中文摘要：

电网调峰需求日益迫切,如何利用高效清洁的水电能源调节电网峰谷差异成为缓解电网调峰压力的关键问题之一。本文以电网剩余负荷均方差最小为目标建立梯级水电站短期调峰模型,并提出一种逐步粒子群优化混合算法,外层利用逐步优化算法压缩解空间降维,内层采用粒子群算法随机连续搜索;同时提出动态约束廊道方法处理模型复杂约束,自适应更新可行域范围。将所提方法运用于华中电网直调沅水梯级电站短期调峰优化运行中,实例计算表明,所提建模思路调峰效果明显,电网余荷均方差降幅达93.6%,经削峰后的负荷曲线趋于平稳,可为梯级电站短期调峰提供求解思路。

英文摘要：

How to make use of efficient clean hydropower for peak-valley adjustment is a key technique to release peak regulation pressure under the circumstance of increasingly urgent demand for peak shaving. A short-term peak shaving model for cascade hydropower stations has been developed in this study considering the minimum of residual load variance. A hybrid progressive particle swarm optimization algorithm is also described herein, combining a progressive optimality algorithm（POA） in the outer layer for compressing solution space and reducing dimension with a particle swarm optimization（PSO） in the inner layer for searching continuously and randomly. To update feasible regions adaptively, a new dynamic corridor technique was used for handling complicated time-space coupling. Application of this model to four stations on the Yuan River shows that the residual load variance of Hunan Grid can be decreased by 93.6% while keeping a steady and smooth curve of residual load. The method is very effective and useful in short-term peak shaving for cascade hydropower stations.