Utilising reliability-constrained optimisation approach to model microgrid operator and private investor participation in a planning horizon

被引:9
作者
Hosseinnia, Hamed [1 ]
Nazarpour, Daryoush [1 ]
Talavat, Vahid [1 ]
机构
[1] Urmia Univ, Fac Elect & Comp Engn, Orumiyeh, Iran
关键词
distributed power generation; power generation planning; power generation reliability; Monte Carlo methods; Pareto optimisation; genetic algorithms; power generation economics; fuzzy set theory; reliability-constrained optimisation; microgrid operator; private investor; planning horizon; microgrid financial issues; optimal operational strategy; energy production; benefit sharing factor; planning problem; random unit outage; generation units; nonsequential Monte Carlo method; two-stage heuristic method; genetic algorithm; Pareto optimal front; fuzzy satisfying method; PROBABILISTIC POWER-FLOW; DEMAND RESPONSE; ENERGY MANAGEMENT; SYSTEM; WIND;
D O I
10.1049/iet-gtd.2018.5930
中图分类号
TM [电工技术]; TN [电子技术、通信技术];
学科分类号
080906 [电磁信息功能材料与结构]; 082806 [农业信息与电气工程];
摘要
A huge motivation has recently made on microgrid (MG) financial issues, aimed to investigate the contribution of MG operator (MGO) and private investor to reach an optimal operational strategy. Motivating the private investors to contribute in an energy production, is a considering benefit sharing factor by MGO to satisfy both of MGO and private investor. In this study, a reliability-constrained optimisation approach is presented to calculate the number and size of MG system components. To this aim, planning problem is solved in two cases; full available state and state with considering random outage of units. Furthermore, all uncertainties of generation units are considered in the problem formulation. Non-sequential Monte Carlo method is used to generate all scenarios. The proposed model simultaneously optimises two objectives, namely the benefits of MGO. The two-stage heuristic method is used to solve the objective function. In the first stage, by utilising genetic algorithm, the solution to form the Pareto optimal front is found. In the second stage, to select the trade-off solution among obtained Pareto solutions, the fuzzy satisfying method has been used. Simulations are carried out in two cases, with and without considering the share of a private investor of MGO's benefit, i.e. beta.
引用
收藏
页码:5798 / 5810
页数:13
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