Accelerated Benders Decomposition for Enhanced Co-Optimized T&D System Planning

This paper addresses the decision-making problem associated with generation and network investments within the context of co-optimized transmission and distribution system planning. The proposed expansion planning problem differs from existing formulations due to the joint consideration of three maj...

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Bibliographic Details
Published in:IEEE transactions on power systems 2024-09, p.1-13
Main Authors: Faria, Wandry R., Munoz-Delgado, Gregorio, Arroyo, Jose M., Contreras, Javier, Pereira, Benvindo R.
Format: Article
Language:English
Subjects:
Benders decomposition
Biological system modeling
co-optimized transmission and distribution planning
Costs
Generators
Indexes
Investment
network and generation investment decisions
Planning
stochastic programming
Uncertainty
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Summary:This paper addresses the decision-making problem associated with generation and network investments within the context of co-optimized transmission and distribution system planning. The proposed expansion planning problem differs from existing formulations due to the joint consideration of three major complicating factors. First, discrete generation investments are considered at both system levels, thereby requiring binary decision variables. Secondly, the nonlinear behavior of the distribution network is accurately modeled using second-order cone programming. In addition, both long- and short-term uncertainty sources are precisely characterized by a scenario-based stochastic programming framework. The proposed model is cast as a mixed-integer second-order cone program that is challenging for the methodologies previously used for solving simpler instances of co-optimized transmission and distribution planning. In order to circumvent this computational issue, this paper presents an enhanced and novel application of Benders decomposition featuring two acceleration strategies respectively tailored to the master problem and the subproblem into which the problem at hand is decomposed. Numerical simulations demonstrate the economic and operational advantages of the proposed approach, in the form of 75.2% cost savings and load shedding decrease down to 0, as well as its computational superiority over available solution techniques, which is backed by reductions in the running times ranging between 74.5% and 99.8%.
ISSN:0885-8950
1558-0679
DOI:10.1109/TPWRS.2024.3467909