Optimization of grid-connected solar PV systems with Hybrid Energy Storage System: A case study of the Sri Lankan power system

Abstract

Abstract Greenhouse gas emissions from fossil fuel-based electricity generation significantly contribute to climate change. This research aims to mitigate these emissions by reducing reliance on fossil fuels and maximizing solar photovoltaic (PV) energy generation. It is supported by a grid-connected Hybrid Energy Storage System (HESS) integrating lithium-ion Battery Storage (BS) and Pumped Hydro Storage (PHS). An optimization algorithm is employed to minimize total expenditures, including capital, operational, replacement costs, as well as costs associated with solar curtailment and supplemental dominated Sorting thermal energy over the lifetime of the system. The Non Genetic Algorithm (NSGA-II) is used to solve the multi-objective optimization problem through the open-source Python framework Pymoo (multi-objective optimization in Python). Optimal capacities for solar PV and BS are determined for short-term intervals, while PHS capacities are evaluated based on regional geological feasibility. A novel energy management strategy ensures an effective balance between energy generation and storage. Optimization results are visualized through Pareto optimal charts and supplemented by economic and environmental analyses to identify sustainable scenarios. Sensitivity analysis further refines the optimal capacities for solar PV, BS, and PHS. The proposed methodology is validated using the Sri Lankan power system. A detailed roadmap is developed to guide the incremental additions of solar PV, BS, and PHS capacities. Final results indicate minimal total cost variations, ranging from −14.05% to 56.69%, and an increase in solar PV generation between 2.19% and 5.13%, due to fluctuations in the interest rate of the country.