基于螺旋法向逼近遗传算法的水电站动态不确定优化调度研究

Study on uncertain dynamic optimization of hydropower station dispatching based on spiral-vertical genetic algorithm

针对水电站存在的诸多动态不确定因素,打破传统的动态确定性模型,在分析模糊动力特性、瞬时给定负荷和不确定检修计划三个基本动态不确定因素基础上,建立了水电站优化调度动态不确定模型,采用基于螺旋法向逼近遗传算法(SVQA算法)对其进行求解,编制计算机软件模块,升级了水电站动态不确定优化调度决策系统。实践应用表明,建立的动态不确定优化调度模型更加符合水电站的实际应用,且SVQA算法比动态规划法更加适合于模型的求解,优化性能指标较好。

Hydropower stations are featured with factors of dynamic uncertainties that are difficult to consider in a traditional deterministic dynamic model. This study analyzes three factors of fuzzy dynamic eharacteristics, given variable load, and uncertainty maintenance plan, and develops an uncertain dynamic model for optimization of hydropower station operation. This model solves the optimization problem using a spiral-vertical genetic algorithm （SVQA） and it is coded into a computer software module, upgrading the existing system of hydropower station dispatching. Its applications show that it is of has better performances and more effective in iteration and convergence to the final solution of an actual hydropower station dispatehing problem than dynamic programming methods.