A Long-Term Evaluation on Transmission Line Expansion Planning with Multistage Stochastic Programming

Sini Han, Hyeon-Jin Kim and Duehee Lee
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Sini Han: Department of Electrical Engineering, Konkuk University, Seoul 05029, Korea
Hyeon-Jin Kim: Department of Electrical Engineering, Konkuk University, Seoul 05029, Korea
Duehee Lee: Department of Electrical Engineering, Konkuk University, Seoul 05029, Korea

Energies, 2020, vol. 13, issue 8, 1-18

Abstract: The purpose of this paper is to apply multistage stochastic programming to the transmission line expansion planning problem, especially when uncertain demand scenarios exist. Since the problem of transmission line expansion planning requires an intensive computational load, dual decomposition is used to decompose the problem into smaller problems. Following this, progressive hedging and proximal bundle methods are used to restore the decomposed solutions to the original problems. Mixed-integer linear programming is involved in the problem to decide where new transmission lines should be constructed or reinforced. However, integer variables in multistage stochastic programming (MSSP) are intractable since integer variables are not restored. Therefore, the branch-and-bound algorithm is applied to multistage stochastic programming methods to force convergence of integer variables.In addition, this paper suggests combining progressive hedging and dual decomposition in stochastic integer programming by sharing penalty parameters. The simulation results tested on the IEEE 30-bus system verify that our combined model sped up the computation and achieved higher accuracy by achieving the minimised cost.

Keywords: mixed-integer linear programming; transmission line expansion planning; multistage stochastic optimisation; decomposition method (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2020
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (4)

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