New distributed energy resources (DER) are rapidly replacing centralized power generation due to their environmental, economic and resiliency benefits. Previous analyses of DER systems have been limited in their ability to account for socio-technical complexities, such as intermittent supply, heterogeneous demand and balance-of-system cost dynamics. Here we develop ReMatch, an interdisciplinary modelling framework, spanning engineering, consumer behaviour and data science, and apply it to 10,000 consumers in California, USA. Our results show that deploying DER would yield nearly a 50% reduction in the levelized cost of electricity (LCOE) over the status quo even after accounting for socio-technical complexities. We abstract a detailed matching of consumers to DER infrastructure from our results and discuss how this matching can facilitate the development of smart and targeted renewable energy policies, programmes and incentives. Our findings point to the large-scale economic and technical feasibility of DER and underscore the pertinent role DER can play in achieving sustainable energy goals.
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This material is based upon the work supported in part by the Stanford Precourt Institute for Energy, a Terman Faculty Fellowship (R.K.J.), a Powell Foundation Fellowship (R.R.) and the US National Science Foundation under a SEES Fellows award (1461549) and a CAREER award (1554178). Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the National Science Foundation.
The authors declare no competing financial interests.
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Jain, R., Qin, J. & Rajagopal, R. Data-driven planning of distributed energy resources amidst socio-technical complexities. Nat Energy 2, 17112 (2017). https://doi.org/10.1038/nenergy.2017.112
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