Reconfiguration of distribution systems in the presence of distributed generation considering protective constraints and uncertainties

被引:14
作者
Fathi, Vahid [1 ]
Seyedi, Heresh [1 ]
Ivatloo, Behnam Mohammadi [1 ]
机构
[1] Univ Tabriz, Fac Elect & Comp Engn, Tabriz, Iran
来源
INTERNATIONAL TRANSACTIONS ON ELECTRICAL ENERGY SYSTEMS | 2020年 / 30卷 / 05期
关键词
genetic algorithm; Monte Carlo simulations; protective constraints; reconfiguration of distribution networks; DISTRIBUTION FEEDER RECONFIGURATION; DISTRIBUTION NETWORK RECONFIGURATION; POWER DISTRIBUTION-SYSTEMS; DYNAMIC RECONFIGURATION; LOSS MINIMIZATION; DEMAND RESPONSE; LOSS REDUCTION; EXPERT-SYSTEM; ALGORITHM; RELIABILITY;
D O I
10.1002/2050-7038.12346
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
Distribution network reconfiguration is a common method to achieve objectives such as minimization of power loss and voltage deviations. There exist several different algorithms to find the optimal configuration of network. Almost all of these algorithms consider the main operational constraints such as voltage, current, and power limits. Although it is crucially important to incorporate protective devices and their constraints in the reconfiguration problem, there are few works in network reconfiguration considering protection system. Accordingly, this paper presents a new reconfiguration algorithm considering protective constraints in the presence of distributed generation (DG) units. The objective function of this paper is power loss, which is minimized subject to both operation and protection constraints. In addition to the protection constraints, the effects of DG units on the reconfiguration are considered in this work. Uncertainties in loads and power generation of DG units are also investigated, using a new scenario-based algorithm. Two test systems are utilized to illustrate the efficiency of the proposed method and to compare it with conventional approaches. Moreover, in order to evaluate the performance of new scenario-based algorithm, it is compared with Monte Carlo simulations.
引用
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页数:25
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