The problem of optimal management of a water distribution system includes the determination of the operation regime for each hydrophore station. The optimal operation of a water distribution system means a maximum attention to assess the demands of the water, with minimum electrical energy consumption. The analysis of load profiles corresponding to a water distribution system can be the first step that water companies must make to assess the electrical energy consumption. This paper presents a new method to assess the electrical load in water distribution systems, taking into account the time-dependent evolution of loads from the hydrophore stations. The proposed method is tested on a real urban water distribution system, showing its effectiveness in obtaining the electrical energy consumption with a relatively low computational burden. 1. Introduction Water and energy are critical resources that affect virtually all aspects of daily life. A huge amount of electrical energy is necessary for the transportation, treatment, and distribution of water for drinking and industrial consumption and for different internal technological processes of water distribution systems. Water distribution systems are massive consumers of energy, which is consumed in each of the stages of the water production and supply chain: starting from pumping the water to the water treatment plant, followed by the treatment processwhile distributing the water via the network. In the Report Watergy by Alliance to Save Energy, it has been asserted that 2-3% of the world’s electrical energy consumption is used to pump and treat water for civil and industrial supply [1]. Energy costs constitute the largest expenditure for nearly all water utilities worldwide and can consume up to 65 percent of a water utility’s annual operating budget [2]. The energy requirements vary significantly from city to city, depending on local factors such as topography, location and quality of water sources, pipe dimensions and configurations, treatment standards required, and the types and numbers of consumers [1–8]. Water industry decisions on operational strategies and technology selection can also significantly influence electrical energy consumption [5]. A high electrical energy consumption may be due to various reasons: inefficient pump stations, poor design, installation or maintenance, old pipes with high head loss, bottlenecks in the supply networks, excessive supply pressure, or inefficient operation strategies of various supply facilities [2–4, 9–16]. Energy-saving measures in water supply systems can
References
[1]
Alliance to Save Energy, “What watergy involves,” 2014, http://www.watergy.net.
[2]
P. Boulos, Z. Wu, C. Orr, M. Moore, P. Hsiung, and D. Thomas, Optimal Pump Operation of Water Distribution Systems Using Genetic Algorithms, H2ONET-Users Guide, MW Software Consulting, Dallas County, Tex, USA, 2000.
[3]
European Commission, Directorate General for Environment, 2013, http://ec.europa.eu/environment/water/ index_en.htm.
[4]
A. Anton and S. Perju, “Monitoring the main parameters of a water supply pumping station over ten years,” in Proceedings of the 6th International Conference on Hydraulic Machinery and Hydrodynamics, Timisoara, Romania, 2004.
[5]
S. J. Kenway, A. Priestley, S. Cook et al., “Energy use in the provision and consumption of urban water in Australia and New Zealand,” Water for a Healthy Country National Research Flagship Report, CSIRO, 2008.
[6]
L. House, Water Supply Related Electricity Demand in California, Report for California Energy Commission, 2006.
[7]
R. Goldstein and W. Smith, “Water and sustainability: U.S. electricity consumption for water supply & treatment—the next half century,” EPRI Report, Electric Power Research Institute, Palo Alto, Calif, USA, 2000.
[8]
I. Pulido-Calvo and J. C. Gutiérrez-Estrada, “Selection and operation of pumping stations of water distribution systems,” Environmental Research Journal, vol. 5, no. 3, pp. 1–20, 2011.
[9]
K. Feldman, “Aspects of energy efficiency in water distribution systems,” in Proceedings of the International Conference on Water Loss, pp. 26–30, IWA, Cape Town, South Africa, April 2009.
[10]
G. C. Ionescu and D. S. Ionescu, “The optimization of energy consumption in water supply systems,” Acta Electrotehnica, vol. 46, pp. 191–194, 2005.
[11]
M. Istrate and G. Grigora？, “Energy consumption estimation in water distribution systems using fuzzy techniques,” Environmental Engineering and Management Journal, vol. 9, no. 2, pp. 249–256, 2010.
[12]
I. Sarbu, Energetic Optimization of the Water Distribution Systems, Publishing House of the Romanian Academy, Bucharest, Romania, 1997.
[13]
L. Szychta, “Energy consumption of water pumping for selected control systems,” Electrical Power Quality and Utilization Journal, vol. 12, pp. 21–27, 2006.
[14]
G. Grigoras and M. Istrate, “An efficient clustering method in evaluation of the electric energy consumption from water hydrophore stations,” International Review on Modelling and Simulations, vol. 4, no. 2, pp. 813–818, 2011.
[15]
U. Zessler and U. Shamir, “Optimal operation of water distribution systems,” Journal of Water Resources Planning and Management, vol. 115, no. 6, pp. 735–752, 1989.
[16]
P. Cutore, A. Campisano, Z. Kapelan, C. Modica, and D. Savic, “Probabilistic prediction of urban water consumption using the SCEM-UA algorithm,” Urban Water Journal, vol. 5, no. 2, pp. 125–132, 2008.
[17]
G. Chicco, “Overview and performance assessment of the clustering methods for electrical load pattern grouping,” Energy, vol. 42, no. 1, pp. 68–80, 2012.
[18]
A. K. Jain, M. N. Murty, and P. J. Flynn, “Data clustering: a review,” http://dataclustering.cse.msu.edu/papers/MSU-CSE-00-16.pdf.
[19]
R. Siddheswar and R. Turi, “Determination of number of clusters in K-Means clustering and application in color image segmentation,” in Proceedings of the 4th International Conference on Advanced in Pattern Recognition and Digital Techniques, Calcutta, India, 1999.
[20]
I. Yatskiv and L. Gusarova, “The methods of cluster analysis results validation,” in Proceedings of International Conference RelStat'04, Riga, Latvia, 2004.
[21]
J. Yu and Q. Cheng, “The upper bound of the optimal number of clusters in fuzzy clustering,” Science in China Series F, vol. 44, pp. 119–124, 2001.
[22]
“A tutorial on clustering algorithms,” http://home.dei.polimi.it/matteucc/Clustering/tutorial_html.
[23]
P. J. Rousseeuw, “Silhouettes: a graphical aid to the interpretation and validation of cluster analysis,” Journal of Computational and Applied Mathematics, vol. 20, pp. 53–65, 1987.
[24]
M. Halkidi, Y. Batistakis, and M. Vazirgiannis, “On clustering validation techniques,” Journal of Intelligent Information Systems, vol. 17, no. 2-3, pp. 107–145, 2001.
[25]
M. R. Rezaee, B. P. F. Lelieveldt, and J. H. C. Reiber, “A new cluster validity index for the fuzzy C-mean,” Pattern Recognition Letters, vol. 19, no. 3-4, pp. 237–246, 1998.
[26]
P. Berkhin, “Survey of clustering data mining techniques,” Tech. Rep., Accrue Software, San Jose, Calif, USA, 2002.
[27]
M. Holgersson, “The limited value of cophenetic correlation as a clustering criterion,” Pattern Recognition, vol. 10, no. 4, pp. 287–295, 1978.
[28]
A. D. Gordon, Classification, Chapman & Hall, New York, NY, USA, 2nd edition, 1999.
[29]
C. G. Carter-Brown, “Load profile modeling for integrated energy planning,” in Proceedings of the Domestic Use of Electrical Energy Conference, pp. 13–18, Cape Town, South Africa, April 1999.
[30]
British Electricity Boards, “Report on the design of low voltage underground networks for new housing,” ACE Report No. 105, 1986.
[31]
British Electricity Boards, “Report on the computer program DEBUTE for the design of LV radial networks. Part 1—general considerations , part 2—program user guide,” Tech. Rep. 115, 1988.
[32]
A. Sepp？l？, Load research and load estimation in electricity distribution [Ph.D. thesis], Helsinki University of Technology, Esbo, Finland, 1996.
[33]
J. Nazarko and Z. A. Styczynski, “Application of statistical and neural approaches to the daily load profiles modelling in power distribution systems,” in Proceedings of the IEEE/PES Transmission and Distribution Conference, pp. 320–325, New Orleans, La, USA, May 1999.
[34]
Z. Zakaria, M. N. Othman, and M. H. Sohod, “Consumer load profiling using fuzzy clustering and statistical approach,” in Proceedings of the 4th Student Conference on Research and Development (SCOReD '06), 274, p. 270, IEEE, Selangor, Malaysia, June 2006.
[35]
I. H. Yu, J. K. Lee, J. M. Ko, and S. I. Kim, “A method for classification of electricity demands using load profile data,” in Proceedings of the 4th Annual ACIS International Conference on Computer and Information Science (ICIS '05), pp. 164–168, Jeju Island, South Korea, July 2006.
[36]
G. Chicco, R. Napoli, and F. Piglione, “Comparisons among clustering techniques for electricity customer classification,” IEEE Transactions on Power Systems, vol. 21, no. 2, pp. 933–940, 2006.
[37]
G. Chicco, R. Napoli, and F. Piglione, “Application of clustering algorithms and self organising maps to classify electricity customers,” in Proceedings of the IEEE Bologna PowerTech Conference, vol. 1, Bologna, Italy,, June 2003.
[38]
D. Gerbek, S. Gasperic, and F. Gubina, “Comparison of different classification methods for the consumers' load profile determination,” in Proceedings of the 17th International Conference on Electricity Distribution, Barcelona, Spain, 2003.
[39]
G. Grigoras, M. Istrate, and F. Scarlatache, “Electrical energy consumption estimation in water distribution systems using a clustering based method,” in Proceedings of the International Conference on Electronics, Computers and Artificial Intelligence (ECAI '13), pp. 1–6, Pitesti, Romania, June 2013.
[40]
N. Mahmoudi-Kohan, M. P. Moghaddam, and S. M. Bidaki, “Evaluating performance of WFA K-means and modified follow the leader methods for clustering load curves,” in Proceedings of the IEEE/PES Power Systems Conference and Exposition (PSCE '09), Seatle, Wash, USA, March 2009.
[41]
G. Grigoras, M. Istrate, and F. Scarlatache, “Electrical energy consumption estimation in water distribution systems using a clustering based method,” in Proceedings of the International Conference on Electronics, Computers and Artificial Intelligence (ECAI '13), pp. 1–6, Pitesti, Romania, June 2013.
[42]
A. Mutanen, M. Ruska, S. Repo, and P. J？rventausta, “Customer classification and load profiling method for distribution systems,” IEEE Transactions on Power Delivery, vol. 26, no. 3, pp. 1755–1763, 2011.
[43]
G. Grigoras, C. Barbulescu, G. Cartina, and D. Comanescu, “A comparative study regarding efficiency of the hierarchical clustering techniques in typical load profiles determination,” Acta Electrotehnica, vol. 52, no. 5, pp. 184–188, 2011.
[44]
B. D. Pitt and D. S. Kirschen, “Application of data mining techniques to load profiling,” in Proceedings of the 21st IEEE International Conference Power Industry Computer Applications, Santa Clara, Calif, USA, 1999.
[45]
S. Ramos and Z. Vale, “Data mining techniques application in power distribution utilities,” in Proceedings of the IEEE/PES Transmission and Distribution Conference and Exposition, pp. 1–8, Chicago, Ill, USA, April 2008.
[46]
V. Figueiredo, F. Rodrigues, Z. Vale, and J. B. Gouveia, “An electric energy consumer characterization framework based on data mining techniques,” IEEE Transactions on Power Systems, vol. 20, no. 2, pp. 596–602, 2005.
[47]
A. H. Nizar, Z. Y. Dong, and J. H. Zhao, “Load profiling and data mining techniques in electricity deregulated market,” in Proceedings of the IEEE Power Engineering Society General Meeting (PES '06), Montreal, Canada, June 2006.
[48]
G. J. Tsekouras, N. D. Hatziargyriou, and E. N. Dialynas, “Two-stage pattern recognition of load curves for classification of electricity customers,” IEEE Transactions on Power Systems, vol. 22, no. 3, pp. 1120–1128, 2007.
[49]
S. V. Verdú, M. O. García, F. J. G. Franco et al., “Characterization and identification of electrical customers through the use of self-organizing maps and daily load parameters,” in Proceedings of the IEEE PES Power System Conference and Exposition, vol. 2, pp. 899–906, Atlanta, Ga, USA, 2004.
[50]
K. L. Lo and Z. Zakaria, “Electricity consumer classification using artificial intelligence,” in Proceedings of the 39th International Universities Power Engineering Conference, Bristol, UK, 2004.
[51]
D. Gerbec, S. Ga？peri？, I. ？mon, and F. Gubina, “Determining the load profiles of consumers based on fuzzy logic and probability neural networks,” IEE Proceedings: Generation, Transmission and Distribution, vol. 151, no. 3, pp. 395–400, 2004.
[52]
D. Gerbec, S. Ga？peri？, I. ？mon, and F. Gubina, “Allocation of the load profiles to consumers using probabilistic neural networks,” IEEE Transactions on Power Systems, vol. 20, no. 2, pp. 548–555, 2005.
[53]
M. Sarlak, T. Ebrahimi, and S. S. Karimi Madahi, “Enhancement the accuracy of daily and hourly short time load forecasting using neural network,” Journal of Basic and Applied Scientific Research, vol. 2, no. 1, pp. 247–255, 2012.
[54]
H. K. Alfares and M. Nazeeruddin, “Electric load forecasting: Literature survey and classification of methods,” International Journal of Systems Science, vol. 33, no. 1, pp. 23–34, 2002.
[55]
L. A. Garcia-Escudero and A. Gordaliza, “A proposal for robust curve clustering,” Journal of Classification, vol. 22, no. 2, pp. 185–201, 2005.
[56]
Z. Zisman and G. Cartina, “Application of fuzzy logic for distribution system estimation,” in Proceedings of the 22nd Seminar on Fundamentals of Electrotechnics and Circuit Theory, Ustroń, Poland, May 1999.
[57]
L. Jentgen, H. Kidder, R. Hill, and S. Conrad, Water Consumption Forecasting to Improve Energy Efficiency of Pumping Operations, Awwa Research Foundation, 2007.
