基于机会约束的电厂混煤煤质和成本的Pareto前沿

Pareto Fronts of Mixed Coal Quality and Cost in Power Plant Based on Chance Constraints

电厂混煤掺配模型通常将混煤成分或性质限定在一定范围内,以寻找混煤成本最低的掺配方案,这种掺配模型实际上未对煤质进行优化。为此,定义了锅炉设计煤种的最大似然煤质,将掺配原煤的成分或性质视为随机变量,建立了基于机会约束的电厂混煤煤质和成本多目标优化模型,采用遗传算法得到该多目标优化模型的Pareto前沿。对一台实际运行机组Pareto前沿的分析表明:混煤煤质和成本的优化数据合理,结果满足机会约束要求。掺配模型还可以增加混煤煤质稳定性作为优化目标,考虑机组不同运行特性和掺配原煤实际状况,掺配模型选用不同优化目标和约束条件的组合,具有较强的灵活性和实用性。

Coal blending models usually take the composition or properties of coal as constraints to optimize the cost of mixed coal for the blending scheme in power plant.Properties of coal are essentially not optimized with this kind of blending model.Therefore,the maximum likelihood quality of design coal of boiler was defined,when the compositions of raw coal can be regarded as random variables.A multi-objective optimization model of coal properties and cost based on chance constraint was established.Pareto fronts of the multi-objective model were presented by genetic algorithm.Analysis of Pareto fronts shows that the optimal data of coal properties and cost are reasonable,and the chance constraints are met well.The blending model can employ the stability of mixed coal as the optimization objective additionally.According to different characteristics of the generator unit and the actual coal to be blended,the optimization model can have different objectives and constraint combinations,which means the good flexibility and practicability.