考虑电池动态退化与权重自适应的微网双层MPC调度

Battery Dynamic Degradation and Weight-Adaptive Two-Layer MPC Scheduling for Microgrids

摘要: 针对高比例可再生能源接入下微网运行的经济性与稳定性矛盾，提出一种融合电池动态健康状态与下层权重自适应的双层模型预测控制(MPC)能量管理方法。上层在15~60分钟尺度以购售电成本与电池退化成本之和最小为目标，引入基于实时健康状态(SOH)的惩罚因子修正退化模型，并联合设备与网络约束滚动求解。下层在秒级以并网功率跟踪和直流母线电压稳定为核心，设计自适应权重切换机制，根据电压偏差与跟踪误差动态调整超级电容与电池的协调策略。基于中国西北地区典型气象数据的仿真结果表明，所提方法相比传统双层模型，总运行成本降低约11.0%，电池等效循环次数减少30.4%，并网功率最大波动量从26 kW降至17 kW，验证了该方法在经济性、寿命保护与动态适应性方面的综合优势。

Abstract: To address the economic and stability challenges of microgrid operation with high penetration of renewable energy, a two-layer model predictive control (MPC) energy management method integrating battery dynamic state-of-health (SOH) and lower-layer weight adaptation is proposed. The upper layer minimizes the sum of electricity purchase/sale costs and battery degradation costs over a 15~60 min rolling horizon, incorporating a real-time SOH penalty factor to revise the degradation model, while considering equipment and network constraints. The lower layer operates on a second-by-second timescale, focusing on grid-connected power tracking and DC bus voltage stabilization. An adaptive weight switching mechanism is designed to dynamically adjust the coordination between the supercapacitor and battery based on voltage deviation and tracking error. Simulation results based on typical meteorological data from Northwest China show that, compared with the conventional two-layer model, the proposed method reduces the total operating cost by approximately 11.0%, decreases the battery equivalent full-cycle count by 30.4%, and reduces the maximum grid-connected power fluctuation from 26 kW to 17 kW, demonstrating comprehensive improvements in economy, lifespan protection, and dynamic adaptability.