Water supply optimization scheduling method based on multi-objective particle swarm optimization

Abstract

The invention discloses a water supply optimization scheduling method based on a multi-objective particle swarm algorithm, and relates to the technical field of water supply optimization scheduling, and the method comprises the steps: obtaining the pressure and flow data of each node in a water supply network, calculating the water transfer demand index of each node, marking a demand water supply scheduling node, and calculating the maximum scheduling time of the demand water supply scheduling node; setting a water loss objective function by taking the water loss of water supply scheduling as an optimization objective, setting an energy demand objective function by taking the energy demand of water supply scheduling as an optimization objective, and performing weighting summation on the two optimization objectives of the water loss and the energy demand to obtain a comprehensive optimization objective function; and acquiring a comprehensive optimization objective function of each demand water supply scheduling node, calculating an objective function of the whole water supply scheduling system, and importing the objective function of the whole water supply scheduling system and constraint conditions into a multi-objective particle swarm algorithm to seek an optimal solution for output. Performance and benefits of a water supply dispatching system are remarkably improved, and energy consumption and water loss are reduced.

Description

Technical Field

[0001] The present invention relates to the technical field of water supply optimization scheduling, and in particular to a water supply optimization scheduling method based on a multi-objective particle swarm algorithm. Background Art

[0002] Water supply optimization scheduling is an important issue in the field of water resources management. It involves how to reasonably allocate and schedule limited water resources to meet the water needs of different users while ensuring the stability and economy of the water supply system. Traditional water supply scheduling methods often rely on artificial experience and simple rules, which are difficult to adapt to complex and changeable water supply needs and system states. Therefore, it is of great significance to develop an efficient and intelligent water supply optimization scheduling method. Multi-objective particle swarm optimization (MOPSO) is an optimization algorithm based on swarm intelligence. It simulates the foraging behavior of bird flocks in nature and finds the optimal solution through collaboration and information sharing between particles. The algorithm has the advantages of simple operation, fast convergence speed, and strong global search ability. It is particularly suitable for solving complex multi-objective optimization problems. In water supply optimization scheduling, there are multiple conflicting objectives, such as water supply stability, economy, and environmental protection. There is often a trade-off relationship between these objectives, and it is difficult to achieve the optimal at the same time. Therefore, it is a reasonable choice to use multi-objective particle swarm optimization to solve the problem of water supply optimization scheduling.

[0003] In a Chinese invention application with application publication number CN117910780A, a water supply optimization scheduling method based on a multi-objective particle swarm algorithm is disclosed, including obtaining a water supply network in a water supply area; in response to a change in the water demand of a water demand point, determining the water supply points to be adjusted and the water demand points to be adjusted associated with the water demand point in the water supply network, and collecting the water supply scheduling matrix at the current moment; calculating the control parameters of each water supply pipeline to be adjusted based on the water supply scheduling matrix; constructing a multi-objective optimization problem based on the water supply scheduling matrix, the control parameters and the water demand of each water demand point to be adjusted after the change; solving the multi-objective optimization problem using the multi-objective particle swarm algorithm to obtain the water supply scheduling target matrix, and adjusting the control parameters of each water supply pipeline to be adjusted based on the water supply scheduling target matrix.

[0004] In the above invention application, the optimization goals are to minimize the number of water supply pipelines to be regulated whose water supply volume changes and to minimize the wear of valves and water pumps on each water supply pipeline to be regulated, thereby reducing the loss of water supply equipment and achieving reasonable scheduling of water supply. However, in the actual water supply process, compared with the loss of water supply equipment, the loss of water resources and the energy loss of water pumps usually have a more significant impact. The loss of water resources directly reduces the amount of water available, resulting in a reduction in water supply, while the amount of leaked water and the energy loss of water pumps require additional water production costs to supplement, increasing the operating costs of water supply companies.

[0005] To this end, the present invention provides a water supply optimization scheduling method based on a multi-objective particle swarm algorithm. Summary of the invention

[0006] 1. Technical issues to be solved

[0007] In view of the shortcomings of the prior art, the present invention provides a water supply optimization scheduling method based on a multi-objective particle swarm algorithm. The present invention sets a water loss objective function minA by taking the water loss of water supply scheduling as the optimization objective, sets an energy demand objective function minB by taking the energy demand of water supply scheduling as the optimization objective, performs weighted summation on the two optimization objectives of water loss and energy demand, obtains a comprehensive optimization objective function minC, calculates the objective function minD of the entire water supply scheduling system, and imports the objective function and constraint conditions of the entire water supply scheduling system into a multi-objective particle swarm algorithm to seek the optimal solution output. The optimal solution or approximate optimal solution can be found in a complex water supply scheduling system, significantly improves the performance and efficiency of the water supply scheduling system, and reduces energy consumption and water loss, thereby solving the technical problems recorded in the background technology.

[0008] (II) Technical solution

[0009] To achieve the above objectives, the present invention is implemented through the following technical solutions: a water supply optimization scheduling method based on a multi-objective particle swarm algorithm, comprising the following steps:

[0010] Get the pressure Ya of each node in the water supply network ij and flow rate ij Data, calculate the water transfer demand index Dx of each node i , when the water demand index Dx i Exceed When the node is marked as a demand water supply scheduling node, the maximum scheduling duration Sd of the demand water supply scheduling node is calculated. a ;

[0011] Taking the water loss of water supply scheduling as the optimization target, the water loss objective function minA is set; taking the energy demand of water supply scheduling as the optimization target, the energy demand objective function minB is set; the two optimization targets of water loss and energy demand are weighted and summed to obtain the comprehensive optimization objective function minC;

[0012] Obtain the comprehensive optimization objective function of each demand water supply scheduling node, calculate the objective function minD of the entire water supply scheduling system, and import the objective function and constraints of the entire water supply scheduling system into the multi-objective particle swarm algorithm to seek the optimal solution output.

[0013] Furthermore, pressure sensors and flow meters are installed at key nodes of the water supply network to monitor and record the pressure Ya of each node in the water supply network in real time.ij and flow rate ij data.

[0014] Furthermore, the pressure Ya of each node in the water supply network is obtained ij and flow rate ij Data, calculate the water transfer demand index Dx of each node i :

[0015]

[0016] Among them, i represents the sequential number of the node, and j represents the sequential number of the monitoring time.

[0017] Furthermore, when the water demand index Dx i Exceed When the node is marked as a demand water supply scheduling node, the demand water supply scheduling node is renumbered and the maximum scheduling duration Sd of the demand water supply scheduling node is calculated. a :

[0018]

[0019] Wherein, [] is a floor rounding function, and a represents the sequence number of the demand-water supply scheduling node.

[0020] Furthermore, let the water supply starting point be x0 and the water demand node be x n , the remaining intermediate passing nodes are x μ , and μ∈[1,n-1].

[0021] Furthermore, taking the water loss of water supply scheduling as the optimization target, the water loss objective function is defined as:

[0022]

[0023] Among them, A represents the total water loss rate caused by the water supply scheduling scheme of the demand water supply node, A μ It represents the water loss rate passing through the node in the water supply scheduling scheme of the demand water supply node.

[0024] Furthermore, the energy demand of water supply scheduling is taken as the optimization target, and the energy demand objective function is defined as:

[0025]

[0026] Where B represents the total energy demand generated by the water supply scheduling scheme of the demand water supply node, and B μ It represents the energy demand of water pump operation passing through the node in the water supply scheduling scheme of demand water supply node.

[0027] Furthermore, the water loss objective function and the energy demand objective function are obtained, and the weighted sum of the two optimization objectives of water loss and energy demand is performed to obtain the comprehensive optimization objective function:

[0028]

[0029] The corresponding calculation formula of the comprehensive optimization objective function is as above.

[0030] Furthermore, the comprehensive optimization objective function of each demand water supply scheduling node is obtained, and the objective function of the entire water supply scheduling system is calculated:

[0031]

[0032] Wherein, a represents the sequence number of the demand water supply scheduling node, a=1, 2, ..., m.

[0033] Furthermore, the constraints of the objective function of the entire water supply scheduling system include the transmission speed of all nodes, the maximum scheduling water volume of the water supply node, and the maximum scheduling time Sd of the demand water supply scheduling node. a .

[0034] Multi-Objective Particle Swarm Optimization (MOPSO) is an optimization method based on evolutionary computation, which is used to solve multi-objective optimization problems. The MOPSO algorithm regards a group of particles as potential solutions, and searches for the optimal solution by simulating the foraging behavior of bird flocks in nature. It is similar to the traditional particle swarm algorithm (PSO), which searches for the optimal solution by updating the position and velocity of particles. The difference is that the MOPSO algorithm considers multiple objective functions at the same time and uses some specific strategies to maintain the diversity and convergence of particles.

[0035] (III) Beneficial effects

[0036] The present invention provides a water supply optimization scheduling method based on a multi-objective particle swarm algorithm, which has the following beneficial effects:

[0037] 1. Obtain the pressure Ya of each node in the water supply network ij and flow rate ij Data, calculate the water transfer demand index Dx of each node i , when the water demand index Dx i Exceed When the node is marked as a demand water supply scheduling node, the maximum scheduling duration Sd of the demand water supply scheduling node is calculated. a, it can accurately determine which nodes have insufficient water supply, so as to carry out precise water supply scheduling, minimize the scope of water outage and reduce the impact on users.

[0038] 2. Taking the water loss of water supply scheduling as the optimization target, set the water loss objective function minA; taking the energy demand of water supply scheduling as the optimization target, set the energy demand objective function minB; and perform weighted summation on the two optimization targets of water loss and energy demand to obtain the comprehensive optimization objective function minC. On the one hand, it can save water resources and reduce the impact on the environment; on the other hand, it can reduce operating costs and improve energy efficiency, which is helpful for the water supply industry to achieve a win-win situation of economic and social benefits.

[0039] 3. Obtain the comprehensive optimization objective function of each demand water supply scheduling node, calculate the objective function minD of the entire water supply scheduling system, and import the objective function and constraints of the entire water supply scheduling system into the multi-objective particle swarm algorithm to seek the optimal solution output. It can find the optimal solution or approximate optimal solution in the complex water supply scheduling system, significantly improve the performance and efficiency of the water supply scheduling system, and reduce energy consumption and water loss. BRIEF DESCRIPTION OF THE DRAWINGS

[0040] Figure 1 The present invention is a flow chart of a water supply optimization scheduling method based on a multi-objective particle swarm algorithm. DETAILED DESCRIPTION

[0041] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0042] See also Figure 1 The present invention provides a water supply optimization scheduling method based on a multi-objective particle swarm algorithm, comprising the following steps:

[0043] Step 1: Obtain the pressure Ya of each node in the water supply network ij and flow rate ij Data, calculate the water transfer demand index Dx of each node i , when the water demand index Dx i Exceed When the node is marked as a demand water supply scheduling node, the maximum scheduling duration Sd of the demand water supply scheduling node is calculated. a .

[0044] The step 1 includes the following contents:

[0045] Step 101: Install pressure sensors and flow meters at key nodes of the water supply network to monitor and record the pressure Ya of each node in the water supply network in real time. ij and flow rate ij data.

[0046] Step 102: Obtain the pressure Ya of each node in the water supply network ij and flow rate ij Data, calculate the water transfer demand index Dx of each node i :

[0047]

[0048] Among them, i represents the sequential number of the node, and j represents the sequential number of the monitoring time.

[0049] Step 103: When the water demand index Dx i Exceed When the node is marked as a demand water supply scheduling node, the demand water supply scheduling node is renumbered and the maximum scheduling duration Sd of the demand water supply scheduling node is calculated. a :

[0050]

[0051] Wherein, [] is a floor rounding function, and a represents the sequence number of the demand-water supply scheduling node.

[0052] When using, combine the contents in steps 101 to 103:

[0053] Get the pressure Ya of each node in the water supply network ij and flow rate ij Data, calculate the water transfer demand index Dx of each node i , when the water demand index Dx i Exceed When the node is marked as a demand water supply scheduling node, the maximum scheduling duration Sd of the demand water supply scheduling node is calculated. a , it can accurately determine which nodes have insufficient water supply, so as to carry out precise water supply scheduling, minimize the scope of water outage and reduce the impact on users.

[0054] Step 2: Taking the water loss of water supply scheduling as the optimization target, set the water loss objective function minA; taking the energy demand of water supply scheduling as the optimization target, set the energy demand objective function minB; perform weighted summation on the two optimization targets of water loss and energy demand to obtain the comprehensive optimization objective function minC.

[0055] The step 2 includes the following contents:

[0056] Step 201: Set the water supply starting point node to be x0 and the water demand node to be x n , the remaining intermediate passing nodes are x μ , and μ∈[1,n-1].

[0057] Step 202: Taking the water loss of water supply scheduling as the optimization target, the water loss objective function is defined as:

[0058]

[0059] Among them, A represents the total water loss rate caused by the water supply scheduling scheme of the demand water supply node, A μ It represents the water loss rate passing through the node in the water supply scheduling scheme of the demand water supply node.

[0060] Step 203: Taking the energy demand of water supply scheduling as the optimization target, the energy demand objective function is defined as:

[0061]

[0062] Where B represents the total energy demand generated by the water supply scheduling scheme of the demand water supply node, and B μ It represents the energy demand of water pump operation passing through the node in the water supply scheduling scheme of demand water supply node.

[0063] Step 204: Obtain the water loss objective function and the energy demand objective function, perform weighted summation on the two optimization objectives of water loss and energy demand, and obtain a comprehensive optimization objective function:

[0064]

[0065] The corresponding calculation formula of the comprehensive optimization objective function is as above.

[0066] When using, combine the contents in steps 201 to 204:

[0067] Taking the water loss of water supply scheduling as the optimization target, the water loss objective function minA is set; taking the energy demand of water supply scheduling as the optimization target, the energy demand objective function minB is set. The two optimization targets of water loss and energy demand are weighted and summed to obtain the comprehensive optimization objective function minC. On the one hand, it can save water resources and reduce the impact on the environment; on the other hand, it can reduce operating costs and improve energy efficiency, which is helpful for the water supply industry to achieve a win-win situation of economic and social benefits.

[0068] Step 3: Obtain the comprehensive optimization objective function of each demand water supply scheduling node, calculate the objective function minD of the entire water supply scheduling system, and import the objective function and constraints of the entire water supply scheduling system into the multi-objective particle swarm algorithm to seek the optimal solution output.

[0069] The step three includes the following contents:

[0070] Step 301: Obtain the comprehensive optimization objective function of each demand water supply scheduling node and calculate the objective function of the entire water supply scheduling system:

[0071]

[0072] Wherein, a represents the sequence number of the demand water supply scheduling node, a=1, 2, ..., m.

[0073] Step 302: Set the constraints of the objective function of the entire water supply scheduling system, including the transmission speed of all nodes, the maximum scheduling water volume of the water supply node, and the maximum scheduling time Sd of the demand water supply scheduling node. a .

[0074] Step 303: Import the objective function and constraints of the entire water supply scheduling system into the multi-objective particle swarm algorithm to seek the optimal solution output.

[0075] Multi-Objective Particle Swarm Optimization (MOPSO) is an optimization method based on evolutionary computation, which is used to solve multi-objective optimization problems. The MOPSO algorithm regards a group of particles as potential solutions, and searches for the optimal solution by simulating the foraging behavior of bird flocks in nature. It is similar to the traditional particle swarm algorithm (PSO), which searches for the optimal solution by updating the position and velocity of particles. The difference is that the MOPSO algorithm considers multiple objective functions at the same time and uses some specific strategies to maintain the diversity and convergence of particles.

[0076] When using, combine the contents in steps 301 to 303:

[0077] Obtain the comprehensive optimization objective function of each demand water supply scheduling node, calculate the objective function minD of the entire water supply scheduling system, import the objective function and constraints of the entire water supply scheduling system into the multi-objective particle swarm algorithm to seek the optimal solution output, and be able to find the optimal solution or approximate optimal solution in a complex water supply scheduling system, significantly improve the performance and efficiency of the water supply scheduling system, and reduce energy consumption and water loss.

[0078] The above embodiments may be implemented in whole or in part by software, hardware, firmware or any other combination thereof. When implemented using software, the above embodiments may be implemented in whole or in part in the form of a computer program product. A person of ordinary skill in the art may appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein may be implemented in electronic hardware, or in a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution.

[0079] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0080] The above description is only a specific implementation manner of the present application, but the protection scope of the present application is not limited thereto. Any technician familiar with the technical field can easily think of changes or substitutions within the technical scope disclosed in the present application, which should be included in the protection scope of the present application.

Claims

1. A water supply optimization scheduling method based on a multi-objective particle swarm algorithm, characterized by: The steps include: Get the pressure Ya of each node in the water supply network ij and flow rate ij Data, calculate the water transfer demand index Dx of each node i , when the water demand index Dx i Exceed When the node is marked as a demand water supply scheduling node, the maximum scheduling duration Sd of the demand water supply scheduling node is calculated. a ; Taking the water loss of water supply scheduling as the optimization target, the water loss objective function minA is set; taking the energy demand of water supply scheduling as the optimization target, the energy demand objective function minB is set; the two optimization targets of water loss and energy demand are weighted and summed to obtain the comprehensive optimization objective function minC; The comprehensive optimization objective function of each demand water supply scheduling node is obtained, the objective function minD of the entire water supply scheduling system is calculated, and the objective function and constraints of the entire water supply scheduling system are introduced into the multi-objective particle swarm algorithm to seek the optimal solution output.

2. The water supply optimization scheduling method based on a multi-objective particle swarm algorithm according to claim 1 is characterized in that: Get the pressure Ya of each node in the water supply network ij and flow rate ij Data, calculate the water transfer demand index Dx of each node i : Among them, i represents the sequential number of the node, and j represents the sequential number of the monitoring time.

3. The water supply optimization scheduling method based on multi-objective particle swarm algorithm according to claim 2 is characterized in that: When the water demand index Dx i Exceed When the node is marked as a demand water supply scheduling node, the demand water supply scheduling node is renumbered and the maximum scheduling duration Sd of the demand water supply scheduling node is calculated. a : Wherein, [] is a floor rounding function, and a represents the sequence number of the demand-water supply scheduling node.

4. The water supply optimization scheduling method based on multi-objective particle swarm algorithm according to claim 1 is characterized in that: Assume that the water supply starting point is x0 and the water demand node is x n , the remaining intermediate passing nodes are x μ , and μ∈[1,n-1].

5. The water supply optimization scheduling method based on multi-objective particle swarm algorithm according to claim 4 is characterized in that: Taking the water loss of water supply scheduling as the optimization target, the water loss objective function is defined as: Among them, A represents the total water loss rate caused by the water supply scheduling scheme of the demand water supply node, A μ It represents the water loss rate passing through the node in the water supply scheduling scheme of the demand water supply node.

6. The water supply optimization scheduling method based on multi-objective particle swarm algorithm according to claim 4 is characterized in that: Taking the energy demand of water supply scheduling as the optimization target, the energy demand objective function is defined as: Where B represents the total energy demand generated by the water supply scheduling scheme of the demand water supply node, and B μ It represents the energy demand of water pump operation passing through the node in the water supply scheduling scheme of demand water supply node.

7. The water supply optimization scheduling method based on multi-objective particle swarm algorithm according to claim 6 is characterized in that: Obtain the water loss objective function and energy demand objective function, perform weighted summation on the two optimization objectives of water loss and energy demand, and obtain the comprehensive optimization objective function: The corresponding calculation formula of the comprehensive optimization objective function is as above.

8. The water supply optimization scheduling method based on multi-objective particle swarm algorithm according to claim 7 is characterized in that: Obtain the comprehensive optimization objective function of each demand water supply scheduling node and calculate the objective function of the entire water supply scheduling system: Wherein, a represents the sequence number of the demand water supply scheduling node, a=1, 2, ..., m.

9. The water supply optimization scheduling method based on multi-objective particle swarm algorithm according to claim 8 is characterized in that: The constraints of the objective function of the entire water supply scheduling system include the transmission speed of all nodes, the maximum scheduling water volume of the water supply node, and the maximum scheduling time Sd of the demand water supply scheduling node. a .

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