水电站群发电优化调度的并行求解方法研究与应用
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摘要
经过60多年特别是近十几年的水电开发建设,我国逐步形成了众多跨流域、跨区域的大规模水电站群。水电站群的联合优化调度不但可以充分发挥电站之间的水文补偿、库容补偿和电能补偿效益,提高水电站群运行的整体经济效益,而且更是实现防洪安全、电网稳定和节能减排等社会效益的重要保障。因此,水电站群的优化调度问题受到了人们的高度重视。
水电站群优化调度问题的研究主要集中在模型构建和算法求解两个方面。随着水电站规模的急剧扩大,构建的优化模型越来越精细,然而相应的求解算法要么计算速度慢,要么容易陷于局部优化解。因此,本文以长江上游大规模水电站群的联合优化调度为研究背景,深入分析传统求解方法和智能优化方法的算法结构特点,结合近些年兴起的并行计算技术,提出了针对多种优化方法的并行化策略,并结合标准测试函数和水电站群联合优化调度问题对并行化算法进行检验,主要成果如下：
(1)针对动态规划的维数灾问题,提出基于状态点相互独立的并行动态规划和基于阶段重构的并行动态规划。通过分析动态规划的递推原理,将动态规划的计算过程分为阶段内计算和阶段间递推两个技术环节。针对阶段内计算环节,利用其状态空间内状态点之间的独立性,提出基于状态空间独立性的并行化方法；针对阶段间递推环节,调整不同阶段间的计算次序,并将其重构成为不同的子阶段进行计算,提出基于阶段重构的并行动态规划法。将上述两种并行动态规划法应用于雅砻江某梯级水电站群的发电优化调度中,通过模拟计算,分析总结了控制参数对并行动态规划性能的影响。模拟计算结果表明,并行模式明显优于串行动态规划方法。
(2)针对逐步优化法,提出基于多初始解的并行化方法和阶段关系独立性的并行化方法。通过对逐步优化法的算法结构的分析,将逐步优化法的计算过程划分为初始解生成和两阶段递推寻优两个技术环节。针对初始解生成环节,改变以往只使用一个初始解进行迭代寻优的模式,利用多进程并行计算技术,在不同的进程中以不同的初始解进行迭代寻优,提出基于多初始解的并行逐步优化法；针对两阶段递推环节,利用由不同的两阶段组成的子阶段之间的独立性,提出基于阶段关系独立性的逐步优化法。将上述两种并行逐步优化法应用于雅砻江某梯级水电站群的发电优化调度中,通过模拟计算,分析总结了控制参数对并行逐步优化法计算性能的影响。模拟计算结果表明,并行逐步优化方法明显优于串行模式的逐步优化方法。
(3)针对智能优化算法在求解以水库群联合调度为代表的复杂系统优化问题时,随着问题维度的上升算法求解性能下降的问题,提出基于子群体划分和动态迁移策略的并行差分进化算法。通过对以差分进化算法为代表的智能优化算法的算法结构进行研究,利用其种群天然的并行性,提出基于子群体划分的并行化策略。同时,对子群体之间的信息交流模式进行深入分析,提出自适应动态迁移策略。将差分进化算法应用于测试函数和水库群联合调度问题并率定其控制参数的选取范围,随后在控制参数的选取范围内对并行差分进化算法的计算性能进行测试和分析。模拟结果表明,并行差分进化算法在求解数值计算问题和水库群联合优化调度问题时,其计算性能明显优于串行差分进化算法。
(4)针对大规模水电站群短期联合优化调度问题,提出基于水电站群分解的并行化求解方法。通过对大规模水电站群短期联合优化调度问题的特性进行研究,基于对电站群分解的思想,分别从水电站空间分布特性、水电站之间水流传播特性以及水库调节性能等3个方面对水电站群进行分解,形成若干基本计算单元。将上述计算单元顺次分配给不同的进程,通过并行计算提高对上述问题的求解速度。将该方法应用于长江上游大规模水电站群短期联合调度问题,模拟计算结果表明,该方法可以解决在较短时间内计算出大规模水电站群的短期调度方案,满足短期调度的时效性要求。
Many large-scale groups of hydropower stations cross basins and provinces have been laid out in China after decades of construction, especially the last decade. Joint optimal regulation of hydropower station group, which is supposed to maximizing the compensation benefits from aspects of hydrology, reservoir storage and power generation, has drawn growing attention as an important approach to achieve more economic benefits as well as secure flood control, power supply, energy saving and emission reduction.
Research on optimal operation of station group is mainly focused on model establishment and algorithm design. In previous studies, the rapid expansion of hydropower stations'size and the corresponding increasing complexity of models have highlighted the problems of limited calculation speed and being easily stuck in local optimal solutions. This thesis applies parallel computing techniques in the joint optimal operation of large-scale hydropower station group in upper reach of Yangtze River with various parallel strategies involved with multiple traditional algorithms and intelligent algorithms. Results of parallel calculation are validated through the case study in Yangtze River as well as standard testing function. Main contents and conclusions of the study are as follows:
(1) Parallel dynamic programming algorithms based on the independence of state point and stage reconstruction are proposed against the curse of dimensionality. First, calculation process of dynamic programming is divided into two technical steps including calculation in single stage and recursion among stages by analyzing the principle of dynamic programming recursion. Then parallel dynamic programming based on the independence of state space is suggested in light of the independence among state points in the state space, which is corresponding to the step of calculation in single stage. The second parallel dynamic programming is carried out by adjusting the calculation orders of different stages and reconstructing them into new sub-stages, which is corresponding to the step of recursion among stages. The above two methods are applied to the optimal operation for power generation in a cascade station group in Yalongjiang River, and the effects of parameter control on the performance of parallel dynamic programming are analyzed and summarized. Simulated results reveal the superiority of parallel dynamic programming over serial mode.
(2) Parallel progressive optimization algorithms based on multiple initial solutions and the independence of stages are proposed against the weakness of traditional progressive optimal algorithm. Through the analysis of the structure of progressive optimization algorithm, the calculation process of progressive optimization algorithm is divided into two technical steps:generation of initial solutions and two-stage recursion optimization. In the first step, we implement iterative optimization in multiple processes with different initial solutions instead of single initial solution in the traditional method, which is suggested as parallel progressive optimization algorithm based on multiple initial solutions. For the second step, parallel progressive optimization algorithm based on the independence of stages is suggested with a concern of the independence between different two-stages. These two methods are then applied to the optimal operation for power generation in a cascade station group in Yalongjiang River, and the effects of parameter control on the performance of parallel progressive optimization are analyzed. Simulated results indicate that parallel algorithms are significantly better than serial algorithm.
(3) Parallel differential evolution algorithms based on sub-group division and adaptive immigration are respectively presented for the degrading performance of intelligent algorithms along with the increase of dimension in solving complex optimal issues such as joint operation of cascade reservoirs. The algorithm based on sub-group division is proposed according to the natural parallelism of populations in the structure of differential evolution algorithm while the dynamic adaptive immigration principle is suggested through an analysis of information exchange mode among different sub-groups. Performance of the algorithms are then tested and compared through test function and the case of joint operation of cascade reservoirs after calibrating the selection range of control parameters. Results also suggest that parallel differential evolution algorithms can outperform the serial counterpart.
(4) A parallel optimization algorithm based on decomposition of hydropower stations is proposed for short-term joint optimal scheduling of large-scale hydropower station group. Stations are decomposed to several basic calculation units from three aspects:spatial distribution of stations, propagation characteristics of flow among stations and regulation performance of reservoirs. Then the units are allocated to different parallel processes in order to improve the computing speed. The algorithm is applied in the short-term joint scheduling of large-scale hydropower station group in upper reach of Yangtze River, and results reveal that the suggested algorithm can meet the requirement of timeliness with higher efficiency.
水电站群优化调度问题的研究主要集中在模型构建和算法求解两个方面。随着水电站规模的急剧扩大,构建的优化模型越来越精细,然而相应的求解算法要么计算速度慢,要么容易陷于局部优化解。因此,本文以长江上游大规模水电站群的联合优化调度为研究背景,深入分析传统求解方法和智能优化方法的算法结构特点,结合近些年兴起的并行计算技术,提出了针对多种优化方法的并行化策略,并结合标准测试函数和水电站群联合优化调度问题对并行化算法进行检验,主要成果如下：
(1)针对动态规划的维数灾问题,提出基于状态点相互独立的并行动态规划和基于阶段重构的并行动态规划。通过分析动态规划的递推原理,将动态规划的计算过程分为阶段内计算和阶段间递推两个技术环节。针对阶段内计算环节,利用其状态空间内状态点之间的独立性,提出基于状态空间独立性的并行化方法；针对阶段间递推环节,调整不同阶段间的计算次序,并将其重构成为不同的子阶段进行计算,提出基于阶段重构的并行动态规划法。将上述两种并行动态规划法应用于雅砻江某梯级水电站群的发电优化调度中,通过模拟计算,分析总结了控制参数对并行动态规划性能的影响。模拟计算结果表明,并行模式明显优于串行动态规划方法。
(2)针对逐步优化法,提出基于多初始解的并行化方法和阶段关系独立性的并行化方法。通过对逐步优化法的算法结构的分析,将逐步优化法的计算过程划分为初始解生成和两阶段递推寻优两个技术环节。针对初始解生成环节,改变以往只使用一个初始解进行迭代寻优的模式,利用多进程并行计算技术,在不同的进程中以不同的初始解进行迭代寻优,提出基于多初始解的并行逐步优化法；针对两阶段递推环节,利用由不同的两阶段组成的子阶段之间的独立性,提出基于阶段关系独立性的逐步优化法。将上述两种并行逐步优化法应用于雅砻江某梯级水电站群的发电优化调度中,通过模拟计算,分析总结了控制参数对并行逐步优化法计算性能的影响。模拟计算结果表明,并行逐步优化方法明显优于串行模式的逐步优化方法。
(3)针对智能优化算法在求解以水库群联合调度为代表的复杂系统优化问题时,随着问题维度的上升算法求解性能下降的问题,提出基于子群体划分和动态迁移策略的并行差分进化算法。通过对以差分进化算法为代表的智能优化算法的算法结构进行研究,利用其种群天然的并行性,提出基于子群体划分的并行化策略。同时,对子群体之间的信息交流模式进行深入分析,提出自适应动态迁移策略。将差分进化算法应用于测试函数和水库群联合调度问题并率定其控制参数的选取范围,随后在控制参数的选取范围内对并行差分进化算法的计算性能进行测试和分析。模拟结果表明,并行差分进化算法在求解数值计算问题和水库群联合优化调度问题时,其计算性能明显优于串行差分进化算法。
(4)针对大规模水电站群短期联合优化调度问题,提出基于水电站群分解的并行化求解方法。通过对大规模水电站群短期联合优化调度问题的特性进行研究,基于对电站群分解的思想,分别从水电站空间分布特性、水电站之间水流传播特性以及水库调节性能等3个方面对水电站群进行分解,形成若干基本计算单元。将上述计算单元顺次分配给不同的进程,通过并行计算提高对上述问题的求解速度。将该方法应用于长江上游大规模水电站群短期联合调度问题,模拟计算结果表明,该方法可以解决在较短时间内计算出大规模水电站群的短期调度方案,满足短期调度的时效性要求。
Many large-scale groups of hydropower stations cross basins and provinces have been laid out in China after decades of construction, especially the last decade. Joint optimal regulation of hydropower station group, which is supposed to maximizing the compensation benefits from aspects of hydrology, reservoir storage and power generation, has drawn growing attention as an important approach to achieve more economic benefits as well as secure flood control, power supply, energy saving and emission reduction.
Research on optimal operation of station group is mainly focused on model establishment and algorithm design. In previous studies, the rapid expansion of hydropower stations'size and the corresponding increasing complexity of models have highlighted the problems of limited calculation speed and being easily stuck in local optimal solutions. This thesis applies parallel computing techniques in the joint optimal operation of large-scale hydropower station group in upper reach of Yangtze River with various parallel strategies involved with multiple traditional algorithms and intelligent algorithms. Results of parallel calculation are validated through the case study in Yangtze River as well as standard testing function. Main contents and conclusions of the study are as follows:
(1) Parallel dynamic programming algorithms based on the independence of state point and stage reconstruction are proposed against the curse of dimensionality. First, calculation process of dynamic programming is divided into two technical steps including calculation in single stage and recursion among stages by analyzing the principle of dynamic programming recursion. Then parallel dynamic programming based on the independence of state space is suggested in light of the independence among state points in the state space, which is corresponding to the step of calculation in single stage. The second parallel dynamic programming is carried out by adjusting the calculation orders of different stages and reconstructing them into new sub-stages, which is corresponding to the step of recursion among stages. The above two methods are applied to the optimal operation for power generation in a cascade station group in Yalongjiang River, and the effects of parameter control on the performance of parallel dynamic programming are analyzed and summarized. Simulated results reveal the superiority of parallel dynamic programming over serial mode.
(2) Parallel progressive optimization algorithms based on multiple initial solutions and the independence of stages are proposed against the weakness of traditional progressive optimal algorithm. Through the analysis of the structure of progressive optimization algorithm, the calculation process of progressive optimization algorithm is divided into two technical steps:generation of initial solutions and two-stage recursion optimization. In the first step, we implement iterative optimization in multiple processes with different initial solutions instead of single initial solution in the traditional method, which is suggested as parallel progressive optimization algorithm based on multiple initial solutions. For the second step, parallel progressive optimization algorithm based on the independence of stages is suggested with a concern of the independence between different two-stages. These two methods are then applied to the optimal operation for power generation in a cascade station group in Yalongjiang River, and the effects of parameter control on the performance of parallel progressive optimization are analyzed. Simulated results indicate that parallel algorithms are significantly better than serial algorithm.
(3) Parallel differential evolution algorithms based on sub-group division and adaptive immigration are respectively presented for the degrading performance of intelligent algorithms along with the increase of dimension in solving complex optimal issues such as joint operation of cascade reservoirs. The algorithm based on sub-group division is proposed according to the natural parallelism of populations in the structure of differential evolution algorithm while the dynamic adaptive immigration principle is suggested through an analysis of information exchange mode among different sub-groups. Performance of the algorithms are then tested and compared through test function and the case of joint operation of cascade reservoirs after calibrating the selection range of control parameters. Results also suggest that parallel differential evolution algorithms can outperform the serial counterpart.
(4) A parallel optimization algorithm based on decomposition of hydropower stations is proposed for short-term joint optimal scheduling of large-scale hydropower station group. Stations are decomposed to several basic calculation units from three aspects:spatial distribution of stations, propagation characteristics of flow among stations and regulation performance of reservoirs. Then the units are allocated to different parallel processes in order to improve the computing speed. The algorithm is applied in the short-term joint scheduling of large-scale hydropower station group in upper reach of Yangtze River, and results reveal that the suggested algorithm can meet the requirement of timeliness with higher efficiency.
引文
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