環境保護のための再生可能エネルギー発電導入による化石燃料消費量の削減に関する研究

最近，我成功地完成并发表了一篇关于智能电网系统运行成本和用户努力的比较方法的研究文章。智能电力系统有两个目标，即运行成本最小化和用户需求响应的努力。通常情况下，消费者不想为减少或增加他们的负荷而付出太多的努力，这就是为什么这项研究是为了减少消费者的努力，除了降低热代的燃料成本。消费者的努力已经通过电力的实时定价得到考虑。主要采用非支配排序遗传算法（NSGA-II）等多目标算法来确定最优Pareto解。并与Epsilon多目标遗传算法（Epsilon- moga）进行了比较。有三个不同的场景反映在这篇论文中，如案例1，案例2和案例3了解消费者努力的重要性。案例1考虑最小的操作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。此外，我还为阿富汗电力系统研究智能电网管理。此外，我还与其他一些研究人员成功完成了电力系统电压稳定、技术经济分析和可持续发展方面的合作研究工作。

Recently, I have successfully accomplished and published a research article that 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. Typically, 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, besides lowering 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 have been compared with Epsilon Multi-Objective Genetic Algorithm (Epsilon-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. Case 1 considers the minimum operational cost and maximum customer efforts. Case 2 considers the moderate operational cost and moderate customer efforts, and case 3 considers the maximum operational cost and minimum customer efforts. Also, I work on smart grid management for the Afghanistan power system. Additionally, I have successfully completed some collaborative research work with some other researchers for power system voltage stability, techno-economic analysis and sustainable development.