A multi-objective optimization strategy of microgrid energy management toward coordinated charging for electric vehicles and economic costing

The rapid proliferation of electric vehicles (EVs) presents significant grid challenges to the construction and stable operation of urban electricity microgrids. To mitigate operational instability risks induced by uncoordinated large-scale EV charging integration in microgrids in urban and built-up areas, this study proposes a hierarchical two-layer optimization framework for power management in high-penetration EV-grid scenarios. The upper-layer of the hierarchical two-layer optimization framework model focuses on EV-related objectives, incorporating constraints, battery depreciation costs, and user satisfaction into the objective function. The lower-layer model includes microgrid components: photovoltaic units, wind turbines, micro wind turbines, and diesel engines. The aim is to minimize operational costs and load variance within the microgrid, thereby enhancing the power grid stability. To solve this model effectively, a hybrid optimization algorithm combining an improved sparrow search algorithm with a neural network is proposed, and the algorithm is benchmarked and validated against three meta-heuristic optimization algorithms for its efficacy. The study evaluates the scheduling outcomes under three distinct EV strategies, and simulation results demonstrate the effectiveness of the proposed algorithm in solving the model. In the coordinated charge-discharge mode, moving charging loads from peak tariff periods to off-peak tariff periods lowers daily costs by 2.9%. Adding distributed generation lowers daily costs by an additional 6.1%, which helps the microgrid system stay stable.