Joint Robust Planning of Wind-Photovoltaic-Energy Storage System Based on Multi-scenario Confidence Gap Decision

Chunhua PENG; Zhisheng XIONG; Yi ZHANG; Huijuan SUN

doi:10.7500/AEPS20211221004

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Joint Robust Planning of Wind-Photovoltaic-Energy Storage System Based on Multi-scenario Confidence Gap Decision

Basic Research | 更新时间：2025-06-16

- Joint Robust Planning of Wind-Photovoltaic-Energy Storage System Based on Multi-scenario Confidence Gap Decision
- Automation of Electric Power Systems Vol. 46, Issue 16, Pages: 178-187(2022)
- 作者机构：
  
  华东交通大学电气与自动化工程学院，江西省南昌市 330013
- 作者简介：
- 基金信息：
  
  National Natural Science Foundation(52167009);Jiangxi Academic and Technical Leader Project(20204BCJL22038);Jiangxi Postgraduate Innovation Special Foundation(YC2020-S325)
- DOI：10.7500/AEPS20211221004
  CLC：
- Received：21 December 2021，
  
  Revised：2022-03-15，
  
  Published Online：16 June 2022，
  
  Published：25 August 2022
- 稿件说明：
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彭春华,熊志盛,张艺等.基于多场景置信间隙决策的风光储联合鲁棒规划[J].电力系统自动化,2022,46(16):178-187.

PENG Chunhua,XIONG Zhisheng,ZHANG Yi,et al.Joint Robust Planning of Wind-Photovoltaic-Energy Storage System Based on Multi-scenario Confidence Gap Decision[J].Automation of Electric Power Systems,2022,46(16):178-187.
彭春华,熊志盛,张艺等.基于多场景置信间隙决策的风光储联合鲁棒规划[J].电力系统自动化,2022,46(16):178-187. DOI： 10.7500/AEPS20211221004.

PENG Chunhua,XIONG Zhisheng,ZHANG Yi,et al.Joint Robust Planning of Wind-Photovoltaic-Energy Storage System Based on Multi-scenario Confidence Gap Decision[J].Automation of Electric Power Systems,2022,46(16):178-187. DOI： 10.7500/AEPS20211221004.

摘要

为实现具有鲁棒性的风光储联合规划以应对可再生能源出力不确定性带来的负面影响，提出基于鲁棒驱动的多场景置信间隙决策理论(MCGDT)；以风光消纳率最大化和总投资成本最小化为优化目标，构建了基于MCGDT的风光储联合鲁棒规划模型；基于不确定性理论将模型中复杂的机会约束进行等效确定性转换，设计了一种新颖的交叉熵-雷达扫描微分进化算法高效求解模型。MCGDT不仅能弥补常规鲁棒优化和信息间隙决策理论未能计及鲁棒准确性以及随机因素多态性的不足，还能解决随机规划中典型场景不具有区间遍历性的问题，可实现更为合理而准确的不确定性规划。所提理论与方法的有效性和优越性通过算例分析得到了验证。

Abstract

In order to achieve the joint planning with robustness of wind-photovoltaic-energy storage system (ESS) to deal with the negative impact of the uncertainties of renewable energy output

a robust-driven multi-scenario confidence gap decision theory (MCGDT) is proposed. Considering the optimization objectives of maximizing the wind/photovoltaic power consumption rate and minimizing the total investment cost

a wind-photovoltaic-ESS joint robust planning model based on MCGDT is established. Furthermore

the complex chance constraints are transformed into the equivalent determinate constraints according to the uncertainty theory

and a novel cross entropy-radar scanning differential evolution (CE-RSDE) algorithm is proposed to solve the model efficiently. MCGDT can not only make up for the inadequacy of conventional robust optimization and information gap decision theory (IGDT) that fails to measure the robustness accuracy and embody the polymorphism of randomness

but also solve the problem of non-interval ergodicity of typical scenarios in stochastic programming. Therefore

a more reasonable and accurate uncertainty planning can be realized. The validity and superiority of the proposed theory and method are verified by case study.

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references

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