摘要
提出了一种基于混合整数线性规划(MILP)的随机优化方法,来求解考虑风电出力不确定性、以总运行成本最小化为目标的机组组合优化问题。为计及风电的波动性,采用时间序列分析自回归滑动平均(ARMA)模型和拉丁立方采样(LHS),将随机优化模型转化为确定性模型,通过场景削减技术来解决场景数量很多时的计算量庞大问题。采用10机组和100机组系统对所提出的方法进行了模拟测试。仿真结果表明:计及风电出力不确定性后,系统总的运行成本趋于增加;非风电机组的爬坡速度和风电预测精度对所提出的算法的计算效率有明显的影响;将风电作为旋转备用资源可以明显降低系统总的运行成本。
A stochastic optimization approach is proposed for the unit commitment problem with the uncertainty of wind power generation taken into account, based on mixed-integer linear programming (MILP). The problem is formulated to minirnize the total operation cost of thermal units. In considering wind power generation, scenarios are generated by auto-regressive and moving average (ARMA) time series model and Latin hypercube sampling (LHS) method and the stochastic optimization problem is then transformed to a deterministic one. A large number of scenarios lead to computing complexity, scenario reduction technology is introduced to decrease scenario number in order to reduce computing cost. The proposed formulation is tested on a ]0-unit system and a 100-unit system. Simulation results show that the varying wind power generally leads to the increase of total cost. In addition, the ramping rates of non-wind generators and the prediction precision of wind power are significant in making generation scheduling with volatile wind power generation. Moreover, the system operation cost decreases significantly if wind power is considered as a spinning reserve resource.
出处
《电力系统自动化》
EI
CSCD
北大核心
2010年第6期79-88,共10页
Automation of Electric Power Systems
基金
supported by Mega-projects of Science Research for the 11th Five-year Plan(No.2008BAA13B10)