Hassan Naroui - Department of Electrical Engineering, Azad University of Zahedan, Zahedan, Iran
AmirKeyvan Momtaz - Amirkeyvan Momtaz, Civil Aviation Technology College,

Transmission network expansion planning is defined as expanding and strengthening the network in order to optimally meet the growing needs of load level under the power and economic constraints. Transmission network planning aims at determining the route, technical specifications, and schedules of new lines construction based on load growth and production capacity predictions. The basic criteria in transmission network planning include economic issues, reliability, and minimum damage to the environment which need to be meticulously observed in the planning of these networks. Given that network planning aims at long-term power systems, transmission expansion is performed in two forms: dynamic and static. In the first type, planning is carried out in multiple stages and the unknown parameters are determined simultaneously. However, in the static planning, time parameters are eliminated from the unknown parameters, so that the planning is performed for a predetermined horizon. Due to the limited financial resources and the lack of exact information on consumer demand changes, network expansion planning is performed statically. Two fundamental points must be taken into consideration in the process of long-term planning of transmission lines. First, since the required data in network planning, such as network load values, costs of line construction, and restrictions on production and transmission, are not clearly available, certain uncertainties may arise. Second, the issue of transmission network planning is in fact a complex optimization problem that classical methods of optimization are not efficient enough to solve it. Therefore, in this study, the neural network algorithm is used to predict the unknown parameters such as network load level and reduce the uncertainties. Furthermore, meta-heuristic genetic algorithm is utilized in solving the optimization problem in order to increase the possibility of achieving convergence and the global optimal point. The proposed method is implemented on 03 bus test system.

Transmission network expansion, Load uncertainty, Neural networks, Genetic algorithm