Abstract
In a power system, a price-based demand-response program offers end electricity users time-varying prices, incentivizing them to shift demand from high-price hours to low-price hours during a day. Heating/cooling (H/C) loads are typical flexible loads to be shifted. Specifically, end users optimize the hourly H/C load to balance electricity costs and comfort. In this paper, a two-time-scale neurodynamic optimization approach is applied for this multi-objective optimization problem. As a result, optimal use of H/C loads is derived that yields significant savings and acceptable comfort. A case study of the Houston City is presented to show the effectiveness of the proposed neurodynamic approach.
J. Xi—The work described in the paper was supported by National Natural Science Foundation of China (Grant No. 51505286), Scientific Research Project of Shanghai Science and Technology Committee (15111107902) and National Key Technology Research and Development of the Ministry of Science and Technology of China (04 project 2014ZX04015021).
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Le, X., Chen, S., Zheng, Y., Xi, J. (2017). A Multiple-objective Neurodynamic Optimization to Electric Load Management Under Demand-Response Program. In: Cong, F., Leung, A., Wei, Q. (eds) Advances in Neural Networks - ISNN 2017. ISNN 2017. Lecture Notes in Computer Science(), vol 10262. Springer, Cham. https://doi.org/10.1007/978-3-319-59081-3_21
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