Techno‐Economic and Uncertainty Analyses of an Optimized Levelized Cost of Hybrid Energy Storage System Model

ABSTRACT

This work provides a techno‐economic framework for evaluating a levelized cost of hybrid energy storage system (LCOHESS) model. Four battery chemistries—lithium iron phosphate (LFP), nickel manganese cobalt oxide (NMC), valve‐regulated lead‐acid (VRLA) and flooded lead‐acid (FLA)—are hybridized with supercapacitors (SCs) to evaluate the model. The model integrates an energy management strategy (EMS) designed to: provide optimal energy to the system through an optimal SC power allocation, estimate HESS charging cost and temperature‐dependent self‐discharge rates. Each of the hybrid energy storage system (HESS) configurations is optimized using genetic algorithm (GA), particle swarm optimization (PSO), and Grey wolf optimizer (GWO). PSO emerges as the best optimizer across the configurations, reaching the global minimum faster than GWO and GA. Optimization results show that the NMC‐SC has the lowest LCOHESS, thereby making it the most cost‐effective configuration. The model is simulated using load profile data from a solar‐powered mini‐grid in Gwam, a rural community in Niger State, Nigeria. Effects of battery and SC degradations, and aging due to the thermal self‐discharge rates are investigated to prove the model's fidelity and superiority over a benchmark study. A post‐optimization uncertainty analysis is conducted using What‐if analysis and MCS to assess the effects of parameter variations on the LCOHESS. The What‐if analysis identifies SC capital cost, discount rate, and charging cost as the most sensitive parameters influencing the LCOHESS. The subsequent MCS using beta, normal, and a combined normal‐beta distribution shows that the lithium‐ion HESS configurations, particularly the NMC‐SC, are more robust than the lead‐acid HESS configurations. This validates the cost‐effectiveness of the NMC‐SC as the optimal configuration. It is expected that the findings of this research provide investors with actionable insights, enabling informed HESS selection, justification of investment choices, and assessment of potential profitability and investment risks.