Transmission network planning (TEP) is very important task in electric power systems. It begins with the establishment
of power demand growth scenarios, in accordance with forecasts along the time and to obtain the
optimal expansion plan, while fulfilling power systems constraints of. This paper proposes a two-stage TEP
procedure for two realistic transmission Egyptian networks, Western Delta Network (WDN) and 500 kV of
Extra High Voltage Network (EHVN). In the first stage, the Adaptive Neuro-Fuzzy Inference System (ANFIS)
is employed to obtain the predicted long-term load forecasting (LTLF) up to 2030. In the second one,
the Integer Based Particle Swarm Optimization (IBPSO) technique is developed for solving the static TEP
problem. The proposed TEP aims at finding the optimal transmission routes, at least capital investment costs,
to meet the forecasted load. The TEP problem is formulated as non-linear, large scale, mixed-integer and
non-convex optimization problem. The static TEP problem is employed using DC power flow model. The
proposed TEP methodology is tested on standard Garver 6-bus test systems. The load forecasting methodology
is dependent on the historical (Actual) peak load data for the UEN from 1993 to 2015. The location and
capacity of new site of generation station are selected to meet the demand required. Also, the AC load flow
method is emerged with the TEP solution employed by IBPSO for Garver and EHVN to assess the voltage,
reactive power and security constraints. Numerical results show the capability of the proposed procedure to
solve TEP problem at acceptable economical and technical benefits |
