CN115455812A - Water supply pump station optimization method and system - Google Patents
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Abstract
The invention discloses a water supply pump station optimization method and a system, wherein the method comprises the following steps: the method comprises the following steps: based on real number coding, improving a genetic algorithm to obtain a water pump operation characteristic curve; establishing a water pump optimization mathematical model based on the water pump operation characteristic curve; obtaining a constraint condition based on the water pump optimization mathematical model; and solving the water pump optimization mathematical model by using an artificial electric field algorithm based on the constraint condition to obtain an optimization scheme of the water supply pump station. The system comprises a curve establishing module, an optimization model module, a condition constraint module and an algorithm solving module. The invention can calculate the optimal operation condition of the water pump according to the current operation characteristics of the water pump, thereby reducing energy consumption and having important significance for safe and efficient operation of a water plant.
Description
Technical Field
The invention belongs to the field of pump station control, and particularly relates to a water supply pump station optimization method and system.
Background
The pump station makes great contribution to the development of national economy in China, and simultaneously consumes a large amount of energy. More than 90% of the electric energy in the water supply system is consumed by the operation of the water pump, so the operating efficiency of the water pump directly determines the energy consumption level of the whole water supply industry.
The research on energy conservation and optimization of the urban water supply system is carried out, the reduction of energy consumption is one of the targets of the water supply industry in China, the method is an important means for increasing the economic benefit of water supply enterprises, is a powerful way for solving the situation of power supply shortage limited by the brake-off in China at present, can promote the healthy and rapid development of the water supply industry in China, and has important economic and social significance.
Therefore, energy-saving research needs to be carried out on the water pump to ensure that the water pump can run in the high-efficiency section of the water supply engineering for a long time, and reasonable protective measures are provided. Therefore, the research on the energy-saving technology of the water supply pump station has very important significance for improving the operation efficiency of the water supply project, achieving the purposes of energy conservation and consumption reduction and realizing the efficient, safe and economic operation of the water supply project.
Disclosure of Invention
In order to solve the technical problems, the invention provides a water supply pump station optimization method and a water supply pump station optimization system, wherein an integer programming mathematical model is established by taking the annual cost (including annual power consumption cost and annual production cost) of a pump station as the minimum as an objective function, an appropriate optimization method is adopted for optimization solution, and on the premise of obtaining the mathematical model under variable speed regulation and the integer solution of the number of water pumps, the minimum shaft power or the highest system efficiency is taken as the objective function, and different regulation operation methods are established for optimizing and regulating the operation of a water supply project of the pump station so as to achieve the purpose of economic operation.
In order to achieve the above object, the present invention provides a method for optimizing a water supply pumping station, comprising the steps of:
establishing a water pump operation characteristic curve based on a real number coding improved genetic algorithm;
establishing a water pump optimization mathematical model based on the water pump operation characteristic curve;
obtaining a constraint condition based on the water pump optimization mathematical model;
and solving the water pump optimization mathematical model by using an artificial electric field algorithm based on the constraint conditions to obtain an optimization scheme of the water supply pump station.
Optionally, the process of establishing the water pump operation characteristic curve includes:
based on a single-pump experiment platform, testing various working conditions in the operation range of the water pump to obtain data points which are uniformly distributed;
taking the data points as reference points of a least square method fitting curve, establishing an overdetermined equation set according to an empirical formula or a quadratic polynomial of a water pump operation characteristic curve, calculating coefficients of all terms of the quadratic polynomial by taking the minimum sum of errors between the data points and the fitting points as a target, and drawing a curve of a working range of the water pump;
determining a search range for each parameter based on a result of a least square method;
initializing population scale, formulating a corresponding fitness function according to a target function, and selecting proper selection operators, crossover operators and mutation operators;
and iterating the result based on the selection operator, the intersection operator, the mutation operator and the fitness function to obtain the water pump operation characteristic curve.
Optionally, the empirical formula expression of the water pump operation characteristic curve is as follows:
H＝H x -S x Q 2
P＝d 0 +d 1 Q+d 2 Q 2
wherein H x ,S x ,d 0 ,d 1 ,d 2 Is a parameter of the characteristic curve, namely an unknown variable, H represents the pump lift, P is the pump power, and Q is the pump flow.
Optionally, the process of establishing the water pump optimization mathematical model includes: the pump station is provided with n water pumps, the front m water pumps are variable-frequency speed-regulating water pumps, and the (m + 1) th to the nth water pumps run at constant speed; and establishing an optimized operation mathematical model by taking the sum of the shaft power of the pump station as a target function, wherein the form is as follows:
wherein m is the number of power frequency pumps, n is the total number of pumps of the pump station, wherein the pump station comprises a variable frequency pump station, F is the total power of the water pump unit, and omega is i And (4) taking 1 as a water pump control decision variable to represent that the water pump operates, and taking 0 as a closing state.
Optionally, the constraint condition obtained according to the water pump operation fitting curve is:
D imin ≤D i ≤1(i＝1,2…,m)
Q imin ≤Q i ≤Q imax
wherein, hx i -the ith water pump virtual lift, m; sx i -the ith water pump virtual resistance coefficient; q e -the amount of water required for the water supply, m3/h; he-water pressure required for water supply, m; q imin 、Q imax Minimum and maximum flow in the high-efficiency section of the water pump, m3/h. When D is present imin Below 0.5, the pump efficiency drops dramatically. Thus, in the present invention, D imin Take 0.5.
Optionally, the process of solving the artificial electric field includes:
randomly initiating a charge population in a search space;
randomly initializing the speed and the position of the charge, and calculating the fitness value of each charge;
calculating coulomb constant, global optimum and worst value of charge;
calculating coulomb force and acceleration of the charge, and updating the speed and position of the particles;
if the termination condition is not met, returning to the initial charge speed; otherwise, outputting the optimal solution and terminating the circulation;
and solving the constraint of the water supply capacity of the pump station in the scheduling model by constructing a penalty function.
Optionally, the expression of the penalty function is:
adding constraint f 1 、f 2 The final form of the objective function is available:
F it ＝minF 2 +c 1 f 1 +c 2 f 2
in the formula, F 2 Optimizing a target function of a scheduling model for the pump station; c. C 1 ,c 2 The punishment degree of the flow and pressure constraint is respectively.
In another aspect, to achieve the above objects, the present invention provides a water supply pumping station optimization system, including: the system comprises a curve establishing module, an optimization model module, a condition constraint module and an algorithm solving module;
the curve establishing module is used for establishing a water pump operation characteristic curve based on a real number coding improved genetic algorithm;
the optimization model module is used for establishing a water pump optimization mathematical model based on the water pump operation characteristic curve,
the condition constraint module is used for obtaining constraint conditions based on the water pump optimization mathematical model;
and the algorithm solving module is used for solving the water pump optimization mathematical model by using an artificial electric field algorithm based on the constraint conditions to obtain an optimization scheme of the water supply pump station.
The invention provides a method and a system for optimizing a water supply pump station, which fit a water pump operation characteristic curve according to a real number coding-based improved genetic algorithm, thereby obtaining a lift-flow curve and a power-flow curve of a water pump, establishing a pump station optimal operation minimum shaft power mathematical model for ensuring that the water pump operates in a high-efficiency area, and introducing a method for solving a water pump variable speed regulation model by using an artificial electric field algorithm. The application of the pump station optimization operation mathematical model and the water pump energy-saving technology is an effective way for realizing energy saving and consumption reduction of water supply of the pump station, and finally the purpose of energy saving and optimization is achieved.
Drawings
The accompanying drawings, which are incorporated in and constitute a part of this application, illustrate embodiments of the application and, together with the description, serve to explain the application and are not intended to limit the application. In the drawings:
FIG. 1 is a schematic flow diagram of a method for optimizing a water supply pumping station according to the present invention.
Detailed Description
It should be noted that the embodiments and features of the embodiments in the present application may be combined with each other without conflict. The present application will be described in detail below with reference to the embodiments with reference to the attached drawings.
Example one
The invention discloses a water supply pump station optimization method, which specifically comprises the following steps:
based on a real number coding improved genetic algorithm, establishing a water pump operation characteristic curve, wherein the water pump operation characteristic curve comprises a head-flow curve and a power-flow curve of the water pump;
establishing a water pump optimization mathematical model based on a flow-lift curve and a power-flow curve of the water pump;
obtaining a constraint condition based on a water pump optimization mathematical model;
and solving the water pump optimization mathematical model by using an artificial electric field algorithm based on the constraint condition to obtain an optimization scheme of the water supply pump station.
Referring to fig. 1, a water pump characteristic curve is fitted based on a real number coding improved genetic algorithm, and firstly, a theoretical curve empirical formula of the water pump is determined as follows:
H＝H x -S x Q 2 (1)
P＝d 0 +d 1 Q+d 2 Q 2 (2)
wherein H x ,S x ,d 0 ,d 1 ,d 2 Is a parameter of the characteristic curve, i.e. an unknown variable;
firstly, testing various working conditions in the operation range of the water pump through an experimental platform of a single pump to obtain data points which are distributed uniformly and have different states; then, the points are used as reference points of a least square method fitting curve, an overdetermined equation set is established according to an empirical formula or a quadratic polynomial of a water pump characteristic curve, then the minimum sum of the square errors between each data point and the fitting point is taken as a target, coefficients of each item of the quadratic polynomial are calculated, and then a curve is drawn for a working range of the water pump;
determining a search range for each parameter based on a result of a least square method, namely fitting a water pump characteristic curve according to the least square method to obtain unknown variable values in the formulas (1) and (2);
initializing population scale, formulating a corresponding fitness function according to a target function, selecting proper selection operators, crossover operators and mutation operators, wherein the crossover operators and the mutation operators are different from those in a binary genetic algorithm due to the adoption of a real number coded genetic algorithm, and the crossover operators add a random variable beta on the basis of the original traditional genetic algorithm, namely the crossover operators add the random variable beta
X i (t+1)＝αX i (t)+(1-α)X j (t)+β(X i (t)-X j (t)) (3)
X j (t+1)＝αX j (t)+(1-α)X j (t)+β(X i (t)-X j (t)) (4)
α is limited to the range of [0,1 ];
the mutation operator selects non-uniform mutation, and the value range of a mutation point is [ Xmin, xmax ]:
in the formula, delta (t, y) is a random number which accords with non-uniform distribution in the range of [0,y ], and the probability that delta (t, y) is close to 0 is increased along with the increase of an evolution algebra t;
after least square fitting is carried out on a characteristic curve of the water pump, the upper and lower 30% of the obtained curve parameters are defined as search domains, and optimization is carried out in the range by using a real number-based genetic algorithm, so that a head-flow and power-flow fitting curve of the water pump superior to the least square fitting curve is obtained;
and (3) establishing a water pump variable speed regulation mathematical model, wherein if the pump station is provided with n water pumps, the front m water pumps are variable frequency speed regulation water pumps, and the (m + 1) th to the nth water pumps run at constant speed. The pump station operation optimization aims to enable each water pump to operate efficiently under the condition that the flow and the lift required by a user are met, and the energy consumption is minimum. Therefore, the sum of the shaft powers of the pump stations is used as an objective function, and an optimized operation mathematical model is established, wherein the form is as follows:
wherein m is the number of power frequency pumps, n is the total number of pumps (including frequency conversion pump stations) of the pump station, F is the total power of the water pump unit, and omega is i Controlling decision variables for the water pump, taking 1 to represent that the water pump operates, and taking 0 to represent that the water pump is closed;
all water pumps work in the high-efficiency area, and the constraint conditions obtained according to the water pump operation fitting curve are as follows:
D imin ≤D i ≤1(i＝1,2…,m) (9)
Q imin ≤Q i ≤Q imax (10)
wherein, hx i -the ith water pump virtual lift, m; sx i -the ith water pump virtual resistance loss coefficient; qe-water quantity required for water supply, m3/h; he-water pressure required for water supply, m; q imin 、Q imax The minimum and maximum flow in the high-efficiency section of the water pump is m3/h. When D is present imin 5, the water pump efficiency drops sharply, so, in the present invention, D imin Taking 0.5;
solving the optimized model using an artificial electric field algorithm to randomly initiate a charge population (X) in the search space 1 (t),X 2 (t),…,X N (t)), speed V of random initialization charge j (t) and position X i (t) and calculating a fitness value for each charge
Calculating coulomb constant K (t) and global optimum b of charge est (t) and the worst value w orst (t) calculating coulomb force and acceleration of the charge, updating the speed and position of the particles, and returning to the initial charge speed if the termination condition is not met; otherwise, outputting the optimal solution and terminating the circulation;
for the constraint of the water supply capacity of the pump station in the scheduling model, a penalty function f is constructed 1 、f 2 To solve for:
adding constraint f 1 、f 2 The final form of the objective function is available:
F it ＝minF 2 +c 1 f 1 +c 2 f 2 (13)
in the formula, F 2 Optimizing a target function of a scheduling model for the pump station; c. C 1 ,c 2 Punishment degrees of flow and pressure constraint are respectively;
and solving according to the model and the parameters to finally obtain an optimization scheme of the water supply pump station.
In the embodiment, as known from historical records of the company Limited temporarily melt Water utilities, the actual water pump condition records are shown in Table 1, and because the water pump unit is frequently started and stopped and the efficiency is not high, the invention provides an optimized operation scheme.
TABLE 1
Considering the pump types 300S58 and 250S65A as speed-regulating pumps, and setting the speed-regulating ratio as D, the pump type shaft power fitting curves are respectively selected as:
200S42: unregulated, N =59.61-0.0012Q 1.8 (14)
250S65: when not adjusting speed, N =57.28+0.0001Q 2.0 (15)
In speed regulation, N =57.28D 3 +0.0001DQ 2.0 (16)
300S58: when not adjusting speed, N =91.97+0.0738Q (17)
At the time of speed regulation, N =91.97D 3 +0.0738D 2 Q (18)
Setting the parameters of the artificial electric field algorithm solving model as follows, setting an initial coulomb constant as 500, setting an Euclidean norm as 2, setting a population scale as 50, setting the maximum iteration number as 100, and solving a minimum axial power mathematical model to obtain a water pump optimization operation scheme:
TABLE 2
The above description is only for the preferred embodiment of the present invention, but the scope of the present invention is not limited thereto, and any changes or substitutions that can be easily conceived by those skilled in the art within the technical scope of the present invention are also included in the scope of the present invention. Therefore, the protection scope of the present invention shall be subject to the protection scope of the claims.
Example two
The invention also provides a water supply pump station optimization system, which comprises a curve establishing module, an optimization model module, a condition constraint module and an algorithm solving module;
the curve establishing module is used for establishing a water pump operation characteristic curve based on a real number coding improved genetic algorithm;
the optimization model module is used for establishing a water pump optimization mathematical model based on the water pump operation characteristic curve;
the condition constraint module is used for obtaining constraint conditions based on the water pump optimization mathematical model;
and the algorithm solving module is used for solving the water pump optimization mathematical model by utilizing an artificial electric field algorithm based on the constraint conditions to obtain an optimization scheme of the water supply pump station.
The above description is only for the preferred embodiments of the present application, but the scope of the present application is not limited thereto, and any changes or substitutions that can be easily conceived by those skilled in the art within the technical scope of the present application should be covered within the scope of the present application. Therefore, the protection scope of the present application shall be subject to the protection scope of the claims.
Claims (8)
1. A method of optimizing a water supply pumping station, comprising the steps of:
establishing a water pump operation characteristic curve based on a real number coding improved genetic algorithm;
establishing a water pump optimization mathematical model based on the water pump operation characteristic curve;
obtaining constraint conditions based on the water pump optimization mathematical model;
and solving the water pump optimization mathematical model by using an artificial electric field algorithm based on the constraint condition to obtain an optimization scheme of the water supply pump station.
2. The water supply pump station optimizing method according to claim 1, wherein the process of establishing the water pump operation characteristic curve includes:
testing various working conditions in the operation range of the water pump based on a single-pump experiment platform to obtain uniformly distributed data points;
taking the data points as reference points of a least square method fitting curve, establishing an overdetermined equation set according to a quadratic polynomial of a water pump characteristic curve, calculating coefficients of all terms of the quadratic polynomial by taking the minimum sum of the square errors between the data points and the fitting points as a target, and drawing a curve of a working range of the water pump;
determining a search range for each parameter based on a result of a least square method;
initializing population scale, formulating a corresponding fitness function according to a target function, and selecting proper selection operators, crossover operators and mutation operators;
and iterating the result based on the selection operator, the intersection operator, the mutation operator and the fitness function to obtain the water pump operation characteristic curve.
3. The water supply pump station optimizing method according to claim 2, wherein the quadratic expression of the water pump operation characteristic curve conforms to the following empirical formula:
H＝H x -S x Q 2
P＝d 0 +d 1 Q+d 2 Q 2
wherein H x ,S x ,d 0 ,d 1 ,d 2 Is a parameter of the characteristic curve, i.e. an unknown variable, H represents the pump head, P isPower, Q is flow.
4. The water supply pump station optimization method according to claim 1, wherein the process of establishing the water pump optimization mathematical model includes: the pump station is provided with n water pumps, the front m water pumps are variable-frequency speed-regulating water pumps, and the (m + 1) th to the nth water pumps run at constant speed; and establishing an optimized operation mathematical model by taking the sum of the shaft power of the pump station as a target function, wherein the form is as follows:
wherein m is the number of variable frequency water pumps, n is the total number of pump station water pumps, F is the total power of the water pump unit, and omega i And (4) taking 1 as a water pump control decision variable to represent that the water pump operates, and taking 0 as a closing state.
5. The water supply pump station optimizing method according to claim 1, wherein the constraint condition obtained from the water pump operation characteristic curve is:
D imin ≤D i ≤1(i＝1,2…,m)
Q imin ≤Q i ≤Q imax
wherein, hx i -the ith water pump virtual lift, m; sx i -the ith water pump virtual resistance loss coefficient; q e -the amount of water required for the water supply, m3/h; he-water pressure required for water supply, m; q imin 、Q imax Minimum and maximum flow in the high-efficiency section of the water pump, m3/h; d imin Take 0.5.
6. The water supply pumping station optimization method according to claim 1, wherein the artificial electric field solving process comprises:
randomly initiating a charge population in a search space;
randomly initializing the speed and the position of the charge, and calculating the fitness value of each charge;
calculating the coulomb constant, the global optimum value and the worst value of the charge;
calculating coulomb force and acceleration of the charge, and updating the speed and position of the particles;
if the termination condition is not met, returning to the initial charge speed; otherwise, outputting the optimal solution and terminating the circulation;
and solving the constraint of the water supply capacity of the pump station in the scheduling model by constructing a penalty function.
7. The water supply pumping station optimization method according to claim 6, wherein the expression of the penalty function is:
wherein Q is i Is the flow rate, Q, of the ith water pump pump The amount of water, omega, required for the water supply network i Control decision variables, H, for the Water Pump i i Supply pressure of water pump i, H pump Adding constraint condition f for pressure of pipe network 1 、f 2 The final form of the objective function is available:
F it ＝minF 2 +c 1 f 1 +c 2 f 2
in the formula, F 2 Optimizing a target function of a scheduling model for the pump station; c. C 1 ,c 2 The punishment degree of the flow and pressure constraint is respectively.
8. A water supply pump station optimization system is characterized by comprising a curve establishing module, an optimization model module, a condition constraint module and an algorithm solving module;
the curve establishment is used for establishing a water pump operation characteristic curve based on a real number coding improved genetic algorithm;
the optimization model module is used for establishing a water pump optimization mathematical model based on the water pump operation characteristic curve;
the condition constraint module is used for obtaining constraint conditions based on the water pump optimization mathematical model;
and the algorithm solving module is used for solving the water pump optimization mathematical model by using an artificial electric field algorithm based on the constraint conditions to obtain an optimization scheme of the water supply pump station.
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| CN116629033A (en) * | 2023-07-21 | 2023-08-22 | 昆明理工大学 | Pump station optimal scheduling method based on multiple swarm genetic algorithms |
| CN116629033B (en) * | 2023-07-21 | 2023-10-27 | 昆明理工大学 | Pump station optimal scheduling method based on multiple swarm genetic algorithms |
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