• CSCD核心库收录期刊
  • 中文核心期刊
  • 中国科技核心期刊

ELECTRIC POWER CONSTRUCTION ›› 2022, Vol. 43 ›› Issue (12): 27-36.doi: 10.12204/j.issn.1000-7229.2022.12.003

• Planning and Design • Previous Articles     Next Articles

Research on Low Carbon Planning Based on Data Driven Robust Optimization for User-Side Integrated Energy Module

XU Chengying1(), ZHU Xu1(), DOU Zhenlan2(), YANG Jun1(), ZHANG Chunyan2()   

  1. 1. School of Electrical and Automation, Wuhan University, Wuhan 430072, China
    2. State Grid Shanghai Municipal Electric Power Company, Shanghai 200023, China
  • Received:2022-06-11 Online:2022-12-01 Published:2022-12-06
  • Contact: ZHU Xu E-mail:845491588@qq.com;zxdq2013@126.com;douzhl@126.com;JYang@whu.edu.cn
  • Supported by:
    State Grid Corporation of China Research Program(5400-202217177A-1-1-ZN)


Under the dual carbon target, it is imperative to develop integrated energy system planning and research, and the flexible loading, unloading and configuration of the user-side integrated energy module is one of the emerging research objects. In this paper, a two-stage integrated energy planning model is established with the introduction of multiple hybrid energy storages and carbon capture devices, which achieves the complementary use of energy and carbon emission recovery and utilization in the system. To deal with new energy output such as photovoltaic and wind power, and user load uncertainty, extreme scenario is proposed. According to ellipsoid set data driven method of robust optimization in uncertainty, the paper accurately describes the correlation between variables and improves the conservation of traditional robust optimization result. Compared with the traditional method with mass probability calculation, a simpler ellipsoid endpoint extraction method is used to obtain extreme scenes. In this paper, the steps of column and constraint generation (CCG) solution are improved by using the advantage of ellipsoidal extreme scenarios to avoid the complicated duality processing of sub-problems. Finally, by example simulation and comparison with the traditional interval uncertain set robust optimization method, it is proved that method proposed in this paper has advantages in reducing economic cost, energy saving and reducing carbon emissions.

Key words: data-driven robust optimization, integrated energy module planning, ellipsoidal uncertain set, improved column and constraint generation (CCG)

CLC Number: