WO2009020402A1 - Distribution d'eau - Google Patents

Distribution d'eau Download PDF

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Publication number
WO2009020402A1
WO2009020402A1 PCT/NZ2008/000192 NZ2008000192W WO2009020402A1 WO 2009020402 A1 WO2009020402 A1 WO 2009020402A1 NZ 2008000192 W NZ2008000192 W NZ 2008000192W WO 2009020402 A1 WO2009020402 A1 WO 2009020402A1
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WO
WIPO (PCT)
Prior art keywords
pump
demand
pumps
operating
speed
Prior art date
Application number
PCT/NZ2008/000192
Other languages
English (en)
Inventor
Simon Michael Bunn
Sarah-Jane Thorstensen
Evan Peter Henry Atkinson
Sanjay Unka Patel
Sarah Elizabeth Clark
Stephanie Anne Pegg
Original Assignee
Derceto Limited
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by Derceto Limited filed Critical Derceto Limited
Publication of WO2009020402A1 publication Critical patent/WO2009020402A1/fr

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Classifications

    • F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
    • F04—POSITIVE - DISPLACEMENT MACHINES FOR LIQUIDS; PUMPS FOR LIQUIDS OR ELASTIC FLUIDS
    • F04D—NON-POSITIVE-DISPLACEMENT PUMPS
    • F04D15/00—Control, e.g. regulation, of pumps, pumping installations or systems
    • F04D15/02—Stopping of pumps, or operating valves, on occurrence of unwanted conditions
    • F04D15/029—Stopping of pumps, or operating valves, on occurrence of unwanted conditions for pumps operating in parallel
    • F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
    • F04—POSITIVE - DISPLACEMENT MACHINES FOR LIQUIDS; PUMPS FOR LIQUIDS OR ELASTIC FLUIDS
    • F04D—NON-POSITIVE-DISPLACEMENT PUMPS
    • F04D15/00—Control, e.g. regulation, of pumps, pumping installations or systems
    • F04D15/0066—Control, e.g. regulation, of pumps, pumping installations or systems by changing the speed, e.g. of the driving engine

Definitions

  • This invention relates to distribution of water such as by water utilities. More particularly, the invention relates to control of the distribution of water so as to provide for efficient, or more efficient distribution.
  • Energy costs for pumping are typically one of the largest expenses in a water utility's operations budget and may be more than US$ 5 million per annum. The significance of the scale of this expenditure means that improvements in energy efficiency and consumption reduction can have a substantial effect.
  • water utilities have significant flexibility in how they operate their water distribution system even taking into account strict constraints on minimum fire-fighting storage and water quality requirements. Many utilities practice the strategy of filling storage tanks overnight to cope with the morning demand, and then refill them before the evening peak. Such practice may necessitate pumping during high tariff afternoon hours. If a water utility can accurately predict the water usage profile and manipulate the substantial storage prevalent in the water distribution industry, then there are numerous opportunities to be smarter in the time at which you choose to use this energy, and which pumps you choose to run. The amount of energy consumed by these pumps is considerable, and the ability to control when this energy is consumed can have a significant impact on electricity costs.
  • an automatic pump scheduling system can be added on top of an existing SCADA (Supervisory Control And Data Acquisition) and telemetry system to control the starting and stopping of pumps in response to electricity tariff schedules as well as system production demands, operational constraints and predicted water usage.
  • SCADA Supervisory Control And Data Acquisition
  • Factors taken into account when looking at the optimization of pump and valve control may include: o Differences in efficiency between pumps at a pump station (which can vary during the day depending on conditions); o Time of use energy tariffs, where the energy price is dependent on the time of use; o The changing efficiency of variable speed drive pumps, with speed set point; o Monthly maximum electrical demand charges, which affect the strategy for optimum selection of pumps at each station; o Multiple possible paths for- directing water throughout a network, some of which will be more efficient than others; o Physical constraints in the network such as storage volumes, min and max flow limitations and min/max pressure requirements; and o Comparison of alternatives including gas, diesel and hydro powered pumps to electrically driven pumps.
  • the problem is approached by providing an automatic pump scheduling system, preferably software embodied, which sits on top of the utility company's SCADA system, and reads in all flows and reservoir levels to calculate and store the current water usage.
  • This information provides the basis for predictions of water usage throughout a predetermined time period, preferably, the coming 48 hours.
  • This demand prediction is used to generate optimized schedules for all pumps and valves in the network to meet the water demand at minimum cost, while meeting all physical constraints of the distribution network.
  • the schedule is then taken and implemented directly and automatically, setting the status of the pumps and valves utilizing the SCADA system.
  • the invention provides a real time, or near real time, automated optimizer. Data may be read in, say, every 10 minutes and a new solution produced, say, at least every 30 minutes, with the updated schedules being implemented automatically.
  • This real time or near real time approach means that it is possible to more quickly adapt to changing demand, equipment failure and automated or more fully automated operation is enabled. While 30 minutes is a preferred time interval, the invention is not limited thereto. The presently preferred range is from 15 minutes to an hour but the selected range will depend on parameters of the system to which the invention is applied.
  • n ⁇ n ⁇ j* J r ⁇ (n — ⁇ ) ⁇ where n is the number of pumps in the station, and r is the number of pumps you want to run. For example, if there are 4 pumps at a station (n), for the number of pumps to run options (r) are (0,1,2,3,4 pumps).
  • the number of combinations given by this formula are (1 ,4,6,4,1), with respect to the number of pumps r, making a total of 16 possible pump combinations. More generally, the relationship between the number of different combinations and the number of pumps in a pump station is governed by the equation:
  • physical pumps within a network are combined, preferably in real time, into logical pumps so as to simplify the pump scheduling optimization problem.
  • physical pump combinations i.e., combinations of 2 or more pumps
  • two or more physical pumps may be combined together to form a new, single logical pump, with performance properties equal to the operation of all pumps together.
  • a simple example of a pump station having 4 pumps of identical or “near" identical size it is possible to reduce the number of possible combinations from 16 using conventional approaches to only 5 pump combinations using combined Logical Pumps as illustrated in Figure 1. While specific reference is made to pumps having the same or similar size, the invention is not limited thereto and also covers combinations of pumps having different sizes.
  • the problem of determining which pumps to run is simplified by limiting to selection between a plurality of single logical pumps, thereby greatly reducing the complexity of the problem.
  • each combination of pumps may be predetermined, according to preferred embodiments, the combinations may be varied so as to achieve proper pump duty rotation and better match the run hours for each pump.
  • the substitution of one pump for another is most easily performed when multiple pumps are provided of the same or similar size but those skilled in the art will be aware that such cycling of pumps may also be applied to systems having differently sized pumps, particularly where one or more of the pumps is a variable speed pump.
  • the first aspect of the invention may be embodied in systems and/or methods, or additionally or alternatively, an apparatus, such as a processing apparatus for determining which pump(s) should be run and which may be included within systems of the invention.
  • modelling of variable speed drive pumps using best efficiency point linear approximations for the simplification of the pump scheduling optimization problem may also be embodied in systems and/or methods, or additionally or alternatively, an apparatus, such as a processing apparatus.
  • Variable speed pumps can be run at any chosen speed within their speed ranges. From a mathematical perspective, this provides an unlimited number of speed options, each with their own pump efficiency and power curves. The way in which the changing speeds of a pump are modelled is via the pump affinity laws listed below.
  • VSD Very Speed Drive
  • a linear approximation of flow and cost may be used by the solver to select any desired flow.
  • a piecewise linear approximation method is adopted. More particularly, a number of piecewise linear approximations to the cost function of running a VSD pump at fixed head are used. The same min - max range of flows are used, but a specified number of additional points are included, for which are entered into the solver, the pump and efficiency curves for each point.
  • the example in Figure 4 is based on using 5 ppints. Inside the solver, constraints are enforced which means that the solver must choose an operating speed which lies on one of the straight lines between the points entered into the solver. This greatly increases the accuracy of the approximation to the actual cost function.
  • the Demand Prediction Algorithm is a process that is carried out every half hour throughout the day, thereby continually adapting to changing historically recorded demand readings.
  • the average standard demand curve is input which contains the average water usage for each half hour period for the zone for each particular day type (i.e., day of the week, whether the day is a public holiday, etc) along with the operator entered estimated total system demand for the day.
  • the algorithm may be incorporated in a method and/or a system and/or an apparatus (e.g. a controller/processor) for controlling distribution, particularly of water.
  • the LOESS algorithm is defined as a locally weighted polynomial regression. The idea is generic and can be applied to a range of cases. The Applicant has created a surprising new technique as a branch off from this LOESS algorithm to predict water demand. 1 A step by step approach explaining components of the algorithm and what makes it so unique is explained herein below.
  • Figure 1 is a schematic view of an embodiment of the invention
  • FIG. 1 charts variable speed pump operating variation
  • Figure 3 charts variable speed drive costing based on a linear approximation
  • Figure 4 charts variable speed drive costing using best efficiency point analysis
  • FIG. 1 charts variable speed pump and efficiency curves
  • Figure 6 charts minimum and maximum flows for a variable speed drive pump
  • FFiigguurree 77 charts scaling of a standard demand curve
  • Figures 8-10 are charts showing demand prediction according to one method
  • Figure 11 charts scaling of the average standard demand curve
  • Figures 12-14 are charts showing demand prediction according to another method
  • Figure 15 provides a graphical depiction of the demand prediction parameters of the second method
  • Figure 16 illustrates a method according to one embodiment of the invention.
  • Figure 17 is a schematic representation of a system according to an embodiment.
  • the solver is limited to selecting from a combination of these new Combined Logical Pumps.
  • the new constraint is as follows:
  • the pump curves which define the performance of the logical pump combinations are generated using the sequence order described above, to determine which pump curves will make up the logical pump curve. This combination is most easily undertaken as a one-off exercise, and this is generally sufficient when all pumps are near identical, or where the sequence doesn't change.
  • the pump sequencing can be fixed, but in most systems, it is preferable to have this sequence order dynamically altered from within the client SCADA system, so as to achieve proper pump duty rotation and cycling to match the run hours for each pump. It may also be necessary to change the pump sequence due to pump servicing and outages. As this sequence changes dynamically, to maintain the accuracy of the logical pump model, it is also necessary to dynamically alter the pump curve of the logical pump.
  • Embodiments of the invention preferably incorporate the technique of numerically combining the physical pump curves every time the scheduling problem is solved with the latest sequence settings. This curve generation greatly increases the accuracy of the logical pumps and enables the linking together of pumps which are of non-equal size to simplify the pump scheduling optimization problem.
  • the "Best Efficiency Point" of operation is explicitly calculated on the fly, given the current Head across the pump, for every variable speed pump. This new operating speed is entered into the solver as one of the choice of operating points. By placing this point in the solution directly, the solver is actively encouraged to run at optimum efficiency where possible.
  • a worked example is provided below of the process used to calculate the values of the points which are entered into the solver of the invention to model the cost of running a VSD pump at any chosen flow rate. The invention is not limited to this specific example and may be applied to pumps having different parameters operating in the same or other conditions.
  • Min Speed 70% (the highest of these speeds) o Calculate the maximum speed possible to run This includes the following considerations
  • Max Speed 100% (The smallest of these speeds)
  • Figure 6 shows minimum and maximum flows for a VSD pump, together with efficiency, o Calculate the Best Efficiency Speed From 100% speed curve, Max efficiency at 6.71 MGD
  • Figure 16 shows a method of predicting demand which has several unfavourable characteristics in terms of accuracy and essentially involves scaling up or down a curve of standard water usage (standard demand curve) by comparing demand recorded previously in the day with this standard demand curve and scaling future predictions of demand based on the difference.
  • standard demand curve standard water usage
  • the initial. step involves scaling the demand curve as shown in Figure 7 based on the estimated operator entered total system demand.
  • Figures 8 and 9 show demand predictions generated at two different points during a day based on the difference between the scaled curve and the observed values. The predictions do not reflect the current trend and do not adapt quickly to changing demand throughout the day.
  • Figure 10 shows that this method is not very accurate at predicting demand in the current half hour periods (shown by divergence of the dotted and dashed lines of Actual and Predicted demand). Systems using this approach may be slow to adapt to changes in demand.
  • embodiments of the invention provide for a more accurate prediction of the half hourly demand values for up to 24 hours, preferably by manipulating an adjusted standard demand curve to predict future demands by using recent historically recorded half hour demands. This is outlined in the following. Inputs and Outputs according to Another Method
  • the first step is to adjust the average Standard Demand.
  • the average system wide demand was 106MG.
  • the operator entered value for the estimated total system demand was 114MG.
  • the standard curve is scaled up as shown in Figure 11.
  • a Loess algorithm is used to predict water demand. For each demand prediction, one or more of the following specified fixed parameters are used:
  • MaxDemandChange This determines how far the predicted demand can stray from the standard demand curve
  • MaxDemandAdaptPeriods This determines how many past periods, immediately preceding the current period, are used to predict the future expectations
  • ScaleFactorSlope This determines the effect that the scale factor has on points beyond two periods into the future.
  • the calculated scale factor is used to scale the first two points after which the value of the scale factor approaches 1 as we predict further into the future. This parameter determines the slope at which it approaches 1
  • SteplnMaxDemandChange This is the amount per unit that the MaxDemandChange parameter is allowed to increase. The importance of these parameters is shown graphically in Figure 15.
  • the inputs for each particular demand prediction are as follows:
  • Standard Demand Curve o
  • typical average demand curves are preferably generated which give a water prediction usage for each half hour in the day.
  • Demand curves are calculated and stored for each season and each day type.
  • the Standard Demand Curve is scaled up or down depending on the estimated daily total system demand compared to average
  • Historical Demand Readings o Historical half hour demand readings up to 24 hours prior to the current time, are used
  • a series of weights are used to determine the importance of the recent half hour historical data.
  • An Exponential Distribution for the weights is used for all instances of this algorithm.
  • Max Demand AdaptPeriods number of weights where each value corresponds to the (MaxDemandAdaptPeriods - (/+1)th most recent half hour demand value) where:
  • K1 is a constant. It has been found that the algorithm performs well across a range of conditions if K1 is set to 1.075.
  • a scale factor is determined by dividing these sums and is used to scale the future standard curve points to obtain an estimate for the rest of the day.
  • the scale factor is capped by the MaxDemandChange parameter.
  • the original MaxDemandChange is no longer large enough. This would occur when demand picks up or drops off throughout the day and the standard curve is limited by how much it can move due to MaxDemandChange and therefore cannot get high enough / low enough to accurately predict future demand.
  • PrevPeriodsToCompareSF is the number of previous periods to compare the scale factor that would have been used in that particular period. A count is performed of any scale factors that are above and another count of any scale factors that are below the limits that MaxDemandChange imposes. These counts are then used to shift the allowable range beyond the MaxDemandChange for the Scale Factor. This allows for a larger scale factor to be used where required without the detrimental effects that a spike in the demand would cause.
  • the first two predicted values are the standard values scaled by multiplying by the Scale Factor (capped by the adjusted MaxDemandChange parameter). However the remaining standard values are scaled by a lesser amount.
  • the following steps are performed: o Scale the standard demand curve for the zone by the estimated total system demand o Create the exponential weighting function based on MaxDemandAdaptPeriods o Calculate the Current Scale Factor by dividing the weighted sum of actual demand by weighted sum of standard demand o Loop from Current Solve Period-1 back to Current Solve Pe ⁇ o ⁇ -PrevPeriodsToCompareSF Calculate the Scale Factor for each of these periods as though each period was the current solve period
  • New Lower Limit 1 - (MaxDemandChange + abs(a - b) * SteplnMaxDemandChange) Endif o Cap the Current Scale Factor using the New upper and lower limits o Determine the Scale Factor Vector that will be used to scale each future standard demand point:
  • the first two Scale Factor vector elements are the capped Current Scale Factor.
  • the ScaleFactorSlope is used to slope the scale factor towards 1 for the remaining vector elements up to period 48.
  • the minimum / maximum 1 o Scale Each Standard Demand Point for the current period to period 48 to determine the predicted Demand.
  • FIG 17 is a schematic representation of a system, generally marked 170, according to the invention.
  • System 170 includes reservoir or other water source 171, pumps 172 fluidly coupled to reservoir 171 , optional holding tank or reservoir 173 for receiving water from reservoir 171 through operation of pumps 172, demand points 174 for receiving water from reservoir 171 (via holding tank 173, where applicable) and outputting water depending on consumer demand, and control station 175.
  • Control station 175 is communicatively coupled to pumps 172 so as to enable monitoring and control thereof.
  • system 170 includes a number of elements not shown in Figure 17.
  • system 170 may include a plurality of reservoirs and control station 175 is preferably communicatively coupled to a plurality of sensors (e.g. pressure sensors) positioned around system 170 so as to enable monitoring of additional parameters of the system.
  • control station 175 is configured to include the novel functionality of one or more of the aforementioned aspects of the invention.
  • Control station 175 may include or be coupled to the SCADA of a conventional water distribution system.

Abstract

Cette invention concerne plusieurs approches permettant d'optimiser l'utilisation d'une pompe dans un système de distribution d'eau. Une approche consiste à utiliser des combinaisons de pompes physiques en tant que pompes logiques uniques afin de résoudre le problème d'optimisation de l'ordonnancement du pompage. Une autre approche consiste à sélectionner une vitesse de fonctionnement d'une pompe à variateur de vitesse utilisée pour distribuer un fluide dans un système de distribution de fluide à une charge hydraulique donnée. Un autre approche consiste à prédire une demande en fluide par ajustement de la différence entre une demande estimée et une demande réelle.
PCT/NZ2008/000192 2007-08-03 2008-07-31 Distribution d'eau WO2009020402A1 (fr)

Applications Claiming Priority (2)

Application Number Priority Date Filing Date Title
NZ560373 2007-08-03
NZ56037307 2007-08-03

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Cited By (7)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
WO2011088983A1 (fr) * 2010-01-19 2011-07-28 Grundfos Management A/S Procédé pour optimiser l'énergie de pompes
CN101509680B (zh) * 2009-03-16 2011-09-07 哈尔滨工业大学 调节同步变速变流量供热系统中水泵台数的节能控制方法
CN109426923A (zh) * 2017-08-30 2019-03-05 西门子股份公司 供水网络的控制
US11018610B2 (en) 2017-01-27 2021-05-25 Franklin Electric Co., Inc. Motor drive system and method
JP2021101113A (ja) * 2017-03-10 2021-07-08 カーエスベー ソシエタス ヨーロピア ウント コンパニー コマンディート ゲゼルシャフト アウフ アクチェンＫＳＢ ＳＥ ＆ Ｃｏ． ＫＧａＡ 遠心ポンプの回転速度を制御する方法
US11248598B2 (en) 2018-06-08 2022-02-15 Fluid Handling Llc Optimal efficiency operation in parallel pumping system with machine learning
WO2022217254A1 (fr) * 2021-04-08 2022-10-13 Cdm Smith Inc. Appareil et procédé d'efficacité de pompage

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US3294023A (en) * 1963-05-31 1966-12-27 Hersey Sparling Meter Co Automatic motor controller
GB2010959A (en) * 1977-12-21 1979-07-04 Danfoss As Controlleddelivery pump systems
JPH0626466A (ja) * 1992-07-10 1994-02-01 Meidensha Corp 浄水場のポンプ制御装置
JP2002005075A (ja) * 2000-06-26 2002-01-09 Toshihiro Omi ポンプ制御方式

Patent Citations (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US3294023A (en) * 1963-05-31 1966-12-27 Hersey Sparling Meter Co Automatic motor controller
GB2010959A (en) * 1977-12-21 1979-07-04 Danfoss As Controlleddelivery pump systems
JPH0626466A (ja) * 1992-07-10 1994-02-01 Meidensha Corp 浄水場のポンプ制御装置
JP2002005075A (ja) * 2000-06-26 2002-01-09 Toshihiro Omi ポンプ制御方式

Cited By (15)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN101509680B (zh) * 2009-03-16 2011-09-07 哈尔滨工业大学 调节同步变速变流量供热系统中水泵台数的节能控制方法
EA025057B1 (ru) * 2010-01-19 2016-11-30 Грундфос Менеджмент А/С Способ энергетической оптимизации насосов
CN102753831A (zh) * 2010-01-19 2012-10-24 格伦德福斯管理联合股份公司 对泵进行能源优化的方法
US9051936B2 (en) 2010-01-19 2015-06-09 Grundfos Management A/S Method for optimizing the energy of pumps
EP2354555B1 (fr) 2010-01-19 2015-12-16 Grundfos Management A/S Procédé d'optimisation de l'énergie de pompes
WO2011088983A1 (fr) * 2010-01-19 2011-07-28 Grundfos Management A/S Procédé pour optimiser l'énergie de pompes
EP2354555A1 (fr) * 2010-01-19 2011-08-10 Grundfos Management A/S Procédé d'optimisation de l'énergie de pompes
US11349419B2 (en) 2017-01-27 2022-05-31 Franklin Electric Co., Inc. Motor drive system including removable bypass circuit and/or cooling features
US11018610B2 (en) 2017-01-27 2021-05-25 Franklin Electric Co., Inc. Motor drive system and method
JP2021101113A (ja) * 2017-03-10 2021-07-08 カーエスベー ソシエタス ヨーロピア ウント コンパニー コマンディート ゲゼルシャフト アウフ アクチェンＫＳＢ ＳＥ ＆ Ｃｏ． ＫＧａＡ 遠心ポンプの回転速度を制御する方法
US10782663B2 (en) 2017-08-30 2020-09-22 Siemens Aktiengesellschaft Control of a water supply network
CN109426923A (zh) * 2017-08-30 2019-03-05 西门子股份公司 供水网络的控制
CN109426923B (zh) * 2017-08-30 2022-11-29 西门子股份公司 供水网络的控制
US11248598B2 (en) 2018-06-08 2022-02-15 Fluid Handling Llc Optimal efficiency operation in parallel pumping system with machine learning
WO2022217254A1 (fr) * 2021-04-08 2022-10-13 Cdm Smith Inc. Appareil et procédé d'efficacité de pompage

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