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Issue No.09  Sept. (2012 vol.23)
pp: 15931606
Yuanxiong Guo , University of Florida, Gainesville
Miao Pan , University of Florida, Gainesville
Yuguang Fang , University of Florida, Gainesville
DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/TPDS.2012.25
ABSTRACT
Recently intensive efforts have been made on the transformation of the world's largest physical system, the power grid, into a &#x201C;smart grid&#x201D; by incorporating extensive information and communication infrastructures. Key features in such a &#x201C;smart grid&#x201D; include high penetration of renewable and distributed energy sources, largescale energy storage, marketbased online electricity pricing, and widespread demand response programs. From the perspective of residential customers, we can investigate how to minimize the expected electricity cost with realtime electricity pricing, which is the focus of this paper. By jointly considering energy storage, local distributed generation such as photovoltaic (PV) modules or small wind turbines, and inelastic or elastic energy demands, we mathematically formulate this problem as a stochastic optimization problem and approximately solve it by using the Lyapunov optimization approach. From the theoretical analysis, we have also found a good tradeoff between cost saving and storage capacity. A salient feature of our proposed approach is that it can operate without any future knowledge on the related stochastic models (e.g., the distribution) and is easy to implement in real time. We have also evaluated our proposed solution with practical data sets and validated its effectiveness.
INDEX TERMS
Batteries, Electricity, Renewable energy resources, Smart grids, Real time systems, Pricing, renewable energy generation, Batteries, Electricity, Renewable energy resources, Smart grids, Real time systems, Pricing, realtime pricing., Smart grid, optimal power management, Lyapunov optimization, energy storage
CITATION
Yuanxiong Guo, Miao Pan, Yuguang Fang, "Optimal Power Management of Residential Customers in the Smart Grid", IEEE Transactions on Parallel & Distributed Systems, vol.23, no. 9, pp. 15931606, Sept. 2012, doi:10.1109/TPDS.2012.25
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