M. Hajibeigy; V. Talavat; S. Galvani
Abstract
Due to ever-increasing energy requirements, modern distribution systems are integrated with renewable energy sources (RESs), such as wind turbines and photovoltaics. They also bring economic, environmental, and technical advantages. However, they face the network operator with decision-making challenges ...
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Due to ever-increasing energy requirements, modern distribution systems are integrated with renewable energy sources (RESs), such as wind turbines and photovoltaics. They also bring economic, environmental, and technical advantages. However, they face the network operator with decision-making challenges due to their uncertain nature. Modern distribution systems usually operate at safety margins, and any contingency may lead to power supply losses. In this regard, any attempts to increase the planner/operator's awareness of the network situation will help improve the decision quality. This paper determines the optimal locations of the RESs to enhance the expected power not served as a reliability index. Besides, it reduces power losses and minimizes the 95\% confidence interval of power losses, as much as possible for having more awareness of network states. The K-medoids data clustering method is applied to handle the uncertainties of the RESs and demand loads. The MOPSO, NSGA II, and MOGWO algorithms are used to solve the proposed problem. The efficiency of the proposed approach is tested on the IEEE standard 33-bus and 118-bus distribution networks. The obtained results show that it is possible to reach a better confidence interval while keeping the losses and reliability index at a desired level. Considering solutions with identical losses and reliability index, the confidence interval of power losses using the MOPSO algorithm is 6.86% and 39.82% better rather than the NSGA II and MOGWO algorithms in the 33-bus distribution network and it is 30.23% and 129.63% better in the 118-bus distribution network.
