Community energy storage: A smart choice for the smart grid?

Abstract

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-minute 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.

Organizing prosumers into electricity trading communities: Costs to attain electricity transfer limitations and self‐sufficiency goals

“…The present study optimizes prosumer PV battery investments and operation so as to minimize the cost for the prosumer. Analyzing a case with a fixed share of solar PV that is already installed (instead of joint optimization of PV and battery capacities as in our study), Barbour et al have shown that less storage capacity is required for a prosumer community energy storage system than for individual household storage systems, which is in line with the results of our study. In this work the incentive to avoid taxes and fees on bought electricity through local self‐consumption of electricity has a greater impact on the optimization and operation of PV battery investments than do the peaks and lows in the hourly electricity price.…”

“…Research has been conducted on the topics of energy autarky and self‐consumption on different scales, from individual buildings to neighborhoods and districts, as well as on the clustering of prosumers to optimize power grid operation and reduce costs . Community energy storage has been investigated in terms of economic benefits and possibilities to integrate renewable energy sources and demand‐side management . There have also been studies on the interactions that occur on local markets, the different energy carriers in a local energy community or energy hub, and the effects of different load curves on neighborhood clusters …”

“…23,24 Community energy storage has been investigated in terms of economic benefits and possibilities to integrate renewable energy sources and demand-side management. [25][26][27][28] There have also been studies on the interactions that occur on local markets, [29][30][31] the different energy carriers in a local energy community or energy hub, 32,33 and the effects of different load curves on neighborhood clusters. 34 The present study adds to the understanding of local grouping of residential prosumers of electricity through direct comparisons of investment and hourly operation of PV plus battery systems for (i) individual prosumers and (ii) prosumers acting as part of an electricity trading community, which has not been provided by studies cited above.…”

Technologies of Engagement: How Battery Storage Technologies Shape Householder Participation in Energy Transitions

“…In the literature, a common way to describe renewable energy communities is as 'those projects where communities (of place or interest) exhibit a high degree of ownership and control in renewable energy production as well as benefiting collectively from the outcomes' [23]. Such communities for example consist of local energy cooperatives that develop collective energy practices [24], such as collectively generating solar energy for local use.…”

Electricity consumption forecasting for smart grid using the multi-factor back-propagation neural network

“…The effects of different factors are analyzed for a deeper understanding. More specifically, using the historical data of the Pecan Street smart community 8 and the corresponding weather information, a BPNN-based prediction has been conducted. The performances under different time resolutions, hourly, daily, weekly, and monthly, are investigated.…”