Energy Savings through Water Conservation in Municipal Water Distribution Systems

Transcription

1 Energy Savings through Water Conservation in Municipal Water Distribution Systems Santosh Ghimire PhD Candidate Civil & Environmental Engineering Department, Michigan Tech University, Houghton, MI Advisor: Dr. Brian Barkdoll Tuesday, October 30,

2 Agenda 1. Introduction 2. Objectives/New Contribution 3. Procedures 4. System Description 5. Results and Discussion 6. Conclusions 7. References 2

3 1. Introduction Municipal Drinking Water Distribution Systems (MDWDS) are major water suppliers to the public. 87% of US public water (Vickers 2001) is supplied by MDWDS. The growing population will pressurize the MDWDSs to reduce water consumption and reduce the energy utilization. Approximately 80% of municipal water processing and distribution costs are for electricity (EPRI 2006). Energy minimization has become a growing g interest for research. The energy minimization by water conservation might help to reduce green house gas emissions. The minimization of pumping costs is one of the techniques to reduce the energy cost. Some of the research related to the energy cost for pumping are: Optimization of pump scheduling for their performance and reliability (Farmani et al. 2004, Pezeshk and Helweg 1996), high-peak usage times (Dandy and Gibbs 2003), pump efficiency (Korn and Stratulat 2001), initial design (Alperovits and Shamir 1977) 3

4 1. Introduction Contd. Water conservation is another factor that may help reduce the energy consumption. There is a growing interest for water conservation. For example, Albuquerque, New Mexico successfully attempted to reduce per capita water consumption by 20% in 9 years ( ) (Vickers 2001). However, the quantification of demand is stochastic in nature (Alcocer et al. 2004, Sasikumar et al. 2003, Barkdoll and Didigam 2003) Seven realistic municipal water distribution systems were analyzed here for the effect of water conservation on energy use. 4

5 2. Objectives/ New Contribution The sensitivity of pumping energy to the system-wide demand reduction for seven realistic systems was analyzed. The current research produced a relationship between water demand and energy utilization. The stochastic nature of the demand was addressed by use of a daily demand pattern. A freely-available network modeling software called EPANET 2.0 was used to model the systems. In contrast to traditional method of analyzing water systems independently due to their complexity, an inter-system approach was used here in which a common rule was sought for many systems. Demand reduction is chosen for the analysis because it is somewhat controllable through education of the users. 5

6 3. Procedures EPANET 2.0 network solver was used to simulate the seven real systems. The seven systems were simulated for the demand reduction for two cases: 1. Wide range of normal demand 2. 20% reduction of normal demand Both energy and demand were normalized using the following equations: E*=E/E o (1) Q D *=Q D /Q Do (2) where E* = the normalized system-wide energy usage, E = the modeled system-wide energy usage, E o = the current (unaltered) system-wide energy usage, Q D * = the normalized global demand, Q D = the modeled global demand, and Q Do = the current (unaltered) global demand. 6

7 1. Case of a Wide Range of Demand Values The global demand of the systems were changed to determine the corresponding energy consumption. For some of the systems, the pumps were modified for the demand greater than 1.0x the normal demand in order to ensure the adequate pressure greater than 138 kpa (20 psi). 7

8 3. Procedures Contd. 2. Case of a 20% Reduction of Demand Values The global demands were reduced by 20% and corresponding energy utilized was determined. Both the energy and water demand were normalized as shown in Case 1. 8

9 4. System Description There were seven systems of varying characteristics analyzed System Junctions Pipes Pumps Reservoirs Tanks Valves z max F (#) (#) (#) (#) (#) (#) m (ft) m/m 4.87 (ft/ft 4.87 ) S (130) (108,767.7) S (110.5) 2,695.3 (649,894.3) S (712.1) (60,953.3) S (305) 7,602.1 (1,833,024.0) S (329) 16,354.9 (3,943,504.6) S (90) 79,291.4 (19,118,852.9) S7 12,525 14, (241.7) 1,928,561.4 (465,017,213.8) The Right hand side of the equation below is the system wide friction factor (F) obtained from Hazen-Williams Equation (Haestad Methods 2004). Where, h L = head loss due to friction (m,ft), Q= Flow (cfs,cms), C f = Conversion factor = 4.73: English, 7.3 :SI, C= Pipe Roughness, D= Pipe diameter ( m,ft) h L C L Q C D f = = F 9

10 The smallest system of 7 junctions (EPA 2000) 10

11 The largest system of 12,525 junctions (Ostfeld et al. 2006). 11

12 5. Results and Discussion Non-normalized energy vs. normalized demand The pattern of energy usage of seven systems for the corresponding normalized demand variation is shown above. 12

13 Normalized energy usage All of the plots fall onto the same line after normalizing both energy and demand. 13

14 1. Wide range of demand values Linear relationship of average normalized energy, E*, for variation of normalized demand, Q D *, (error bars denote ± one standard deviation) along with the standard deviation and Coefficient of Variation. 14

15 2. 20% reduction of demand values Normalized energy for variation of normalized demand for up to a 20% water demand reduction, Q D *=0.8 to

16 Linear relationship of average normalized energy for variation of normalized demand for up to a 20% water demand reduction, Q D *=0.8 to 1.0 (error bars denote ± one standard deviation) 16

17 6. Conclusions The inter-system approach applied in this study might be applicable to any other pump-dominated systems. The relationship between average energy utilized and water demand is linear for the analyzed seven systems. The energy saving equation for 20% demand reduction for the seven systems was determined as: E* =07Q 0.7 D * , withtheenergythe e ergy Elasticity coefficient = The 20% water conservation for the seven systems may save approximately 14% average energy utilized. The above equation and coefficient might be similar to other pumpdominated water distribution systems. 17

18 7. References Alcocer, V.H.Y.; Tzatchkov, V.G.; Buchberger, S.G.; Arreguin, F.I.C.; Feliciano, D.G. (2004) Stochastic residential water demand characterization ASCE, Proceedings of the 2004 World Water and Environmental Resources Congress: Critical Transitions in Water and Environmental Resources Management, Jun 27-Jul , Salt Lake City, UT, p Alperovits, E.; Shamir, U. (1977) Design Of Optimal Water Distribution Systems. Water Resources Research, v 13, n 6, Dec, 1977, p Barkdoll, B.D.; Didigam, H. (2003) Effect of User Demand on Water Quality and Hydraulics of Distribution Systems ASCE World Water and Environmental Resources Congress, 2003, Jun , Philadelphia, PA, United States, p Dandy, G.; Gibbs, M. (2003) Optimizing i i System Operations and Water Quality Proceedings of ASCE World Water and Environmental Resources Congress, 2003, Jun , Philadelphia, PA, United States, p EPA (2000), EPANET 2, Users Manual, by Lewis A. Rossman, Water Supply and Water Resources Division National Risk Management Research Laboratory Cincinnati, OH EPRI (2006) Electric Power Research Institute, Water and Sustainability (volume 4): U.S. Electricity Consumption for Water Supply and Treatment The Next Half Century. Technical Report 18

19 7. References Farmani, R.; Savic, D.A.; Walters, G.A. (2004) The simultaneous multi-objective optimization of anytown pipe rehabilitation, tank sizing, tank siting and pump operation schedules Proceedings of the 2004 World Water and Environmetal Resources Congress: Critical Transitions in Water and Environmetal Resources Management, 2004, Jun 27-Jul , Salt Lake City, UT, p Haestad Methods (2004) Advanced Water Distribution Modeling and Management, Haestad Press, Waterbury, CT Korn, D.L.; Stratulat, A. (2001) Energy efficiency improvements to a water distribution system, Galati, Romania Proceedings ACEEE Summer Study on Energy Efficiency in Industry, v 2, 2001, 5th Biennial ACEEE Conference on Energy Efficiency in Industry, Jul , Tarrytown, NY, p Ostfeld A., Uber J., and Salomons E. (2006). "Battle of the Water Sensor Networks (BWSN): A Design Challe llenge for Engineers a nd Algorithms. " Proceedings of 8th Annu ual Water Distribution System Analysis Symposium, Cincinnati, Ohio, USA, published on CD. Pezeshk, S.; Helweg, O.J. (1996) Adaptive search optimization in reducing pump operating costs ASCE Journal of Water Resources Planning and Management, v 122, n 1, Jan-Feb, 1996, p Sasikumar, M.S.; Sheikh, M. Ayaz; Gupta, A. (2003) A new methodology for nodal water demand estimation in water distribution system using Geographical Information System (GIS) Institution of Engineers (India), Journal of Indian Water Works Association, v 35, n 1, January/March, 2003, p Vickers, Amy (2001), Handbook of Water Use and Conservation, Water Plow Press, Amherst, Massachusetts, USA, First Edition, ISBN

20 Acknowledgement The authors gratefully acknowledge support from the Sustainable Futures IGERT project sponsored by the National Science Foundation (under Grant No. DGE ). Dr. Brian Barkdoll, Assoc. Professor CEE Department, MTU Dr. Neil Hutzler, Prof. CEE Department, MTU Dr. David Hand, Prof. CEE Department, MTU Dr. Leonard Bohmann, Assoc. Prof. ECE Department, MTU 20

21 Questions? Thank you! 21