Adaptive weighted multi-objective battery storage optimization in virtual power plant-controlled networks integrating electric vehicles and photovoltaics

The rapid integration of electric vehicles (EVs) and solar photovoltaic (PV) systems introduces significant uncertainty and operational stress in low-voltage (LV) distribution networks, often leading to voltage violations at high penetration levels. Residential battery energy storage systems (BESSs), when operated within a virtual power plant (VPP) framework, play an important role in supporting voltage regulation while improving consumer economic benefits. However, BESS scheduling remains a challenge due to the conflicting objectives of network safety and cost minimization, particularly under realistic operating conditions. This paper proposes an adaptive weighted multi-objective optimization (AW-MOO) framework. It explicitly prioritizes voltage violation mitigation (network impact) while subsequently minimizing consumer cost. Owing to the multimodal nature of the optimization landscape, where multiple scheduling solutions can yield same network-level performance, Pareto-based multi-objective optimization methods struggle to identify economically optimal solutions. To address this issue, the proposed framework reformulates the problem into a single-objective optimization with an adaptively tuned weight that dynamically balances network and economic objectives. AW-MOO is implemented by a constrained particle swarm optimization algorithm, where the weight is adaptively adjusted during the evolutionary process. AW-MOO is validated on a real-world LV distribution network with 108 residential consumers. Its superiority is demonstrated through comparisons with constant weighting strategies and a no-BESS case. The results show that AW-MOO can eliminate voltage violations and reduce consumer costs by more than 25%, highlighting its effectiveness in network and economic benefits.