Multi-objective MPC for Mitigating Degradation and Maximizing Profit of BESS in LV Distribution Systems

This paper presents a multi-objective model predictive control (MPC) strategy for a grid-connected battery energy storage system (BESS) that simultaneously accounts for voltage regulation, battery degradation, and price-based arbitrage in a weak low-voltage distribution feeder. The controller leverages a linear voltage–power sensitivity model and a physics-based battery aging model to predict system behavior and optimize BESS dispatch over a receding horizon. The proposed MPC framework is implemented and validated through a CYME Python co-simulation platform, where a Python-based MPC optimizer interacts with CYME’s detailed load-flow engine at each simulation step. Case studies on a 380-V radial feeder demonstrate that the BESS operating pattern is highly sensitive to the weighting of voltage, degradation, and price objectives. When PV and load forecasts are incorporated, the MPC adopts a more conservative and smoother power schedule that mitigates rapid voltage excursions and tends to reduce cumulative battery degradation, while slightly lowering energy-arbitrage revenue compared with a persistence-based MPC. These results highlight the importance of forecast-informed control in balancing grid-support performance, battery lifetime, and economic profitability. The proposed framework provides a practical and deployable solution for operating BESS units in PV-rich weak distribution networks.