A Computationally Intelligent Power Transmission Expansion Strategy in a Deregulated Energy System

This paper aims to simulate a computationally intelligent electrical power transmission expansion system and study the factors affecting power transmission expansion in a deregulated energy system to improve on the current economic conditions. The main problem facing most power system transmission is the failure to actually forecast the load expansion accurately this leads to failure in the transmission expansion design. a hybrid algorithm for the ac/dc transmission expansion planning (HTEP) and multi algebraic formulation of the stochastic TEP model in a multi-stage planning framework will be used to analyze the transmission expansion system, optimization problem will considers a weighted sum of multiple objectives including cost of operation and maintenance, emission, load shedding and line investments, simulation method would consider random outages of generating units and ac/dc transmission lines as well as load forecast .The independent system operator would utilize the proposed method to select the optimal set of ac/dc transmission lines for satisfying TEP criteria. The proposed set of dc transmission system may use either current source converters or voltage source converters. The proposed algorithms are simulated on IEEE 24-bus reliability test system (RTS) and Gerner’s 6 bus system  to compare optimal plans between the original and equivalent system. Further assumptions and adjustments are searched and tested to get more accurate optimal plans. results obtained showed that the hybrid model was capable of handling future generation and load patterns in deregulated, unbundled, and competitive electricity system. the results of the study showed the hybrid model was tested in the Gerner’s 6 bus system and the expansion model after a load forecast. On the IEEE 24-bus system showed that the hybrid expansion model was able to take care of the load forecast for future expansion.