Chu and Beasley Genetic Algorithm to Solve the Transmission Network Expansion Planning Problem Considering Active Power Losses
Keywords:Transmission network expansion planning, DC model, power losses, Chu and Beasley genetic algorithm
Due to the accelerated growth of electricity demand, the scarcity of primary resources to produce electricity, and technological advances in recent years, electricity companies must face and solve these challenges in the best possible way, and for that, the Transmission Network Expansion Planning (TNEP) plays a crucial role, since the decisions taken in long-term planning determine the optimal form of expansion of the networks, to respond to these needs of electricity demands. On the other hand, there is also the tendency to leave the TNEP problem more efficient, robust, and closer to what happens in real electrical networks. For these reasons, this article proposes a methodology to solve the TNEP problem considering active power losses. The problem is formulated as a mixed-integer nonlinear programming (MINLP) problem. The Chu-Beasley Genetic Algorithm (CBGA) is used to transform the MINLP problem into a linear programming (LP) problem. Furthermore, the Villasana Garver constructive heuristic (VGCH) algorithm is used to make the investment proposals made by the CBGA feasible. To measure the efficiency and effectiveness of the proposed methodology several tests are performed on the 6-bus Garver system, the IEEE 24-bus test system, and the South Brazilian 46-bus test system.
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