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).
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Chen, S., Liu, C.: From demand response to transactive energy: state of the art. J. Mod. Power Syst. Clean Energy (2017). doi:10.1007/s40565-016-0256-x
Chen, S., Love, H.A., Liu, C.C.: Optimal opt-in residential time-of-use contract based on principal-agent theory. IEEE Trans. Power Syst. 31(6), 4415–4426 (2016)
Chen, S., Chen, Q., Xu, Y.: Strategic bidding and compensation mechanism for a load aggregator with direct thermostat control capabilities. IEEE Trans. Smart Grid (2017). doi:10.1109/TSG.2016.2611611
Cheng, L., Hou, Z.G., Lin, Y., Tan, M., Zhang, W.C., Wu, F.X.: Recurrent neural network for non-smooth convex optimization problems with application to the identification of genetic regulatory networks. IEEE Trans. Neural Netw. 22(5), 714–726 (2011)
Chicca, E., Stefanini, F., Bartolozzi, C., Indiveri, G.: Neuromorphic electronic circuits for building autonomous cognitive systems. Proc. IEEE 102(9), 1367–1388 (2014)
Coello, C.A.C., Van Veldhuizen, D.A., Lamont, G.B.: Evolutionary Algorithms for Solving Multi-objective Problems, vol. 242. Springer, Heidelberg (2002)
Deb, K.: Multi-objective Optimization Using Evolutionary Algorithms, vol. 16. Wiley, Hoboken (2001)
Forti, M., Nistri, P., Quincampoix, M.: Generalized neural network for nonsmooth nonlinear programming problems. IEEE Trans. Circ. Syst. I: Regul. Pap. 51(9), 1741–1754 (2004)
Ilic, M., Black, J.W., Watz, J.L.: Potential benefits of implementing load control. In: IEEE Power Engineering Society Winter Meeting, vol. 1, pp. 177–182. IEEE (2002)
Kennedy, M.P., Chua, L.O.: Neural networks for nonlinear programming. IEEE Trans. Circ. Syst. 35(5), 554–562 (1988)
Le, X., Wang, J.: Robust pole assignment for synthesizing feedback control systems using recurrent neural networks. IEEE Trans. Neural Netw. Learn. Syst. 25(2), 383–393 (2014)
Le, X., Wang, J.: Neurodynamics-based robust pole assignment for high-order descriptor systems. IEEE Trans. Neural Netw. Learn. Syst. 26(11), 2962–2971 (2015)
Le, X., Wang, J.: A two-time-scale neurodynamic approach to robust pole assignment. In: Eighth International Conference on Advanced Computational Intelligence (ICACI), pp. 60–67. IEEE (2016)
Le, X., Wang, J.: A two-time-scale neurodynamic approach to constrained minimax optimization. IEEE Trans. Neural Netw. Learn. Syst. 28(3), 620–629 (2017). doi:10.1109/TNNLS.2016.2538288
Liu, Q., Guo, Z., Wang, J.: A one-layer recurrent neural network for constrained pseudoconvex optimization and its application for dynamic portfolio optimization. Neural Netw. 26(1), 99–109 (2012)
Marler, R.T., Arora, J.S.: Survey of multi-objective optimization methods for engineering. Struct. Multidiscip. Optim. 26(6), 369–395 (2004)
Menniti, D., Costanzo, F., Scordino, N., Sorrentino, N.: Purchase-bidding strategies of an energy coalition with demand-response capabilities. IEEE Trans. Power Syst. 24(3), 1241–1255 (2009)
Miettinen, K.: Nonlinear Multiobjective Optimization, vol. 12. Springer Science & Business Media, Heidelberg (2012)
Qin, S., Le, X., Wang, J.: A neurodynamic optimization approach to bilevel quadratic programming. IEEE Trans. Neural Netw. Learn. Syst. (2016)
Tank, D., Hopfield, J.: Simple neural optimization networks: an A/D converter, signal decision circuit, and a linear programming circuit. IEEE Trans. Circ. Syst. 33(5), 533–541 (1986)
Wang, J.: A deterministic annealing neural network for convex programming. Neural Netw. 7(4), 629–641 (1994)
Wunderground. https://www.wunderground.com/history/
Yang, S., Wang, J., Liu, Q.: Multiple-objective optimization based on a two-time-scale neurodynamic system. In: Eighth International Conference on Advanced Computational Intelligence (ICACI), pp. 193–199. IEEE (2016)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2017 Springer International Publishing AG
About this paper
Cite this paper
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
Download citation
DOI: https://doi.org/10.1007/978-3-319-59081-3_21
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-59080-6
Online ISBN: 978-3-319-59081-3
eBook Packages: Computer ScienceComputer Science (R0)