Expansion planning for active distribution networks considering deployment of smart management technologies

被引:12
|
作者
Feng, Changsen [1 ]
Liu, Weijia [1 ]
Wen, Fushuan [1 ]
Li, Zhiyi [2 ]
Shahidehpour, Mohammad [2 ]
Shen, Xinwei [3 ]
机构
[1] Zhejiang Univ, Sch Elect Engn, Hangzhou 310027, Zhejiang, Peoples R China
[2] IIT, Dept Elect & Comp Engn, Chicago, IL 60605 USA
[3] Tsinghua Berkeley Shenzhen Inst, Shenzhen 518055, Peoples R China
基金
中国国家自然科学基金;
关键词
power distribution planning; distributed power generation; power generation planning; quadratic programming; static VAr compensators; integer programming; investment; power distribution economics; power generation economics; power system management; power cables; substations; voltage regulators; minimisation; approximation theory; smart management technologies; operation cost; cable circuits; static VAR generators; connection points; distributed generators; investment decisions; operation strategies; ADN topology reconfiguration; mixed-integer quadratic constrained programming problem; active distribution networks; substation capacity; multistage long-term expansion planning model; investment minimisation; linearisation technique; approximation technique; 24-node test system; DISTRIBUTION-SYSTEMS; OPTIMIZATION; GENERATION;
D O I
10.1049/iet-gtd.2018.5882
中图分类号
TM [电工技术]; TN [电子技术、通信技术];
学科分类号
0808 ; 0809 ;
摘要
In this study, a multi-stage long-term expansion planning model for an active distribution network (ADN) is presented, with the aim of minimising the investment and operation cost in a coordinated manner over an established horizon. The planning model optimises the following alternatives: upgrading the capacities of substations, reinforcing and/or constructing cable circuits, placing voltage regulators (VRs) and/or static VAR generators, and determining the connection points for distributed generators (DGs). The investment decisions are optimised over the entire planning horizon which can be further divided into multiple periods, and the operation strategies, e.g. active management of DG as well as ADN topology reconfiguration, are determined according to the profiles of representative scenarios. To relieve the computational burden, the original model is properly simplified as a mixed-integer quadratic constrained programming problem through linearisation and approximation techniques, and the solution optimality is guaranteed after invoking the off-the-shell solver. A 24-node test system is employed to validate the effectiveness of the proposed model.
引用
收藏
页码:4605 / 4614
页数:10
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