Economic Sizing of Batteries for the Smart Home

Growing rooftop photovoltaics (PV) adoption is beginning to challenge the electric power grid in some locations today. During many hours of the year, power is flowing out of PV-enabled buildings and back into grid. When this happens in many homes, the entire feeder voltage is raised significantly, which can be unsafe for grid assets and home appliances. Battery storage provides a good solution to this problem. By storing energy within the home, less energy flows back onto the grid. Scheduling a battery to charge during times a home would otherwise export energy, and discharge when it would otherwise import energy, the bidirectional flow of energy on the feeder is reduced. However, batteries are still expensive and need to be introduced optimally. Battery sizing is not well studied in the literature; most research uses rule of thumb to determine the battery size whereas others use tool-based methods. This paper presents a methodology to economically size a residential battery based on parametric analysis using a home energy management system (HEMS) software to optimally dispatch the battery along with controllable loads under several use cases. The study accounts for connected equipment, controls, renewable resources, and accounts for other factors such as occupancy patterns, and house characteristics. The paper defines an initial analytical pathway for such a sizing tool, develops initial sizing guidance, and clarifies technical and market opportunities for home batteries in the context of existing and emerging equipment and control technologies. One of the unique contributions of our paper is that we demonstrate how dynamic control of building equipment may change the selection, operation, lifespan and economics of solar and battery storage. We use the HEMS to optimally control the same solar-enabled residential building in three different use cases: a) with controllable loads, b) with controllable battery, and c) with controllable loads and battery. As more loads can be interactively controlled, they can relieve the burden of some of the battery’s use. This may change the cycling use cases for the battery, and possibly enable smaller batteries to be used in conjunction with equipment homeowners are already purchasing to achieve the same outcomes. In our initial study, a parametric analysis that included 132 scenarios has been performed based on different combinations of pertinent parameters. Results indicate that four parameters dominate the decision-making process: utility tariffs, application scenarios, existence of HEMS, and the anticipated payback time. Life-cycle cost analysis indicated in the absence of utility incentives, batteries plus HEMS haveat least 10-year payback time for new construction under time-of-use rate structure and feed-in tariff; larger batteries have longer payback time but may provide more benefits to utilities on power backfeed reduction under certain circumstances