Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/145371
Title: A mixed-integer SDP solution to distributionally robust unit commitment with second order moment constraints
Authors: Zheng, Xiaodong
Chen, Haoyong
Xu, Yan
Li, Zhengmao
Lin, Zhenjia
Liang, Zipeng
Keywords: Engineering::Electrical and electronic engineering
Issue Date: 2020
Source: Zheng, X., Chen, H., Xu, Y., Li, Z., Lin, Z., & Liang, Z. (2020). A mixed-integer SDP solution to distributionally robust unit commitment with second order moment constraints. CSEE Journal of Power and Energy Systems, 6(2), 374-383. doi:10.17775/CSEEJPES.2019.00930
Journal: CSEE Journal of Power and Energy Systems
Abstract: A power system unit commitment (UC) problem considering uncertainties of renewable energy sources is investigated in this paper, through a distributionally robust optimization approach. We assume that the first and second order moments of stochastic parameters can be inferred from historical data, and then employed to model the set of probability distributions. The resulting problem is a two-stage distributionally robust unit commitment with second order moment constraints, and we show that it can be recast as a mixed-integer semidefinite programming (MI-SDP) with finite constraints. The solution algorithm of the problem comprises solving a series of relaxed MI-SDPs and a subroutine of feasibility checking and vertex generation. Based on the verification of strong duality of the semidefinite programming (SDP) problems, we propose a cutting plane algorithm for solving the MI-SDPs; we also introduce a SDP relaxation for the feasibility checking problem, which is an intractable biconvex optimization. Experimental results on a IEEE 6-bus system are presented, showing that without any tunings of parameters, the real-time operation cost of distributionally robust UC method outperforms those of deterministic UC and two-stage robust UC methods in general, and our method also enjoys higher reliability of dispatch operation.
URI: https://hdl.handle.net/10356/145371
ISSN: 2096-0042
DOI: 10.17775/CSEEJPES.2019.00930
Rights: © 2019 CSEE. This is an open-access article distributed under the terms of the Creative Commons Attribution License.
Fulltext Permission: open
Fulltext Availability: With Fulltext
Appears in Collections:EEE Journal Articles

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