-population optimization (SRO-NSGA-II). SRO-NSGA-II first decouples the problem and updates the population using rotated binary crossovers to accelerate the search for feasible domains. Results indicate that SRO-NSGA-II concurrently maintains solution diversity and convergence speed, outperforming NSGA-II in hypervolume metrics. Particularly, the novel approach demonstrates faster scheduling plans development and improves grid-connection friendliness by 90.76% with a 4.86% cost variation compared to benchmark methods, which provide a systematic approach to realize friendly grid integration while ensuring economic viability in MGs' applications.