计及风电成本的电力系统短期经济调度建模

Short-term economic dispatch of power system modeling considering the cost of wind power

与传统发电方式相比,风力发电具有无煤耗和无污染的优势,但风速的间歇性和不确定性使得大容量风电并网后会给电力系统的安全性和稳定性造成了影响。将风速的不确定性量化为风电成本纳入到短期调度模型中,同时考虑到风电的不足和盈余对于调度策略的影响及风能无污染的优势,在模型中还分别加入了备用罚函数、风电盈余罚函数和污染评估罚函数,从而建立了计及风电成本的电力系统短期调度模型。在优化方法方面,依据混沌理论,将免疫算法和粒子群算法相结合,建立了基于人工免疫系统的混沌粒子群算法(ICPSO),通过混沌初始化和浓度的控制,克服了粒子群易陷入局部最优解的劣势,并在仿真中证明了其有效性。

Compared with the traditional power generation,wind power has the advantages of no coal consumption and free pollution.However,because of intermittent and uncertainties of wind speed,grid will exist potential security and stability when large-capacity wind power integrating it.This paper quantifies the uncertainty of wind speed for the cost of wind power into a short-term scheduling model.At the same time,considering the impact of insufficiency and surplus of the wind power to scheduling strategy and its advantages of non-polluting,the reserve-penalty function,surplus-penalty function and pollution assessment of penalty function are joined in the short-term scheduling of power system model.T o overcome the inferior local optimal solution of the particle swarm,this paper combines the chaos theory,manual immunity theory and particle swarm optimization to create an effective method—immune chaotic partical swarm optimization（ICPSO）.The simulation of system verifies the feasibility of models and algorithms.