Multi-objective active distribution networks expansion planning by scenario-based stochastic programming considering uncertain and random weight of network

•The uncertain random network theory is originally applied in the ADN planning model.•A modified scenario-generation procedure is proposed to describe uncertainty sources.•A numerical second-order cone programming is applied to solve the MINLP model.•A novel 3-dimensional uncertain space is built to...

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Bibliographic Details
Published in:Applied energy Vol. 219; pp. 207 - 225
Main Authors: Xie, Shiwei, Hu, Zhijian, Zhou, Daming, Li, Yan, Kong, Shunfei, Lin, Weiwei, Zheng, Yunfei
Format: Journal Article
Language:English
Published: Elsevier Ltd 01.06.2018
Subjects:
Active distribution network
energy
Energy management
Minimum spanning tree
Multi-objective planning
planning
Stochastic programming
Uncertain random network
uncertainty
Active distribution network
Uncertain random network
Energy management
Multi-objective planning
Minimum spanning tree
Stochastic programming
ISSN:0306-2619, 1872-9118
Online Access:Get full text
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Summary:•The uncertain random network theory is originally applied in the ADN planning model.•A modified scenario-generation procedure is proposed to describe uncertainty sources.•A numerical second-order cone programming is applied to solve the MINLP model.•A novel 3-dimensional uncertain space is built to cope with multi-objective problem.•A sensitivity analysis is performed to find compromise between costs and system performance. This paper presents a novel multi-objective model of active distribution network planning based on stochastic programming and uncertain random network (URN) theory. The planning model is proposed to find the final scheme with optimal alternative, location, size and operational strategy for the candidate distribution lines, transformer substations (TSs), distribution generations (DGs), static var compensators (SVCs) and on-load tap changers (OLTCs). Firstly, a scenario-based approach is developed to analyse the uncertainties in network system, such as the demand and intermittency of renewable sources. Since the impact of multiple uncertain factors on network cannot be ignored, a network frame is then modelled by uncertain and random weights of spanning tree (ST) instead of fixed value. In order to achieve the minimization of total cost, and further the selection of a minimum spanning tree (MST) with the uncertain and random weight, a 3-dimensional uncertain space is constructed based on the combination of the previous two targets. In addition, a second-order cone programming (SOCP) is applied to cope with the multi-objective, mixed-integer nonlinear nature of the proposed planning model. Simulation is performed on a modified Pacific Gas and Electric Company (PG&E) 69-bus distribution system, and the results demonstrate the effectiveness of the proposed model.
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ISSN:0306-2619
1872-9118
DOI:10.1016/j.apenergy.2018.03.023