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Optimal operation of multi-storage tank multi-source system based on storage policy

Optimal operation of multi-storage tank multi-source system based on storage policy
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摘要 A two-stage method is developed to solve a new class of multi-storage tank multi-source (MTMS) systems. In the first stage, the optimal storage policy of each tank is determined according to the electricity tariff, and the ground-level storage tank is modeled as a node. In the second stage, the genetic algorithm, combined with a repairing scheme, is applied to solve the pump scheduling problem. The objective of the pump scheduling problem is to ensure that the required volume is adequately provided by the pumps while minimizing the operation cost (energy cost and treatment cost). The decision variables are the settings of the pumps and speed ratio of variable-speed pumps at time steps of the total operational time horizon. A mixed coding methodology is developed according to the characteristics of the decision variables. Daily operation cost savings of approximately 11% are obtained by application of the proposed method to a pressure zone of S. Y. water distribution system (WDS), China. A two-stage method is developed to solve a new class of multi-storage tank multi-source （MTMS） systems. In the first stage, the optimal storage policy of each tank is determined according to the electricity tariff, and the ground-level storage tank is modeled as a node. In the second stage, the genetic algorithm, combined with a repairing scheme, is applied to solve the pump scheduling problem. The objective of the pump scheduling problem is to ensure that the required volume is adequately provided by the pumps while minimizing the operation cost （energy cost and treatment cost）. The decision variables are the settings of the pumps and speed ratio of variable-speed pumps at time steps of the total operational time horizon. A mixed coding methodology is developed according to the characteristics of the decision variables. Daily operation cost savings of approximately 11% are obtained by application of the proposed method to a pressure zone of S. Y. water distribution system （WDS）, China.
作者 Hai-en FANG Jie ZHANG Jin-liang GAO
机构地区 School of Municipal and Environment Engineering
出处 《Journal of Zhejiang University-Science A(Applied Physics & Engineering)》 SCIE EI CAS CSCD 2010年第8期571-579,共9页 浙江大学学报（英文版）A辑（应用物理与工程）
基金 Project supported by the National Science & Technology Pillar Program during the Eleventh Five-year Plan Period (No. 2006BAJ08B03), China
关键词 Multi-storage tank system Storage policy Genetic algorithm Repairing scheme Pump scheduling Multi-storage tank system, Storage policy, Genetic algorithm, Repairing scheme, Pump scheduling
分类号 TU991.31 [建筑科学—市政工程]
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参考文献26

  • 1Alvisi, S., Franchini, M., Marinelli, A., 2007. A short-term, pattern-based model for water demand forecasting. Journal of Hydroinformatics, 9(1):39-50. [doi:10.2166/ hydro.2006.016].
  • 2Beckwith, S.P., Wong, K.P., 1996. A genetic algorithm approach for electric pump scheduling in watersupply systems. Evolutionary Computation, 1:21-26. [doi:10. 1109/ICEC. 1995.489113].
  • 3Brion, L.M., Mays, L.W., 1991. Methodology for optimal operation of pumping stations in water distribution systems. Journal of Hydraulic Engineering, 117(11): 1551-1569. [doh10.1061/(ASCE)0733-9429(1991)117: 11(1551)].
  • 4Chase, D., Ormsbee, L., 1991. An Alternate Formulation of Time as a Decision Variable to Faciliate Real-time Operation of Water Supply Systems. Proc. 18tb Annual Conf. ASCE Water Resources Planning and Management Division, p.923-927.
  • 5Coulbeck, B., Brdys, M., Orr, C., Rance, J., 1988a. A hierarchial approach to optimized control of water distribution systemsz: Part I decomposition. Journal of Optimal Control Applications and Methods, 9(1):51-61. [doi:10.1002/oca.4660090105].
  • 6Coulbeck, B., Brdys, M., On', C., Rance, J., 1988b. A hierarchial approach to optimized control of water distribution systems: Part II lower-level algorithm. Journal of Optimal Control Applications and Methods, 9(2):109-126. [doi: 10.1002/oca,4660090202].
  • 7Deb, K., 2000. An efficient constraint handling method for genetic algorithms. Computer Methods in Applied Mechanics and Engineering, 186(2-4):311-338.
  • 8Deb, K., Jainz, S., 2002. Multi-speed Gearbox Design Using Multi-objective Evolutionary Algorithms. Kangal, Kanpur.
  • 9Ertin, E., Dean, A.N., Moore, M.L., Priddy, K.L., 2001. Dynamic Optimization for Optimal Control of Water Distribution Systems. Conference on Applications and Science of Computational Intelligence IV, Orlando, USA, p.142-149.
  • 10Gato, S., Jayasuriya, N., Roberts, P., 2007. Forecasting residential water demand: case study. Journal of Water Resources Planning and Management, 133(4):309-319. [doi: 10.10611(ASGE)0733-9496(2007)133:4(309)].
Journal of Zhejiang University-Science A(Applied Physics & Engineering)
2010年 第8期

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