Rolling Energy Management of Grid-Connected Microgrids Considering Battery Degradation and Main Grid Power Smoothing

Abstract

With the transformation of the global energy structure, microgrids are gradually emerging as a key technological platform for enhancing the integration capacity of renewable energy and the flexibility of energy supply to consumers. In response to the time-varying characteristics of renewable energy and loads in grid-connected microgrids, this paper proposes a rolling optimization strategy that accounts for battery degradation and the smoothing of power exchange with the main grid. Based on an analysis of microgrid power balance, battery state-of-charge constraints and time-varying electricity price characteristics, a rolling optimization model is established to reduce the computational burden of complex multi-level optimization models and improve practical engineering feasibility. This model sets objectives comprising battery degradation costs, electricity purchase costs, penalties for curtailed wind and solar power, penalties for peak electricity purchases from the main grid, penalties for battery end-state conditions, and penalties for fluctuations in power exchange with the main grid. By constructing scenarios that account for forecasting errors in wind and solar loads, we compare three approaches: traditional rolling optimization, day-ahead static optimization, and the proposed method. The results of the case studies demonstrate that the proposed method improves grid compatibility and energy storage lifespan, reduces the total objective value, grid peak demand and grid fluctuation metrics, and exhibits better adaptability to dispatch.