Intelligent Energy Management Method for Parallel-Connected Household Hybrid Inverter Systems
Parallel operation of household hybrid inverters introduces complex internal energy interactions and diverse coordination objects, exacerbating the difficulty of energy management. This study proposes a mathematical programming-based energy management strategy for parallel hybrid inverter systems. The method extracts power supply and consumption characteristics from household energy storage battery capacity, photovoltaic (PV) installed capacity, inverter power ratings, and electricity consumption and price data. A multi-objective optimization model is established to maximize PV utilization, enhance user economic benefits, and extend battery lifespan. The model incorporates six operating modes for a two-unit parallel system, and a logic pre-positioning method reduces the number of decision variables. The problem is solved using mixed-integer linear programming (MILP) with CPLEX. Simulation results demonstrate that the proposed method reduces user electricity costs while maximizing PV resource utilization, and adapts to different battery configurations. The efficacy coefficient method linearly combines the three objectives into a single objective, with weights summing to 1. The approach provides a dynamic planning reference for energy management modes, addressing the lack of data and theoretical support in existing experience-based mode selection. The study validates the economic efficiency and applicability of the method under various system configurations, offering a robust solution for flexible capacity expansion and intelligent energy management in household PV-storage systems.