Simulated Annealing in Optimization of Energy Production in a Water Supply Network
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- 作者：Irene Samora ; Mário J. Franca ; Anton J. Schleiss…
- 关键词：Micro ; hydropower ; Water supply systems ; Pipeline network ; Optimization ; Simulated annealing
- 刊名：Water Resources Management
- 出版年：2016
- 出版时间：March 2016
- 年：2016
- 卷：30
- 期：4
- 页码：1533-1547
- 全文大小：713 KB
- 参考文献：Araújo L, Ramos H, Coelho S (2006) Pressure control for leakage minimization in water distribution systems management. Water Resour Manag 20(1):133–149. doi:10.1007/s11269-006-4635-3 CrossRef
Azizi N, Zolfaghari S (2004) Adaptive temperature control for simulated annealing: a comparative study. Comput Oper Res 31:2439–2451. doi:10.1016/S0305-0548(03)00197-7 CrossRef
Bertsimas D, Tsitsikis J (1993) Simulated annealing. Stat Sci 8(1):10–15CrossRef
Carravetta A, Giuseppe G, Fecarotta O, Ramos H (2012) Energy production in water distribution networks: a PAT design strategy. Water Resour Manag 26(13):3947–3959. doi:10.1007/s11269-012-0114-1 CrossRef
Carravetta A, Del Giudice G, Fecarotta O, Ramos H (2013) PAT design strategy for energy recovery in water distribution networks by electrical regulation. Energies 6(1):411–424. doi:10.3390/en6010411 CrossRef
Cunha MC, Sousa J (2001) Hydraulic infrastructures design using simulated annealing. J Infrastruct Syst 7(1):32–39, ISSN 1076-0342/01/0001-0032CrossRef
European Commission (2012) Periodic Report Summary 2 – HYLOW (Hydropower converters with very low head differences), United Kingdom. http://cordis.europa.eu/result/rcn/53053_en.html
Farmani R, Savic DA, Walters GA (2005) Evolutionary multi-objective optimization in water distribution network design. Eng Optim 37(2):167–183. doi:10.1080/03052150512331303436 CrossRef
Fayzul M, Pasha K, Lansey K (2014) Strategies to develop warm solutions for real-time pump scheduling for water distribution systems. Water Resour Manag 28:3975–3987. doi:10.1007/s11269-014-0721-0 CrossRef
Fecarotta O, Costanza A, Carravetta A, Martino R, Ramos HM (2015) Hydropower potential in water distribution networks: pressure control by PATs. Water Resour Manag 29:699–714. doi:10.1007/s11269-014-0836-3 CrossRef
Henderson H, Jacobson SH, Johnson AW (2003) The theory and practice of simulated annealing. Int Ser Opera Res Manag Sci, Handbook Metaheurist 57:287–319. doi:10.1007/0-306-48056-5_10
Keeling M, Sullivan M (2012) Fixing the future. why we need smarter water management for the world’s most essential resource. IBM Global Business Services, Executive Report
Kirkpatrick S, Gelatt CD, Vecchi MP (1983) Optimization by simulated annealing. Sci, New Ser 220(4598):671–680. doi:10.1126/science.220.4598.671
Kougias I, PatsialisT ZA, Theodossiou N (2014) Exploring the potential of energy recovery using micro hydropower systems in water supply systems. Water Utility J 7:25–33
Liu J (1999) The impact of neighbourhood size on the process of simulated annealing: computational experiments on the flowshop scheduling problem. Comput Ind Eng 37:285–288. doi:10.1016/S0360-8352(99)00075-3 CrossRef
Martins TC, Sato AK, Tsuzuki MSG et al. (2012) Adaptive Neighborhood Heuristics for Simulated Annealing over Continuous Variables. Simulated Annealing - Advances, Applications and Hybridizations, Marcos Sales Guerra Tsuzuki (Ed.), ISBN: 978-953-51-0710-1, InTech, doi: 10.5772/50302
McNabola A, Coughlan P, Corcoran L, Power C, Williams AP, Harris I, Gallagher J, Styles D (2014) Energy recovery in the water industry using micro-hydropower: an opportunity to improve sustainability. Water Policy 16:168–183. doi:10.2166/wp.2013.164 CrossRef
Olsson G (2012) Water energy: conflicts and connections. Int Water Assoc, Water 21(October):12–16
Pardalos PM, Romeijn HE, Tuy H (2000) Recent developments and trends in global optimization. Num AnalVol IV: Optim Nonlinear Equa 124(1-2):209–228. doi:10.1016/S0377-0427(00)00425-8
Ramos H, Borga A, Simão M (2009) New design for low-power energy production in water pipe systems. Water Sci Eng 2(4):69–84. doi:10.3882/j.issn.1674-2370.2009.04.007
Ramos H, Mello M, De P (2010) Clean power in water supply systems as a sustainable solution: from planning to practical implementation. Water Sci Technol Water Supply 79(10):24–26. doi:10.2166/ws.2010.720
Ramos H, Kenov K, Vieira F (2011) Environmentally friendly hybrid solutions to improve the energy and hydraulic efficiency in water supply systems. Energ Sustain Dev 15(4):436–442. doi:10.1016/j.esd.2011.07.009 CrossRef
Ramos HM, Simão M, Borga A (2012) CFD and experimental study in the optimization of an energy converter for low heads. Energ Sci Technol 4(2):69–84. doi:10.3968/pdf
Ramos HM, Simão M, Borga A (2013a) Experiments and CFD analyses for a new reaction microhydro propeller with five blades. J Energ Eng 139:109–117. doi:10.1061/(ASCE)EY.1943-7897.0000096 CrossRef
Ramos H, Theyssier C, Samora I, Schleiss AJ (2013b) Energy recovery in SUDS towards smart water grids: a case study. Energ Policy 62:463–472. doi:10.1016/j.enpol.2013.08.014 CrossRef
Rossman LA (2000) EPANET 2 users manual. water supply and water resources division, national risk management research laboratory, Cincinnati, United States Environmental Protection Agency
Rutenbar RA (1989) Simulated annealing algorithms: an overview. Circ Dev Mag 5(1):19–26. doi:10.1109/101.17235 CrossRef
Sitzenfrei R, von Leon J (2014) Long-time simulation of water distribution systems for the design of small hydro power systems. Renew Energy 72:182–187. doi:10.1016/j.renene.2014.07.013 CrossRef
Van Laarhoven PJM, Aarts EHL, Lenstra JK (1992) Job shop scheduling by simulated annealing. Oper Res 40(1):113–125. doi:10.1287/opre.40.1.113 CrossRef
Varanelli J, Cohoon J (1999) A fast method for generalized starting temperature determination in homogeneous two-stage simulated annealing systems. Comput Oper Res 26:481–503. doi:10.1016/S0305-0548(98)00062-8 CrossRef
Vieira F, Ramos H (2008) Hybrid solution and pump-storage optimization in water supply system efficiency: a case study. Energ Policy 36(11):4142–4148. doi:10.1016/j.enpol.2008.07.040 CrossRef
Walid B (2004) Computing the initial temperature of simulated annealing. Comput Optim Appl 29:369–385. doi:10.1023/B:COAP.0000044187.23143.bd CrossRef
Xu Q, Chen Q, Ma J, Blanckaert K, Wan Z (2014) Water saving and energy reduction through pressure management in urban water distribution networks. Water Resour Manag 28:3715–3726. doi:10.1007/s11269-014-0704-1 CrossRef
Yao X (1993) Comparison of different neighborhood sizes in simulated annealing. Proc Fourth Austral Conf Neural Networks (A CNN’9 3 ), 216-219
Youssef H, Sait SM, Adiche H (2001) Evolutionary algorithms, simulated annealing and tabu search: a comparative study. Eng Appl Artif Intell 14:167–181. doi:10.1016/S0952-1976(00)00065-8 CrossRef - 作者单位：Irene Samora (1) (2)
Mário J. Franca (1)
Anton J. Schleiss (1)
Helena M. Ramos (2)
1. Laboratory of Hydraulic Constructions, École Polytecnique Fédérale de Lausanne, Lausanne, Switzerland
2. Civil Engineering Research and Innovation for Sustainability, Instituto Superior Técnico - Universidade de Lisboa, Lisboa, Portugal - 刊物类别：Earth and Environmental Science
- 刊物主题：Earth sciences
Hydrogeology
Geotechnical Engineering
Meteorology and Climatology
Civil Engineering
Environment - 出版者：Springer Netherlands
- ISSN：1573-1650
文摘
In water supply systems, the potential exists for micro-hydropower that uses the pressure excess in the networks to produce electricity. However, because urban drinking water networks are complex systems in which flows and pressure vary constantly, identification of the ideal locations for turbines is not straightforward, and assessment implies the need for simulation. In this paper, an optimization algorithm is proposed to provide a selection of optimal locations for the installation of a given number of turbines in a distribution network. A simulated annealing process was developed to optimize the location of the turbines by taking into account the hourly variation of flows throughout an average year and the consequent impact of this variation on the turbine efficiency. The optimization is achieved by considering the characteristic and efficiency curves of a turbine model for different impeller diameters as well as simulations of the annual energy production in a coupled hydraulic model. The developed algorithm was applied to the water supply system of the city Lausanne (Switzerland). This work focuses on the definition of the neighborhood of the simulated annealing process and the analysis of convergence towards the optimal solution for different restrictions and numbers of installed turbines. Keywords Micro-hydropower Water supply systems Pipeline network Optimization Simulated annealing