基于改进灰狼算法在多锅炉负荷优化分配中的应用

Application of Improved Grey Wolf Algorithm in Multi-boiler Load Optimization

根据不同锅炉机组特性,使工厂热负荷在机组间进行负荷优化分配,是提高能源利用率的重要方法。通过分析某工厂实际供热系统,建立多锅炉特性与负荷分配优化模型。采用随机分配调整和概率扰动策略改进灰狼优化算法(GWO)对目标函数进行求解,提出一种新型锅炉集群负荷优化分配策略,并在MATLAB2018a上进行验证。仿真结果表明,所提的改进GWO优化分配模型可以对锅炉集群负荷进行有效的优化分配,减少能源消耗,提高能源利用率。相较于标准灰狼算法(GWO)与粒子群优化算法(PSO),本文所改进的灰狼算法具有一定的有效性和优越性。

According to the characteristics of different boiler units,it is an important method to optimize the distribution of plant heat load among units to improve the energy utilization rate.By analyzing the actual heating system of a factory,an optimization model of multi-boiler characteristics and load distribution is established.The objective function is solved by the improved Grey Wolf Optimization Algorithm(GWO)based on the random allocation adjustment and probability disturbance strategy,and a new boiler cluster load optimization allocation strategy is proposed and verified on MATLAB 2018a.The simulation results show that the proposed modified GWO optimal distribution model can effectively optimize the distribution of boiler cluster load,reduce energy consumption and improve energy utilization.Compared with the standard grey wolf algorithm(GWO)and particle swarm optimization(PSO)algorithm,the grey wolf algorithm improved in this paper has certain effectiveness and advantages.