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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| Vydáno v: | Applied energy Ročník 219; s. 207 - 225 |
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| Hlavní autoři: | , , , , , , |
| Médium: | Journal Article |
| Jazyk: | angličtina |
| Vydáno: |
Elsevier Ltd
01.06.2018
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| Témata: | |
| ISSN: | 0306-2619, 1872-9118 |
| On-line přístup: | Získat plný text |
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| Abstract | •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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| AbstractList | 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. •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. |
| Author | Zhou, Daming Li, Yan Zheng, Yunfei Lin, Weiwei Xie, Shiwei Hu, Zhijian Kong, Shunfei |
| Author_xml | – sequence: 1 givenname: Shiwei orcidid: 0000-0002-4304-6709 surname: Xie fullname: Xie, Shiwei email: Shiwei_Xie@126.com organization: School of Electrical Engineering, Wuhan University, Wuhan City, Hubei Province, China – sequence: 2 givenname: Zhijian surname: Hu fullname: Hu, Zhijian organization: School of Electrical Engineering, Wuhan University, Wuhan City, Hubei Province, China – sequence: 3 givenname: Daming surname: Zhou fullname: Zhou, Daming organization: FEMTO-ST (UMR CNRS 6174), Energy Department, Univ. Bourgogne Franche-Comte, UTBM, Rue Thierry Mieg, F-90010 Belfort Cedex, France – sequence: 4 givenname: Yan surname: Li fullname: Li, Yan organization: School of Electrical Engineering, Wuhan University, Wuhan City, Hubei Province, China – sequence: 5 givenname: Shunfei surname: Kong fullname: Kong, Shunfei organization: School of Electrical Engineering, Wuhan University, Wuhan City, Hubei Province, China – sequence: 6 givenname: Weiwei surname: Lin fullname: Lin, Weiwei organization: School of Electrical Engineering, Wuhan University, Wuhan City, Hubei Province, China – sequence: 7 givenname: Yunfei surname: Zheng fullname: Zheng, Yunfei organization: School of Electrical Engineering, Wuhan University, Wuhan City, Hubei Province, China |
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| Keywords | Active distribution network Uncertain random network Energy management Multi-objective planning Minimum spanning tree Stochastic programming |
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| SubjectTerms | Active distribution network energy Energy management Minimum spanning tree Multi-objective planning planning Stochastic programming Uncertain random network uncertainty |
| Title | Multi-objective active distribution networks expansion planning by scenario-based stochastic programming considering uncertain and random weight of network |
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