Parallel Strategies for Solving Large Unit Commitment Problems in the California ISO Planning Model
Parallel Strategies for Solving Large Unit Commitment Problems in the California ISO Planning Model
复制标题
解决加州 ISO 规划模型中大型机组承诺问题的并行策略
DOI：
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发表时间：
2015
期刊：
影响因子：
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通讯作者：
T. Parriani
中科院分区：
文献类型：
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作者：
Guojing Cong;C. Meyers;D. Rajan;T. Parriani
我们提出了我们的研究解决大机组的承诺问题，在加州ISO规划模型。该模型每小时计算一天前的机组承诺，所有实例都需要在一小时内解决接近最优。目前最先进的求解器CPLEX分别需要5小时和10小时来求解确定性实例和5场景随机实例。20个场景的实例实际上是无法解决的，因为在24小时后没有找到可行的解决方案。我们考虑通过分布式内存并行化来改善解决方案的时间。以前的技术，如分布式分支和绑定表现不佳，我们的问题。我们提出了协同并发搜索解决集群上的确定性实例。对于随机的情况下，我们提出了并行化策略，结合基于神经网络的分解和异步解决引导的中间结果从渐进对冲。我们的分解创建线性子问题，而不是二次的，往往是棘手的。在16台IBM Power7机器的集群上，我们的并行实现分别为确定性实例和5场景随机实例实现了平均12.7和22倍的加速。所有的问题都在一个小时内解决，包括以前无法解决的20个场景的随机实例。
We present our study of solving large unit commitment problems in the California ISO planning model. The model calculates hourly day-ahead unit commitments, and all instances need to be solved close to optimality within an hour. It takes CPLEX, the current state-of-the-art solver, up to 5 and 10 hours to solve the deterministic instances and the 5-scenario stochastic instances, respectively. The 20-scenario instances are practically unsolvable as no feasible solutions are found after 24 hours.We consider improving solution times through distributed-memory parallelization. Prior techniques such as distributed branch- and-bound perform poorly for our problems. We propose coordinated concurrent search to solve the deterministic instances on a cluster. For stochastic instances, we propose parallelization strategy that combines scenario-based decomposition and asynchronous solves guided by intermediate results from progressive hedging. Our decomposition creates linear sub problems instead of quadratic ones that are oftentimes intractable. On a cluster of 16 IBM Power7 machines, our parallel implementation achieves on average 12.7 and 22 times speedup for the deterministic instances and the 5-scenario stochastic instances, respectively. All problems are solved within an hour to near optimality including the previously unsolvable 20-scenario stochastic instances.