A parallel dynamic programming algorithm for multi-reservoir system optimization |
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| Institution: | 1. School of Soil and Water Conservation, Beijing Forestry University, Beijing 100083, China;2. Beijing Collaborative Innovation Center for Eco-environmental Improvement with Forestry and Fruit Trees, Beijing 102206, China;3. Institute of Soil and Water Conservation, Northwest A&F University, Yangling, Shaanxi 712100, China;4. Department of Biological and Environmental Sciences, Alabama A & M University, Normal, AL 35762, USA;5. Department of Water Resources Engineering, Lund University, Lund 233 62, Sweden;6. China National Forestry Economics and Development Research Center, National Forestry and Grassland Administration, Beijing 100714, China;7. USDA Forest Service, Pacific Southwest Research Station, Albany, CA 94710, USA |
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| Abstract: | This paper develops a parallel dynamic programming algorithm to optimize the joint operation of a multi-reservoir system. First, a multi-dimensional dynamic programming (DP) model is formulated for a multi-reservoir system. Second, the DP algorithm is parallelized using a peer-to-peer parallel paradigm. The parallelization is based on the distributed memory architecture and the message passing interface (MPI) protocol. We consider both the distributed computing and distributed computer memory in the parallelization. The parallel paradigm aims at reducing the computation time as well as alleviating the computer memory requirement associated with running a multi-dimensional DP model. Next, we test the parallel DP algorithm on the classic, benchmark four-reservoir problem on a high-performance computing (HPC) system with up to 350 cores. Results indicate that the parallel DP algorithm exhibits good performance in parallel efficiency; the parallel DP algorithm is scalable and will not be restricted by the number of cores. Finally, the parallel DP algorithm is applied to a real-world, five-reservoir system in China. The results demonstrate the parallel efficiency and practical utility of the proposed methodology. |
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| Keywords: | Dynamic programming Multi-reservoir system optimization Joint operation Parallel computing |
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