Session: 03-04: Innovative Energy Storage Solutions for Resilient Communities

Paper Number: 169830

169830 - Sizing of Standalone Residential Battery Energy Storage Systems 

Abstract: 

Lithium-ion battery energy storage systems (BESS) can provide load shifting opportunities and cost or CO_2 emissions savings for low-income households. BESS is commonly combined with local energy generation technologies to increase the benefit they provide through increased self consumption. However, there are many potential buyers who are unable to install local energy generation technologies, but still wish to receive the resilience benefits of storage. Furthermore, there has been an emergence of storage-based incentive programs which do not require the purchase of any other distributed energy resource. These incentives make it not only reasonable to purchase BESS, but even economically beneficial depending on local electricity pricing. Due to the prevalence of combined energy generation and storage systems, there are numerous studies which examine the sizing of combined systems and their economic, emissions, and resilience benefits. However, few studies consider the sizing of standalone BESS without any local generation technologies. Therefore, this work offers two methodologies for the sizing of standalone home BESS. The life-cycle-cost approach relies on the application of incentive programs for energy storage technologies to balance the large upfront capital expenditures against the lifetime electricity cost savings offered by the system. By contrast, the iterative-heuristic approach increases the quantity of batteries by comparing an objective metric of the system to that of a system with one additional battery module until a stopping tolerance is reached. There are five potential objectives for the iterative-heuristic method: Energy Cost, Emissions, and Energy Cost + Emissions minimization, an Energy Cost minimization with an emissions limit, and an Emissions minimization with an energy cost limit. The iterative-heuristic method does not consider the overall capital cost, but, nonetheless, selects “reasonably sized” systems due to the diminishing returns to scale. The life-cycle-cost approach suits system owners who are only interested in the economic benefit, whereas the iterative-heuristic approach appeals to those who decide to install storage for resilience or emissions purposes. These sizing methodologies are used in two case studies. In the first, The National Renewable Energy Laboratory’s Building Energy Optimization (BEopt) tool is used to generate hypothetical loads for 29 homes from a low-income housing community located in Lake County, Colorado, after electrification and retrofitting modifications. We pair the electric loads with the grid emissions rates for the Public Service Company of Colorado and electricity pricing structures from across the United States to determine potential cost and emissions savings. Both methodologies achieve electricity cost savings of -50 to 40% and emissions savings of -12 to 23% if systems are installed. However, the life-cycle-cost approach selects a large system to obtain all of the incentive, or does not recommend the purchase of a system at all. The iterative-heuristic approach selects comparatively smaller systems that still attain all electricity cost savings, but may not achieve minimum capital cost. In the second case study, we generate electric load profiles for the Lake County homes based on weather data from San Francisco, California; Long Island, New York; Corpus Christi, Texas; Miami, Florida; and, Lincoln, Nebraska. We then pair these new electric loads with the local emissions rates associated with the electric grid and local pricing structures. The home-pricing-emissions cases represent realistic combinations which would exist if the homes existed in each location. These cases produce electricity cost savings of -50 to 45% and emissions savings of -20 to 72%.  Cost savings are highest in locations with pricing structures designed for electrified buildings, while emissions savings potential depends on the combination of annual energy usage and grid-based emission rates of the local balancing authority.

Presenting Author: Andrew Aikman Colorado School of Mines

Presenting Author Biography: Andrew is a third-year Ph.D. student in Operations Research with Engineering at Colorado School of Mines. He completed his B.S. in Mechanical Engineering and M.S. in Mechanical Engineering at Mines, and has been an intern at Calpine's Delta Energy Center, a combined cycle power plant in Pittsburg, California, and was a 2023 Mickey Leland Energy Fellow, which gave him the opportunity to perform technoeconomic analysis for a novel carbon capture technology at Lawrence Livermore National Laboratory. His current projects involve developing sizing and dispatch strategies for standalone residential battery energy storage and creating a design for a concentrating solar power testing facility located in the front range of Colorado.