摘要
在节能减排背景下,综合考虑最小化机组煤耗量和污染物排放量,提出了求解多目标节能减排负荷调度的模型及改进多目标粒子群算法。该改进算法引入半可行域的概念处理约束条件,避免了惩罚因子复杂的选取过程;采用精英归档技术构建了外部精英集和个体非支配解集,提高了算法的收敛速度和解的质量;采用了自适应网格法维护外部精英集,获得了分布均匀的Pareto前沿;并提出了基于半可行域概念的个体极值和全局极值选取规则。利用该方法对某电厂6台机组系统进行了节能减排最优负荷调度,获得了分布良好的Pareto最优解,有效降低了系统煤耗和污染物排放量,分析结果验证了该方法的有效性和可行性。
In order to solve the problems of economic emission load d^spatch,a model of multi-objective energy-saving and emission- reduction load dispatch was established, along with an improved multi-objective particle swarm optimization algorithm. The conception of semi-feasible region was introduced to treat constrained conditions, the complicated process of finding appropriate penalty parameters was avoided. Elite filing technology were used to enhance the speed of convergence and the quality of solutions. Adaptive mesh method was adopted to renew and maintain the external elite set in order to gain the Pareto front distributed uniformly. The rules of personal best selection and global best selection were proposed based on the conception of semi-feasible region. This method was applied to multi- objective load dispatch of a real power plant of six power units, and well-distribution Pareto-optimal solutions were obtained. Fuel cost and pollution emission were reduced effectively. The analysis result confirms the feasibihty and validity of this approach.
出处
《机电工程》
CAS
2012年第3期353-358,共6页
Journal of Mechanical & Electrical Engineering
关键词
节能减排负荷调度
多目标粒子群算法
半可行域
精英归档技术
自适应网格法
高斯变异
energy-saving and emission-reduction load dispatch
multi-objective particle swarm optimization algorithm
semi-feasibleregion
elite filing technology
adaptive mesh
Gauss mutation
