Energy Optimization of Grid-Connected Hybrid PV- Battery Systems

Abstract

The growing integration of renewable energy sources into modern power systems has intensified the need for efficient energy storage and management solutions to address supply variability, ensure grid reliability, and minimize operational costs. This study is motivated by the technical and operational challenges associated with Battery Energy Storage Systems (BESS), particularly lithium-ion technologies, within hybrid photovoltaic (PV)-battery systems. The research aims to investigate these challenges, explore optimal control strategies, and develop practical recommendations to enhance the performance, lifespan, and economic viability of BESS. Specific objectives include formulating advanced control algorithms, analyzing system dynamics, and proposing best practices for BESS management in smart grid and microgrid environments.

A hybrid energy management system (EMS) was developed, combining Model Predictive Control (MPC) and Fuzzy Logic Control (FLC) to optimize power flow among PV generation, the battery, and the utility grid. Mathematical models capturing battery behavior, PV output, and grid interaction were designed under dynamic conditions such as variable tariffs and load profiles. The model was implemented and simulated using Python, with synthetic and real-world data over 24-hour periods to reflect residential, commercial, and industrial usage scenarios.

The results show that the proposed EMS significantly improves solar energy self-consumption, reduces reliance on the grid during peak tariff periods, and enhances battery cycling efficiency. FLC offered real-time responsiveness to rapid changes, while MPC provided anticipatory optimization over a forecast horizon. These findings highlight the potential of intelligent control strategies to support energy resilience and cost efficiency, offering scalable solutions for sustainable energy transitions in smart grid applications.