Risk-Averse Two-Stage Distributionally Robust Economic Dispatch Model Under Uncertain Renewable Energy

Ce Yang; Weiqing Sun; Jiannan Yang; Dong Han

doi:10.17775/CSEEJPES.2020.03430

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Risk-Averse Two-Stage Distributionally Robust Economic Dispatch Model Under Uncertain Renewable Energy

Regular Papers | 更新时间：2026-10-15

- Risk-Averse Two-Stage Distributionally Robust Economic Dispatch Model Under Uncertain Renewable Energy
- CSEE Journal of Power and Energy Systems Vol. 10, Issue 4, Pages: 1514-1524(2024)
- 作者机构：
  
  1. Department of Control Science and Engineering, University of Shanghai for Science and Technology,Shanghai,China
  2. Department of Electrical Engineering, University of Shanghai for Science and Technology,Shanghai,China
  3. School of Economics and Business Administration, Chongqing University,Chongqing,China
- 作者简介：
- 基金信息：
- DOI：10.17775/CSEEJPES.2020.03430
  CLC：
- Published：01 July 2024
- 稿件说明：
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Ce Yang, Weiqing Sun, Jiannan Yang, et al. Risk-Averse Two-Stage Distributionally Robust Economic Dispatch Model Under Uncertain Renewable Energy[J]. CSEE Journal of Power and Energy Systems, 2024, 10(4): 1514-1524.
DOI：

Ce Yang, Weiqing Sun, Jiannan Yang, et al. Risk-Averse Two-Stage Distributionally Robust Economic Dispatch Model Under Uncertain Renewable Energy[J]. CSEE Journal of Power and Energy Systems, 2024, 10(4): 1514-1524. DOI： 10.17775/CSEEJPES.2020.03430.

摘要

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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