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Measurement and verification of a motor sequencing controller on a conveyor belt
Energy 28 (2003) 913–927 www.elsevier.com/locate/energy
Measurement and verification of a motor sequencing controller on a conveyor belt A.Z. Dalgleish ∗, L.J. Grobler School of Mechanical and Material Engineering, Potchefstroom University for CHE, Private Bag X6001, Potchefstroom, 2522, South Africa Received 15 August 2002
Abstract One important question that is almost always asked when any demand-side management activity is implemented is: “How much is it saving?” That is an easy question to ask and it can almost always be expected. However, it is not necessarily easy to answer. One reason is because one cannot measure savings. Savings can at best be determined. The facility in this study installed a motor sequencing controller on a conveyor belt to decrease the energy costs. The plant personnel wanted to quantify the reduction in energy consumption and cost savings due to the installation of the controller. Therefore, an independent auditor was needed to measure and verify the actual savings. To determine the energy and cost savings of the controller, a baseline was needed. The baseline was developed from actual data using regression analysis. It was verified that the installation of the controller saved the facility R865 per month. With a cost of R6 000 for the controller, the straight payback is 0.58 years, the net present value is R29 615 and the internal rate of return is 172%. 2003 Elsevier Science Ltd. All rights reserved.
1. Introduction The facility in this study installed a motor sequencing controller on a coal conveyor belt to control the motors in correlation to the amount of coal on the conveyor. The controller was installed to reduce the energy use and the energy cost. Initial saving calculations were performed, but it provided insufficient saving information for the plant personnel. The plant personnel wanted to quantify the energy and cost savings due to the installation of the controller.
∗
Corresponding author. Fax: +27-18-299-1320. E-mail address: [email protected] (A.Z. Dalgleish).
0360-5442/03/$ - see front matter 2003 Elsevier Science Ltd. All rights reserved. doi:10.1016/S0360-5442(03)00034-3
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These savings were quantified by an independent auditor. To determine the savings, a baseline was developed. The savings were calculated as the difference between actual data and the baseline.
2. The functioning of the controller The conveyor belt is equipped with four identical motors with an electrical installed capacity of 110 kW each. One of these motors is referred to as the master motor. Controlling of the motors is done according to the current that the master motor draws. Plant personnel have selected a low limit of 34 A and a high limit of 55 A to which the motors are controlled. A motor will switch off when the current of the master motor drops below 34 A. The conveyor will as a consequence be driven by the master motor and the remaining motor(s) that has not switched off. A current higher than 55 A measured on the master motor will result in one of the four motors that were inoperative to start. One might argue that shutting off some motors might also decrease the electricity consumption considerably. However, that is not the case. The following scenario illustrates that point. One motor will stop when the master motor uses 33 A. All four motors together (the system) used 132 A. The moment a motor stops, the current that that motor used must be divided between the other motors that are still running. The system will still use 132 A but only three motors are operational. Thus, each motor will use 44 A. If, say, the motors were rated 130 A at full load, the motors would operate 9% closer to their full load capacity. The result of the motor running closer to maximum capacity means that the efficiency of the motor will increase slightly. This increase in motor efficiency will probably result in slightly less (1 to 4%) electricity being consumed. It was expected that most of the energy savings would be due to the reduction of reactive power (kVAr). However, baselines were developed for electricity consumption (kWh), maximum demand (kVA and kW) and reactive power (kVAr).
Table 1 MegaFlex time-of-use periods Peak
Standard
Off-peak
Weekdays
7:00 to 10:00 18:00 to 20:00
0:00 to 6:00 22:00 to 0:00
Saturdays
None
6:00 to 7:00 10:00 to 18:00 20:00 to 22:00 7:00 to 12:00 18:00 to 20:00
Sundays
None
None
0:00 to 7:00 12:00 to 18:00 22:00 to 0:00 All-day
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3. The tariff structure The tariff structure, MegaFlex, used by the facility is a time-of-use tariff structure [1]. The time-of-use periods are shown in Table 1. The charges of MegaFlex are shown in Table 2. The customer pays for the following on MegaFlex: 1. Monthly basic charge. 2. Maximum demand charge measured in kW. The customer will pay for maximum demand recorded during peak and standard periods. 3. Active energy charge measured in kWh. Electricity consumption is paid for during peak, standard and off-peak periods. 4. Reactive energy charge measured in kVArh. The customer will only pay this charge when the power factor is less than 0.96 during peak and standard periods. The integration period of MegaFlex is 30 minutes.
4. Initial business case The project was initially justified by the following savings calculations and assumptions: The majority of the savings will be due to the reduction of reactive power. The conveyor belt operates on average 17 hours (7:00 to 0:00) on a weekday, and on average 5 hours (8:00 to 13:00) on Saturdays. The average power factor for weekdays and Saturdays without the controller is 0.54. The average power factor for weekdays and Saturdays with the controller will increase with 12% to 0.61. [0.54+(0.12×0.54)]. There are 4 Saturdays and 22 weekdays in a month. Average reactive power data were obtained from actual measurements [2,3].
Table 2 MegaFlex charges Description
High season tariff (June–August)
Low season tariff (September–May)
Basic charge Maximum demand (kW) Electricity consumption (kWh)
R65.34 R13.67/kW Peak: 32.98c/kWh Standard: 13.84c/kWh Off-peak: 7.93c/kWh 2.85c/kVArh
R65.34 R12.31/kW Peak: 22.20c/kWh Standard: 12.41c/kWh Off-peak: 7.14c/kWh 2.85c/kVArh
Reactive energy (kVArh)
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Table 3 Expected reactive energy savings due to the installation of the controller Day
Controlled reactive power per month Uncontrolled reactive power per month
Weekday Saturday Total
51 463 kVArh 2817 kVArh 54 280 kVArh
Reactive power savings per month
=76 896–54 280 =22 616 kVArh 22 616×2.85c/kVArh R7734/year
Monthly cost savings Yearly cost savings
73 027 kVArh 3868 kVArh 76 896 kVArh
=R645/month
The expected savings are summarised in Table 3. Cost savings of R77341 per year was expected solely due to the reduction of the reactive power. A drastic difference between the expected and the actual savings suggests that the assumptions are either incorrect or the controller is not functioning as expected. Note that these savings are just expected and are useful when determining a budget or an initial business case. An initial business case was constructed based on the figures above. The initial business case is shown in Table 4. 5. Measurement and verification of the project The expected energy and cost savings as well as an initial business case were calculated in the previous paragraph. The purpose of the rest of the paper is to verify what the actual savings have been after installation of the controller. A baseline was developed and used to determine the savings. Table 4 Initial business case Yearly savings: Implementation cost: Project period: Company discount rate: Straight payback: Net present value: Internal rate of return:
1
1 US dollar is approximately equal to 10 South African rand.
R7734 R6000 5 years 14% 0.78 years R20 554 127%
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5.1. What is a baseline? The baseline is a model of what the energy consumption would have been if no changes were made to the system. The baselines were developed and calibrated with actual data prior to any changes were made to the system. Savings calculations then were performed by using the following equation from the International Performance Measurement and Verification Protocol (IPMVP): EnergySavings ⫽ (BaselineEnergyUse) ⫺ (PostEnergyUse) ⫾ Adjustments.
(1)
Adjustments might be needed if there are any changes to the conveyor system in the future. That process is called baseline adjustment. 5.2. Determining of the baseline According to the IPMVP, the baseline can be developed by using one of the following options: 5.2.1. Option A: partially measured retrofit isolation “Savings are determined by partial field measurement of the energy use of the system(s) to which an energy conservation measure was applied, separate from the energy use of the rest of the facility. Measurements may be either short-term or continuous. Partial measurement means that some but not all parameter(s) may be stipulated, if the total impact of possible stipulation error(s) is not significant to the resultant savings. Careful review of energy conservation measure design and installation will ensure that stipulated values fairly represent the probable actual value.” 5.2.2. Option B: retrofit isolation “Savings are determined by field measurement of the energy use of the systems to which the energy conservation measure was applied, separate from the energy use of the rest of the facility. Short-term or continuous measurements are taken throughout the post-retrofit period.” 5.2.3. Option C: whole facility “Savings are determined by measuring energy use at the whole facility level. Short-term or continuous measurements are taken throughout the post-retrofit period.” 5.2.4. Option D: calibrated simulation “Savings are determined through simulation of the energy use of components or the whole facility. Simulation routines must be demonstrated to adequately model actual energy perform-
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ance measured in the facility. This option usually requires considerable skill in calibrated simulation.” In this study, option A with partial metering was used to develop the baseline. The baselines correlate the ton of product and the electricity usage. Regression analysis was used to develop the baselines. 5.3. The baselines Baselines for kWh, kVA, kW and kVArh were developed to determine the energy and cost savings. These baselines will remain valid if no changes are made to either the conveyor or the motors of the conveyor. 5.3.1. Electricity consumption baseline The electricity consumption (kWh) baseline is shown in Fig. 1. The equation for this baseline is kWh = 0.0801 × Ton + 38.031. The R2 value for this equation is 0.8513. Actual calibration data is presented later in this paper. 5.3.2. Maximum demand (kW) baseline The maximum demand, metered in kW, is shown in Fig. 2. The equation for the baseline is kW = 0.1602 × Ton + 76.062. The R2 value for this equation is 85.13%. Actual calibration data for this baseline is discussed later in this paper. 5.3.3. Reactive power baseline The reactive power baseline is shown in Fig. 3. It was seen that some points that are not near the baseline. These points could be due to one or more of the motors having broken down or some metering errors.
Fig. 1. The electricity consumption (kWh) baseline.
A.Z. Dalgleish, L.J. Grobler / Energy 28 (2003) 913–927
Fig. 2.
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The maximum demand (kW) baseline.
Fig. 3. Reactive power baseline.
The equation for the reactive power baseline is kVArh = 0.0311 × Ton + 85.859. The R2 value for this baseline is 0.1985. Different indices are discussed later in this paper to determine if the baseline is accurate. The baseline predicts that the reactive power will be 85.859 kVArh when no product is conveyed. This value might be a bit higher if the indicated points in Fig. 3 were neglected. 5.3.4. Maximum demand (kVA) baseline The last baseline, the total power (kVA) baseline is shown in Fig. 4. The baseline was not used in any of the savings calculations. This was because the tariff structure, MegaFlex, uses kW for maximum demand calculations. The kVA-baseline is included purely for academic purposes.
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Fig. 4. The kVA-baseline.
The baseline equation is kVA = 0.1382 × Ton + 186.79 and the R2 value for this equation is 0.51. Actual baseline calibration results are presented later in this paper. 5.4. Baseline calibration The baseline was calibrated to see if they could be used to determine savings once the controller is installed. Two indices were calculated to determine if the baselines are accurate. The two indices are: 1. The Mean Bias Error (MBE), and 2. The Coefficient of the Root-mean-squared error (CV(RMSE)). The indices are calculated as follows: MBE(%) ⫽
冘
(M ⫺ S)h
冘
period
Mh
⫻ 100
(2)
period
where: Mh is the measured hourly system usage and Sh is the hourly simulated (baseline) usage. In this study, half-hourly data instead of hourly data have been used. Calculating the MBE is not enough to determine if the model is accurate. This is because positive and negative differences cancel each other out. Therefore, the CV(RMSE) have been calculated as well. The CV(RMSE) was calculated with the following equations: First, one needs to calculate the Root Mean Square Error (RMSE). The RMSE for the monitored period is:
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Table 5 Acceptable tolerance for calibration of the baseline Index
Value
MBEperiod CV(RMSEperiod)
±7% ±15%
RMSEperiod ⫽
冪
冘
(M⫺S)2h
period
(3)
Nh
where, Nh is the number of hours in the monitored period. In this study, the number of half-hours in the monitored period was used. The mean of the measured data for the period was calculated as follows: Aperiod ⫽
冘
Mh
period
(4)
Nh
The CV(RMSE) was then calculated as: CV(RMSEperiod) ⫽
RMSEperiod ⫻ 100 Aperiod
(5)
The acceptable tolerances for calibration of this baseline are summarised in Table 5. The indices have been calculated for the baseline. The results are shown in Table 6. Comparing the values in Table 6 with the required values shown in Table 5, it was seen that all the indices are satisfied. The baselines could therefore be used to determine the savings. It was also seen that the MBE for the baselines is fairly small. It was noted previously that positive and negative differences might cancel each other out as happened in this case. 6. Determining of the savings The developed baselines proved to be accurate and could thus be used to determine the savings. The next step was to use the baselines and determine the savings due to the installation of the Table 6 Indices for the developed baselines Index
kVAr baseline
kWh baseline
kVA baseline
MBEperiod CV(RMSEperiod)
⫺0.01% 8.85%
0.007% 6.88%
⫺2.14% 9.14%
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controller. In this case, the baselines were used to predict what the relevant electricity unit would have been if the same ton of product was conveyed. Calculating the difference between the baseline and the actual data gave the savings. 6.1. Electricity consumption savings Fig. 5 shows the electricity consumption baseline and actual post-installation data. It was seen that there was not a great difference between the baseline and post-installation data. This was expected. The difference was explained as a result of the increase in motor efficiency.
Fig. 5. Electricity consumption baseline vs. actual post-installation data.
6.1.1. Savings calculations methodology The savings were calculated with the following equations: kWhsaved,t ⫽ kWhbaseline model,t⫺kWhpost-implementation,t
(6)
where: kWhsaved,t=kWh savings realised during time period t; kWhbaseline,t=description of what the electricity consumption would have been under the same conditions (production, operating hours, etc.) as the post-implementation conveyor, for the selected time period t; kWhpost-implementation,t=actual kWh consumption of the conveyor with the control equipment for the selected time period t. The total electricity consumption reduction was found by adding all the kWh savings realised during time period t for the total period. This is:
冘
period
kWhsaving ⫽
kWhsaved,t.
t⫽1
Monthly savings were calculated and yearly savings were extrapolated.
(7)
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Table 7 Summary of the electricity consumption savings Baseline and actual electricity consumption and savings kWh
Rand
Actual Baseline Savings
R6107 R6225 R118
50 227 51 027 800
6.1.2. Quantifying of the monthly electricity consumption savings A summary of the monthly electricity consumption savings is shown in Table 7. It is seen that the controller realised an electricity consumption saving of 800 kWh and a cost saving of R118. 6.2. Maximum demand savings Fig. 6 shows the maximum demand (kW) baseline and actual metered post-installation data. Just as with the electricity consumption, the small difference between the baseline and the postinstallation data was due to motor efficiency improvement.
Fig. 6. Maximum demand (kW) baseline vs. post-installation data.
6.2.1. Maximum demand (kW) savings methodology To calculate the monthly maximum demand impact, the actual monthly maximum demand was extracted from the baseline monthly maximum demand. kWsaved,t ⫽ kWbaseline,t⫺kWpost-implementation,t
(8)
where: kWsaved,t=kW maximum demand savings realised during time period t; kWbaseline,t=descrip-
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Table 8 Summary of the maximum demand savings Maximum demand (kW) savings
Actual Baseline Savings
kW
Rand
173 181 8
2130 2228 98
tion of what the maximum demand would have been under the same conditions (production, operating hours, etc.) as the post-implementation conveyor, for the selected time period t; kWpostimplementation,t=actual maximum demand of the conveyor with the control equipment for the selected time period t. 6.2.2. Quantifying of the maximum demand savings A summary of the maximum demand savings is shown in Table 8. It is seen that the controller realised an 8 kW saving during the month. 6.3. Reactive power (kVArh) savings Fig. 7 shows the baseline with actual data after installation of the controller. It was seen that the installation of the controller had a significant reduction in reactive power. 6.3.1. Saving calculations methodology The savings was calculated by using the following equations: kVArhsaved,t ⫽ kVArhbaseline,t⫺kVArhpost-implementation,t
Fig. 7. The kVArh-baseline with the actual reactive power after installation.
(9)
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where: kVArhsaved,t=reactive power savings realised during time period t; kVArhbaseline,t=description of what the reactive power of the conveyor would have been under the same conditions (production, operating hours, etc.) as the post-implementation conveyor, for the selected time period t; kVArhpost-impl model,t=actual reactive power of the conveyor with the control equipment for the selected time period t. The total reactive power savings were found by adding all the kVArh savings realised during time period t for the total period. This is:
冘
period
kVArhsaving ⫽
kVArhsaved,t
(10)
t⫽1
The period could be a day, a week, a month or a year. Monthly savings were calculated in this paper. 6.3.2. Quantifying of monthly reactive power savings A summary of the monthly reactive power savings is shown in Table 9. It was seen that the reactive power savings were 22 863 kVArh. This relates to a cost saving of R652 per month. This verified reactive power saving was fairly close to the expected 22 616 kVArh although the power factor actually increased to 0.69. The difference probably was due to a combination of the increased power factor and unforeseen breakdowns and some shorter than normal working days experienced during the metering period. 6.4. Maximum demand (kVA) savings The tariff used by the client does not need kVA for bill calculations. The maximum demand metered in kVA was thus included purely for academic purposes. The kVA-baseline with actual metered data is shown in Fig. 8. If the client was on a tariff with maximum demand charges in kVA, the maximum demand savings would also be significant. 6.5. Financial impacts The largest contributor to the savings was the reduction of the reactive power component. The total cost savings are shown in Table 10. It was seen that the total saving was R868 per month. This is about R10 380 per annum. The implementation cost of the controller was about R6000. Table 9 Summary of the reactive power savings Reactive power (kVArh) and savings
Actual Baseline Savings
kVArh
Rand
51 973 74 835 22 863
R1481 R2133 R652
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Fig. 8.
Maximum demand (kVA) baseline vs. actual post-installation kVA-data.
Table 10 Total cost savings Total cost savings Electricity consumption Maximum demand Reactive power Total savings
R118 R98 R652 R868
To calculate the straight payback (SPB), the following equation was used: SPB ⫽
Cost Yearly savings
(11)
The Net Present Value (NPV) and the Internal Rate of Return (IRR) was calculated by using those functions in Microsoft’s Excel. The verified financial viability is summarised in Table 11. The differences were because the Table 11 Verified cost savings
Monthly savings Yearly savings Straight payback Net present value Internal rate of return
Expected
Verified
Percent Difference
R645 R7734 0.78 years R20 554 127%
R865 R10 374 0.58 years R29 615 172%
25% 25% ⫺34% 31% 26%
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expected savings took only the reactive power into consideration. The verified savings took all energy savings into account. 7. Conclusion A motor sequencing controller was installed for the motors of a conveyor belt with the purpose of reducing the energy consumption and energy cost. Expected cost savings were calculated to determine if the project would be financially feasible. It was found that the expected monthly cost savings were about R650. The straight payback of the project was calculated as 0.78 years. The net present value and internal rate of return of this project were calculated as R20 554 and 127%, respectively. Based on this initial business case, it was decided that the controller should be installed. The actual savings after installation were measured and verified by an independent auditor. The verified monthly cost saving was R868. The straight payback was calculated as 0.58 years. The net present value and internal rate of return of this project was calculated as R29 615 and 172%, respectively. References [1] Eskom. MegaFlex tariff structure. http://www.eskom.co.za/tariffs/2002/megaflex.htm [2] United States Department of Energy, International Performance Measurement and Verification Protocol Committee. International Performance Measurement and Verification Protocol. Vol. I. March 2002. http://www.ipmvp.org [3] California Public Utilities Commission. California’s 2000 Large Non-Residential standard performance contract program: Procedure manual. May 2000; pp. 130–131. http://www.scespc.com/largenonresmanuals.htm
Measurement and verification of a motor sequencing controller on a conveyor belt A.Z. Dalgleish ∗, L.J. Grobler School of Mechanical and Material Engineering, Potchefstroom University for CHE, Private Bag X6001, Potchefstroom, 2522, South Africa Received 15 August 2002
Abstract One important question that is almost always asked when any demand-side management activity is implemented is: “How much is it saving?” That is an easy question to ask and it can almost always be expected. However, it is not necessarily easy to answer. One reason is because one cannot measure savings. Savings can at best be determined. The facility in this study installed a motor sequencing controller on a conveyor belt to decrease the energy costs. The plant personnel wanted to quantify the reduction in energy consumption and cost savings due to the installation of the controller. Therefore, an independent auditor was needed to measure and verify the actual savings. To determine the energy and cost savings of the controller, a baseline was needed. The baseline was developed from actual data using regression analysis. It was verified that the installation of the controller saved the facility R865 per month. With a cost of R6 000 for the controller, the straight payback is 0.58 years, the net present value is R29 615 and the internal rate of return is 172%. 2003 Elsevier Science Ltd. All rights reserved.
1. Introduction The facility in this study installed a motor sequencing controller on a coal conveyor belt to control the motors in correlation to the amount of coal on the conveyor. The controller was installed to reduce the energy use and the energy cost. Initial saving calculations were performed, but it provided insufficient saving information for the plant personnel. The plant personnel wanted to quantify the energy and cost savings due to the installation of the controller.
∗
Corresponding author. Fax: +27-18-299-1320. E-mail address: [email protected] (A.Z. Dalgleish).
0360-5442/03/$ - see front matter 2003 Elsevier Science Ltd. All rights reserved. doi:10.1016/S0360-5442(03)00034-3
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These savings were quantified by an independent auditor. To determine the savings, a baseline was developed. The savings were calculated as the difference between actual data and the baseline.
2. The functioning of the controller The conveyor belt is equipped with four identical motors with an electrical installed capacity of 110 kW each. One of these motors is referred to as the master motor. Controlling of the motors is done according to the current that the master motor draws. Plant personnel have selected a low limit of 34 A and a high limit of 55 A to which the motors are controlled. A motor will switch off when the current of the master motor drops below 34 A. The conveyor will as a consequence be driven by the master motor and the remaining motor(s) that has not switched off. A current higher than 55 A measured on the master motor will result in one of the four motors that were inoperative to start. One might argue that shutting off some motors might also decrease the electricity consumption considerably. However, that is not the case. The following scenario illustrates that point. One motor will stop when the master motor uses 33 A. All four motors together (the system) used 132 A. The moment a motor stops, the current that that motor used must be divided between the other motors that are still running. The system will still use 132 A but only three motors are operational. Thus, each motor will use 44 A. If, say, the motors were rated 130 A at full load, the motors would operate 9% closer to their full load capacity. The result of the motor running closer to maximum capacity means that the efficiency of the motor will increase slightly. This increase in motor efficiency will probably result in slightly less (1 to 4%) electricity being consumed. It was expected that most of the energy savings would be due to the reduction of reactive power (kVAr). However, baselines were developed for electricity consumption (kWh), maximum demand (kVA and kW) and reactive power (kVAr).
Table 1 MegaFlex time-of-use periods Peak
Standard
Off-peak
Weekdays
7:00 to 10:00 18:00 to 20:00
0:00 to 6:00 22:00 to 0:00
Saturdays
None
6:00 to 7:00 10:00 to 18:00 20:00 to 22:00 7:00 to 12:00 18:00 to 20:00
Sundays
None
None
0:00 to 7:00 12:00 to 18:00 22:00 to 0:00 All-day
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3. The tariff structure The tariff structure, MegaFlex, used by the facility is a time-of-use tariff structure [1]. The time-of-use periods are shown in Table 1. The charges of MegaFlex are shown in Table 2. The customer pays for the following on MegaFlex: 1. Monthly basic charge. 2. Maximum demand charge measured in kW. The customer will pay for maximum demand recorded during peak and standard periods. 3. Active energy charge measured in kWh. Electricity consumption is paid for during peak, standard and off-peak periods. 4. Reactive energy charge measured in kVArh. The customer will only pay this charge when the power factor is less than 0.96 during peak and standard periods. The integration period of MegaFlex is 30 minutes.
4. Initial business case The project was initially justified by the following savings calculations and assumptions: The majority of the savings will be due to the reduction of reactive power. The conveyor belt operates on average 17 hours (7:00 to 0:00) on a weekday, and on average 5 hours (8:00 to 13:00) on Saturdays. The average power factor for weekdays and Saturdays without the controller is 0.54. The average power factor for weekdays and Saturdays with the controller will increase with 12% to 0.61. [0.54+(0.12×0.54)]. There are 4 Saturdays and 22 weekdays in a month. Average reactive power data were obtained from actual measurements [2,3].
Table 2 MegaFlex charges Description
High season tariff (June–August)
Low season tariff (September–May)
Basic charge Maximum demand (kW) Electricity consumption (kWh)
R65.34 R13.67/kW Peak: 32.98c/kWh Standard: 13.84c/kWh Off-peak: 7.93c/kWh 2.85c/kVArh
R65.34 R12.31/kW Peak: 22.20c/kWh Standard: 12.41c/kWh Off-peak: 7.14c/kWh 2.85c/kVArh
Reactive energy (kVArh)
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Table 3 Expected reactive energy savings due to the installation of the controller Day
Controlled reactive power per month Uncontrolled reactive power per month
Weekday Saturday Total
51 463 kVArh 2817 kVArh 54 280 kVArh
Reactive power savings per month
=76 896–54 280 =22 616 kVArh 22 616×2.85c/kVArh R7734/year
Monthly cost savings Yearly cost savings
73 027 kVArh 3868 kVArh 76 896 kVArh
=R645/month
The expected savings are summarised in Table 3. Cost savings of R77341 per year was expected solely due to the reduction of the reactive power. A drastic difference between the expected and the actual savings suggests that the assumptions are either incorrect or the controller is not functioning as expected. Note that these savings are just expected and are useful when determining a budget or an initial business case. An initial business case was constructed based on the figures above. The initial business case is shown in Table 4. 5. Measurement and verification of the project The expected energy and cost savings as well as an initial business case were calculated in the previous paragraph. The purpose of the rest of the paper is to verify what the actual savings have been after installation of the controller. A baseline was developed and used to determine the savings. Table 4 Initial business case Yearly savings: Implementation cost: Project period: Company discount rate: Straight payback: Net present value: Internal rate of return:
1
1 US dollar is approximately equal to 10 South African rand.
R7734 R6000 5 years 14% 0.78 years R20 554 127%
A.Z. Dalgleish, L.J. Grobler / Energy 28 (2003) 913–927
917
5.1. What is a baseline? The baseline is a model of what the energy consumption would have been if no changes were made to the system. The baselines were developed and calibrated with actual data prior to any changes were made to the system. Savings calculations then were performed by using the following equation from the International Performance Measurement and Verification Protocol (IPMVP): EnergySavings ⫽ (BaselineEnergyUse) ⫺ (PostEnergyUse) ⫾ Adjustments.
(1)
Adjustments might be needed if there are any changes to the conveyor system in the future. That process is called baseline adjustment. 5.2. Determining of the baseline According to the IPMVP, the baseline can be developed by using one of the following options: 5.2.1. Option A: partially measured retrofit isolation “Savings are determined by partial field measurement of the energy use of the system(s) to which an energy conservation measure was applied, separate from the energy use of the rest of the facility. Measurements may be either short-term or continuous. Partial measurement means that some but not all parameter(s) may be stipulated, if the total impact of possible stipulation error(s) is not significant to the resultant savings. Careful review of energy conservation measure design and installation will ensure that stipulated values fairly represent the probable actual value.” 5.2.2. Option B: retrofit isolation “Savings are determined by field measurement of the energy use of the systems to which the energy conservation measure was applied, separate from the energy use of the rest of the facility. Short-term or continuous measurements are taken throughout the post-retrofit period.” 5.2.3. Option C: whole facility “Savings are determined by measuring energy use at the whole facility level. Short-term or continuous measurements are taken throughout the post-retrofit period.” 5.2.4. Option D: calibrated simulation “Savings are determined through simulation of the energy use of components or the whole facility. Simulation routines must be demonstrated to adequately model actual energy perform-
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ance measured in the facility. This option usually requires considerable skill in calibrated simulation.” In this study, option A with partial metering was used to develop the baseline. The baselines correlate the ton of product and the electricity usage. Regression analysis was used to develop the baselines. 5.3. The baselines Baselines for kWh, kVA, kW and kVArh were developed to determine the energy and cost savings. These baselines will remain valid if no changes are made to either the conveyor or the motors of the conveyor. 5.3.1. Electricity consumption baseline The electricity consumption (kWh) baseline is shown in Fig. 1. The equation for this baseline is kWh = 0.0801 × Ton + 38.031. The R2 value for this equation is 0.8513. Actual calibration data is presented later in this paper. 5.3.2. Maximum demand (kW) baseline The maximum demand, metered in kW, is shown in Fig. 2. The equation for the baseline is kW = 0.1602 × Ton + 76.062. The R2 value for this equation is 85.13%. Actual calibration data for this baseline is discussed later in this paper. 5.3.3. Reactive power baseline The reactive power baseline is shown in Fig. 3. It was seen that some points that are not near the baseline. These points could be due to one or more of the motors having broken down or some metering errors.
Fig. 1. The electricity consumption (kWh) baseline.
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Fig. 2.
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The maximum demand (kW) baseline.
Fig. 3. Reactive power baseline.
The equation for the reactive power baseline is kVArh = 0.0311 × Ton + 85.859. The R2 value for this baseline is 0.1985. Different indices are discussed later in this paper to determine if the baseline is accurate. The baseline predicts that the reactive power will be 85.859 kVArh when no product is conveyed. This value might be a bit higher if the indicated points in Fig. 3 were neglected. 5.3.4. Maximum demand (kVA) baseline The last baseline, the total power (kVA) baseline is shown in Fig. 4. The baseline was not used in any of the savings calculations. This was because the tariff structure, MegaFlex, uses kW for maximum demand calculations. The kVA-baseline is included purely for academic purposes.
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Fig. 4. The kVA-baseline.
The baseline equation is kVA = 0.1382 × Ton + 186.79 and the R2 value for this equation is 0.51. Actual baseline calibration results are presented later in this paper. 5.4. Baseline calibration The baseline was calibrated to see if they could be used to determine savings once the controller is installed. Two indices were calculated to determine if the baselines are accurate. The two indices are: 1. The Mean Bias Error (MBE), and 2. The Coefficient of the Root-mean-squared error (CV(RMSE)). The indices are calculated as follows: MBE(%) ⫽
冘
(M ⫺ S)h
冘
period
Mh
⫻ 100
(2)
period
where: Mh is the measured hourly system usage and Sh is the hourly simulated (baseline) usage. In this study, half-hourly data instead of hourly data have been used. Calculating the MBE is not enough to determine if the model is accurate. This is because positive and negative differences cancel each other out. Therefore, the CV(RMSE) have been calculated as well. The CV(RMSE) was calculated with the following equations: First, one needs to calculate the Root Mean Square Error (RMSE). The RMSE for the monitored period is:
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Table 5 Acceptable tolerance for calibration of the baseline Index
Value
MBEperiod CV(RMSEperiod)
±7% ±15%
RMSEperiod ⫽
冪
冘
(M⫺S)2h
period
(3)
Nh
where, Nh is the number of hours in the monitored period. In this study, the number of half-hours in the monitored period was used. The mean of the measured data for the period was calculated as follows: Aperiod ⫽
冘
Mh
period
(4)
Nh
The CV(RMSE) was then calculated as: CV(RMSEperiod) ⫽
RMSEperiod ⫻ 100 Aperiod
(5)
The acceptable tolerances for calibration of this baseline are summarised in Table 5. The indices have been calculated for the baseline. The results are shown in Table 6. Comparing the values in Table 6 with the required values shown in Table 5, it was seen that all the indices are satisfied. The baselines could therefore be used to determine the savings. It was also seen that the MBE for the baselines is fairly small. It was noted previously that positive and negative differences might cancel each other out as happened in this case. 6. Determining of the savings The developed baselines proved to be accurate and could thus be used to determine the savings. The next step was to use the baselines and determine the savings due to the installation of the Table 6 Indices for the developed baselines Index
kVAr baseline
kWh baseline
kVA baseline
MBEperiod CV(RMSEperiod)
⫺0.01% 8.85%
0.007% 6.88%
⫺2.14% 9.14%
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controller. In this case, the baselines were used to predict what the relevant electricity unit would have been if the same ton of product was conveyed. Calculating the difference between the baseline and the actual data gave the savings. 6.1. Electricity consumption savings Fig. 5 shows the electricity consumption baseline and actual post-installation data. It was seen that there was not a great difference between the baseline and post-installation data. This was expected. The difference was explained as a result of the increase in motor efficiency.
Fig. 5. Electricity consumption baseline vs. actual post-installation data.
6.1.1. Savings calculations methodology The savings were calculated with the following equations: kWhsaved,t ⫽ kWhbaseline model,t⫺kWhpost-implementation,t
(6)
where: kWhsaved,t=kWh savings realised during time period t; kWhbaseline,t=description of what the electricity consumption would have been under the same conditions (production, operating hours, etc.) as the post-implementation conveyor, for the selected time period t; kWhpost-implementation,t=actual kWh consumption of the conveyor with the control equipment for the selected time period t. The total electricity consumption reduction was found by adding all the kWh savings realised during time period t for the total period. This is:
冘
period
kWhsaving ⫽
kWhsaved,t.
t⫽1
Monthly savings were calculated and yearly savings were extrapolated.
(7)
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Table 7 Summary of the electricity consumption savings Baseline and actual electricity consumption and savings kWh
Rand
Actual Baseline Savings
R6107 R6225 R118
50 227 51 027 800
6.1.2. Quantifying of the monthly electricity consumption savings A summary of the monthly electricity consumption savings is shown in Table 7. It is seen that the controller realised an electricity consumption saving of 800 kWh and a cost saving of R118. 6.2. Maximum demand savings Fig. 6 shows the maximum demand (kW) baseline and actual metered post-installation data. Just as with the electricity consumption, the small difference between the baseline and the postinstallation data was due to motor efficiency improvement.
Fig. 6. Maximum demand (kW) baseline vs. post-installation data.
6.2.1. Maximum demand (kW) savings methodology To calculate the monthly maximum demand impact, the actual monthly maximum demand was extracted from the baseline monthly maximum demand. kWsaved,t ⫽ kWbaseline,t⫺kWpost-implementation,t
(8)
where: kWsaved,t=kW maximum demand savings realised during time period t; kWbaseline,t=descrip-
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Table 8 Summary of the maximum demand savings Maximum demand (kW) savings
Actual Baseline Savings
kW
Rand
173 181 8
2130 2228 98
tion of what the maximum demand would have been under the same conditions (production, operating hours, etc.) as the post-implementation conveyor, for the selected time period t; kWpostimplementation,t=actual maximum demand of the conveyor with the control equipment for the selected time period t. 6.2.2. Quantifying of the maximum demand savings A summary of the maximum demand savings is shown in Table 8. It is seen that the controller realised an 8 kW saving during the month. 6.3. Reactive power (kVArh) savings Fig. 7 shows the baseline with actual data after installation of the controller. It was seen that the installation of the controller had a significant reduction in reactive power. 6.3.1. Saving calculations methodology The savings was calculated by using the following equations: kVArhsaved,t ⫽ kVArhbaseline,t⫺kVArhpost-implementation,t
Fig. 7. The kVArh-baseline with the actual reactive power after installation.
(9)
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where: kVArhsaved,t=reactive power savings realised during time period t; kVArhbaseline,t=description of what the reactive power of the conveyor would have been under the same conditions (production, operating hours, etc.) as the post-implementation conveyor, for the selected time period t; kVArhpost-impl model,t=actual reactive power of the conveyor with the control equipment for the selected time period t. The total reactive power savings were found by adding all the kVArh savings realised during time period t for the total period. This is:
冘
period
kVArhsaving ⫽
kVArhsaved,t
(10)
t⫽1
The period could be a day, a week, a month or a year. Monthly savings were calculated in this paper. 6.3.2. Quantifying of monthly reactive power savings A summary of the monthly reactive power savings is shown in Table 9. It was seen that the reactive power savings were 22 863 kVArh. This relates to a cost saving of R652 per month. This verified reactive power saving was fairly close to the expected 22 616 kVArh although the power factor actually increased to 0.69. The difference probably was due to a combination of the increased power factor and unforeseen breakdowns and some shorter than normal working days experienced during the metering period. 6.4. Maximum demand (kVA) savings The tariff used by the client does not need kVA for bill calculations. The maximum demand metered in kVA was thus included purely for academic purposes. The kVA-baseline with actual metered data is shown in Fig. 8. If the client was on a tariff with maximum demand charges in kVA, the maximum demand savings would also be significant. 6.5. Financial impacts The largest contributor to the savings was the reduction of the reactive power component. The total cost savings are shown in Table 10. It was seen that the total saving was R868 per month. This is about R10 380 per annum. The implementation cost of the controller was about R6000. Table 9 Summary of the reactive power savings Reactive power (kVArh) and savings
Actual Baseline Savings
kVArh
Rand
51 973 74 835 22 863
R1481 R2133 R652
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Fig. 8.
Maximum demand (kVA) baseline vs. actual post-installation kVA-data.
Table 10 Total cost savings Total cost savings Electricity consumption Maximum demand Reactive power Total savings
R118 R98 R652 R868
To calculate the straight payback (SPB), the following equation was used: SPB ⫽
Cost Yearly savings
(11)
The Net Present Value (NPV) and the Internal Rate of Return (IRR) was calculated by using those functions in Microsoft’s Excel. The verified financial viability is summarised in Table 11. The differences were because the Table 11 Verified cost savings
Monthly savings Yearly savings Straight payback Net present value Internal rate of return
Expected
Verified
Percent Difference
R645 R7734 0.78 years R20 554 127%
R865 R10 374 0.58 years R29 615 172%
25% 25% ⫺34% 31% 26%
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expected savings took only the reactive power into consideration. The verified savings took all energy savings into account. 7. Conclusion A motor sequencing controller was installed for the motors of a conveyor belt with the purpose of reducing the energy consumption and energy cost. Expected cost savings were calculated to determine if the project would be financially feasible. It was found that the expected monthly cost savings were about R650. The straight payback of the project was calculated as 0.78 years. The net present value and internal rate of return of this project were calculated as R20 554 and 127%, respectively. Based on this initial business case, it was decided that the controller should be installed. The actual savings after installation were measured and verified by an independent auditor. The verified monthly cost saving was R868. The straight payback was calculated as 0.58 years. The net present value and internal rate of return of this project was calculated as R29 615 and 172%, respectively. References [1] Eskom. MegaFlex tariff structure. http://www.eskom.co.za/tariffs/2002/megaflex.htm [2] United States Department of Energy, International Performance Measurement and Verification Protocol Committee. International Performance Measurement and Verification Protocol. Vol. I. March 2002. http://www.ipmvp.org [3] California Public Utilities Commission. California’s 2000 Large Non-Residential standard performance contract program: Procedure manual. May 2000; pp. 130–131. http://www.scespc.com/largenonresmanuals.htm