Investment decision optimization for distribution network planning with correlation constraint
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Accepted version
Author(s)
Type
Journal Article
Abstract
With the increasing access of distributed generation (DG), the investment decision of distribution network (DN) has become a large‐scale portfolio optimization problem with various reconstruction strategies, which reduces the applicability of the traditional investment decision optimization model. Therefore, aiming at reliability of DN, a novel deep belief networks (DBN)‐based correlation constraint‐integrated investment decision model is proposed in this paper. With the DBN‐based correlation constraint replacing the nonlinear and nonconvex constraints in the traditional model, a new investment decision model is established aiming at maximizing the reliability index and minimizing the total investment cost. In this way, the effects of different reconstruction strategies can be analysed, from which the optimal investment reconstruction plans are identified. Finally, an example of a regional distribution network in a city is provided to verify the rapidity, feasibility, and effectiveness of the investment decision model.
Date Issued
2020-07-01
Date Acceptance
2019-12-17
Citation
International Transactions on Electrical Energy Systems, 2020, 30 (7)
ISSN
2050-7038
Publisher
Wiley
Journal / Book Title
International Transactions on Electrical Energy Systems
Volume
30
Issue
7
Copyright Statement
© 2020 John Wiley & Sons Ltd. This is the accepted version of the following article: Chai, Y, Xiang, Y, Liu, J, Teng, F, Yao, H, Wang, Y. Investment decision optimization for distribution network planning with correlation constraint. Int Trans Electr Energ Syst. 2020;e12323, which has been published in final form at https://doi.org/10.1002/2050-7038.12323
Sponsor
Economic & Social Research Council (ESRC)
Identifier
https://onlinelibrary.wiley.com/doi/full/10.1002/2050-7038.12323
Grant Number
ES/T000112/1
Subjects
0906 Electrical and Electronic Engineering
Energy
Publication Status
Published
Article Number
ARTN e12323
Date Publish Online
2020-01-27