Fuel Power Generation Business Model And Multi-objective Optimization Of The Procurement

For thermal power plants, only the fuel cost may account for nearly 70% of he total cost. With the rapid rise of coal prices currently, the cost of thermal power plant is increasing dramatically. At the same time, electric power market in China is still not perfect, which makes electricity price can not be determined by the market mechanism.So the price can not increase with the rising of generator cost, which gives a great challenge to the normal operation of power generation companies and even brought profits. Therefore, paying more attention for the power plant fuel management, reduceing the cost of fuel by effective technical means is of great necessity.Taking Dalate power plant for example, based on basic dynamic blending model, this paper comprehensively discussed linear additive property conditions of various thecarbonificated index at first, so it draw coal's calorific value and volatile which did not have linear additive property. Then through literature review, it found the corresponding technical method to processing calorific value and volatile copies to make them meet the assumptions of dynamic blending model. Subsequently, the paper builded programming model and considered the environmental factors to get the main influencing factors, namely mixed coal's ash and sulfur,adding them into the objective function to establish dynamic blending model which based on boiler design standards of Dalate power plant. The next, this paper applicated Genetic algorithm and Pareto Multi-objective optimization method to optimization the model. Through designing code and fitness function to obtain optimal solution set of Pareto Multi-objective optimization. Then according to the actual situation of power plant, the paper considered the preferences of economic and environmental factors and through fuzzy number to identify weight and fuzzy multi-objective decision-making etc, getting mixed scheme to meet Dalate power plant, finally the thesis determined procurement scheme of DaLaTe power plant.