A reliable approach for solving the transmission network expansion planning problem using genetic algorithms |
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| Affiliation: | 1. Graduate Institute of Ferrous Technology (GIFT), Pohang University of Science and Technology (POSTECH), 77 Cheongam-ro, Nam-gu, Pohang, Gyeongbuk 790-784, Republic of Korea;2. Department of Materials Science and Engineering, Korea University, 145 Anam-ro, Seongbuk-gu, Seoul 136-701, Republic of Korea;3. Center for Mechanical Technology and Automation, Department of Mechanical Engineering, University of Aveiro, 3810-193, Portugal;1. Beijing Tianrun New Energy Investment Co., Ltd., Beijing 100029, China;2. Center for Energy and Environmental Policy, University of Delaware, Newark 19716, USA;3. Underground Polis Academy, College of Civil Engineering, Shenzhen University, Guangdong Province 518060, China;1. Department of Engineering and Computer Science, Tarleton State University, Stephenville, TX 76402, United States;2. Department of Civil, Environmental, and Geo- Engineering, University of Minnesota, Twin Cities, Minneapolis, MN 55455, United States;1. Centre for Innovation Management Research, Xinjiang University, Urumqi, 830046, China;2. School of Economics and Management, Xinjiang University, Urumqi, 830046, China |
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| Abstract: | This paper presents a reliable approach for solving the transmission network expansion planning (TNEP) problem through a genetic algorithm (GA). GAs have demonstrated the ability to deal with non-convex, non-linear, integer-mixed optimization problems, such as the TNEP problem, better than a number of mathematical methodologies. The procedure presented consists on finding unfeasible solutions for the problem through the GA. These solutions are used for predicting the cost of the optimum solution using a ‘loss of load limit curve’, of the transmission system. Once this cost is estimated, the optimum solution can be found by performing a local search starting from the unfeasible solutions that have costs close to the estimated cost. This approach makes the GA more robust and reliable for solving the problem for different transmission systems. |
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