Optimization of Energy Storage System Economics and Controls by Incorporating Battery Degradation Costs in REopt®

Abstract

The use of stationary electrochemical energy storage systems utilizing lithium-ion batteries has increased rapidly as the production scale has grown and price for lithium-ion batteries has decreased. These energy storage systems are crucial for maintaining grid resiliency, especially for grids operating with high penetration of renewable energy generation assets or with a variety of distributed energy generation and storage systems. One challenging factor for the adoption of battery energy storage systems is estimating the proper sizing, in terms of both power and energy, that minimizes total cost; properly sizing energy storage systems is difficult in simple cases, where a battery is costed independently of any other systems, but is extremely challenging when building loads and electrical generation by photovoltaic resources are also considered. REopt® is a techoeconomic optimization tool developed by NREL to address these challenges. Previously, battery degradation has been priced by simply assuming a 10-year replacement schedule for battery systems. However, this does not account for varying degradation trends observed across real-world batteries, or inform degradation-aware control strategies to optimize battery dispatch and extend lifetime.This work incorporates a battery life model into REopt. The battery life model is linearized so that it may be solvable within the constrains of a mixed-integer linear optimization problem. To achieve the best possible accuracy for lifetime estimates given these constraints, parameters for the battery life model in REopt are estimated by fitting 20-year simulations of battery life after identifying a state-space battery degradation model from accelerated aging data. Comparisons of battery life predicted in REopt and from the state-space battery degradation model are made to ensure validity of lifetime estimates made by REopt.Battery life impacts system cost through three decision variables: battery sizing, daily state-of-charge, and daily energy-throughput. The cost of battery degradation as a function of these control variables is then estimated assuming two possible maintenance strategies: replacement, where the entire battery system is replaced if cells reach an end-of-life capacity threshold; and augmentation, which establishes a fund to pay for continual purchase of new batteries to maintain the initial energy capacity of the system. These two strategies offer conservative (for replacement) and optimistic (for augmentation) bounds for total system cost. The degradation cost incurred by these strategies is then used to inform battery dispatch decisions, operating the battery in a degradation-aware manner that maximizes battery lifetime while also providing energy when economically favorable. Because the mixed-integer linear program has perfect foresight of future energy needs, batteries are often operated using ‘just-in-time’ charging, which is unrealistic, as no energy is left in the storage system to perform other energy services or to serve as emergency back-up power. To combat this, an inequality constraint on the average annual state-of-charge is imposed, and the sensitivity of system cost to average stored energy, e.g., the cost of system resiliency, can be quantified. Analysis of results has several conclusions, for instance, increasing battery capacity to provide more flexibility for dispatch, enabling the extension of useful life beyond 10 years and resulting in greater avoided utility costs per unit for energy storage while providing more energy storage for grid resiliency.