Simultaneous Generation and Transmission Expansion Planning in the Power Market using Multi-objective Genetic Algorithm

Nowadays, due to the growth of energy consumption, power systems planning is one of the most important affair which simultaneously effects on the operation and maintenances of power systems in the future. Hence, in this paper a new multi-objective framework is proposed for dynamic Generation and Transmission Expansion Planning (GTEP) in deregulated power systems. The investment cost and operation cost are considered as long term and short term economical criteria. Also, congestion cost and average loss of load cost are considered as market and reliability criteria, respectively. Non-dominated Sorting Genetic Algorithm (NSGA-II) is adopted to cope with mentioned optimization problem. Moreover, fuzzy satisfying method has been applied to obtain the final optimal plan according to different satisfaction levels of objectives. To show the effectiveness and applicability of the proposed algorithm in electricity market environment, the planning methodology has been tested on the IEEE-RTS and a part of Iranian power system in Khorasan province (a real life case study). Obtained results also are separately compared with Generation Expansion Planning (GEP) and Transmission Expansion Planning (TEP).