Co-optimization of photovoltaic-energy storage capacity and wireless charging infrastructure for industrial AGVs: A bilevel programming approach solved by an improved differential evolution algorithm

This paper puts forward a two-tier coordinated configuration scheme for photovoltaic-storage microgrid and automatic guidance vehicles wireless charging system based on improved differential evolution algorithm (IDE), aiming at the coordination optimization of photovoltaic-storage microgrid (PV-SM) and automated guided vehicle (AGV) wireless charging system. The upper layer configures power and PV storage and wireless charging number, then the lower layer implements the “PV+storage+Wireless Charging” system’s 24-h scheduling in response to the time-of-use electricity pricing mechanism. The model co-synchronizes the life cycle cost (LCC) and the real-time operation efficiency. Compared with traditional algorithms including particle swarm optimization (PSO), genetic algorithm (GA), and standard differential evolution (DE), the proposed IDE incorporates a dynamic mutation operator, adaptive crossover mechanism, and randomized selection strategy, achieving a faster convergence speed while enhancing solution accuracy and global optimization stability in handling the high-dimensional constraints of the bilayer energy-logistics coordination problem. Taking an automated logistics factory in China’s Jiangsu province industrial park as a validation case, we get the configuration of 10 kW single charging piles as the economic optimal, a net benefit of 157.31 United States Dollar (USD) per day of factory’s operation, a total net present value (NPV) income of 495,575.85 USD for 20 years. This research presents an economically and reliable plan for constructing a “photovoltaic + energy storage + wireless charging” system in the industry.