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Water-Energy Nexus Management for Power Systems

Pengfei Zhao, Chenghong Gu

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Abstract

The water system management problem has been widely investigated. However, the interdependencies between water and energy systems are significant and the effective co-optimization is required considering strong interconnections. This paper proposes a two-stage distributionally robust operation model for integrated water-energy nexus systems including power, gas and water systems networked with energy hub systems at a distribution level considering wind uncertainty. The presence of wind power uncertainty inevitably leads to risks in the optimization model. Accordingly, a coherent risk measure, i.e., conditional value-at-risk, is combined with the optimization objective to determine risk-averse operation schemes. This two-stage mean-risk distributionally robust optimization is solved by Bender's decomposition method. Both the day-ahead and real-time operation cost are minimized with an optimal set of scheduling the multi-energy infrastructures. Case studies focus on investigating the strong interdependencies among the four interconnected energy systems. Numerical results validate the economic effectiveness of IES through optimally coordinating the multi-energy infrastructures. The proposed model can provide system operators a powerful two-stage operation scheme to minimise operation cost under water-energy nexus considering risk caused by renewable uncertainties, thus benefiting customers with lower utility bills.
Original languageEnglish
Pages (from-to)2542 - 2554
JournalIEEE Transactions on Power Systems
Volume36
Issue number3
DOIs
Publication statusPublished - 31 May 2021

Funding

Manuscript received May 25, 2020; revised October 10, 2020; accepted November 8, 2020. Date of publication November 16, 2020; date of current version April 19, 2021. This work was supported in part by the National Science Fund for Distinguished Young Scholars under Grant 72025404, and in part by the National Natural Science Foundation of China under Grants 72042018 and 71621002. Paper no. TPWRS-00866-2020. (Corresponding author: Chenghong Gu.) Pengfei Zhao is with the Institute of Automation, Chinese Academy of Sciences, Beijing, China, also with School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing, China, and also with the Department of Electronic & Electrical Engineering, University of Bath, Bath BA2 7AY, U.K. (e-mail: [email protected]).

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

  1. SDG 7 - Affordable and Clean Energy
    SDG 7 Affordable and Clean Energy

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