ADVANCE OPTIMIZATION OF ECONOMIC EMISSION DISPATCH BY PARTICLE SWARM OPTIMIZATION (PSO) USING CUBIC CRITERION FUNCTIONS AND VARIOUS PRICE PENALTY FACTORS - Joko Pitono; Adi Soepriyanto; Mauridhi Hery Purnomo (Jurnal Ilmiah Kursor, 2014)
ADVANCE OPTIMIZATION OF ECONOMIC EMISSION DISPATCH BY PARTICLE SWARM OPTIMIZATION (PSO) USING CUBIC CRITERION FUNCTIONS AND VARIOUS PRICE PENALTY FACTORS a Joko Pitono, bAdi Soepriyanto, cMauridhi Hery Purnomo aDepartment of Electrical Engineering, PPPPTK/VEDC Malang b,cDepartment of Electrical Engineering, Sepuluh Nopember Institute of Technology, Surabaya Email: j_pitono@yahoo.com Abstract The classical economic dispatch problem could be solved based on single objective function of power system operation by minimizing the fuel cost. However, the single objective function is not sustainable because the environmental issues arise from the emissions generated by fossil-fueled thermal electric power plants. Various pollutants such as sulfur dioxide (SO2), nitrogen oxides (NOX) and carbon dioxide (CO2) affect environmental issues. The economy-environment dispatch problem has been generally solved by considering each objective separately or by applying Weighted Sum Method on both objectives. This paper formulates the solution of dispatch PSO method that considers the impact of various pollutants and various factors such as the price penalty Min-Max, MaxMax, and Average in solving multi-objective problems using cubic criterion function for the cost of fuel and emission values. Multi-objective functions method proposed in this research was validated using IEEE 30-bus systems with six generating units. The results of simulation using Min-Max penalty factor indicated less total fuel cost value compared to the simulation using Max-Max and Average penalty factor. In general, the comparison of Min-Max type= 100%, Max-Max type= 266.9%, and Average type= 191.8%; Max-Max penalty factor provided less emission value with comparison to Min-Max and Average penalty factors. In general, the comparison Max-Max type= 100%, Min-Max type= 102%, and Average type= 100.2% to ETSO while for ETNO and ETCO is not significantly different; Average penalty factor provided less fuel cost value compared to Max-Max and Average penalty factor. In general, the comparison of Average type= 100%, Min-Max type= 101.8%, and Max-Max type= 100.3%. Keywords: Economic-Emission Dispatch, Multi-Objective, Cubic Criterion Function, Price Penalty Factors, Particle Swarm Optimization.