Journal of South China University of Technology(Natural Science) >

2024 , Vol. 52 >Issue 9: 1 - 11

DOI: https://doi.org/10.12141/j.issn.1000-565X.240117

Energy, Power & Electrical Engineering

Two-Stage Distributional Robust Optimization for the Expansion Planning of Photovoltaic Cluster

  • ZENG Jun ,
  • WANG Tianlun ,
  • HUANG Zhipeng ,
  • ZHANG Xuan
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  • 1.School of Electric Power Engineering, South China University of Technology, Guangzhou 510640, Guangdong, China
    2.China Southern Power Gird Beijing Company, Beijing 100020, China
    3.Electric Power Research Institute of Guangdong Power Grid Co. , Ltd. , Guangzhou 510080, Guangdong, China
曾君（1979—），女，博士，教授，主要从事微电网能量管理及优化、可再生能源发电系统中的电力电子及控制技术研究。E-mail： junzeng@ scut.edu.cn

Received date: 2024-03-13

  Online published: 2024-04-07

Supported by

the National Natural Science Foundation of China(62173148);the Natural Science Foundation of Guangdong Province(2022A1515010150);the Basic and Applied Basic Research Foundation of Guangdong Province(2022A1515240026)

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Abstract

With the deepening of the two-carbon target, the penetration rate of renewable energy is increasing year by year, and its consumption problem has attracted much attention. Distributed renewable energy cluster is a new mode of accommodating renewable energy. It is necessary to consider the influence of source-load uncertainty in planning and operation. In this paper, based on the new photovoltaic grid-connected planning of distributed photovoltaic cluster, considering the uncertainty of source and load, a distributed photovoltaic cluster expansion planning method based on two-stage robust optimization was proposed. Considering the difference between the planning stage and the operation stage, it established a two-stage distributed robust optimization model, which takes the minimum annual equivalent cost as the objective and considers the unit output constraint and the power grid carrying capacity. In order to improve the computational efficiency, the historical data of regional distributed renewable energy and random load were reduced and modified by combining K-means clustering with extreme scenario method. Based on the modified scenario set, a probability distribution fuzzy set based on Wasserstein distance was constructed. The column and constraint generation algorithm was used to decompose the two-stage distributed robust optimization model into the main problem and the sub-problem. The main problem and the sub-problem were solved by iteration, which further improves the efficiency of the solution. In order to solve the sub-problem, Lagrange duality was introduced to transform the sub-problem into a deterministic optimization problem. Finally, a distributed photovoltaic cluster was taken as an example to carry out an example analysis. The results show that the proposed two-stage distributional robust optimization method for distributed photovoltaic clusters can coordinate the economy and robustness of the planning operation scheme. Model control parameters can be flexibly adjusted according to the size and reliability of historical scene sets to meet the different requirements of reliability and economy in various engineering application scenarios.

Key words： photovoltaic cluster; extreme scenario; Wasserstein distance; distributional robust optimization; column and constraints generation algorithm

Cite this article

ZENG Jun , WANG Tianlun , HUANG Zhipeng , ZHANG Xuan . Two-Stage Distributional Robust Optimization for the Expansion Planning of Photovoltaic Cluster[J]. Journal of South China University of Technology(Natural Science), 2024 , 52(9) : 1 -11 . DOI: 10.12141/j.issn.1000-565X.240117

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