A bi-level planning approach for hybrid AC-DC distribution system considering N-1 security criterion. (15th November 2018)

• Record Type: Journal Article
• Authors: Wu, Zhi; Liu, Pengxiang; Gu, Wei; Huang, He; Han, Jun
• Is Part Of: Applied energy. Volume 230(2018)
• Page Start: 417
• Page End: 428
• Publication Date: 2018-11-15
• Subjects: AC-DC distribution system expansion planning -- Bi-level optimization -- Robust optimization -- N-1 contingency; Power (Mechanics) -- Periodicals; Energy conservation -- Periodicals; Energy conversion -- Periodicals; 621.042
• DOI: 10.1016/j.apenergy.2018.08.110
• Languages: English
• ISSNs: 0306-2619

Abstract

Highlights: A bi-level planning approach for AC-DC hybrid distribution system is presented. Integration of renewable energy distributed in remote areas is considered. Wind and solar curtailment are prevented under the worst N-1 contingency. A solving strategy combining heuristic algorithm and numerical method is proposed. Improvement in computational efficiency is investigated. Abstract: Renewable energy is widely distributed in remote or coastal areas. In order to improve the economic efficiency and the consumption rate of renewable energy in distribution system, such distributed generators can be connected through a DC network, which provides power supply for AC system through collection lines. This paper presents a bi-level planning model for AC-DC distribution system with consideration of N-1 contingency. The upper-level model optimizes the total investment costs and operating costs in both AC and DC system over the planning horizon. The lower-level model aims to improve the reliability of the DC system by minimizing curtailment cost of wind farm and photovoltaic under the worst N-1 contingency. Dual technology and Big-M method are applied to reformulate lower-level model as a robust optimization. A novel solving strategy is proposed by combining modified genetic algorithm and numerical method in a nested way. Case studies illustrate that the proposed planning approach is more reliable in dealing with N-1 contingency and more effective in solving large-scale optimization compared with the existing planning approach.