Designing a Monte Carlo Model with Python to Predict the Life Cycle Disposal Costs for Grid-Level Electrical Energy Storage Systems

Over the last two decades, researchers have developed techniques and models to establish the total capital costs (TCC) related to installation of electrical energy storage systems (ESSs). There are three major impediments to developing a reliable dataset for the LCC: (1) much of the data needed to properly construct the ESS LCC model depends on proprietary information; (2) newer energy storage technologies do not have a sufficient disposal cost profile; and (3) determination of disposal costs involve uncertainties related to the function, performance, system configuration, and stacking of ESSs. To overcome these three challenges, this study uses a Monte Carlo model to predict and validate life cycle disposal costs. Five battery technologies are considered: lead acid, lithium, sodium sulfur, vanadium redox, and zinc bromine. This study determined that disposal costs are more sensitive to battery lifetime and replacement costs than annual worth, operating costs, and adjusted interest rate.