Abstract
Electricity is an essential element of modern life, and presently most electric power is generated using fossil fuels. Two abundant renewable energy sources, solar and wind, are increasingly cost-competitive and also offer the potential of decentralized, and hence more robust, sourcing. However, the intermittent nature of solar and wind power can present difficulties in connection with integrating them into the main electric power grid. One measure that can address this issue of local, temporal energy deficits is to organize local micro-grid societies in which excess power is traded to those members that need it by market exchange. Different communities may employ differing strategies and policies with respect to their attitudes concerning environmental sustainability and financial outcomes. In this connection it can be valuable to have modeling facilities available that can assist communities to predict what may happen under various circumstances in a society employing mixed trading and storage strategies. In this paper we present an agent-based modeling approach that can be used to examine various strategies that can be used in connection with battery storage and market-based energy trading strategies for a set of communities locally connected into an electric micro-grid. We demonstrate that by means of agent-based what-if simulations, battery strategies can be selected that provide financial advantages to local communities and also lead to reduced greenhouse gas emissions (from a policy modeling perspective).
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Yasir, M., Purvis, M.K., Purvis, M., Savarimuthu, B.T.R. (2014). Intelligent Battery Strategies for Local Energy Distribution. In: Balke, T., Dignum, F., van Riemsdijk, M., Chopra, A. (eds) Coordination, Organizations, Institutions, and Norms in Agent Systems IX. COIN 2013. Lecture Notes in Computer Science(), vol 8386. Springer, Cham. https://doi.org/10.1007/978-3-319-07314-9_4
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DOI: https://doi.org/10.1007/978-3-319-07314-9_4
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