PEPSO: Reducing Electricity Usage and Associated Pollution Emissions of Water Pumps
Abstract
:1. Introduction
2. Literature Review
3. Tool Development and Methodology
3.1. Transition from the First Version of PEPSO to the Second Version
3.2. Introducing the New PEPSO
4. Experimental Demonstration
Design of Experiment
- Penalty associated with water pressure violation at junctions
- Penalty associated with water level violation at tanks
- Time step index starts from the 1st time block and goes to the Ith time block
- Junction index starts from the 1st junction and goes to the Jth junction
- Tank index starts from the 1st tank and goes to the Kth tank
- A power defined to increase the penalty by increasing the amount of violation. x = 1.5 is used.
- Water pressure of junction j at time block i
- Maximum allowed water pressure of junction j
- Minimum allowed water pressure of junction j
- Water level of tank k at time block i
- Maximum allowed water level of tank k
- Minimum allowed water level of tank k
5. Results and Discussion
6. Conclusions
- Optimizing based on all three objectives (S1) reduces the CO2 emissions of the Monroe and Richmond WDSs by 1.3–3.4%. Optimizing based on all three objectives at the same time is more effective than optimizing based on only the electricity cost or total penalty.
- Optimizing based on just penalty (S2 scenario) reduced the total penalty on Monroe and Richmond WDSs by 10 and 5.8% respectively.
- Calculating the Undesirability Index helped PEPSO to find more practical optimized solutions with fewer EPANET warnings and less tank drainage. However, on average, the undesirability calculation increased the required optimization time by 8.9%. The effect of the UI on finding high-quality solutions for a complex system with vast solution space needs to be evaluated.
- In the S4 scenario, the Monroe WDS was optimized without tank level constraints. The water level penalty of tanks of the S4 scenario is more than four times the water level penalties of the base scenario (S0). Like the Monroe WDS, optimizing without tank level constraints reduced the electricity cost and CO2 emissions of the Richmond WDS. However, it considerably increases the water level penalty of tanks (35.1%). Removing water level constraints increases both water level and water pressure penalties and led to impractical and unacceptable solutions.
- The time-of-use electricity tariff forces PEPSO to shift 1.7% of energy consumption from on-peak hours to off-peak hours. Including the power demand charge in the electricity tariff also, on average, reduces the peak power demand of the Monroe WDS by 9.7%. In the Richmond test, using a flat rate energy consumption charge enables PEPSO to consume energy at the time of high demand. This eliminated the need to store more water during off-peak hours which was causing 1.5% energy losses. In addition, by this method, PEPSO reduced tank drainage by about 10%.
- PEPSO used a multi-objective optimization algorithm to optimize three objectives independent of each other and report the final Pareto frontier that can be used in system studies and research. However, for practical use, one of the solutions among the Pareto frontier should be selected for operation. This selection is made by considering user preference based on user-defined weighting factors and also by removing impractical solution from the Pareto frontier (e.g., a solution with zero energy usage but high penalties). Defining different weighting factors can change the selected solution. Weighting factors are dependent on geographical, social, economic, etc., characteristics of the water system, defined constraints and practical preferences of operators. This area needs to be studied further to create a guideline that can help users to define weighting factors in such a way that results in the selection of the most desirable solution from the Pareto frontier.
- In this study, the net electricity cost and net CO2 emissions are calculated to take into account the effect of deficit or surplus water volume of tanks within the acceptable range. However, using the average electricity charge ($/kWh) and CO2 emission factor (kg/MWh) might not match real operation conditions. Therefore, we suggest running tests and simulations for a longer period (e.g., a week instead of 24 h) or using better calculation methods to take into account the effect of tank level changes at the end of simulation in a more accurate way.
Acknowledgments
Author Contributions
Conflicts of Interest
References
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Item | Monroe | Richmond Skeletonized |
---|---|---|
No. of Fixed Speed Pumps | 11 | 7 |
No. of Variable Speed Pumps | 2 | 0 |
No. of Pump Stations | 2 | 6 |
No. of Tanks | 3 | 6 |
No. of Water Sources | 1 | 2 |
No. of Pipes | 1945 | 44 |
No. of Junctions | 1531 | 41 |
Total Length of Pipes (km) | 450 | 22.69 |
Pipe Size Range (mm) | 50–910 | 76–300 |
Total Demand (m3/day) | 36,500 | 3921 |
Storage volume (m3) | 3974 | 2598 |
Storage to Daily Demand Ratio | 11/100 | 66/100 |
Range of Power of Pumps (kW) | 36–220 | 3–60 |
Max. Static Water Head (m) | 60 | 199 |
Demand Pattern Duration (hr) | 24 | 24 |
Demand Pattern Time Step (hr) | 1 | 1 |
Min and Max. Demand Multiplier | 0.67–1.19 | 0.39–1.53 |
Test Case | Strategic Junction ID | Min. Water Pressure (psi) | Max. Water Pressure (psi) |
---|---|---|---|
Monroe | J-6 | 42 | 52 |
J-27 | 32 | 46 | |
J-131 | 28 | 42 | |
J-514 | 42 | 56 | |
Richmond | 42 | 20 | 140 |
1302 | 0 | 100 | |
10 | 0 | 100 | |
312 | 0 | 100 | |
325 | 0 | 100 | |
701 | 0 | 100 | |
745 | 20 | 100 | |
249 | 20 | 100 | |
753 | 20 | 100 | |
637 | 20 | 140 |
Test Case | Tank ID | Min. Water Level (m) | Max. Water Level (m) |
---|---|---|---|
Monroe | T-2 | 1.56 | 8.12 |
T-3 | 1.41 | 7.28 | |
T-5 | 1.78 | 8.66 | |
Richmond | A | 0.30 | 1.70 |
B | 0.50 | 2.86 | |
C | 0.32 | 1.79 | |
D | 0.55 | 3.10 | |
E | 0.44 | 2.29 | |
F | 0.33 | 1.86 |
Pump Station | On-Peak Rate ($/kWh) | Off-Peak Rate ($/kWh) |
---|---|---|
A | 0.0679 | 0.0241 |
B | 0.0754 | 0.0241 |
C | 0.1234 | 0.0246 |
D | 0.0987 | 0.0246 |
E | 0.1122 | 0.0246 |
F | 0.1194 | 0.0244 |
Time | CO2 Emission Factor (kg/MWh) | Time | CO2 Emission Factor (kg/MWh) |
---|---|---|---|
00:00 | 767.771 | 12:00 | 662.793 |
01:00 | 738.324 | 13:00 | 630.703 |
02:00 | 702.904 | 14:00 | 630.531 |
03:00 | 702.904 | 15:00 | 628.591 |
04:00 | 702.904 | 16:00 | 628.882 |
05:00 | 767.771 | 17:00 | 666.549 |
06:00 | 781.469 | 18:00 | 693.607 |
07:00 | 808.212 | 19:00 | 665.274 |
08:00 | 764.333 | 20:00 | 730.766 |
09:00 | 719.768 | 21:00 | 790.628 |
10:00 | 719.768 | 22:00 | 808.212 |
11:00 | 695.334 | 23:00 | 780.477 |
Parameter | Value |
---|---|
Max. No. of Solution Evaluations | 16,600 |
Population Size | 100 |
Percentage of Elite Solution | 20% |
Crossover Percentage | 50% |
Crossover Rate | 50% |
Mutation Percentage | 5% |
Mutation Rate | 10% |
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Sadatiyan A., S.M.; Miller, C.J. PEPSO: Reducing Electricity Usage and Associated Pollution Emissions of Water Pumps. Water 2017, 9, 640. https://doi.org/10.3390/w9090640
Sadatiyan A. SM, Miller CJ. PEPSO: Reducing Electricity Usage and Associated Pollution Emissions of Water Pumps. Water. 2017; 9(9):640. https://doi.org/10.3390/w9090640
Chicago/Turabian StyleSadatiyan A., S. Mohsen, and Carol J. Miller. 2017. "PEPSO: Reducing Electricity Usage and Associated Pollution Emissions of Water Pumps" Water 9, no. 9: 640. https://doi.org/10.3390/w9090640
APA StyleSadatiyan A., S. M., & Miller, C. J. (2017). PEPSO: Reducing Electricity Usage and Associated Pollution Emissions of Water Pumps. Water, 9(9), 640. https://doi.org/10.3390/w9090640