中国电机工程学报2017，Vol.37Issue(12)：3437-3448,12.DOI:10.13334/j.0258-8013.pcsee.160280
梯级水电站群长期优化调度云计算随机动态规划算法
Cloud Computing Stochastic Dynamic Programming Algorithms for Long-term Optimal Operation of Cascaded Hydropower Stations
摘要
Abstract
In order to solve the problem of "curse of dimensionality" in long-term optimal operation of cascade hydropower stations,parallel methods for stochastic dynamic programming are widely studied.The scalability of multi-core parallel algorithms for single computer is limited;the traditional distributed parallel algorithms are difficult for programming;in addition,they lack the load balance and fault tolerance mechanism.As a new distributed computing platform,cloud computing platform can make full use of resources and has many other advantages.In order to explore implementations of distributed parallel stochastic dynamic programming models on cloud platforms,this paper implemented traditional cluster and cloud computing distributed parallel method on stochastic dynamic programming algorithms based on message passing interface (MPI) and spark framework respectively.The algorithm on spark framework transformed calculation model into a data processing model for computation.These algorithms were compared with each other through three reservoirs' optimal experimentations.Analysis and Experimental results show that distributed parallel stochastic dynamic programming based on cloud computing can make full advantages of cloud platform and has effective fault tolerance and load balancing mechanisms.关键词
随机动态规划/并行计算/云计算/消息传递接口(MPI)/spark框架Key words
stochastic dynamic programming/parallel computation/cloud computing/message passing interface (MPI)/spark分类
信息技术与安全科学引用本文复制引用
周东清,彭世玉,程春田,王健..梯级水电站群长期优化调度云计算随机动态规划算法[J].中国电机工程学报,2017,37(12):3437-3448,12.基金项目
国家自然科学基金重大计划重点支持项目(91547201).The Key Support Program of Major Plan of National Natural Science Foundation of China (91547201). （91547201）
