Multi-Objective Optimization for Energy Storage Configuration in Photovoltaic DC Collection Systems Based on Improved Ivy Algorithm

With the increasing deployment of large-scale photovoltaic (PV) power generation, efficient energy storage configuration has become increasingly important for ensuring the reliable and economical operation of direct current (DC) collection systems. However, existing studies have paid limited attention to coordinated multi-node energy storage allocation in asymmetric, multi-voltage-level DC networks, particularly considering the coupled effects of storage location, capacity, and network operating conditions. To address these challenges, this study investigates an 85-node asymmetric PV DC collection system and establishes a multi-objective optimization model considering investment cost, voltage deviation, network power loss, and energy storage power-support capability. An improved Multi-Objective Ivy Algorithm (MOIVYA) is employed by integrating non-dominated sorting, Pareto-based elite guidance, and iteration-dependent growth regulation into the ivy-inspired growth and climbing processes. Comparative simulations with Non-dominated Sorting Genetic Algorithm II (NSGA-II) and multi-objective particle swarm optimization (MOPSO) demonstrate that MOIVYA achieves competitive convergence and improved Pareto-solution distribution uniformity, with a spacing (SP) value of 0.0111, approximately 31.5% lower than that of NSGA-II. Although NSGA-II achieves a higher hypervolume (HV), MOIVYA exhibits lower HV variability across independent runs. Additional benchmark experiments further support its effectiveness under the investigated test conditions. The resulting Pareto solutions provide alternative energy storage configurations that balance economic and operational requirements, offering quantitative support for energy storage planning in large-scale PV DC collection systems.