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CLC number: TU991.31

On-line Access: 2010-08-02

Received: 2009-12-23

Revision Accepted: 2010-07-02

Crosschecked: 2010-07-15

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Journal of Zhejiang University SCIENCE A 2010 Vol.11 No.8 P.571-579

http://doi.org/10.1631/jzus.A0900784


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


Author(s):  Hai-en Fang, Jie Zhang, Jin-liang Gao

Affiliation(s):  School of Municipal and Environment Engineering, Harbin Institute of Technology, Harbin 150090, China

Corresponding email(s):   haien699@yahoo.com.cn

Key Words:  Multi-storage tank system, Storage policy, Genetic algorithm, Repairing scheme, Pump scheduling


Hai-en Fang, Jie Zhang, Jin-liang Gao. Optimal operation of multi-storage tank multi-source system based on storage policy[J]. Journal of Zhejiang University Science A, 2010, 11(8): 571-579.

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author="Hai-en Fang, Jie Zhang, Jin-liang Gao",
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%T Optimal operation of multi-storage tank multi-source system based on storage policy
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%A Jin-liang Gao
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%I Zhejiang University Press & Springer
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PB - Zhejiang University Press & Springer
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DOI - 10.1631/jzus.A0900784


Abstract: 
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.

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Reference

[1]Alvisi, S., Franchini, M., Marinelli, A., 2007. A short-term, pattern-based model for water demand forecasting. Journal of Hydroinformatics, 9(1):39-50.

[2]Beckwith, S.P., Wong, K.P., 1996. A genetic algorithm approach for electric pump scheduling in watersupply systems. Evolutionary Computation, 1:21-26.

[3]Brion, 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.

[4]Chase, D., Ormsbee, L., 1991. An Alternate Formulation of Time as a Decision Variable to Faciliate Real-time Operation of Water Supply Systems. Proc. 18th Annual Conf. ASCE Water Resources Planning and Management Division, p.923-927.

[5]Coulbeck, 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.

[6]Coulbeck, B., Brdys, M., Orr, 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.

[7]Deb, K., 2000. An efficient constraint handling method for genetic algorithms. Computer Methods in Applied Mechanics and Engineering, 186(2-4):311-338.

[8]Deb, K., Jainz, S., 2002. Multi-speed Gearbox Design Using Multi-objective Evolutionary Algorithms. Kangal, Kanpur.

[9]Ertin, 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.

[10]Gato, S., Jayasuriya, N., Roberts, P., 2007. Forecasting residential water demand: case study. Journal of Water Resources Planning and Management, 133(4):309-319.

[11]Ghiassi, M., Zimbra, D.K., Saidane, H., 2008. Urban water demand forecasting with a dynamic artificial neural network model. Journal of Water Resources Planning and Management, 134(2):138-146.

[12]Goldber, D.E., 1989. Genetic Algorithms in Search, Optimization and Machine Learning. Addison-Wesley Publishing Inc.

[13]Lansey, K., Zhong, Q., 1990. A Methodology for Optimal Control of Pump Station. Water Resources Infrastructure. Proc. ASCE Water Water Resources Planning and Management Specialty Conf., p.58-61.

[14]López-Ibáñez, M., Prasad, T.D., Paechter, B., 2008. Ant colony optimization for optimal control of pumps in water distribution networks. Journal of Water Resources Planning and Management, 134(4):337-346.

[15]Mackle, G., Savic, G.A., Walters, G.A., 1995. Application of Genetic Algorithms to Pump Scheduling for Water Supply. Genetic Algorithms in Engineering Systems, Innovations and Applications, Galeria, Sheffield, UK, p.400-405.

[16]Magini, R., Pallavicini, I., Guercio, R., 2008. Spatial and temporal scaling properties of water demand. Journal of Water Resources Planning and Management, 134(3):276-284.

[17]Ormsbee, L., Lansey, K., 1994. Optimal control of water supply pumping systems. Journal of Water Resources Planning and Management, 120(2):237-252.

[18]Ormsbee, L., Walski, T., Chase, D., Sharp, W., 1989. Methodology for improving pump operation efficience. Journal of Water Resources Planning and Management, 115(2):148-164.

[19]Pasha, M.F.K., Lansey, K., 2009. Optimal Pump Scheduling by Linear Programming. Proceedings of World Environmental and Water Resources Congress American Society of Civil Engineers, Kansas City, MO, USA, p.395-404.

[20]Pezeshk, S., Helweg, O.J., 1996. Adaptive search optimization in reducing pump operating costs. Journal of Water Resources Planning and Management, 122(1):57-63.

[21]Rossman, L.A., 2002. Epanet 2 User’s Manual. Water Supply and Water Resources Division, National Risk Management Research Laboratory, Cincinnati.

[22]Ulanicki, B., Orr, C.H., 1991. Unified approach for the optimization of nonlinear hydraulic systems. Journal of Optimization Theory and Applications, 68(1):161-171.

[23]Ulanicki, B., Kahler, J., See, H., 2007. Dynamic optimization approach for solving an optimal scheduling problem in water distribution system. Journal of Water Resources Planning and Management, 133(1):23-32.

[24]Vamvakeridou-Lyroudia, L.S., Savic, D.A., Walters, G.A., 2007. Tank simulation for the optimization of water distribution networks. Journal of Hydraulic Engineering, 133(6):625-636.

[25]Yu, T.C., Zhang, T.Q., Li, X., 2005. Optimal operation of water supply systems with tanks based on genetic algorithm. Journal of Zhejiang University SCIENCE, 6A(8):886-893.

[26]Zessler, U., Shamir, U., 1989. Optimal operation of water distribution systems. Journal of Water Resources Planning and Management, 115(6):735-752.

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