Integrated optimization for sizing, placement, and energy management of hybrid energy storage systems in renewable power systems

Abstract

Power systems reliant on renewable energy sources (RES) encounter supply-demand imbalances and stability challenges due to their inherent uncertainties. Hybrid energy storage systems (HESS) have emerged as a flexible and cost-effective solution to address these issues. This paper proposes an integrated optimization method for the capacity, location, and energy management of a HESS in RES-based power systems. The method optimizes battery energy storage system (BESS), electrolyzer (EL), fuel cell (FC), and hydrogen storage tank (HST) to minimize total costs, including power purchase, curtailment compensation, operation and maintenance (O&M), and investment costs, alongside voltage profile deviation index (VPDI) and active power loss index (APLI). A novel hybrid framework is proposed, utilizing mixed-integer linear programming (MILP) optimization at the master level and non-dominated sorting genetic algorithm II (NSGA-II) optimization at the slave level. This framework ensures that MILP provides robust solutions, while NSGA-II derives balanced optimal solutions from the optimal Pareto front. The proposed method was validated using real seasonal data from Korea and tested in IEEE 33 and IEEE 69 bus systems against various benchmark cases. Results indicate significant improvements, with VPDI enhanced by 16.00 % and 10.11 %, APLI by 15.64 % and 12.15 %, and total costs reduced by 58.67 % and 50.56 %, respectively. The sensitivity analysis demonstrated the robustness of the proposed method, showing zero sensitivity to iterations and positive correlations with increased levels of RES. Further verification against other optimization algorithms showed that the proposed method excels in cost efficiency, voltage stability, and line loss reduction, providing highly reliable results.