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.
Highlights
Development of renewable energy has been expedited by the global effort to reduce greenhouse gas emissions
Based on the empirical test for the initial parameters, we report the Transmission line expansion planning (TLEP) results based on multistage stochastic programming (MSSP)
We introduce the methodological basis of dual decomposition methods for multistage stochastic programming, tailored toward the transmission line expansion problem
Summary
Development of renewable energy has been expedited by the global effort to reduce greenhouse gas emissions. These include emissions from fossil fuels used in transportation, which are declining as electric vehicle use increases [1,2,3]. Transmission line expansion planning (TLEP) has been suggested to avoid future shortages. The TLEP is usually implemented by system planners to analyse old transmission lines that would be uneconomical in a long-term evaluation of the growth of electricity demand [6]. To find optimal expansions in the TLEP process, the planner reconciles system reliability and energy economy over a long-term time horizon
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