Candidate line selection for transmission expansion planning considering long- and short-term uncertainty. (September 2018)

• Record Type: Journal Article
• Authors: Zhang, Xuan; Conejo, Antonio J.
• Is Part Of: International journal of electrical power & energy systems. Volume 100(2018)
• Page Start: 320
• Page End: 330
• Publication Date: 2018-09
• Subjects: Candidate line selection -- Transmission expansion planning -- Robust optimization; Electrical engineering -- Periodicals; Electric power systems -- Periodicals; Électrotechnique -- Périodiques; Réseaux électriques (Énergie) -- Périodiques; Electric power systems; Electrical engineering; Periodicals; 621.3
• DOI: 10.1016/j.ijepes.2018.02.024
• Languages: English
• ISSNs: 0142-0615

Abstract

Highlights: An effective candidate-line set is essential for transmission expansion planning. The proposed algorithm is capable of generating an effective candidate-line set. A set of updating rules efficiently manages the size of the candidate-line set. Long- and short-term uncertainty are differentiated and accurately represented. The final candidate-line set depends on the uncertainty level. Abstract: The objective of transmission expansion planning (TEP) is to expand and/or reinforce the transmission network to satisfy the increasing future demand for electricity and to integrate new power plants while maintaining an efficient operation of the system. The candidate lines initially considered for investment largely depend on the expertise of the system planner, which may result in inaccuracies if large networks are considered, as a result of the necessarily limited expertise of the planner. In this paper, we propose an algorithm to generate an effective candidate-line set for TEP considering both long- and short-term uncertainty. The long-term uncertainty includes the peak demand and available generating capacity of the system during the target year (e.g., 10 years from now) and it is described via an uncertainty set. Then, within the target year, the short-term uncertainty pertaining to different operating conditions is represented via a scenario set.