Abstract
A common assumption for economic power dispatch (EPD) is a perfect knowledge of cost functions. However, this assumption can be violated in cases when it is too difficult to establish an accurate model of the generation unit. In this paper, we formulate the EPD problem in a unified notation, based on which various reinforcement learning techniques can be applied. Then, a consensus based distributed reinforcement learning (CBDRL) algorithm is developed to solve the EPD problem. The CBDRL algorithm is fully distributed in sense that it requires only local computation and communication, which will contribute to a microgrid of higher scalability and robustness. Finally, the effectiveness and performance of the proposed algorithm is verified through case studies.
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References
Wood, A.J., Wollenberg, B.F.: Power Generation, Operation, and Control. Wiley, New York (2012)
Boyd, S., Vandenberghe, L.: Convex Optimization. Cambridge University Press, Cambridge (2004)
Tan, S., Yang, S., Xu, J.X.: Consensus based approach for economic dispatch problem in a smart grid. In: IECON 2013, pp. 2011–2015 (2013)
Li, C., Yu, X., Yu, W., Huang, T., Liu, Z.W.: Distributed event-triggered scheme for economic dispatch in smart grids. IEEE TII 12(5), 1775–1785 (2016)
Qin, J., Ma, Q., Shi, Y., Wang, L.: Recent advances in consensus of multi-agent systems: a brief survey. IEEE TIE. doi:10.1109/TIE.2016.2636810
Sinha, N., Chakrabarti, R., Chattopadhyay, P.K.: Evolutionary programming techniques for economic load dispatch. IEEE TEVC 7(1), 83–94 (2003)
Park, J.B., Jeong, Y.W., Shin, J.R., Lee, K.Y.: An improved particle swarm optimization for nonconvex economic dispatch problems. IEEE TWRS 25(1), 156–166 (2010)
El-Naggar, K., AlRashidi, M., Al-Othman, A.: Estimating the input-output parameters of thermal power plants using PSO. Energy Convers. Mgmt. 50(7), 1767–1772 (2009)
Olfati-Saber, R., Murray, R.M.: Consensus problems in networks of agents with switching topology and time-delays. IEEE TAC 49(9), 1520–1533 (2004)
Qin, J., Gao, H., Yu, C.: On discrete-time convergence for general linear multi-agent systems under dynamic topology. IEEE TAC 59(4), 1054–1059 (2014)
Sutton, R.S., Barto, A.G.: Reinforcement Learning: An Introduction. MIT Press, Cambridge (1998)
Acknowledgement
This work was supported in part by the National Natural Science Foundation of China under Grants 61422307, 61473269, 61673361, the Youth Innovation Promotion Association of Chinese Academy of Sciences, the Youth Top-Notch Talent Support Program, and the Youth Yangtze River Scholar, and the Australian Research Council under Grant DP120104986.
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Li, F., Qin, J., Kang, Y., Zheng, W.X. (2017). Consensus Based Distributed Reinforcement Learning for Nonconvex Economic Power Dispatch in Microgrids. In: Liu, D., Xie, S., Li, Y., Zhao, D., El-Alfy, ES. (eds) Neural Information Processing. ICONIP 2017. Lecture Notes in Computer Science(), vol 10634. Springer, Cham. https://doi.org/10.1007/978-3-319-70087-8_85
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DOI: https://doi.org/10.1007/978-3-319-70087-8_85
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