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 (). 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, , 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.