Customized predictions of the installed cost of behind-the-meter battery energy storage systems

Abstract

Behind-the-meter (BTM) battery energy storage systems (BESS) are undergoing rapid deployment. Simple equations to estimate the installed cost of BTM BESS are often necessary when a rigorous, bottom-up cost estimate is not available or not appropriate, in applications such as energy system modeling, informing a BESS sizing decision, and cost benchmarking. Drawing on project-level data from California, I estimate several predictive regression models of the installed cost of a BTM BESS as a function of energy capacity and power capacity. The models are evaluated for in-sample goodness-of-fit and out-of-sample predictive accuracy. The results of these analyses indicate stronger empirical support for models with natural log transformations of installed cost, energy, and power as compared against widely-used models that posit a linear relationship among the untransformed versions of these variables. Building on these results, I present a logarithmic model that can predict installed cost conditional on energy capacity, power capacity, AC or DC coupling with distributed generation, customer sector, and local wages for electricians. I document how the model can be easily extrapolated to future years, either with forecasts from other sources or by re-estimating the parameters with the latest data.

Keywords

• Energy storage
• Batteries
• Behind-the-meter
• Cost
• Model selection
• Prediction