基于CVaR的风电场并网容量优化计算

Optimal capacity limit calculation of wind power based on CVaR model

由于风力发电出力与风速具有随机性,风电场接入系统的极限容量较传统电厂的影响更大,计算更困难。条件风险价值理论(CVaR)作为一种新的随机性计算方法被用来建立和求解风电场并网极限容量的优化模型。该模型可计算不同置信度水平下的极限容量,可有效处理置信度水平以外极端情况的影响。引用变换函数,将难以解析的条件风险函数转化为可微的概率密度函数积分形式;引入辅助变量,用离散点代替连续积分计算,简化模型为线性优化问题。仿真算例分别计算和分析了系统接入单个风电场与多个风电场时不同置信度水平下的风电场并网容量,表明方法在处理随机性变量及随机性问题中具有明显、独特、便捷、有效的优点。

Due to the randomness of wind power output, the wind power capacity limit calculation is more important and difficult than that of traditional power plant. Based on a new random method, namely Conditional Value-at-Risk （CVaR）, a new optimization model is proposed to calculate the wind power capacity limit in the system. The limits under different confidence levels can be calculated in this model. Moreover, the extreme case outside confidence level can also be dealt with effectively. Because of the difficulty to analysis CVaR function, it is transformed into integral form of probability density function. Aided variable is introduced to linear optimization and continuous integral calculation is performed instead of discrete calculation. The wind farm capacity limits are calculated and analyzed respectively when access to both single and multiple wind farms under different confidence levels. The simulation results show that the proposed method is distinct, particular, convenient and effective.