Pump station optimization scheduling method

Abstract

The invention discloses a pump station optimal scheduling method, which comprises the following steps of: setting a target function by taking the minimum power consumption carbon emission intensity Ee of a pump station as a target and taking the startup and shutdown variable and the water pump speed regulation ratio of each water pump in the pump station as decision variables; setting constraint conditions for solving the objective function, wherein the constraint conditions comprise water supply flow and water supply pressure constraints of the pump station; and solving a pump station optimization scheduling scheme. And using a plurality of different intelligent optimization algorithms to solve the new objective function to obtain a plurality of pump station optimization scheduling schemes, thereby determining an optimal pump station optimization scheduling scheme. The water supply requirement can be met, and low-carbon water supply can be achieved to the maximum extent.

Description

Technical Field

[0001] The invention relates to the field of optimized operation of urban water supply pumping stations, and in particular to a method for optimizing the dispatching of pumping stations. Background Art

[0002] At present, a key issue that water supply companies are more concerned about is the operating energy consumption of water delivery pump stations in water supply plants and stations. Reducing the energy consumption of pump stations under the conditions of ensuring water supply safety and meeting water supply needs is of great significance for reducing pump station operating costs, saving urban electricity consumption, and reducing greenhouse gas emissions. At the same time, with the prominent environmental problems such as excessive resource consumption and excessive greenhouse gas emissions, low-carbon concepts and low-carbon planning have emerged and developed, and carbon evaluation has gradually become one of the important contents of low-carbon construction. Pump stations account for a large proportion of the total energy consumption of water supply systems. The carbon emissions related to pump stations mainly come from electricity consumption and are rising year by year. According to relevant literature, the carbon emissions of pump stations account for more than 50% of the total carbon emissions of electricity consumption of pump stations. Pumps, motors and other equipment are the focus of high energy consumption. How to improve the operating efficiency of water pumps and motors under working conditions is the key to equipment energy saving. Pump stations should increase investment and research on low-carbon planning, analyze carbon reduction potential through carbon evaluation, gradually implement low-carbon transformation projects, and promote the continuous improvement of energy utilization efficiency.

[0003] Pump station control systems generally rely on traditional monitoring and control methods. Pump station dispatching methods are generally determined by technicians based on their own experience, with low overall operating efficiency. Existing pump station dispatching methods rely heavily on subjective experience, resulting in different dispatching strategies formulated by different technicians. Dispatching strategies formulated based on historical data have lags and cannot guarantee the optimal pump station optimization dispatching scheme, resulting in low dispatching efficiency and failure to meet the dispatching needs of all working conditions. Moreover, due to differences in the technical level and experience of dispatchers, there is a certain degree of arbitrariness, resulting in complex manual dispatching, difficult reliability, high energy consumption of pump stations, and energy waste. For cities with a population of tens of millions, the use of manual experience for dispatching cannot provide a pump station optimization dispatching scheme that can both ensure safe water supply and achieve low-carbon water supply to the greatest extent. The dispatching method based on intelligent algorithms has high requirements for the acquisition of flow, pressure and other data of water demand terminals in the water supply network, the transmission of real-time signals, the construction of system platforms, intelligent algorithm calculations, and the technical level of operators. Although the algorithm has the advantages of strong search capabilities, few parameters, and fast calculation speed, it also has the disadvantages of being easily trapped in local optimality, low calculation efficiency, and unstable results. Summary of the invention

[0004] The purpose of this application is to provide a pump station optimization scheduling method that can both meet water supply needs and achieve low-carbon water supply to the greatest extent possible.

[0005] In the first aspect, the present application provides a method for optimizing the scheduling of a pump station, comprising: e The lowest value is taken as the goal, and the start and stop variables of each water pump in the pump station and the water pump speed ratio are taken as decision variables to set the objective function; set the constraint conditions for solving the objective function, and the constraint conditions include the water supply flow and water supply pressure constraints of the pump station; solve the optimal scheduling plan of the pump station.

[0006] In a possible implementation, the objective function is:

[0007]

[0008] Among them, v i is the start and stop variable of the i-th pump; v i =1 means the i-th water pump is turned on, v i =0 means the i-th water pump is turned off; E e is the carbon emission intensity of the power consumption of the pumping station, f e is the carbon emission factor of the power consumption of the pumping station; m is the total number of water pumps in the pumping station; Q i is the flow rate of the i-th water pump in the pumping station; H av N is the average net head of the pump station during operation in a certain period of time; i is the shaft power of the i-th water pump in the pumping station;

[0009] If the i-th water pump is a constant speed pump, then:

[0010]

[0011] Among them, H i is the head of the i-th pump in the pumping station; H xi is the virtual head of the ith pump, S xi is the virtual resistance coefficient of the i-th water pump, a i , b i 、c i These are the fitting parameters of the characteristic curve of the i-th water pump;

[0012] If the i-th water pump is a speed-regulating pump, then:

[0013]

[0014] Among them, a si , b si 、c si These are the fitting parameters of the characteristic curve of the i-th water pump;

[0015] S i =Q i / Q 0i , is the speed ratio of the i-th water pump, Q 0i is the flow rate of the i-th water pump when it runs at rated speed; Hxi is the virtual head of the ith pump, S xi is the virtual resistance coefficient of the i-th water pump;

[0016] The water supply flow and water supply pressure constraints of the pump station are:

[0017]

[0018] Among them, H P is the water supply pressure of the pump station; Q P The water supply flow of the pumping station.

[0019] In a possible implementation, the constraint conditions also include a speed ratio constraint of a speed regulating pump and a number of water pumps turned on constraint:

[0020] S i,min ≤S i ≤1

[0021] 0≤n≤n max

[0022] Among them, S i,min is the minimum value of the speed ratio of the i-th speed regulating pump; n is the number of pumps turned on in the pump station; n max Design the maximum number of pumps to be turned on for the pump station.

[0023] In one possible implementation, the constraint condition is converted into a penalty function, and the constructed penalty function is combined with the original objective function to form a new objective function:

[0024]

[0025] Where L is the new objective function value and λ is the penalty coefficient.

[0026] In a possible implementation, solving the pump station optimization scheduling scheme includes: using a plurality of different intelligent optimization algorithms to respectively solve the new objective function to obtain a plurality of pump station optimization scheduling schemes and corresponding objective function values ​​L: L1, L2, ..., L Num , L1,L2,...,L Num etc., and take the min{L1,L2,...,L Num The pump station optimization scheduling scheme of} is taken as the optimal pump station optimization scheduling scheme solved by the intelligent algorithm; where Num is the number of pump station optimization scheduling schemes.

[0027] In one possible implementation, based on the range Q of flow and head data within the high efficiency interval of the pump station min ~Q max and H min ~H max , Q min~Q max and H min ~H max All discrete flow and head data points within the range are combined one by one and used as parameter combinations (Q P ,H P ), and solve different parameter combinations (Q P ,H P ) under the optimal pump station optimization scheduling scheme; the optimal pump station optimization scheduling scheme is stored in the water pump optimization scheduling database.

[0028] In a possible implementation, the water pump optimization scheduling database is deployed to the corresponding water plant server; after obtaining the actual scheduling demand of the pipe network, the corresponding (Q P ,H P ) The optimal pump station optimization scheduling plan corresponding to the optimal pump station optimization scheduling plan transmits the corresponding scheduling instructions to the water plant pump group control module to control the start and stop and frequency conversion adjustment of the corresponding water pump.

[0029] In this way, efficient scheduling can be achieved.

[0030] In a second aspect, the present application provides an electronic device, including: a memory and a processor;

[0031] The memory is used to store computer programs;

[0032] The processor is used to call the computer program to execute the method as described above.

[0033] In a third aspect, the present application provides a computer-readable storage medium, in which a computer program is stored. When the computer program is executed on an electronic device, the electronic device implements the method as described above.

[0034] In a fourth aspect, the present application provides a computer program product, including a computer program, and when the computer program is executed on an electronic device, the electronic device implements the method as described above.

[0035] The specific implementation methods of the second to fourth aspects of the present application can refer to the implementation methods of the first aspect, which will not be repeated here.

[0036] Beneficial effects:

[0037] This application is based on the carbon emission intensity of pump station electricity consumption E eThe lowest is the goal, the start and stop variables of each water pump in the pump station and the speed ratio of the water pump are used as decision variables, the objective function is set, and the water supply flow and water supply pressure of the pump station are used as constraints to solve the optimal dispatching scheme of the pump station. It can reduce the energy consumption of the pump station and reduce the carbon emission intensity of electricity consumption while ensuring that the water supply demand is met. It has high reliability and high dispatching efficiency. BRIEF DESCRIPTION OF THE DRAWINGS

[0038] Figure 1 This is a schematic diagram of an embodiment of the present application. DETAILED DESCRIPTION

[0039] In order to enable those skilled in the art to better understand the solution of the present application, the technical solution in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work should fall within the scope of protection of the present application.

[0040] In response to the problems of instability in the current pump group scheduling methods, the existing optimization scheduling methods do not consider the objective function of low carbon emissions. The pump station optimization scheduling plan given by the algorithm alone is not the global optimal solution. It does not fully consider the various constraints of the pump station operation, the actual scheduling situation and the scheduling habits of personnel. Sometimes the credibility is not high, resulting in a low adoption rate or execution rate of the intelligent pump station optimization scheduling plan, and cannot guarantee actual effectiveness and safety. Therefore, it is necessary to establish a new set of pump station optimization scheduling methods to reduce carbon emissions.

[0041] Therefore, this application proposes a pump station optimization scheduling method, which includes: based on the carbon emission intensity E of the pump station power consumption e The lowest value is taken as the goal, the start and stop variables of each water pump in the pump station and the speed ratio of the water pump are taken as decision variables, the objective function is set, and the water supply flow and water supply pressure of the pump station are taken as constraints to solve the optimal scheduling plan of the pump station.

[0042] Preferably, the embodiment of the present application may also include pump station status assessment and water pump fault diagnosis. Through the perfect monitoring of various water pump status parameters, the water pump status can be evaluated more intelligently and accurately. When the monitoring indicators are abnormal, the faults in the water pump operation can be judged, and corresponding fault handling suggestions can be given. When the status assessment indicators are qualified, the low-carbon operation scheduling of the pump group is assisted by the intelligent algorithm combined with expert verification, and the water pump maintenance or repair is performed when the status assessment indicators are unqualified. The water pump fault diagnosis and pump station optimization scheduling scheme are stored in the water pump optimization scheduling database, and deployed on the water plant server for access and continuous improvement, which can not only obtain more accurate and feasible pump group fault correction and pump station optimization scheduling schemes, but also reduce the hardware investment of the water supply company. This method is based on the dual guarantees of multiple monitoring data, multiple intelligent algorithm selection and expert verification, which can ensure the reliability, uniqueness and operability of the pump station optimization scheduling scheme under each working condition calculated by the water pump fault diagnosis and intelligent algorithm after expert verification.

[0043] The following references Figure 1 The steps that may be included in the embodiments of the present application are described in detail:

[0044] 1. Pump status assessment

[0045] Three water pump status evaluation indicators are provided, and one indicator can be selected to evaluate the water pump according to the actual situation. When the evaluation result is excellent, the operating status can be left unchanged; when the evaluation result is qualified, the water pump operation can be optimized; when the evaluation result is unqualified, water pump maintenance or fault diagnosis and repair are required.

[0046] (1) Actual efficiency of the pump:

[0047]

[0048] Where η i,re is the actual efficiency of the i-th pump in the pumping station, ρ is the density of water, which is 1000 kg / m 3 ; g is the acceleration due to gravity, which is 9.8m / s 2 ;Q i is the flow rate of the i-th pump in the pumping station, in m 3 / h; H i is the head of the i-th pump in the pumping station, m; N i is the shaft power of the i-th water pump in the pumping station, kW.

[0049] According to the Technical Management Regulations for Pumping Stations (GB / T 30948-2021), the efficiency of a centrifugal pumping station that transports clean water is ≥ 60%. For example, if the actual efficiency of the pump is below 60%, the result of the pump status assessment is unqualified; if the actual efficiency of the pump is between 60% and 70%, the result of the pump status assessment is qualified; if the actual efficiency of the pump is 70% or above, the result of the pump status assessment is excellent (the assessment standard can be determined according to the actual situation of each pumping station).

[0050] (2) Energy consumption of pumping station:

[0051]

[0052] Where N is the total shaft power of the pump station, kW; v i is the start and stop variable of the i-th pump; v i =1 means the i-th water pump is turned on, v i =0 means the i-th water pump is turned off; m is the total number of water pumps in the pumping station; e is the energy consumption of the pumping station, that is, the energy consumed by the water pump for every 1000t of water lifted and the lifting height of 1m, kW·h / (kt·m); is the total flow rate of the pump station during operation in a certain period, m 3 / s;H av It is the average net head of the pump station during operation in a certain period of time, m.

[0053] According to the Technical Management Regulations for Pumping Stations (GB / T 30948-2021), for electric pumping stations, the energy consumption of axial flow pumping stations or guide vane mixed flow pumping stations with a net head of less than 3m and centrifugal pumping stations or volute mixed flow pumping stations transporting sediment-containing water should not be greater than 4.95kW·h / (kt·m); for other pumping stations, it should not be greater than 4.53kW·h / (kt·m). For example, e≤4.53kW·h / (kt·m) can be set, and the water pump status assessment result is excellent; when e exceeds 4.53kW·h / (kt·m) by less than 10%, that is, e is in the range of 4.53 to 4.98kW·h / (kt·m), the water pump status assessment result is qualified; when e exceeds 4.53kW·h / (kt·m) by more than 10%, that is, e≥4.98kW·h / (kt·m), the water pump status assessment result is unqualified (the assessment standard can be determined according to the actual situation of each pumping station).

[0054] (3) Carbon emission intensity of electricity consumption at pumping stations:

[0055] E e =e×f e

[0056] E e is the carbon emission intensity of power consumption of the pumping station, kgCO2 / (kt·m); f eis the carbon emission factor of electricity consumption of the pump station, kgCO2 / (kW·h), which can be taken as the average emission factor of the national power grid of the Ministry of Ecology and Environment in 2022, 0.5703kgCO2 / (kW·h).

[0057] According to the "Guidelines for the Evaluation of the Implementation Effect of the Carbon Emission Management System Construction" DB11 / T 1558-2018: According to the latest published carbon emission accounting guidelines and the corresponding advanced carbon emission values, when the advanced carbon emission intensity values ​​of the carbon emission units exist and are applicable, under the same boundary conditions, the carbon emission intensity of the carbon emission units reaches the latest valid industry advanced value during the evaluation reporting period, and the score is 100 points. If the gap with the industry advanced value is within 10% (including 10%), the score is 80 points, and if it is beyond 10%, the score is 60 points. Therefore, E can be set e The latest effective industry advanced value was achieved during the evaluation reporting period, and the pump status evaluation result was excellent; E e The difference with the industry advanced value is within 10% (including 10%), and the pump status assessment result is qualified; E e If the gap with the industry advanced value is more than 10%, the water pump condition assessment result is unqualified.

[0058] 2. Water pump fault diagnosis

[0059] Water pump parameter monitoring method:

[0060] Existing water pumps can be equipped with magnetic sensors to monitor the shaft temperature and vibration of the water pump. For example, for monitoring vibration signals, the vibration sensor used has a collection frequency of 0-50000Hz, collecting vibration information 20 times per second. The vibration signal data measured within 5s is {g1, g2, ..., g 100}.

[0061] 1 Hardware:

[0062] (1) Pressure sensor: installed in the main pipeline and key nodes of the pump station to monitor the changes in water pressure at the pump inlet and outlet in real time;

[0063] (2) Flow sensor: installed on the main water supply pipe to monitor water flow and convert it into a readable electrical signal;

[0064] (3) Bearing temperature sensor: installed on the bearing housing to identify mechanical faults related to bearing temperature changes;

[0065] (4) Temperature sensor: installed in the water pump body, used to identify mechanical faults related to pump body temperature;

[0066] (5) Vibration sensor: installed on the motor base, used to detect the vibration frequency and intensity of the equipment.

[0067] The voltage, current and other parameters of the water pump are monitored by a multi-function meter.

[0068] 2Software:

[0069] The software processes and analyzes the various parameters of the pump, and uses the envelope curve to determine the upper and lower limits of the data range within a period of time. If the data exceeds the threshold for a certain period of time (such as 10s), it will be diagnosed as the corresponding fault type. If there is a monitoring parameter signal, the possible type of fault will be automatically determined first, and the others will be manually determined by experts. The results of the judgment will be recorded in the fault phenomenon-fault cause-corrective measure correspondence table of the water pump fault diagnosis database, and new fault items will be continuously added to improve it as the basis for future fault diagnosis.

[0070] Construction of fault diagnosis software model:

[0071] (1) Extraction of pump pressure, flow, vibration and other signals: including data cleaning (filtering), data normalization, trend analysis, etc.

[0072] (2) Characteristic data threshold extraction (obtain the maximum and minimum values ​​of each monitoring data signal, use cubic spline curve interpolation to obtain the upper and lower envelopes of the signal as the highest and lowest values ​​within a period of time, determine the range of characteristic threshold values, and add coefficients to freely adjust the fault thresholds of each parameter).

[0073] 1) Insufficient pump flow: The pump flow monitoring data is ≤ the lowest value of the historical monitoring data in the past month * a1, where a1 is a coefficient, for example, 90%;

[0074] 2) The pump outlet pressure is too high: the pump pressure monitoring data ≥ the highest value of the historical monitoring data in the past month * a2, where a2 is a coefficient, for example, it can be 110%;

[0075] 3) The pump cannot generate enough pressure: the pressure monitoring data of the pump is ≤ the lowest value of the historical monitoring data in the past month * a3, where a3 is a coefficient, for example, 80%;

[0076] 4) Bearing temperature rise is too large: the temperature rise monitoring data of the pump bearing ≥ the highest value of the historical monitoring data in the past month * a4, where a4 is a coefficient, for example, it can be 120%;

[0077] 5) Excessive vibration during pump operation: The vibration monitoring data of the pump ≥ the highest value of the historical monitoring data in the past month * a5, where a5 is a coefficient, for example, it can be 110%;

[0078] 6) Pump temperature rise is too high: the pump temperature rise monitoring data ≥ the highest value of the historical monitoring data in the past month * a6, a6 is a coefficient, for example, it can be 120%;

[0079] 7) The power consumed by the pump is too large: the power monitoring data of the pump ≥ the highest value of the historical monitoring data in the past month * a7, where a7 is a coefficient, for example, it can be 110%;

[0080] 8) Motor overload: The motor's operating current ≥ the motor's rated current.

[0081] (3) Fault diagnosis

[0082] During the operation of each device, the faults automatically determined by monitoring data include:

[0083] 1) When the instrument automatically determines that the fault is a fault signal of insufficient pump flow, the corresponding fault causes include excessive pump outlet pressure, too high device head or too much resistance, etc.;

[0084] 2) When the instrument automatically determines that the fault is a fault signal of high pump outlet pressure, the corresponding fault cause type includes high speed;

[0085] 3) When the instrument automatically determines that the fault is a fault signal that the pump cannot generate sufficient pressure, the corresponding fault cause types include air leakage in the inlet pipe or flange, too low pump speed, etc.;

[0086] 4) When the instrument automatically determines that the fault is a fault signal of excessive bearing temperature rise, the corresponding fault causes include deformation of the pump due to additional external force from the pipeline, resonance between the pump and the pipeline, etc.;

[0087] 5) When the instrument automatically determines that the fault is a fault signal of excessive vibration during pump operation, the corresponding fault causes include the pump or suction pipe not completely excluding air or being filled with liquid, the pump being deformed by the additional external force of the pipe, etc.

[0088] 6) When the instrument automatically determines that the fault is a fault signal of excessive pump temperature rise, the corresponding fault causes include the pump or suction pipe not completely excluding air or filled with liquid, friction between rotating parts and fixed parts, etc.;

[0089] 7) When the instrument automatically determines that the fault is a fault signal of excessive power consumption of the pump, the corresponding fault causes include operation with excessively large flow rate, excessive compression of the packing, etc.;

[0090] 8) When the instrument automatically determines that the fault is a motor overload fault signal, the corresponding fault cause types include friction between rotating parts and fixed parts, too low operating voltage, etc.

[0091] In addition, experts analyze the data from on-site monitoring, the differences in the flow, head, efficiency and other curves of the pump under actual and theoretical conditions, and combine experience to determine the pump failure. The faults that are manually determined are:

[0092] 1) When the fault is manually judged as a pump leakage fault signal, the corresponding fault cause types include bolts not tightened, sealant and sealing gasket failure, etc.;

[0093] 2) When the fault is manually judged to be a fault signal of excessive shaft seal leakage, the corresponding fault cause types include deformation of the pump due to additional external force on the pipeline, resonance between the pump and the pipeline, etc.;

[0094] 3) When the fault is manually judged to be a fault signal that the pump does not pump liquid, the corresponding fault cause types include pump not primed, pump priming incomplete, etc.;

[0095] 4) When the fault is manually judged as a fault signal that the pump cannot deliver enough liquid, the corresponding fault cause types include the suction lift or static lift is too high, air leakage in the inlet pipe or flange, etc.;

[0096] 5) When the fault is manually judged as a fault signal that the pump stops running for a short period of time, the corresponding fault cause types include air leakage in the packing seal, wear of the impeller or wear ring, etc.

[0097] After automatically judging the fault signal or manually judging the fault through on-site analysis, the fault judgment model calls the fault phenomenon-fault cause-corrective measure correspondence table in the general water pump fault diagnosis database to analyze the cause of the fault, and combines experts to determine the only result, and take corresponding fault correction measures. When there is no corresponding fault in the general water pump fault diagnosis database, the corresponding fault record is added according to the actual fault type encountered. Record each fault judgment, analysis and correction record in the water pump fault diagnosis database, and sort them according to the number of occurrences or the amount of maintenance, continuously improve and optimize the water pump fault diagnosis database of the pump station, and form a personalized general water pump fault diagnosis database for each pump station.

[0098] The embodiment of the present application combines multiple parameters to determine the state of the water pump and diagnose the fault, and combines expert experience to make accurate judgments, which can make the fault correction plan more reasonable and feasible.

[0099] Table 1 Fault phenomenon-fault cause-corrective measures correspondence table (universal water pump fault diagnosis database)

[0100]

[0101]

[0102]

[0103]

[0104]

[0105] Note: Mainly refer to the relevant instructions or operating manuals of centrifugal pumps of KENFLO, GRUNDFOS and WILO brands.

[0106] 3. Pump station optimization and dispatching

[0107] When the pump station is put into operation or put back into operation after fault repair, the characteristic curve of the water pump can be fitted, and the carbon emission intensity can be used as the target to optimize it through an intelligent scheduling model to achieve low-carbon operation of the pump group.

[0108] 3.1. Pump characteristic curve fitting

[0109] According to the scattered data of the water pump flow rate Q and head H, shaft power N, and efficiency η, the characteristic curves of the water pump flow rate and head, power, and efficiency can be fitted by a polynomial or exponential curve, and H = f1(Q), N = f2(Q), and η = f3(Q) can be obtained.

[0110] If the i-th water pump is a fixed speed pump, it runs at the rated speed, and its flow rate and head Q i -H i , flow and power Q i -N i , flow and efficiency Q i -η i The characteristic curves (polynomial fitting curves) are:

[0111]

[0112] Among them, H xi is the virtual head of the i-th water pump, S i is the speed ratio of the i-th water pump, S xi is the virtual resistance coefficient of the i-th water pump, η i is the theoretical efficiency of the i-th water pump, a i , b i 、c i 、s i 、e i 、f i are the fitting parameters of the characteristic curve of the i-th water pump.

[0113] According to the water pump similarity law, if the i-th water pump is a speed-regulating pump, its speed ratio S i The characteristic curve is:

[0114]

[0115] Among them, a si , b si 、c si ,d si 、e si 、fsi These are the fitting parameters of the characteristic curve of the i-th water pump;

[0116] Speed ​​ratio S i =Q i / Q 0i , Q 0i is the flow rate of the i-th water pump when it runs at rated speed, in m 3 / h.

[0117] 3.2. Establishment of pump station optimization dispatch model

[0118] In the process of optimizing the dispatch of the pump station, the pump group operation plan that meets the water supply requirements (water flow and water pressure) of the pump station is obtained by adjusting the start-up model, number and speed of the water pumps in the pump station. Taking the start-up and shutdown variables and the speed ratio of the water pumps in the pump station as the decision variables, on the basis of meeting the water supply flow and water supply pressure of the pump station, the carbon emission intensity E of the power consumption of the pump station is made e Minimum, its main task is to determine the carbon emission intensity E of electricity consumption in pumping stations e The starting model, number and speed of the water pumps in the pumping station under the lowest condition.

[0119] Objective function:

[0120] Carbon emission intensity of electricity consumption of pumping station E e lowest:

[0121] f(v i ,S i )=min E e

[0122] The constraints include:

[0123] (1) Constraints on water supply flow and pressure at the pumping station;

[0124] The parallel operation of m water pumps should meet the following requirements:

[0125]

[0126] Among them, H P is the water supply pressure of the pumping station, that is, the water discharge head of the pumping station (the water supply pressure of the pumping station), m; Q P The water supply flow rate of the pump station is determined according to the total flow rate required by the pipe network, that is, the total flow rate required by the pipe network users;

[0127] (2) Speed ​​ratio constraints of speed-regulating pumps. If the speed of a speed-regulating pump is higher than the rated speed, the pump is prone to mechanical damage and safety hazards, so the speed ratio should not be greater than 1; when the speed ratio is less than a certain value, the operating efficiency of the pump will drop sharply. The lower limit of the speed ratio can be determined based on the performance of the speed-regulating pump.

[0128] Si,min ≤S i ≤1

[0129] Among them, S i,min is the minimum speed ratio of the i-th speed regulating pump.

[0130] (3) Constraints on the number of pumps turned on:

[0131] 0≤n≤n max

[0132] Where n is the number of pumps turned on in the pump station; n max Design the maximum number of pumps to be turned on for the pump station.

[0133] As mentioned above, the constraints mainly include three aspects: the water supply capacity of the pump station, the speed regulation performance of the pump, and the number of pumps turned on. The constraints on the speed regulation performance of the pump and the number of pumps turned on can be achieved by setting upper and lower limits on the position of each dimension of the variable. The relationship between the head and flow of the pump can be expressed by the characteristic curve of the pump, so the constraint on the head can be converted into a constraint on the flow, but the constraint function on the flow cannot be directly described by the position of the variable, so the penalty function method is used to convert the constraints of the original problem into a penalty function. The constructed penalty function is combined with the original objective function to form a new objective function L:

[0134]

[0135] Where: λ is the penalty coefficient, which should be a sufficiently large number.

[0136] The penalty coefficient is generally a positive number. By adjusting the penalty coefficient, the influence of the constraint condition is controlled, so as to gradually approach the solution of the original constrained optimization problem. The larger the penalty coefficient, the greater the penalty for variables that are not in the feasible domain. The minimum value of the objective function is expressed as The closer it is to 0. The penalty coefficient needs to be determined in program debugging. If the strict minimum point of the objective function cannot be solved when the penalty coefficient is small, you can consider increasing the penalty coefficient.

[0137] By investigating the total flow rate required by the pipeline network at each time period of the day and the head required for the pump station to discharge water in the historical data of the pipeline network, the pump startup plan for each time period of the pump station can be determined (Q P ,H P )’s scope.

[0138] Common intelligent optimization algorithms for solving such pump station optimization scheduling problems include, but are not limited to, genetic algorithms, particle swarm algorithms, and artificial bee colony algorithms. A variety of different intelligent optimization algorithms are used to solve new objective functions and obtain multiple pump station optimization scheduling schemes and corresponding objective function values ​​L: L1, L2, ..., L Num, L1,L2,...,L Num etc., and take the min{L1,L2,...,L Num The pump station optimization scheduling scheme of} is taken as the optimal pump station optimization scheduling scheme solved by the intelligent algorithm; where Num is the number of pump station optimization scheduling schemes.

[0139] 3.3 Solve the model and establish a pump optimization scheduling database

[0140] Range Q based on flow and head data within the high efficiency interval of the pump station min ~Q max and H min ~H max (can be an integer), Q min ~Q max and H min ~H max All discrete flow and head data points within the range are combined one by one, and the flow data point interval can be 50m 3 / s, the head data point interval can be 0.2m, such as (Q min ,H mi n), ..., (Q max ,H max ), respectively as parameter combinations (Q P ,H P ), and use a variety of intelligent algorithms to calculate different parameter combinations (Q P ,H P ) under each parameter combination (Q P ,H P ) is the optimal pumping station optimization scheduling scheme that takes the minimum value of the objective function.

[0141] In addition, after the experts with rich practical engineering experience manually verified the plan, the abnormal pump station optimization scheduling plans such as those that did not meet the water supply demand and those with too high power were eliminated, and the confirmed optimal pump station optimization scheduling plan was stored in the water pump optimization scheduling database. The corresponding parameters of the optimal pump station optimization scheduling plan include: the water supply flow rate Q of the pump station P , water supply pressure of the pump station H P , the combination of pump group start-stop schemes, the speed ratio of each speed regulating pump, the theoretical efficiency and power of each water pump; the water supply flow rate Q of the pump station P , water supply pressure of the pump station H P It is a known condition in the pump station optimization scheduling model; the combination of pump group start and stop schemes and the speed ratio of each speed regulating pump are all optimized as the variables to be optimized in the objective function, and then the theoretical calculation efficiency and power of each water pump can be obtained.

[0142] By adding a manual verification process, unreasonable plans can be discarded after expert verification, making the pump station optimization scheduling plan more reasonable and feasible.

[0143] At present, most pumping stations still use local servers to process and calculate relevant data. For this purpose, more servers are equipped to meet the needs of future data analysis and calculation. The use of algorithm calculations requires high computer processing power, so the initial software and hardware investment costs of water supply companies are high. The embodiment of the present application establishes a water pump optimization scheduling database and provides it to the water supply company after running the positive integer combination working conditions of the flow and head required by all users on an external server, thereby reducing the hardware investment of the water supply company.

[0144] 3.4. Deploy database in water supply company for pump scheduling decision and control

[0145] The water pump optimization scheduling database is deployed to the corresponding water plant server; after obtaining the actual scheduling requirements of the pipe network, the corresponding (Q P ,H P ) The optimal pump station optimization scheduling plan corresponding to the water plant transmits the instructions to the water plant pump group control module to control the start and stop and frequency conversion adjustment of the corresponding water pumps.

[0146] 3.5. Database modification and updating

[0147] After the pump group has been running for a period of time, an alarm will be taken for the actual dispatching demand of the pipeline network that exceeds the range of the water pump optimization dispatching database, that is, the actual dispatching demand of the pipeline network that exceeds the water supply capacity of the pump station; for the actual dispatching demand of the pipeline network that is within the water supply capacity of the pump station but exceeds the high efficiency range of the pump station, the water pump motor frequency conversion or frequency conversion combined with valve adjustment is used to adjust to the required flow and head. When the pump station is dispatched, the actual efficiency η of a water pump i,re Exceeds the theoretical calculation efficiency η of the water pump in the corresponding pump station optimization scheduling scheme in the water pump optimization scheduling database i A certain deviation p (such as 3%, which can be set and adjusted according to actual conditions), that is, It is necessary to correct the pump characteristic curve through experiments, and update the corrected data and algorithm calculation results to the pump optimization scheduling database.

[0148] Traditional water pump status assessment, fault judgment and manual experience scheduling have certain subjectivity due to differences in technical skills and experience of technicians, which makes manual judgment and scheduling complex and reliability difficult to guarantee. The method provided in the embodiment of the present application is based on multiple monitoring data, multiple intelligent algorithm selection and expert verification, and can obtain a unique and reliable fault correction and pump station optimization scheduling plan; the use of intelligent algorithms in pump group scheduling is prone to fall into local optimal solutions, lack of stability, and the results of each operation are not exactly the same. The embodiment of the present application uses multiple intelligent algorithms to solve, and after using intelligent algorithms to optimize scheduling, these shortcomings can be avoided through expert verification. The embodiment of the present application realizes pump group fault judgment and precise scheduling through a database deployment method, which does not require high on-site software and hardware, has higher operating efficiency, is more stable, and has lower costs.

[0149] The embodiment of the present application also provides an electronic device, including: a memory and a processor;

[0150] The memory is used to store computer programs;

[0151] The processor is used to call the computer program to execute the method as described above.

[0152] An embodiment of the present application further provides a computer-readable storage medium, in which a computer program is stored. When the computer program is executed on an electronic device, the electronic device implements the method as described above.

[0153] An embodiment of the present application further provides a computer program product, including a computer program. When the computer program is executed on an electronic device, the electronic device implements the method as described above.

[0154] The specific implementation methods of a system, electronic device, computer-readable storage medium, and computer program product provided in the embodiments of the present application can refer to the specific embodiments of the above-mentioned method and will not be repeated here.

[0155] Obviously, those skilled in the art should understand that the above-mentioned units or steps of the present application can be implemented by a general-purpose computing device, they can be concentrated on a single computing device, or distributed on a network composed of multiple computing devices, and optionally, they can be implemented by a program code executable by a computing device, so that they can be stored in a storage device and executed by the computing device, or they can be made into individual integrated circuit modules, or multiple modules or steps therein can be made into a single integrated circuit module for implementation. In this way, the present application is not limited to any specific combination of hardware and software.

[0156] The above description is only the preferred embodiment of the present application and is not intended to limit the present application. For those skilled in the art, the present application may have various modifications and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application shall be included in the protection scope of the present application.

Claims

1. A method for optimizing the dispatching of a pump station, characterized in that: include: The carbon emission intensity of the power consumption of the pumping station is E e The lowest value is taken as the goal, and the start and stop variables of each water pump in the pump station and the water pump speed ratio are taken as decision variables to set the objective function; set the constraint conditions for solving the objective function, and the constraint conditions include the water supply flow and water supply pressure constraints of the pump station; solve the optimal scheduling plan of the pump station.

2. The method according to claim 1, characterized in that The objective function is: Among them, v i is the start and stop variable of the i-th water pump; v i =1 means the i-th water pump is turned on, v i =0 means the i-th water pump is turned off; E e is the carbon emission intensity of the power consumption of the pumping station, f e is the carbon emission factor of the power consumption of the pumping station; m is the total number of water pumps in the pumping station; Q i is the flow rate of the i-th water pump in the pumping station; H av N is the average net head of the pump station during operation in a certain period of time; i is the shaft power of the i-th water pump in the pumping station; If the i-th water pump is a constant speed pump, then: Among them, H i is the head of the i-th pump in the pumping station; H xi is the virtual head of the i-th water pump, S xi is the virtual resistance coefficient of the i-th water pump, a i 、b i 、c i These are the fitting parameters of the characteristic curve of the i-th water pump; If the i-th water pump is a speed-regulating pump, then: Among them, a si , b si 、c si These are the fitting parameters of the characteristic curve of the i-th water pump; S i =Q i / Q 0i , is the speed ratio of the i-th water pump, Q 0i is the flow rate of the i-th water pump when it runs at rated speed; H xi is the virtual head of the i-th water pump, S xi is the virtual resistance coefficient of the i-th water pump; The water supply flow and water supply pressure constraints of the pump station are: H P =H1=H2=…=H m Among them, H P is the water supply pressure of the pump station; Q P The water supply flow of the pumping station.

3. The method according to claim 2, characterized in that The constraints also include the speed ratio constraint of the speed regulating pump and the number of pumps turned on constraint: S i,min ≤S i ≤1 0≤n≤n max Among them, S i,min is the minimum value of the speed ratio of the i-th speed regulating pump; n is the number of pumps turned on in the pump station; n max Design the maximum number of pumps to be turned on for the pump station.

4. The method according to claim 2 or 3, characterized in that: The constraints are converted into penalty functions, and the constructed penalty function is combined with the original objective function to form a new objective function: Where L is the new objective function value and λ is the penalty coefficient.

5. The method according to claim 4, characterized in that The method for solving the pump station optimization scheduling scheme includes: using a plurality of different intelligent optimization algorithms to respectively solve the new objective function to obtain a plurality of pump station optimization scheduling schemes and corresponding objective function values ​​L: L1, L2, ..., L Num , L1,L2,...,L Num etc., and take the min{L1,L2,...,L Num The pump station optimization scheduling scheme of} is taken as the optimal pump station optimization scheduling scheme solved by the intelligent algorithm; where Num is the number of pump station optimization scheduling schemes.

6. The method according to claim 5, characterized in that Range Q based on flow and head data within the high efficiency interval of the pump station min ~Q max and H min ~H max , Q min ~Q max and H min ~H max All discrete flow and head data points within the range are combined one by one and used as parameter combinations {Q P ,H P ), and solve different parameter combinations (Q P ,H P ) optimal pump station optimization scheduling scheme under The optimal pump station optimization scheduling plan is stored in the water pump optimization scheduling database.

7. The method according to claim 6, characterized in that Deploy the water pump optimization scheduling database to the corresponding water plant server; after obtaining the actual scheduling requirements of the pipe network, quickly retrieve the corresponding {Q P ,H P ) The optimal pump station optimization scheduling plan corresponding to the optimal pump station optimization scheduling plan transmits the corresponding scheduling instructions to the water plant pump group control module to control the start and stop and frequency conversion adjustment of the corresponding water pump.

8. An electronic device, characterized in that: include: Memory and processor; The memory is used to store computer programs; The processor is configured to call the computer program to execute the method according to any one of claims 1 to 7.

9. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a computer program, and when the computer program is executed on an electronic device, the electronic device implements the method according to any one of claims 1 to 7.

10. A computer program product, comprising a computer program, characterized in that When the computer program is executed on an electronic device, the electronic device implements the method according to any one of claims 1 to 7.

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