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Regular Paper | Open Access

Risk-averse Two-stage Distributionally Robust Economic Dispatch Model under Uncertain Renewable Energy

Ce Yang1Weiqing Sun2( )Jiannan Yang3Dong Han2
Department of Control Science and Engineering, University of Shanghai for Science and Technology, Shanghai 200093, China
Department of Electrical Engineering, University of Shanghai for Science and Technology, Shanghai 200093, China
School of Economics and Business Administration, Chongqing University, Chongqing 400044, China
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Abstract

With the participation of large quantities of renewable energy in power system operations, their volatility and intermittence increases the difficulties and challenges of power system economic scheduling. Considering the uncertainty of renewable energy generation, based on the distributionally robust optimization method, a two-stage economic dispatch model is proposed to minimize the total operation costs. In this paper, it is assumed that the fluctuating of renewable power generation follows the unknown probability distribution that is restricted in an ambiguity set, which is established by utilizing the first-order moment information of available historical data. Furthermore, the theory of conditional value-at-risk is introduced to transform the model into a tractable model, which we call robust counterpart formulation. Based on the stochastic dual dynamic programming method, an improved iterative algorithm is proposed to solve the robust counterpart problem. Specifically, the convergence optimum can be obtained by the improved iterative algorithm, which performs a forward pass and backward pass repeatedly in each iterative process. Finally, by comparing with other methods, the results on the modified IEEE 6-bus, 118-bus, and 300-bus system show the effectiveness and advantages of the proposed model and method.

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CSEE Journal of Power and Energy Systems
Pages 1514-1524
Cite this article:
Yang C, Sun W, Yang J, et al. Risk-averse Two-stage Distributionally Robust Economic Dispatch Model under Uncertain Renewable Energy. CSEE Journal of Power and Energy Systems, 2024, 10(4): 1514-1524. https://doi.org/10.17775/CSEEJPES.2020.03430

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Received: 28 July 2020
Revised: 04 December 2020
Accepted: 13 April 2021
Published: 30 December 2021
© 2020

This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).

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