小水电群智能优化调度方法及系统开发
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摘要
小水电是优质的再生能源,是可持续利用的洁净能源。我国的小水电建设已经取得了很大的成效,现在如何高效地运行管理小水电越来越得到重视。随着传统水利向资源水利、可持续发展水利转变,小水电运行管理也应由常规调度管理向优化调度转变。将小水电联合起来优化调度,可以最大限度地发挥小水电的积极作用,并促进小水电的可持续发展。小水电群优化调度问题是大规模、多目标、非线性优化问题,数学模型和优化调度方法是研究的关键。以发电量最大为目标的优化调度数学模型更多地考虑了水的资源功能,而对水的生态环境功能考虑不足;基于动态规划法的优化调度存在“维数灾”等问题;遗传算法容易出现“早熟”,计算时间随问题规模显著增加。
本文主要研究小水电群优化调度数学模型、优化调度方法以及地方电网小水电群优化调度系统。主要研究工作如下:
(1)首先介绍了小水电群、小水电群优化调度的研究背景及意义,在总结归纳大量文献的基础上,综述了小水电群优化调度问题的主要研究内容和国内外研究现状。
(2)系统研究了小水电群优化调度数学模型。建立了能够统一描述串联、并联和混联小水电群以发电量最大为目标的优化调度数学模型;提出了以控制水位和弃水最小为目标的小水电群优化调度数学模型,能够体现小水电的生态环境功能;分析了发电引用流量约束,并设计了流量调节因子,以保证下游生态需水要求。
(3)研究了基于粒子群优化算法的小水电群优化调度。将自适应粒子群优化算法(APSO)应用于小水电群优化调度问题,实例仿真结果证明了算法可以有效地处理小水电群大规模优化调度问题;将自适应机制引入弹性粒子群优化算法(RPSO),并应用于小水电群优化调度问题,实例仿真证明了RPSO算法可以很好地克服APSO算法容易陷入局部最优的弱点;分析了RPSO算法的时间复杂度,并将RPSO和PSO、APSO算法联合应用,经测试比较,RPSO算法具有较好精度和稳定度。
(4)研究了基于文化算法的小水电群优化调度。深入研究了双空间双演化双促进文化算法,将改进遗传算法作为种群空间的全局搜索策略,将各子群体的优秀个体提升为对应文化单元的知识,以一定概率引入遗传模拟退火算法和粒子群优化算法作为优秀知识指导种群进化的局部变异策略。将算法应用于小水电群优化调度问题,实例仿真并作算法比较,验证了文化算法可以综合各算法的优点,具较好的性能;将文化算法应用于不同水文年的优化调度,且采用不同的数学模型,分析了径流随机性对优化调度结果的影响,并验证了小水电群以控制水位和弃水最小为目标的数学模型简单有效。
(5)研究基于基因拟子协同进化算法的小水电群优化调度。提出了一种新的基因拟子协同进化算法(GMCA),并将其应用于小水电群优化调度问题。针对描述生物进化的遗传算法,提出了编码拟子和特殊拟子,并将它们和遗传操作拟子一起形成算法文化;设计了多种拟子并形成多种文化,使不同的文化指导不同的生物群体进化;提出了文化进化的发展和消亡算子,以粒子群优化算法作为文化感染策略,并以对应种群的适应值增幅大小作为判别文化衰老的标准;分析得出了GMCA的一代算法时间复杂度最高不超过整个群体采用最复杂文化策略的时间复杂度;经实例仿真、算法测试比较,证明了GMCA作为一种综合算法,能组合多种遗传操作算子,有优良的性能;本文对八种小水电群智能优化调度方法进行了比较分析。
(6)开发了地方电网小水电群优化调度系统。结合温州电网实例,提出了系统功能、系统模块以及系统实现方法等;开发了系统并投入了实际的应用。
最后,对全文的研究工作进行了总结,展望了小水电群优化调度问题研究的前景。
Small hydropower is high-quality,sustainable and clean renewable energy.Great achievement has been made in China regarding small hydro development.With the development of society,conservancy is shifted from its traditional type to a resources-focused and sustainable-development-oriented type;the operation of mall hydro should correspondingly be changed from regular experience-based scheduling to optimal scheduling.The small hydro groups in local power grid should be joined together to realize optimal scheduling so as to display the positive role to greatest extent and promote its sustainable development.Small hydro optimal scheduling is a nonlinear problem of large scale and for multi purposes.The key of achieving this is modeling and solution finding.A mathematic model with its optimal scheduling objective of achieving maximum power output does not meet the environmental and ecological demands.Optimal scheduling based on dynamic programming can lead to dimension disasters.Genetic Algorithm(GA) is widely applied to overall study on hydropower operation,but efforts have been making in applying improved genetic algorithm and other new and better optimization algorithms to hydropower optimal scheduling.The paper focuses on the optimal scheduling for small hydro group in local power grid, developing such a system by modeling and solving the problem.The main research works of the paper are listed as follow:
(1) It firstly introduces the background and significance of the study on the optimal scheduling for small hydro group.Based on the summary of many documents and records,it provides an overview of the domestic and oversea researches made on optimal scheduling for small hydro group.
(2) It gives a thorough research on various optimal scheduling mathematic models for small hydro group.An optimal scheduling mathematic model with its objective of achieving maximum power output is set up.It gives a unified description of serial, parallel and mixed group of small hydropower plants.After analyzing the requirements and principles of ecological scheduling for small hydro,a new mathematic model is proposed.Its objectives are normal reservoir level and minimum surplus.The environmental and ecological functions of small hydro are taken into consideration in the restrains.
(3) It studies the optimal scheduling for small hydro group based on Particle Swarm Optimization(PSO).A solution to the optimal scheduling for series small hydro groups by applying Adaptive Particle Swarm Optimization(APSO) is developed.The validity and convergence of APSO are tested and verified in a simulation example of two series small reservoirs.APSO is proved to be of higher performance than standard PSO and simple GA.It also develops a solution to the optimal scheduling for small hydro group by applying Resilient Particle Swarm Optimization Algorithm(RPSO). The self-adaptive strategy is introduced into the RPSO to improve its performance.The time complexity of the RPSO algorithm is analyzed.The validity of the algorithm and the model are tested and verified by making simulations.Differences between standard PSO,APSO and RPSO are also identified and compared in the simulation tests.
(4) It studies the optimal scheduling for small hydro group based on Memetic Algorithm(MA) and Cultural Algorithm(CA).After summarizing the development and application of MA and CA,the paper develops a cultural algorithm which is called MA here.It characterized by co-evolution and co-promotion of the population space and belief space.An improved GA is used to represent the population space,and some updating regulations to describe the belief space.A simulated annealing algorithm and particle swarm algorithm are introduced to deal with the local variation as belief space governing population space.The validity of the algorithm is verified in a simulation test, and the time complexity of the algorithm is analyzed.The performance comparison between different algorithms is made,and the optimal scheduling results basing on MA and delivered by different mathematic models are also analyzed.
(5) It studies the optimal scheduling for small hydro group based on Gene-Meme Co-evolution Algorithms(GMCA).After introducing the definition of meme and culture,the paper develops a gene-meme co-evolution algorithm,in which a coding meme and a particular meme are added,ways to evolute different algorithm cultures are setted,four cultural evolution operators(development,defection,revival and renaissance) are established,and a way of judging cultural senility is proposed.It develops a solution to the optimal scheduling for small hydro group based on GMCA.The validity of the algorithm is tested and verified in the case of small hydro group optimal scheduling.In the experiment comparison with that of GA,APSO,RPSO,MA and etc.the higher performance of GMCA is well demonstrated.
(6) It studies the optimal scheduling system for small hydro group of local power grid,including its functions,components,design methods of individual module,and its realization.The system embodies two functions of administrative information management and query and optimal scheduling for small hydro plants.In particular,it includes runoff forecasting,long-term and short-term optimal scheduling,and daily operation.In combination with practical research project,the application of the system is also dealt with.
Finally,the paper summarizes the studies made here and gives an outlook at the prospect of the optimal scheduling for small hydro group.
本文主要研究小水电群优化调度数学模型、优化调度方法以及地方电网小水电群优化调度系统。主要研究工作如下:
(1)首先介绍了小水电群、小水电群优化调度的研究背景及意义,在总结归纳大量文献的基础上,综述了小水电群优化调度问题的主要研究内容和国内外研究现状。
(2)系统研究了小水电群优化调度数学模型。建立了能够统一描述串联、并联和混联小水电群以发电量最大为目标的优化调度数学模型;提出了以控制水位和弃水最小为目标的小水电群优化调度数学模型,能够体现小水电的生态环境功能;分析了发电引用流量约束,并设计了流量调节因子,以保证下游生态需水要求。
(3)研究了基于粒子群优化算法的小水电群优化调度。将自适应粒子群优化算法(APSO)应用于小水电群优化调度问题,实例仿真结果证明了算法可以有效地处理小水电群大规模优化调度问题;将自适应机制引入弹性粒子群优化算法(RPSO),并应用于小水电群优化调度问题,实例仿真证明了RPSO算法可以很好地克服APSO算法容易陷入局部最优的弱点;分析了RPSO算法的时间复杂度,并将RPSO和PSO、APSO算法联合应用,经测试比较,RPSO算法具有较好精度和稳定度。
(4)研究了基于文化算法的小水电群优化调度。深入研究了双空间双演化双促进文化算法,将改进遗传算法作为种群空间的全局搜索策略,将各子群体的优秀个体提升为对应文化单元的知识,以一定概率引入遗传模拟退火算法和粒子群优化算法作为优秀知识指导种群进化的局部变异策略。将算法应用于小水电群优化调度问题,实例仿真并作算法比较,验证了文化算法可以综合各算法的优点,具较好的性能;将文化算法应用于不同水文年的优化调度,且采用不同的数学模型,分析了径流随机性对优化调度结果的影响,并验证了小水电群以控制水位和弃水最小为目标的数学模型简单有效。
(5)研究基于基因拟子协同进化算法的小水电群优化调度。提出了一种新的基因拟子协同进化算法(GMCA),并将其应用于小水电群优化调度问题。针对描述生物进化的遗传算法,提出了编码拟子和特殊拟子,并将它们和遗传操作拟子一起形成算法文化;设计了多种拟子并形成多种文化,使不同的文化指导不同的生物群体进化;提出了文化进化的发展和消亡算子,以粒子群优化算法作为文化感染策略,并以对应种群的适应值增幅大小作为判别文化衰老的标准;分析得出了GMCA的一代算法时间复杂度最高不超过整个群体采用最复杂文化策略的时间复杂度;经实例仿真、算法测试比较,证明了GMCA作为一种综合算法,能组合多种遗传操作算子,有优良的性能;本文对八种小水电群智能优化调度方法进行了比较分析。
(6)开发了地方电网小水电群优化调度系统。结合温州电网实例,提出了系统功能、系统模块以及系统实现方法等;开发了系统并投入了实际的应用。
最后,对全文的研究工作进行了总结,展望了小水电群优化调度问题研究的前景。
Small hydropower is high-quality,sustainable and clean renewable energy.Great achievement has been made in China regarding small hydro development.With the development of society,conservancy is shifted from its traditional type to a resources-focused and sustainable-development-oriented type;the operation of mall hydro should correspondingly be changed from regular experience-based scheduling to optimal scheduling.The small hydro groups in local power grid should be joined together to realize optimal scheduling so as to display the positive role to greatest extent and promote its sustainable development.Small hydro optimal scheduling is a nonlinear problem of large scale and for multi purposes.The key of achieving this is modeling and solution finding.A mathematic model with its optimal scheduling objective of achieving maximum power output does not meet the environmental and ecological demands.Optimal scheduling based on dynamic programming can lead to dimension disasters.Genetic Algorithm(GA) is widely applied to overall study on hydropower operation,but efforts have been making in applying improved genetic algorithm and other new and better optimization algorithms to hydropower optimal scheduling.The paper focuses on the optimal scheduling for small hydro group in local power grid, developing such a system by modeling and solving the problem.The main research works of the paper are listed as follow:
(1) It firstly introduces the background and significance of the study on the optimal scheduling for small hydro group.Based on the summary of many documents and records,it provides an overview of the domestic and oversea researches made on optimal scheduling for small hydro group.
(2) It gives a thorough research on various optimal scheduling mathematic models for small hydro group.An optimal scheduling mathematic model with its objective of achieving maximum power output is set up.It gives a unified description of serial, parallel and mixed group of small hydropower plants.After analyzing the requirements and principles of ecological scheduling for small hydro,a new mathematic model is proposed.Its objectives are normal reservoir level and minimum surplus.The environmental and ecological functions of small hydro are taken into consideration in the restrains.
(3) It studies the optimal scheduling for small hydro group based on Particle Swarm Optimization(PSO).A solution to the optimal scheduling for series small hydro groups by applying Adaptive Particle Swarm Optimization(APSO) is developed.The validity and convergence of APSO are tested and verified in a simulation example of two series small reservoirs.APSO is proved to be of higher performance than standard PSO and simple GA.It also develops a solution to the optimal scheduling for small hydro group by applying Resilient Particle Swarm Optimization Algorithm(RPSO). The self-adaptive strategy is introduced into the RPSO to improve its performance.The time complexity of the RPSO algorithm is analyzed.The validity of the algorithm and the model are tested and verified by making simulations.Differences between standard PSO,APSO and RPSO are also identified and compared in the simulation tests.
(4) It studies the optimal scheduling for small hydro group based on Memetic Algorithm(MA) and Cultural Algorithm(CA).After summarizing the development and application of MA and CA,the paper develops a cultural algorithm which is called MA here.It characterized by co-evolution and co-promotion of the population space and belief space.An improved GA is used to represent the population space,and some updating regulations to describe the belief space.A simulated annealing algorithm and particle swarm algorithm are introduced to deal with the local variation as belief space governing population space.The validity of the algorithm is verified in a simulation test, and the time complexity of the algorithm is analyzed.The performance comparison between different algorithms is made,and the optimal scheduling results basing on MA and delivered by different mathematic models are also analyzed.
(5) It studies the optimal scheduling for small hydro group based on Gene-Meme Co-evolution Algorithms(GMCA).After introducing the definition of meme and culture,the paper develops a gene-meme co-evolution algorithm,in which a coding meme and a particular meme are added,ways to evolute different algorithm cultures are setted,four cultural evolution operators(development,defection,revival and renaissance) are established,and a way of judging cultural senility is proposed.It develops a solution to the optimal scheduling for small hydro group based on GMCA.The validity of the algorithm is tested and verified in the case of small hydro group optimal scheduling.In the experiment comparison with that of GA,APSO,RPSO,MA and etc.the higher performance of GMCA is well demonstrated.
(6) It studies the optimal scheduling system for small hydro group of local power grid,including its functions,components,design methods of individual module,and its realization.The system embodies two functions of administrative information management and query and optimal scheduling for small hydro plants.In particular,it includes runoff forecasting,long-term and short-term optimal scheduling,and daily operation.In combination with practical research project,the application of the system is also dealt with.
Finally,the paper summarizes the studies made here and gives an outlook at the prospect of the optimal scheduling for small hydro group.
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