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Regular Paper | Open Access

Risk-averse Robust Interval Economic Dispatch for Power Systems with Large-scale Wind Power Integration

Zhenjia Lin1,2, Haoyong Chen1( ), Jinbin Chen1, Jianping Huang1, Mengshi Li1
School of Electric Power Engineering, South China University of Technology, Guangzhou 510641, China
Department of Electrical Engineering, Technical University of Denmark, Denmark
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Abstract

This paper presents a robust interval economic dispatch (RIED) model for power systems with large-scale wind power integration. Differing from existing interval optimization (IO) approaches that merely rely on the upper and lower boundaries of random variables, the distribution information retained in the historical data is introduced to the IO method in this paper. Based on the available probability distribution function (PDF), wind power curtailment and load shedding are quantified as the operational risk and incorporated into the decision-making process. In this model, we need not rely on the forecasted value of wind power, which is randomly fluctuating and quite unpredictable. Furthermore, when the PDFs of wind power are taken into account, the resulting dispatch solution makes a good tradeoff between the generation cost and the operational risk. Finally, the RIED model yields an optimal dispatch solution for thermal units and the allowable intervals of wind power for the wind farms, which efficiently mitigates the uncertainty in wind power generation and provides more practical suggestions for system operators. Simulation studies are conducted on a modified IEEE-118 bus system and the results verify the effectiveness of the proposed RIED model.

Keywords

Economic dispatchinterval optimizationrisk-aversewind power generation

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CSEE Journal of Power and Energy Systems
Volume 10 Issue 1,
January 2024
Pages 105-116
DOI: 10.17775/CSEEJPES.2020.05630
Cite this article:
Lin Z, Chen H, Chen J, et al. Risk-averse Robust Interval Economic Dispatch for Power Systems with Large-scale Wind Power Integration. CSEE Journal of Power and Energy Systems, 2024, 10(1): 105-116. https://doi.org/10.17775/CSEEJPES.2020.05630

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Received: 24 October 2020
Revised: 07 April 2021
Accepted: 02 July 2021
Published: 10 September 2021
© 2020 CSEE.

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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