Turkish Journal of Electrical Engineering and Computer Sciences




This study presents a possible process of simulating power plant generation planning. The process combines expected overall industry costs, associated cost uncertainty, and expected CO$_{2}$ emissions for different generations, variations of future fossil fuel costs, carbon prices, plant investment costs, and demand, including price elasticity impacts. Uncertainty in the decision stems from the elasticity of prices of fuel and electricity. The aim of this paper is to apply fuzzy numbers to power generation planning and to use a Monte Carlo simulation to check. Simulations are demonstrated through a case study of an electricity industry with coal and lignite, combined cycle gas turbines, and supercritical boilers facing future uncertainties. The same simulation was used in planning the generation of electricity from wind, solar, and hydro energy. Comparing the results, decisions were made about the profitability of investments in renewable energy. Based on the results, it can be concluded that the use of fuzzy numbers is a simple and flexible approach to planning and that it can be a serious competitor compared to other methods of planning.


Generation planning, renewable energy, price elasticity, Monte Carlo, fuzzy logic

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