posted on 2019-02-27, 15:49authored byEdward Barbour, David Parra, Zeyad Awwad, Marta Gonzalez
Energy storage can help integrate local renewable generation, however the best deployment level for storage remains an open question. Using a data-driven approach, this paper simulates 15-min electricity consumption for households and groups them into local communities of neighbors using real locations and the road network in Cambridge, MA. We then simulate PV for these households and use this framework to study battery economics in a high PV adoption, high electricity cost scenario, in order to demonstrate significant storage adoption. We compare the results of storage adoption at the level of individual households to storage adoption on the community level using the aggregated community demands. Under the simulated conditions, we find that the optimum storage at the community level was 65% of that at the level of individual households and each kWh of community battery installed was 64–94% more effective at reducing exports from the community to the wider network. Therefore, given the current increasing rates of residential battery deployment, our research highlights the need for energy policy to develop market mechanisms which facilitate the deployment of community storage.
Funding
The research was supported in part by grants from the Center for Complex Engineering Systems at KACST and MIT, the MIT Energy initiative and the Commission for Technology and Innovation in Switzerland (the project of SCCER-HaE – Swiss Competence Centre for Energy Research in Heat and Electricity Storage).
History
School
Mechanical, Electrical and Manufacturing Engineering
Published in
Applied Energy
Volume
212
Pages
489 - 497
Citation
BARBOUR, E. ... et al, 2017. Community energy storage: A smart choice for the smart grid?. Applied Energy, 212, pp.489-497.
This work is made available according to the conditions of the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0) licence. Full details of this licence are available at: https://creativecommons.org/licenses/by-nc-nd/4.0/
Publication date
2017
Notes
This paper was accepted for publication in the journal Applied Energy and the definitive published version is available at https://doi.org/10.1016/j.apenergy.2017.12.056.