Multi-objective Optimization for Phase Balancing in an Energy Community with High PV Penetration and a Shared Energy Storage System

In grids with high penetration of photovoltaic (PV) systems and electric vehicle (EV) charging points, the phase imbalance can happen mainly due to uneven distributions of PV systems and EV charging points across different phases. This study presents a multi-objective optimization for a three-phase smart grid with 55 single-phase connected nodes to balance the grid, minimize electricity costs, and maximize operational efficiency. The research primarily focuses on individual node optimization, where each node autonomously adjusts its devices, aiming to reduce electricity expenses. It considers integrating community battery storage, operated based on multiple objectives, including phase balance and maximization of self-consumption within the community. Our approach involves a series of scenarios exploring various configurations of PV panel sizes and EV penetration levels. A critical aspect of this investigation is assessing how community battery storage can increase the PV hosting capacity of the grid. This enhancement is required to increase PV integration while ensuring grid stability. The study utilized Pyomo for modelling the optimization problems, coupled with the use of generic solvers. This combination provides a robust and free framework for tackling the complex multi-objective optimization tasks inherent in modern smart grids. The findings of this study can be used as a guideline for utility providers and grid operators, offering insights into optimal battery storage operation, phase balancing strategies, and facilitating higher PV integration.