基于改进遗传算法的含风电场的电力系统短期经济调度及其风险管理

Short-term Economic Scheduling Model Including Wind Park Based on Improved Genetic Algorithm and Risk Management

风能是一种不产生任何污染物排放的可再生能源,其作为我国发展新能源的主要方向之一,发展潜力巨大。但由于风速的间歇性和不可控性,使得大容量风电并网给电力系统带来了更多的不确定性因素。考虑了负荷、风速的不确定性,将风险价值(VaR)和条件风险价值(CVaR)理论融入到含风电场的电力系统短期经济调度模型中,在最小化运行成本的同时,降低不确定因素对系统产生的风险。另外采用了遗传算法和免疫算法相结合的优化方法,并引用自适应理论来提高算法的效率,通过十机系统的仿真计算,验证了该模型和算法的可行性。

Wind energy is renewable energy which can not pollute the environment, and it has huge growth potential to be one of the main development directions of China＇ s new energy sources. Because of the intermittent and non-control- lable characteristics of wind speed, large-capacity wind pow- er grid＇ s merging into the power system brings more risk factors. Considering the uncertainty of the load and wind speed, the Value-at-Risk （VaR） and Conditional Value-at- Risk （CVaR） theory are integrated into the short-term economic dispatch model. This model with wind park is expected to reduce the uncertainty of the system risk while minimizing the operating cost. The genetic algorithm, immune algorithm and adaptive algorithm theory are combined to improve the effectiveness of computation. The simulation of system including ten generators and a wind park has verified the feasibility of the model and algorithms.