Abstract:

‘In this study, we investigate households’ investments in behind-the-meter battery storage alongside rooftop solar and examine the effects of these batteries on consumers, the power market, and environmental emissions. We answer three main research questions: 1) Beyond financial aspects, how much do behavioral motivations drive technology adoption of residential storage; 2) How does residential storage impact consumer autonomy and the grid provider; 3) What effects do solar and storage have on carbon emissions. We develop a structural estimation model of residential electricity usage that separates observed demand and consumption preferences and lets us estimate a non-financial utility that households may have for using self-generated solar power over grid-procured electricity. We call this utility nonmarket valuation, provide evidence that it is driven by sustainability and autarky desires, and relate it to the early adoption of residential storage. Applying this model to a novel data set of German households, we find that the median household has a nonmarket valuation of 0.29€ per kilowatt hour (kWh). We then show that owning storage increases a household’s electricity demand (storage rebound ) and marginally increases the emissions by 57 kg CO2 / year / kWh of battery capacity. However, batteries may reduce emissions if solar penetration in the grid is sufficiently high. Lastly, we estimate that, at future technology costs, 2023 European electricity prices, and without subsidies, investing in storage is optimal for 54% of households, which would reduce the residential grid load by 38%, but, counterintuitively, also make it more variable.’

About the Speaker:

Prof. Christian Kaps is an Assistant Professor in the Technology and Operations Management unit at Harvard Business School. His research focuses on emerging topics in sustainable electricity generation and storage - notably how new technologies, sustainability behavior, and policies shape the energy market of the future. Depending on the research question and problem at hand, he has done modelling work, employed structural estimation or utilized machine learning tools.
He received his PhD in Operations Management at the Wharton School of Business of the University of Pennsylvania, where he was advised by Serguei Netessine. Prior to his doctoral studies at Wharton, he graduated summa cum laude from the Rotterdam School of Management with a MSc. in Supply Chain Management. He received his BSc. in Business Administration from WHU.

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