Optimal Consumer Efforts and Operational Costs Based Analysis for a Smart Grid
Optimal Consumer Efforts and Operational Costs Based Analysis for a Smart Grid
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基于智能电网的最佳消费者努力和运营成本分析
DOI：
10.1080/15325008.2019.1663296
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发表时间：
2019
影响因子：
1.5
通讯作者：
Senjyu Tomonobu
中科院分区：
文献类型：
--
作者：
Howlader Harun Or Rashid;Lotfy Mohammed E.;Shigenobu Ryuto;Matayoshi Hidehito;Senjyu Tomonobu
本文论述了智能电网系统的运行成本和用户努力的比较方法。有两个目标考虑的智能电力系统，即最小化的运营成本和消费者的需求响应的努力。通常情况下，消费者不想为减少或增加他们的负荷做太多的努力，这就是为什么，除了降低热发电的燃料成本之外，这项研究还可以减少消费者的努力。消费者的努力已经通过实时电价得到了考虑。主要是一个多目标的算法，如非支配排序遗传算法-II（NSGA-II）已被用来确定最佳的Pareto解决方案。并将NSGA-II算法的最优值与Epperian多目标遗传算法（ε-莫加）的最优值进行了比较。本文中反映了三种不同的场景，如案例1，案例2和案例3，以了解消费者努力的重要性。案例1考虑最小的运营成本和最大的客户努力。案例2考虑适度的运营成本和适度的客户努力，案例3考虑最大的运营成本和最小的客户努力。计算机模拟已通过MATLAB®软件进行。
This paper deals with the comparison approach of operational cost and consumer efforts for the smart grid system. There are two objectives considered for the smart power system i.e. the minimization of the operational cost and consumer efforts of demand responses. Normally, consumers do not want to make much effort for reducing or increasing their loads that is why, this research is done for reducing consumer effort, beside reducing the fuel cost of thermal generations. Consumer efforts have been considered through the real-time pricing of electricity. Mainly a multi-objective algorithm such as Non-Dominated Sort Genetic Algorithm-II (NSGA-II) has been utilized to determine the optimum Pareto solution. Also, optimal values of NSGA-II has been compared with Epsilon Multi Objective Genetic Algorithm (ε-MOGA). There are three different scenarios reflected in this paper such as case 1, case 2, and case 3 for understanding the importance of consumer efforts. The case 1 considers the minimum operational cost and maximum customer efforts. The case 2 considers the moderate operational cost and moderate customer efforts and the case 3 considers the maximum operational cost and minimum customer efforts. The computer simulation has been performed by the MATLAB® software.
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