Design optimization of a grid scale hybrid energy storage system to maximize solar PV integratio

Abstract

The global utilization of renewable energy is increasing as an environmentally sus- tainable approach to reduce greenhouse gas emissions, which are major contributors to air pollution and climate change, by decreasing reliance on oil-fired thermal power generation. Thisresearchaims tominimizeoil-fired thermal generationandmaximize solar photovoltaic (PV) generation through a grid-connected Hybrid Energy Storage System (HESS) that integrates lithium-ion Battery Storage (BS) and Pumped Hydro Storage (PHS). Solar energy is prioritized due to its technological maturity and cost- effectiveness compared to other sustainable energy sources. The HESS is employed to optimize storage performance, utilizing high efficiencyand energy management ca- pabilities of lithium-ion BS and the large-scale energy storage potential of PHS. Both technologies are well-established, offering reliability and low investment risks. A novel optimization algorithm, based on the Non-dominated Sorting Genetic Al- gorithm(NSGA-II),isdevelopedtominimizetotalexpenditures. Thisincludescapital, operational, and replacement costs associated with solar PV, lithium-ion BS, and PHS systems, as well as expenses related to solar power curtailment and supplementary oil-fired thermal energy. The multi-objective optimization problem is solved using the open-source Python framework Pymoo (multi-objective optimization in Python), and Python code developed and executed on Google Colaboratory (Colab). The algorithm determines optimal capacities for lithium-ion BS and solar PV systems at short-term intervals throughout the project planning horizon. The capacity of PHS is identified throughafeasibilitystudyconductedforaspecificregion,consideringgeologicalcon- straints. An innovative energy management strategy is proposed to optimize solar en- ergyutilizationwithintheHESS.Thisstrategyensurestheefficientutilizationofsolar energy, maintains grid stability, optimizes HESS operations within system limitations, and minimizes dependence on thermal power generation. The research includes a case study of the Sri Lankan power system, supported by economic and environmental assessments to identify the most sustainable scenarios. Sensitivity analysis is conducted to evaluate capacity variations under fluctuating in- terest rates. A roadmap for solar PV and BS capacity expansions is developed for two-year intervals throughout the planning horizon, along with the optimal timeline for integrating PHS. The final results indicate minimal variations in total cost, ranging from -14.05 % to 56.69 %, and an increase in solar PV generation by 2.19 % at the lowest interest rate and 5.13 % at highest interest rate. The solar PV capacities deter- mined from the optimization algorithm are compared with the projected capacities of solar power plants outlined in Sri Lankan generation expansion plan to evaluate their alignment with the renewable energy targets in the country. The results demostrats strong agreement with the national energy plan.