摘要
随着大规模风电并网,风电资源的随机性及不确定性给电力系统节能发电调度带来了新问题。为适应上述电力系统新情况,更好地实现电力系统的节能减排,本文基于多目标粒子群算法,对含风电的节能发电调度进行研究。以火电机组总能耗最小和CO2排放量最小为共同目标函数,建立含风电的多目标节能发电调度模型,利用多目标粒子群算法进行模型求解,并引入半可行域的概念进行约束条件的处理。同时以1个含有10台火电机组和1个风电场的系统为算例进行验证。结果表明,多目标优化方案与以火电总能耗最小为目标的方案相比,CO2排放量减少8.16%,火电总能耗仅增加4.73%,与以CO2排放量最小为目标的方案相比,火电总能耗减少7.39%,CO2排放量仅增加1.73%。该方案实现了节省资源及降低排放的目的,对电力系统节能减排具有参考价值。
With the large-scale wind power integration,the randomness and uncertainty of wind power resources have brought new problems to the energy-saving generation dispatch of power system. In order to adapt to the new situation in power system, and to better realize energy conservation and emissions reduc- tion of power system, based on Multi-Objective Particle Swarm Optimization（MOPSO）algorithm, the re search on energy-saving generation dispatch with wind power was done, and minimizing both the total en- ergy consumption of therma generation dispatching mode power units and emissions of CO2 as objective functions, an energy saving was established. MOPSO algorithm was used to solve this model,and semi- feasible region was used to treat constrained conditions. A system with ten generating units and a big-scale wind power was used as an example for energy-saving generation dispatching purpose. The result suggests that：compared with the scheme of minimizing the total energy consumption as the goal,CO2 emissions are reduced by 8.16%, but thermal power energy consumption increased by only 4.73%;compared with the scheme of minimizing CO2 emissions as the goal,thermal power is reduced by 7. 39% ,but CO2 emissions in- creased only 1.73%. Energy was effectively saved, and emissions were reduced. It has a certain reference value to energy conservation and emission reduction in power system .
出处
《青岛大学学报（工程技术版）》
CAS
2014年第2期11-14,28,共5页
Journal of Qingdao University(Engineering & Technology Edition)
关键词
节能发电调度
风电
多目标粒子群算法
PARETO最优解
半可行域
energy-saving generation dispatching
wind power
multi-objective particle swarm optimiza- tion
pareto optimal solution
semi-feasible region
