Substation scheduling optimization method and device, computer equipment, readable storage medium and program product

Abstract

The invention relates to a substation scheduling optimization method and device, computer equipment, a readable storage medium and a program product, and relates to the technical field of substation scheduling. The method comprises the steps of obtaining operation data of a target substation in response to a scheduling optimization instruction for the target substation; performing scheduling optimization analysis on the target transformer substation based on the operation data through a scheduling optimization model to obtain a scheduling optimization result of the target transformer substation; the scheduling optimization model is a model established in advance based on an objective function of minimizing the operation cost of the transformer substation; the operation cost of the transformer substation comprises the power generation cost of the transformer substation and the cost required for processing the environmental influence generated in the operation process of the transformer substation; and obtaining historical scheduling data of the target substation, and controlling the target substation to operate based on the scheduling optimization result when a comparison result between the historical scheduling data and the scheduling optimization result is lower than a preset threshold value. By adopting the method, the dispatching optimization cost of the transformer substation can be reduced.

Description

Technical Field

[0001] The present application relates to the technical field of substation dispatching, and in particular to a substation dispatching optimization method, device, computer equipment, readable storage medium and program product. Background Art

[0002] Substations are places in the power system that transform voltage and current, receive electric energy, and distribute electric energy. Its main functions include boosting the voltage of the electric energy generated by the power plant and feeding it into the high-voltage power grid, and reducing the high-voltage electricity to a suitable low-voltage electricity near the user.

[0003] Based on this, optimizing the dispatching and operation of substations becomes increasingly important. At present, the dispatching optimization of substations is usually achieved by introducing advanced substation units, but this method has the problem of high cost. Summary of the invention

[0004] Based on this, it is necessary to provide a substation scheduling optimization method, device, computer equipment, computer readable storage medium and computer program product that can reduce the substation scheduling optimization cost in response to the above technical problems.

[0005] In a first aspect, the present application provides a substation dispatching optimization method, comprising: in response to a dispatching optimization instruction for a target substation, obtaining operating data of the target substation; performing dispatching optimization analysis on the target substation based on the operating data through a dispatching optimization model to obtain a dispatching optimization result of the target substation; the dispatching optimization model is a model established in advance based on an objective function of minimizing the substation operating cost; the substation operating cost includes the power generation cost and environmental impact cost of the substation; the environmental impact cost refers to the cost required to deal with the environmental impact generated by the substation during operation; obtaining historical dispatching data of the target substation, and when the comparison result between the historical dispatching data and the dispatching optimization result is lower than a preset threshold, controlling the operation of the target substation based on the dispatching optimization result.

[0006] In one of the embodiments, the dispatching optimization instruction includes at least one dispatching optimization indicator; the historical dispatching data includes the historical indicator value of each dispatching optimization indicator; the dispatching optimization result includes the target indicator value of each dispatching optimization indicator; the method also includes: obtaining the difference between the historical indicator value and the target indicator value of each dispatching optimization indicator; when the comparison result between the historical dispatching data and the dispatching optimization result is lower than a preset threshold, controlling the operation of the target substation based on the dispatching optimization result, including: when the average value between the differences is lower than a preset threshold, controlling the operation of the target substation based on the dispatching optimization result.

[0007] In one of the embodiments, a dispatch optimization model is used to perform dispatch optimization analysis on a target substation based on operating data to obtain a dispatch optimization result of the target substation, including: inputting the operating data into the dispatch optimization model, solving the dispatch optimization model based on a particle swarm optimization algorithm, and obtaining an optimal solution output by the dispatch optimization model; and using the optimal solution as the dispatch optimization result of the target substation.

[0008] In one of the embodiments, the method also includes: obtaining sample operating data of the sample substation; performing a cost analysis on the sample substation based on the sample operating data to obtain a sample operating cost of the sample substation; determining operating constraints of the sample substation based on the sample operating data; and training a scheduling optimization model based on the sample operating cost and operating constraints.

[0009] In one embodiment, the sample operating data includes fuel data and line data of the sample substation; the sample operating cost includes the sample power generation cost of the sample substation; based on the sample operating data, a cost analysis is performed on the sample substation to obtain the sample operating cost of the sample substation, including: based on the fuel data, analyzing the first power generation cost required for the sample substation under high load and the second power generation cost required for the sample substation under non-high load; based on the line data, a line loss analysis is performed on the sample substation to obtain the line loss cost of the sample substation during operation; the first power generation cost, the second power generation cost and the line loss cost are collectively used as the sample power generation cost of the sample substation.

[0010] In one embodiment, the sample operating cost also includes the sample environmental impact cost of the sample substation; based on the sample operating data, a cost analysis is performed on the sample substation to obtain the sample operating cost of the sample substation, which also includes: determining the gas emissions of the sample substation based on the fuel data; obtaining the unit processing cost of the emission gas of the sample substation, and taking the product of the unit processing cost and the gas emissions as the environmental impact cost of the sample substation.

[0011] On the second aspect, the present application also provides a substation dispatching optimization device, including: a data acquisition module, which is used to respond to the dispatching optimization instruction for the target substation and obtain the operating data of the target substation; a dispatching optimization module, which is used to perform dispatching optimization analysis on the target substation based on the operating data through a dispatching optimization model to obtain the dispatching optimization result of the target substation; the dispatching optimization model is a model established in advance based on the objective function of minimizing the substation operating cost; the substation operating cost includes the power generation cost and environmental impact cost of the substation; the environmental impact cost refers to the cost required to deal with the environmental impact generated by the substation during operation; an operation module, which is used to obtain the historical dispatching data of the target substation, and when the comparison result between the historical dispatching data and the dispatching optimization result is lower than a preset threshold, the operation of the target substation is controlled based on the dispatching optimization result.

[0012] On the third aspect, the present application also provides a computer device, including a memory and a processor, the memory storing a computer program, and the processor implementing the following steps when executing the computer program: in response to a dispatch optimization instruction for a target substation, obtaining operating data of the target substation; performing dispatch optimization analysis on the target substation based on the operating data through a dispatch optimization model to obtain a dispatch optimization result of the target substation; the dispatch optimization model is a model established in advance based on an objective function of minimizing the operating cost of the substation; the operating cost of the substation includes the power generation cost and environmental impact cost of the substation; the environmental impact cost refers to the cost required to deal with the environmental impact generated by the substation during operation; obtaining historical dispatch data of the target substation, and when the comparison result between the historical dispatch data and the dispatch optimization result is lower than a preset threshold, controlling the operation of the target substation based on the dispatch optimization result.

[0013] In a fourth aspect, the present application also provides a computer-readable storage medium having a computer program stored thereon, which implements the following steps when the computer program is executed by a processor: in response to a dispatch optimization instruction for a target substation, obtaining operating data of the target substation; performing dispatch optimization analysis on the target substation based on the operating data through a dispatch optimization model to obtain a dispatch optimization result of the target substation; the dispatch optimization model is a model established in advance based on an objective function of minimizing the operating cost of the substation; the operating cost of the substation includes the power generation cost and environmental impact cost of the substation; the environmental impact cost refers to the cost required to deal with the environmental impact generated by the substation during operation; obtaining historical dispatch data of the target substation, and controlling the operation of the target substation based on the dispatch optimization result when the comparison result between the historical dispatch data and the dispatch optimization result is lower than a preset threshold.

[0014] In a fifth aspect, the present application also provides a computer program product, including a computer program, which implements the following steps when executed by a processor: in response to a dispatch optimization instruction for a target substation, obtaining operating data of the target substation; performing dispatch optimization analysis on the target substation based on the operating data through a dispatch optimization model to obtain a dispatch optimization result of the target substation; the dispatch optimization model is a model established in advance based on an objective function of minimizing the operating cost of the substation; the operating cost of the substation includes the power generation cost and environmental impact cost of the substation; the environmental impact cost refers to the cost required to deal with the environmental impact generated by the substation during operation; obtaining historical dispatch data of the target substation, and controlling the operation of the target substation based on the dispatch optimization result when the comparison result between the historical dispatch data and the dispatch optimization result is lower than a preset threshold.

[0015] The above-mentioned substation dispatch optimization method, device, computer equipment, computer-readable storage medium and computer program product, in response to the dispatch optimization instruction for the target substation, first obtain the operating data of the target substation. Then, through the dispatch optimization model, based on the operating data, the target substation is dispatched and optimized to obtain the dispatch optimization result of the target substation. Since the dispatch optimization model is a model established in advance based on the objective function of minimizing the substation operation cost, the entire dispatch optimization can ensure that the operating cost of the target substation is minimized, thereby reducing the substation dispatch optimization cost. Moreover, the substation operation cost also includes the power generation cost and environmental impact cost of the substation. The environmental impact cost refers to the cost required to deal with the environmental impact generated by the substation during operation. In other words, the substation operation cost in this application is determined from two aspects: the substation's own operation and environmental impact, which is more in line with the actual situation, so that the output result of the dispatch optimization model is more accurate, thereby improving the accuracy of the substation dispatch optimization. In addition, in order to further verify the reliability of the dispatch optimization results, this application will also obtain the historical dispatch data of the target substation. If the comparison result between the historical dispatch data and the dispatch optimization result is lower than the preset threshold, it means that the dispatch optimization result at this time is not much different from the historical dispatch data, and the dispatch optimization result is considered to be reliable. Finally, based on the dispatch optimization result, the operation of the target substation is controlled. In summary, on the one hand, this application can reduce the cost of substation dispatch optimization and solve the current technical defects of high cost caused by introducing advanced substation units for dispatch optimization. On the other hand, it can improve the accuracy and reliability of substation dispatch optimization. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] In order to more clearly illustrate the technical solutions in the embodiments of the present application or related technologies, the drawings required for use in the embodiments of the present application or related technical descriptions will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other related drawings can be obtained based on these drawings without paying creative work.

[0017] Figure 1 An application environment diagram of a substation dispatch optimization method in an embodiment;

[0018] Figure 2 A schematic diagram of a flow chart of a substation dispatch optimization method in one embodiment;

[0019] Figure 3 A schematic diagram of a flow chart of model solving in one embodiment;

[0020] Figure 4 A schematic diagram of a process for training a scheduling optimization model in one embodiment;

[0021] Figure 5 A schematic diagram of a process for determining power generation cost in one embodiment;

[0022] Figure 6 A schematic diagram of a process for determining environmental impact costs in one embodiment;

[0023] Figure 7 This is a schematic diagram of the optimal output of each power generation unit in an embodiment;

[0024] Figure 8 A real-time power generation monitoring diagram of each generator set in an embodiment;

[0025] Fig. 9 A schematic diagram of the operating costs of each generator set at different power generation amounts in one embodiment;

[0026] Fig.10 A structural block diagram of a substation dispatch optimization device in one embodiment;

[0027] Fig.11 FIG. 4 is a diagram showing the internal structure of a computer device in one embodiment. DETAILED DESCRIPTION

[0028] In order to make the purpose, technical solution and advantages of the present application more clearly understood, the present application is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and are not used to limit the present application.

[0029] The substation dispatch optimization method provided in the embodiment of the present application can be applied to Figure 1In the application environment shown. Among them, the terminal 102 communicates with the server 104 through the network. The data storage system can store the data that the server 104 needs to process. The data storage system can be integrated on the server 104, or it can be placed on the cloud or other network servers. The server 104 responds to the dispatch optimization instruction for the target substation initiated by the terminal 102, and first obtains the operation data of the target substation. Then, through the dispatch optimization model, based on the operation data, the dispatch optimization analysis of the target substation is performed to obtain the dispatch optimization result of the target substation. The dispatch optimization model is a model established in advance based on the objective function of minimizing the operation cost of the substation. The operation cost of the substation includes the power generation cost and the environmental impact cost of the substation. The environmental impact cost refers to the cost required to deal with the environmental impact generated by the substation during operation. Finally, the historical dispatch data of the target substation is obtained. When the comparison result between the historical dispatch data and the dispatch optimization result is lower than the preset threshold, the server 104 controls the operation of the target substation based on the dispatch optimization result. Among them, the terminal 102 can be, but is not limited to, various personal computers, laptops, smart phones, tablet computers and Internet of Things devices. The server 104 may be an independent physical server, or a server cluster or distributed system composed of multiple physical servers, or a cloud server that provides cloud computing services.

[0030] In an exemplary embodiment, Figure 2 As shown, a substation scheduling optimization method is provided, which is applied to Figure 1 The server 104 in FIG. 1 is used as an example to illustrate, including:

[0031] Step S202: In response to a dispatch optimization instruction for a target substation, operation data of the target substation is acquired.

[0032] Among them, the target substation may refer to a substation that needs to be dispatched and optimized. In one example, the target substation may include a high-voltage substation that boosts the electric energy generated by the power plant and feeds it into the high-voltage power grid, and a low-voltage substation that reduces the high-voltage electricity to a low-voltage electricity suitable for users. The dispatch optimization instruction may refer to an instruction to dispatch and optimize the target substation. It can be understood that there may be at least one power generation unit in the substation. The power generation unit can be understood as a unit unit for generating electricity in the substation, which can be composed of a group of generators connected to the main transformer and a matching speed control system, control system and protection system. Therefore, the dispatch optimization in this embodiment can also be understood as dispatching and optimizing each power generation unit in the target substation. Operation data may refer to the basic data of the target substation during operation, which may include but is not limited to fuel data, line data, voltage data, current data and power data of the target substation.

[0033] In one example, fuel data can be understood as data such as the amount of fuel consumed by the target substation during operation, fuel type, etc., and line data can be understood as line-related data of the target substation during operation, such as line resistance, power transmitted by the line, number of lines, etc. It should be noted that the line here refers to the line through which the target substation transmits power to other substations.

[0034] In one example, the operation data may refer to the real-time operation data of the target substation, or the operation data of the target substation within a specified period, such as a week or a month, which may be collected through sensors or other intelligent devices.

[0035] Exemplarily, when the server receives a dispatch optimization instruction for a target substation initiated by a terminal, it may first obtain real-time operation data of the target substation, so as to facilitate subsequent input into a dispatch optimization model to perform dispatch optimization analysis on the target substation.

[0036] In one example, after the server obtains the operating data of the target substation, it can further pre-process the operating data to ensure data quality, and input the pre-processed operating data into the scheduling optimization model to ensure the accuracy of the operating data.

[0037] In one example, the preprocessing of the running data may include data cleaning and data standardization. Data cleaning refers to removing missing values ​​and outliers in the running data. The expression for data standardization may be:

[0038]

[0039] in, represents the i-th type of operation data; Represents the i-th type of running data after data standardization; represents the mean of the i-th type of running data, Represents the standard deviation of the i-th type of running data.

[0040] Step S204, performing a dispatch optimization analysis on the target substation based on the operation data through the dispatch optimization model to obtain a dispatch optimization result of the target substation; the dispatch optimization model is a model established in advance based on the objective function of minimizing the substation operation cost.

[0041] Among them, the substation operation cost includes the power generation cost and environmental impact cost of the substation. Environmental impact cost refers to the cost required to deal with the environmental impact generated by the substation during operation. The power generation cost can refer to the cost required by the substation during operation, that is, the power generation process, which can include but is not limited to the power generation cost required by the target substation under high load and non-high load, the line loss cost of the target substation, and the basic cost of the target substation, including startup cost and maintenance cost, etc. The basic cost refers to the cost that must be borne regardless of whether the target substation is in operation, and has nothing to do with the power generation. It should be noted that high load can be understood as the power generation of the target substation is too high, and non-high load can be understood as the power generation of the target substation is medium or even zero. The load situation can be determined based on the fuel data of the target substation. The larger the amount of fuel, the higher the power generation can be considered, and the less the amount of fuel, the lower the power generation can be considered. Line loss cost can refer to the cost corresponding to the power loss of the substation due to line resistance during operation. Startup cost can be understood as the cost required when the substation is started. Maintenance cost can be understood as the cost required to maintain the normal operation of the substation. Of course, in actual applications, other costs such as transformer loss cost can also be set according to actual conditions. The dispatch optimization result may refer to the result obtained after dispatch optimization of the target substation, which may include but is not limited to the total power generation, the number and number of power generation units performing power generation tasks, the optimal power generation power of each power generation unit, operating cost, operating time, etc.

[0042] Exemplarily, after acquiring the operating data of the target substation, the server can input the operating data into a pre-trained dispatch optimization model. Since the dispatch optimization target is a model established in advance based on an objective function of minimizing the substation operating cost, i.e., the power generation cost and the environmental impact cost, the dispatch optimization model output result, i.e., the dispatch optimization result of the target power station, can be obtained by solving the objective function.

[0043] In one example, when solving the scheduling optimization model, the server can also solve it based on pre-built operating constraints.

[0044] Step S206, obtaining historical dispatching data of the target substation, and when the comparison result between the historical dispatching data and the dispatching optimization result is lower than a preset threshold, controlling the operation of the target substation based on the dispatching optimization result.

[0045] The historical dispatch data may be understood as the dispatch data of the target substation in the historical period, which may be six consecutive months or one consecutive year. The comparison result may refer to the difference between the historical dispatch data and the dispatch optimization result. The preset threshold may be a predefined threshold.

[0046] It should be noted that the substation is actually a complex and changeable system, and its operation is affected by many factors, and historical dispatch data can provide rich actual dispatch information. Therefore, by comparing the historical dispatch data with the dispatch optimization results, we can understand whether the performance of the dispatch optimization model is stable, and thus evaluate whether the dispatch optimization results output by the model are reliable. If the historical dispatch data differs greatly from the dispatch optimization results, this may mean that the dispatch optimization model fails to accurately reflect the actual situation of the substation in some aspects, or there are some unconsidered constraints. At this time, the input parameters, constraints, and solution methods of the model can be reconfigured to ensure its accuracy and reliability.

[0047] Exemplarily, after obtaining the dispatch optimization result output by the dispatch optimization model, the server can compare the dispatch optimization result with the dispatch data of the target substation obtained in the historical time. It is determined whether the difference between the two exceeds a preset threshold. If it does not exceed the preset threshold, it means that the difference between the historical dispatch data and the dispatch optimization result is not large, or in other words, the output of the dispatch optimization model is stable. Therefore, it can be considered that the dispatch optimization result at this time is accurate and reliable, so the operation of the target substation can be controlled based on the dispatch optimization result. If the difference between the historical dispatch data and the dispatch optimization result exceeds the preset threshold, it means that the difference between the historical dispatch data and the dispatch optimization result is large, the output of the dispatch optimization model is not stable, and the dispatch optimization result at this time is not accurate and reliable enough. It is necessary to optimize the dispatch optimization model again before performing dispatch optimization analysis on the target substation.

[0048] In this embodiment, the server responds to the dispatch optimization instruction for the target substation initiated by the terminal, and first obtains the operating data of the target substation. Then, through the dispatch optimization model, based on the operating data, the dispatch optimization analysis of the target substation is performed to obtain the dispatch optimization result of the target substation. Since the dispatch optimization model is a model established in advance based on the objective function of minimizing the substation operation cost, the entire dispatch optimization can ensure that the operation cost of the target substation is minimized, thereby reducing the dispatch optimization cost of the substation. Moreover, the substation operation cost also includes the power generation cost and environmental impact cost of the substation. The environmental impact cost refers to the cost required to deal with the environmental impact generated by the substation during operation. In other words, the substation operation cost in this embodiment is determined from the two aspects of the substation's own operation and environmental impact, which is more in line with the actual situation, so that the output result of the dispatch optimization model is more accurate, thereby improving the accuracy of the substation dispatch optimization. In addition, in order to further verify the reliability of the dispatch optimization result, this embodiment will also obtain the historical dispatch data of the target substation. If the comparison result between the historical dispatch data and the dispatch optimization result is lower than the preset threshold, it means that the dispatch optimization result at this time is not much different from the historical dispatch data, and the dispatch optimization result is considered to be reliable. Finally, based on the dispatch optimization result, the target substation operation is controlled. In summary, this embodiment can reduce the cost of substation dispatch optimization and solve the technical defect of high cost caused by introducing advanced substation units for dispatch optimization, and can improve the accuracy and reliability of substation dispatch optimization.

[0049] In one example, the scheduling optimization instruction may include at least one scheduling optimization index, which may refer to various indicators for scheduling optimization, including but not limited to the number of power generation units, total power generation, operating time, etc. The historical scheduling data may include the historical index value of each scheduling optimization index, which may refer to the historical value of the scheduling optimization index. The scheduling optimization result may include the target index value of each scheduling optimization index, which may refer to the current value of the scheduling optimization index.

[0050] In an exemplary embodiment, the substation dispatch optimization method further includes: obtaining the difference between the historical index value and the target index value of each dispatch optimization index. That is, after obtaining the historical dispatch data of the target substation, the server can query the historical index value corresponding to each dispatch optimization index from the historical dispatch data, and query the target index value corresponding to each dispatch optimization index from the dispatch optimization result output by the dispatch optimization model. Thus, the difference between the historical index value and the target index value corresponding to all dispatch optimization indexes is calculated.

[0051] In an exemplary embodiment, when the comparison result between the historical dispatching data and the dispatching optimization result is lower than a preset threshold, the operation of the target substation is controlled based on the dispatching optimization result, including: when the average value between the differences is lower than a preset threshold, the operation of the target substation is controlled based on the dispatching optimization result.

[0052] In this embodiment, by calculating the difference between the historical index value and the target index value corresponding to each dispatch optimization index, the operation of the target substation is controlled based on the dispatch optimization result when the average value between the differences is lower than the preset threshold value. This improves the accuracy and reliability of the comparison process, thereby improving the verification effect of the dispatch optimization result and making the dispatch optimization process more accurate and reliable.

[0053] In an exemplary embodiment, Figure 3 As shown in the figure, through the dispatch optimization model, based on the operation data, the dispatch optimization analysis of the target substation is carried out to obtain the dispatch optimization results of the target substation, including:

[0054] Step S302: input the operation data into the scheduling optimization model, solve the scheduling optimization model based on the particle swarm optimization algorithm, and obtain the optimal solution output by the scheduling optimization model.

[0055] Step S304: taking the optimal solution as the dispatch optimization result of the target substation.

[0056] Among them, the particle swarm optimization algorithm is an optimization algorithm based on swarm intelligence. It finds the optimal solution by simulating the foraging behavior of bird flocks. The particle swarm optimization algorithm has the advantages of fast convergence speed, few parameters, and simple and easy algorithm implementation. Therefore, it can effectively improve the dispatch optimization efficiency of the substation in this embodiment.

[0057] Exemplarily, solving the scheduling optimization model using the particle swarm optimization algorithm may include the following steps:

[0058] Step 1: Initialize particles. It should be noted that in the particle swarm optimization algorithm, each potential solution is represented as a particle, which moves in the search space to try to find the optimal solution. Each particle has two key properties: position and velocity. The position represents the current position of the particle in the search space, while the velocity represents the direction and speed of the particle's movement. Therefore, a particle can also be represented by a vector: P=[P1, P2...P n ], where P i Indicates the power generation information of the i-th power generation unit, such as the power generation power, i represents any one from 1 to n, and n represents the number of power generation units.

[0059] Step 2: Initialize the position and velocity of the particle. The position and velocity represent different scheduling optimization schemes and the changing trend of the scheduling optimization schemes respectively. The formula for initializing the position and velocity of the particle can be: , j represents the index of the particle, which is usually used to uniquely identify each particle. Indicates the maximum speed of the particle.

[0060] Step 3: Set the particle swarm parameters, such as swarm size, maximum number of iterations, learning factor, and inertia weight.

[0061] Step 4: Calculate the fitness value. Evaluate the quality of each particle according to the pre-designed fitness function, that is, calculate the fitness value of each particle. The fitness function should be determined according to the optimization target, that is, the operating cost. The fitness value calculation expression can be: F(P i )=C(P i ), C represents the objective function.

[0062] Step 5: Update individual extreme values ​​and global extreme values. Individual extreme values ​​can refer to the optimal solution currently found by each particle, and global extreme values ​​can refer to the optimal solution found in the entire particle group. If the current fitness value is better than the particle's historical best fitness value, update the individual extreme value:

[0063]

[0064] Update the global extremum:

[0065] Step 6: Update the particle's velocity and position. The expression can be:

[0066]

[0067] w represents the inertia weight, and Both represent learning factors. In addition, it is also necessary to ensure that the updated position of the particle meets the power generation capacity of the power generation unit, that is, P i,j (t+1) ∈[ P i min , P i max ] .

[0068] Step 7: Convergence judgment: Determine whether the maximum number of iterations has been reached. If so, stop the algorithm. In practical applications, you can also set a suitable fitness convergence threshold to determine whether the current fitness value meets the convergence condition.

[0069] Step eight, output the optimal solution, i.e., the global extreme value, as the scheduling optimization result.

[0070] In this embodiment, by using the particle swarm optimization algorithm to solve the scheduling operation model, the accuracy of the model output results can be effectively improved, thereby improving the accuracy of the scheduling optimization results.

[0071] In an exemplary embodiment, Figure 4 As shown in Figure 1, the training process of the scheduling optimization model includes:

[0072] Step S402, obtaining sample operation data of a sample substation.

[0073] The sample substation may refer to a substation used to train the scheduling optimization model, and the sample substation may be a representative substation or multiple substations. The sample operation data may refer to the basic data of the sample substation during operation, and may include but is not limited to the fuel data, line data, voltage data, current data and power data of the sample substation.

[0074] For example, when the server is training the scheduling optimization model, it needs training samples as model data support. Therefore, the server can obtain the operation data of each sample substation from the database of the power grid system for each sample substation as a training sample for model training.

[0075] Step S404: Based on the sample operation data, cost analysis is performed on the sample substation to obtain the sample operation cost of the sample substation.

[0076] Among them, the sample operating cost includes the sample power generation cost and sample environmental impact cost of the sample substation. The sample power generation cost refers to the cost required for the sample substation during operation, that is, the power generation process. The sample environmental impact cost refers to the cost required to deal with the environmental impact generated by the sample substation during operation.

[0077] For example, the server obtains sample operation data of the sample substation, and can further calculate the cost required for the sample substation in the power generation process and the cost required for processing the environmental impact generated by the sample substation in the operation process based on the sample operation data, so as to train the scheduling optimization model based on the cost information in the future.

[0078] Step S406: determining the operation constraint conditions of the sample substation based on the sample operation data.

[0079] The operation constraints refer to the constraints of the dispatch optimization model, which are constructed based on the operation data of the substation, and may include but are not limited to load balance constraints, power generation capacity constraints, network transmission constraints, and system security constraints.

[0080] In one example, the load balance constraint is used to ensure the supply and demand balance of the sample substation, taking into account the total load and reserve capacity of the sample substation, and its expression can be: ,in, represents the required reserve capacity of the sample substation, and D represents the total load of the sample substation.

[0081] In one example, the power generation capacity constraint may include an output constraint and a start / stop constraint. The output constraint is expressed as: , represents the minimum output of the sample substation, Represents the maximum output of the sample substation. The start-stop constraint includes the start constraint and the stop constraint. The start constraint is used to ensure that the power generation unit has enough time to reach the set target output when it starts. Its expression can be:

[0082]

[0083] in, represents the output of the i-th generating unit at time t, represents the target output of the i-th power generation unit, is a binary variable, indicating the state of the i-th power generation unit (1 for operation, 0 for shutdown). In order to limit the startup time of the power generation unit, a time delay variable can be introduced ,Right now ,in, It is the time required for the power generation unit to reach a stable output after startup. The stop constraint is used to ensure that the power generation unit can operate at the set minimum output level when it is shut down. Its expression can be: , Indicates the minimum output level. And when shutting down, it is necessary to ensure the minimum output constraint during the shutdown process:

[0084]

[0085] in, Indicates the minimum output during shutdown.

[0086] In one example, the network transmission constraint is set based on the capacity, transmission loss and safety of each line. Its expression can be: ,in, represents the power transmitted from sample substation i to other substations j, Indicates the extreme value of power transmission. And considering the influence of transmission loss, the transmission loss factor can be introduced, and the expression can be: ,in, Represents the transmission loss factor.

[0087] In one example, to ensure the system safety of the sample substation, some additional constraints can be introduced, including frequency stability constraints and voltage stability constraints. The expression of the frequency stability constraint can be:

[0088]

[0089] Where f represents the current frequency, Indicates the rated frequency, Indicates the allowable frequency deviation.

[0090] The expression of voltage stability constraint can be expressed as: ,in, Indicates the voltage value of the sample substation.

[0091] Exemplarily, after the server obtains the sample operation data, it can establish operation constraints based on the sample operation data.

[0092] Step S408: training a scheduling optimization model based on the sample operation cost and operation constraints.

[0093] Exemplarily, the server can finally train the pre-trained mathematical model according to the obtained sample operating costs and operating constraints until a preset number of iterations is reached or the training accuracy is met, thereby obtaining a scheduling optimization model.

[0094] In one example, the objective function of the scheduling optimization model may be expressed as:

[0095]

[0096] Where Pi represents the power generation of the i-th power generation unit, represents the first power generation cost of the i-th power generation unit, represents the second power generation cost of the i-th power generation unit, The basic cost of the i-th power generation unit includes the startup cost and maintenance cost. N represents the number of power generation units; M represents the number of transmission lines of the sample substation, and K represents the number of grid nodes. Represents line loss cost. Represents the sample environmental impact cost.

[0097] In this embodiment, a scheduling optimization model is obtained through training and used for actual substation scheduling optimization. On the basis of reducing the cost of substation scheduling optimization, the accuracy and efficiency of substation scheduling optimization are also improved.

[0098] In an exemplary embodiment, Figure 5As shown, based on the sample operation data, the cost analysis of the sample substation is performed to obtain the sample operation cost of the sample substation, including:

[0099] Step S502: Analyze the first power generation cost required by the sample substation under high load and the second power generation cost required by the sample substation under non-high load based on the fuel data.

[0100] The fuel data is actually related to the load of the sample substation. The higher the fuel amount, the higher the load of the sample substation, and the lower the fuel amount, the lower the load of the sample substation. High load can be understood as high power generation of the sample substation, and non-high load can be understood as medium or even zero power generation of the sample substation.

[0101] Exemplarily, the server analyzes the power generation cost required by the sample substation under high load, the fixed cost when the output power of the sample substation is zero, or the power generation cost under medium load based on the fuel data of the sample power station.

[0102] Step S504: Based on the line data, a line loss analysis is performed on the sample substation to obtain the line loss cost of the sample substation during operation.

[0103] Among them, line loss analysis actually refers to the process of analyzing the cost corresponding to the electricity lost due to line resistance during the operation of the sample substation.

[0104] In one example, the expression for line loss cost may be: ,in, represents the transmission loss cost; represents the line resistance between sample substation i and other substation j, represents the power transmitted from sample substation i to other substation j. In order to consider the loss under different load conditions, it can be further refined as: ,in, is the load-dependent loss factor and D is the total load demand.

[0105] Exemplarily, the server calculates the cost loss of the sample substation when transmitting power to other substations based on the line resistance between the sample substation and other substations, thereby obtaining the line loss cost of the sample substation.

[0106] Step S506: The first power generation cost, the second power generation cost and the line loss cost are taken together as the sample power generation cost of the sample substation.

[0107] Exemplarily, the server uses the first power generation cost, the second power generation cost and the line loss cost together as the sample power generation cost of the sample substation for training the scheduling optimization model.

[0108] In this embodiment, the cost analysis of the substation is performed from two aspects of power generation and line loss of the sample substation, which improves the accuracy and reliability of the sample power generation cost, thereby improving the accuracy and reliability of the scheduling optimization model.

[0109] In an exemplary embodiment, Figure 6 As shown, based on the sample operation data, the cost analysis of the sample substation is performed to obtain the sample operation cost of the sample substation, which also includes:

[0110] Step S602: determining the gas emission of the sample substation based on the fuel data.

[0111] Step S604: Obtain the unit processing cost of the emission gas of the sample substation, and use the product of the unit processing cost and the gas emission as the environmental impact cost of the sample substation.

[0112] The gas emission volume may refer to the gas volume of the gas emitted during the operation of the sample substation. The emission gas may be carbon dioxide, sulfur oxides, etc. The unit processing cost may refer to the unit cost of processing the emission gas of the sample substation.

[0113] Exemplarily, the server can obtain the gas emission corresponding to the fuel data based on the fuel data, and thus calculate the product of the gas emission and the unit processing cost of the emission gas, and the product is the environmental impact cost of the sample substation.

[0114] In one example, the calculation expression of the environmental impact cost can be:

[0115]

[0116]

[0117] in, represents the unit treatment cost of the mth type of emission gas, represents the environmental impact cost; represents the emission of the mth type of emission gas, is the unit gas emission factor associated with the ith power generation unit.

[0118] In this embodiment, the environmental impact cost of the sample substation is obtained by calculating the product of the gas emission of the sample substation and the unit processing cost of the emission gas, thereby ensuring the accuracy of the environmental impact cost and thus ensuring the accuracy of the scheduling optimization result.

[0119] In a specific embodiment, the specific process of the substation dispatch optimization method includes: the server responds to the dispatch optimization instruction for the target substation, the dispatch optimization instruction includes at least one dispatch optimization index, and obtains the operating data of the target substation. The operating data is input into the dispatch optimization model, and the dispatch optimization model is solved based on the particle swarm optimization algorithm to obtain the optimal solution output by the dispatch optimization model, and the optimal solution is used as the dispatch optimization result of the target substation. The dispatch optimization model is a model established in advance based on the objective function of minimizing the substation operation cost. The substation operation cost includes the power generation cost and environmental impact cost of the substation. The environmental impact cost refers to the cost required to deal with the environmental impact generated by the substation during operation. The historical indicator value for each dispatch optimization index in the historical dispatch data of the target substation and the target indicator value for each dispatch optimization index in the dispatch optimization result are obtained, and the difference between the two is calculated. When the average value between the differences is lower than the preset threshold, the operation of the target substation is controlled based on the dispatch optimization result. The dispatch optimization result may include the optimal output of each power generation unit, which can be referred to Figure 7 , Figure 7 It shows the optimal output diagram of each power generation unit. The horizontal axis represents different power generation units, also known as generator sets (unit 1, unit 2, unit 3), and the vertical axis represents the optimal output of each unit, that is, the power generation of each generator set after scheduling optimization. The height of each column represents the optimal output of the corresponding unit. A higher column means that the unit has undertaken more power generation tasks in the optimized operation. During the whole process, each generator set can also be monitored in real time. Figure 8 The real-time power generation monitoring diagram of each generator set is shown, reflecting the changes in power generation of the generator set in different time periods.

[0120] The training process for the scheduling operation model includes: obtaining the fuel data and line data of the sample substation. Based on the fuel data, analyze the first power generation cost required by the sample substation under high load and the second power generation cost required by the sample substation under non-high load. Based on the line data, perform line loss analysis on the sample substation to obtain the line loss cost of the sample substation during operation. The first power generation cost, the second power generation cost and the line loss cost are collectively used as the sample power generation cost of the sample substation. Based on the fuel data, determine the gas emissions of the sample substation. Obtain the unit processing cost of the emission gas for the sample substation, and use the product of the unit processing cost and the gas emissions as the environmental impact cost of the sample substation. For reference Fig. 9 , Fig. 9 The operating cost of each generator set in the sample substation under different power generation is shown, and each curve represents the cost function of a generator set. Based on the sample operation data, the operating constraints of the sample substation are determined. Finally, according to the sample operation cost and operation constraints, the scheduling optimization model is trained.

[0121] In this embodiment, the server responds to the dispatch optimization instruction for the target substation initiated by the terminal, and first obtains the operating data of the target substation. Then, through the dispatch optimization model, based on the operating data, the dispatch optimization analysis of the target substation is performed to obtain the dispatch optimization result of the target substation. Since the dispatch optimization model is a model established in advance based on the objective function of minimizing the substation operation cost, the entire dispatch optimization can ensure that the operation cost of the target substation is minimized, thereby reducing the dispatch optimization cost of the substation. Moreover, the substation operation cost also includes the power generation cost and environmental impact cost of the substation. The environmental impact cost refers to the cost required to deal with the environmental impact generated by the substation during operation. In other words, the substation operation cost in this embodiment is determined from the two aspects of the substation's own operation and environmental impact, which is more in line with the actual situation, so that the output result of the dispatch optimization model is more accurate, thereby improving the accuracy of the substation dispatch optimization. In addition, in order to further verify the reliability of the dispatch optimization result, this embodiment will also obtain the historical dispatch data of the target substation. If the comparison result between the historical dispatch data and the dispatch optimization result is lower than the preset threshold, it means that the dispatch optimization result at this time is not much different from the historical dispatch data, and the dispatch optimization result is considered to be reliable. Finally, based on the dispatch optimization result, the target substation operation is controlled. In summary, this embodiment can reduce the cost of substation dispatch optimization and solve the technical defect of high cost caused by introducing advanced substation units for dispatch optimization, and can improve the accuracy and reliability of substation dispatch optimization.

[0122] It should be understood that, although the steps in the flowcharts involved in the above embodiments are displayed in sequence according to the indication of the arrows, these steps are not necessarily executed in sequence according to the order indicated by the arrows. Unless there is a clear explanation in this article, the execution of these steps does not have a strict order restriction, and these steps can be executed in other orders. Moreover, at least a part of the steps in the flowcharts involved in the above embodiments can include multiple steps or multiple stages, and these steps or stages are not necessarily executed at the same time, but can be executed at different times, and the execution order of these steps or stages is not necessarily carried out in sequence, but can be executed in turn or alternately with other steps or at least a part of the steps or stages in other steps.

[0123] Based on the same inventive concept, the embodiment of the present application also provides a substation dispatch optimization device for implementing the substation dispatch optimization method involved above. The implementation scheme for solving the problem provided by the device is similar to the implementation scheme recorded in the above method, so the specific limitations in one or more substation dispatch optimization device embodiments provided below can refer to the limitations of the substation dispatch optimization method above, and will not be repeated here.

[0124] In an exemplary embodiment, Fig.10 As shown, a substation dispatching optimization device is provided, including: a data acquisition module 1002, which is used to respond to a dispatching optimization instruction for a target substation and obtain the operating data of the target substation; a dispatching optimization module 1004, which is used to perform dispatching optimization analysis on the target substation based on the operating data through a dispatching optimization model to obtain a dispatching optimization result of the target substation; the dispatching optimization model is a model established in advance based on an objective function of minimizing the substation operating cost; the substation operating cost includes the power generation cost and environmental impact cost of the substation; the environmental impact cost refers to the cost required to deal with the environmental impact generated by the substation during operation; an operation module 1006, which is used to obtain the historical dispatching data of the target substation, and when the comparison result between the historical dispatching data and the dispatching optimization result is lower than a preset threshold, the operation of the target substation is controlled based on the dispatching optimization result.

[0125] In one embodiment, the dispatching optimization instruction includes at least one dispatching optimization indicator; the historical dispatching data includes the historical indicator value of each dispatching optimization indicator; the dispatching optimization result includes the target indicator value of each dispatching optimization indicator; the device is also used to: obtain the difference between the historical indicator value and the target indicator value of each dispatching optimization indicator; the operation module 1006 is also used to: control the operation of the target substation based on the dispatching optimization result when the average value between the differences is lower than a preset threshold.

[0126] In one embodiment, the dispatch optimization module 1004 is also used to: input the operation data into the dispatch optimization model, solve the dispatch optimization model based on the particle swarm optimization algorithm, and obtain the optimal solution output by the dispatch optimization model; and use the optimal solution as the dispatch optimization result of the target substation.

[0127] In one of the embodiments, the device is also used to: obtain sample operating data of the sample substation; based on the sample operating data, perform cost analysis on the sample substation to obtain sample operating costs of the sample substation; based on the sample operating data, determine operating constraints of the sample substation; and train a scheduling optimization model based on the sample operating costs and operating constraints.

[0128] In one embodiment, the sample operating data includes fuel data and line data of the sample substation; the sample operating cost includes the sample power generation cost of the sample substation; the device is also used to: based on the fuel data, analyze the first power generation cost required for the sample substation under high load and the second power generation cost required for the sample substation under non-high load; based on the line data, perform line loss analysis on the sample substation to obtain the line loss cost of the sample substation during operation; and use the first power generation cost, the second power generation cost and the line loss cost together as the sample power generation cost of the sample substation.

[0129] In one embodiment, the sample operating cost also includes the sample environmental impact cost of the sample substation; the device is also used to: determine the gas emissions of the sample substation based on fuel data; obtain the unit processing cost of the emission gas for the sample substation, and use the product of the unit processing cost and the gas emissions as the environmental impact cost of the sample substation.

[0130] Each module in the above-mentioned substation dispatch optimization device can be implemented in whole or in part by software, hardware and their combination. Each of the above-mentioned modules can be embedded in or independent of the processor in the computer device in the form of hardware, or can be stored in the memory of the computer device in the form of software, so that the processor can call and execute the corresponding operations of each of the above modules.

[0131] In an exemplary embodiment, a computer device is provided. The computer device may be a server, and its internal structure diagram may be as shown in FIG. Fig.11 As shown. The computer device includes a processor, a memory, an input / output interface (Input / Output, referred to as I / O) and a communication interface. Among them, the processor, the memory and the input / output interface are connected through a system bus, and the communication interface is connected to the system bus through the input / output interface. Among them, the processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program and a database. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The database of the computer device is used to store substation dispatch optimization data. The input / output interface of the computer device is used to exchange information between the processor and an external device. The communication interface of the computer device is used to communicate with an external terminal through a network connection. When the computer program is executed by the processor, a substation dispatch optimization method is implemented.

[0132] Those skilled in the art will understand that Fig.11The structure shown in the figure is only a block diagram of a part of the structure related to the solution of the present application, and does not constitute a limitation on the computer device to which the solution of the present application is applied. The specific computer device may include more or fewer components than those shown in the figure, or combine certain components, or have a different arrangement of components.

[0133] In an exemplary embodiment, a computer device is provided, including a memory and a processor, wherein a computer program is stored in the memory, and the processor implements the following steps when executing the computer program: in response to a dispatch optimization instruction for a target substation, obtaining operation data of the target substation; performing dispatch optimization analysis on the target substation based on the operation data through a dispatch optimization model to obtain a dispatch optimization result of the target substation; the dispatch optimization model is a model established in advance based on an objective function of minimizing the substation operation cost; the substation operation cost includes the power generation cost and environmental impact cost of the substation; the environmental impact cost refers to the cost required to deal with the environmental impact generated by the substation during operation; obtaining historical dispatch data of the target substation, and controlling the operation of the target substation based on the dispatch optimization result when the comparison result between the historical dispatch data and the dispatch optimization result is lower than a preset threshold.

[0134] In one embodiment, when the processor executes the computer program, the following steps are also implemented: obtaining the difference between the historical indicator value and the target indicator value of each scheduling optimization indicator; when the comparison result between the historical scheduling data and the scheduling optimization result is lower than a preset threshold, controlling the operation of the target substation based on the scheduling optimization result, including: when the average value between the differences is lower than a preset threshold, controlling the operation of the target substation based on the scheduling optimization result.

[0135] In one embodiment, when the processor executes the computer program, the following steps are also implemented: inputting the operating data into the scheduling optimization model, solving the scheduling optimization model based on the particle swarm optimization algorithm, and obtaining the optimal solution output by the scheduling optimization model; and using the optimal solution as the scheduling optimization result of the target substation.

[0136] In one embodiment, when the processor executes the computer program, the following steps are also implemented: obtaining sample operating data of the sample substation; based on the sample operating data, performing a cost analysis on the sample substation to obtain a sample operating cost of the sample substation; based on the sample operating data, determining the operating constraints of the sample substation; and training a scheduling optimization model based on the sample operating cost and the operating constraints.

[0137] In one embodiment, when the processor executes the computer program, the following steps are also implemented: based on the fuel data, a first power generation cost required for the sample substation under high load and a second power generation cost required for the sample substation under non-high load are analyzed; based on the line data, a line loss analysis is performed on the sample substation to obtain the line loss cost of the sample substation during operation; the first power generation cost, the second power generation cost and the line loss cost are collectively used as the sample power generation cost of the sample substation.

[0138] In one embodiment, when the processor executes the computer program, it also implements the following steps: based on the fuel data, determining the gas emissions of the sample substation; obtaining the unit processing cost of the emission gas for the sample substation, and taking the product of the unit processing cost and the gas emissions as the environmental impact cost of the sample substation.

[0139] In one embodiment, a computer-readable storage medium is provided, on which a computer program is stored, and when the computer program is executed by a processor, the following steps are implemented: in response to a dispatch optimization instruction for a target substation, the operating data of the target substation is obtained; based on the operating data, a dispatch optimization analysis is performed on the target substation through a dispatch optimization model to obtain a dispatch optimization result of the target substation; the dispatch optimization model is a model established in advance based on an objective function of minimizing the substation operating cost; the substation operating cost includes the power generation cost and the environmental impact cost of the substation; the environmental impact cost refers to the cost required to deal with the environmental impact generated by the substation during operation; the historical dispatch data of the target substation is obtained, and when the comparison result between the historical dispatch data and the dispatch optimization result is lower than a preset threshold, the operation of the target substation is controlled based on the dispatch optimization result.

[0140] In one embodiment, when the computer program is executed by the processor, the following steps are also implemented: obtaining the difference between the historical indicator value and the target indicator value of each scheduling optimization indicator; when the comparison result between the historical scheduling data and the scheduling optimization result is lower than a preset threshold, controlling the operation of the target substation based on the scheduling optimization result, including: when the average value between the differences is lower than a preset threshold, controlling the operation of the target substation based on the scheduling optimization result.

[0141] In one embodiment, when the computer program is executed by the processor, the following steps are also implemented: inputting the operating data into the scheduling optimization model, solving the scheduling optimization model based on the particle swarm optimization algorithm, and obtaining the optimal solution output by the scheduling optimization model; and using the optimal solution as the scheduling optimization result of the target substation.

[0142] In one embodiment, when the computer program is executed by the processor, the following steps are also implemented: obtaining sample operating data of the sample substation; based on the sample operating data, performing a cost analysis on the sample substation to obtain the sample operating cost of the sample substation; based on the sample operating data, determining the operating constraints of the sample substation; and training a scheduling optimization model based on the sample operating cost and the operating constraints.

[0143] In one embodiment, when the computer program is executed by the processor, the following steps are also implemented: based on the fuel data, a first power generation cost required for the sample substation under high load and a second power generation cost required for the sample substation under non-high load are analyzed; based on the line data, a line loss analysis is performed on the sample substation to obtain the line loss cost of the sample substation during operation; the first power generation cost, the second power generation cost and the line loss cost are collectively used as the sample power generation cost of the sample substation.

[0144] In one embodiment, when the computer program is executed by a processor, the following steps are also implemented: based on the fuel data, the gas emissions of the sample substation are determined; the unit processing cost of the emission gas for the sample substation is obtained, and the product of the unit processing cost and the gas emissions is used as the environmental impact cost of the sample substation.

[0145] In one embodiment, a computer program product is provided, including a computer program, which implements the following steps when executed by a processor: in response to a dispatch optimization instruction for a target substation, obtaining operating data of the target substation; performing dispatch optimization analysis on the target substation based on the operating data through a dispatch optimization model to obtain a dispatch optimization result of the target substation; the dispatch optimization model is a model established in advance based on an objective function of minimizing the substation operating cost; the substation operating cost includes the power generation cost and environmental impact cost of the substation; the environmental impact cost refers to the cost required to deal with the environmental impact generated by the substation during operation; obtaining historical dispatch data of the target substation, and controlling the operation of the target substation based on the dispatch optimization result when a comparison result between the historical dispatch data and the dispatch optimization result is lower than a preset threshold.

[0146] In one embodiment, when the computer program is executed by the processor, the following steps are also implemented: obtaining the difference between the historical indicator value and the target indicator value of each scheduling optimization indicator; when the comparison result between the historical scheduling data and the scheduling optimization result is lower than a preset threshold, controlling the operation of the target substation based on the scheduling optimization result, including: when the average value between the differences is lower than a preset threshold, controlling the operation of the target substation based on the scheduling optimization result.

[0147] In one embodiment, when the computer program is executed by the processor, the following steps are also implemented: inputting the operating data into the scheduling optimization model, solving the scheduling optimization model based on the particle swarm optimization algorithm, and obtaining the optimal solution output by the scheduling optimization model; and using the optimal solution as the scheduling optimization result of the target substation.

[0148] In one embodiment, when the computer program is executed by the processor, the following steps are also implemented: obtaining sample operating data of the sample substation; based on the sample operating data, performing a cost analysis on the sample substation to obtain the sample operating cost of the sample substation; based on the sample operating data, determining the operating constraints of the sample substation; and training a scheduling optimization model based on the sample operating cost and the operating constraints.

[0149] In one embodiment, when the computer program is executed by the processor, the following steps are also implemented: based on the fuel data, a first power generation cost required for the sample substation under high load and a second power generation cost required for the sample substation under non-high load are analyzed; based on the line data, a line loss analysis is performed on the sample substation to obtain the line loss cost of the sample substation during operation; the first power generation cost, the second power generation cost and the line loss cost are collectively used as the sample power generation cost of the sample substation.

[0150] In one embodiment, when the computer program is executed by a processor, the following steps are also implemented: based on the fuel data, the gas emissions of the sample substation are determined; the unit processing cost of the emission gas for the sample substation is obtained, and the product of the unit processing cost and the gas emissions is used as the environmental impact cost of the sample substation.

[0151] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, stored data, displayed data, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties, and the collection, use and processing of relevant data must comply with relevant regulations.

[0152] Those skilled in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be completed by instructing the relevant hardware through a computer program, and the computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to the memory, database or other medium used in the embodiments provided in the present application can include at least one of non-volatile memory and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetic random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. As an illustration and not limitation, RAM can be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM). The database involved in each embodiment provided in this application may include at least one of a relational database and a non-relational database. Non-relational databases may include distributed databases based on blockchains, etc., but are not limited to this. The processor involved in each embodiment provided in this application may be a general-purpose processor, a central processing unit, a graphics processor, a digital signal processor, a programmable logic device, a data processing logic device based on quantum computing, an artificial intelligence (AI) processor, etc., but are not limited to this.

[0153] The technical features of the above embodiments may be combined arbitrarily. To make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this application.

[0154] The above embodiments only express several implementation methods of the present application, and the descriptions thereof are relatively specific and detailed, but they cannot be understood as limiting the scope of the present application. It should be pointed out that, for a person of ordinary skill in the art, several variations and improvements can be made without departing from the concept of the present application, and these all belong to the protection scope of the present application. Therefore, the protection scope of the present application shall be subject to the attached claims.

Claims

1. A substation dispatch optimization method, characterized in that: The method comprises: In response to a dispatch optimization instruction for a target substation, acquiring operation data of the target substation; By using a scheduling optimization model, based on the operation data, a scheduling optimization analysis is performed on the target substation to obtain a scheduling optimization result of the target substation; The dispatch optimization model is a model established in advance based on the objective function of minimizing the substation operation cost; the substation operation cost includes the power generation cost and environmental impact cost of the substation; the environmental impact cost refers to the cost required to deal with the environmental impact generated by the substation during operation; The historical dispatching data of the target substation is obtained, and when a comparison result between the historical dispatching data and the dispatching optimization result is lower than a preset threshold, the operation of the target substation is controlled based on the dispatching optimization result.

2. The method according to claim 1, characterized in that The scheduling optimization instruction includes at least one scheduling optimization index; the historical scheduling data includes the historical index value of each scheduling optimization index; the scheduling optimization result includes the target index value of each scheduling optimization index; the method further includes: Obtaining the difference between the historical index value and the target index value of each of the scheduling optimization indicators; When the comparison result between the historical dispatching data and the dispatching optimization result is lower than a preset threshold, controlling the operation of the target substation based on the dispatching optimization result includes: When the average value between the differences is lower than a preset threshold, the operation of the target substation is controlled based on the scheduling optimization result.

3. The method according to claim 1, characterized in that The method of performing a scheduling optimization analysis on the target substation based on the operation data through the scheduling optimization model to obtain a scheduling optimization result of the target substation includes: Inputting the operation data into a scheduling optimization model, solving the scheduling optimization model based on a particle swarm optimization algorithm, and obtaining an optimal solution output by the scheduling optimization model; The optimal solution is used as the dispatch optimization result of the target substation.

4. The method according to claim 1, characterized in that: The method further comprises: Obtaining sample operation data of a sample substation; Based on the sample operation data, performing cost analysis on the sample substation to obtain a sample operation cost of the sample substation; Determining operating constraints of the sample substation based on the sample operating data; A scheduling optimization model is trained based on the sample operating costs and the operating constraints.

5. The method according to claim 4, characterized in that The sample operation data includes fuel data and line data of the sample substation; the sample operation cost includes the sample power generation cost of the sample substation; and the cost analysis of the sample substation based on the sample operation data to obtain the sample operation cost of the sample substation includes: Based on the fuel data, analyzing a first power generation cost required by the sample substation under high load and a second power generation cost required by the sample substation under non-high load; Based on the line data, performing line loss analysis on the sample substation to obtain the line loss cost of the sample substation during operation; The first power generation cost, the second power generation cost and the line loss cost are collectively used as the sample power generation cost of the sample substation.

6. The method according to claim 5, characterized in that The sample operation cost also includes the sample environmental impact cost of the sample substation; the cost analysis of the sample substation based on the sample operation data to obtain the sample operation cost of the sample substation also includes: determining gas emissions of the sample substation based on the fuel data; The unit processing cost of the exhaust gas of the sample substation is obtained, and the product of the unit processing cost and the gas emission is used as the environmental impact cost of the sample substation.

7. A substation dispatch optimization device, characterized in that: The device comprises: A data acquisition module, configured to acquire operation data of a target substation in response to a dispatch optimization instruction for the target substation; A dispatch optimization module, configured to perform dispatch optimization analysis on the target substation based on the operation data through a dispatch optimization model to obtain a dispatch optimization result of the target substation; the dispatch optimization model is a model established in advance based on an objective function of minimizing the substation operation cost; the substation operation cost includes the power generation cost and environmental impact cost of the substation; the environmental impact cost refers to the cost required to deal with the environmental impact generated by the substation during operation; An operation module is used to obtain historical dispatching data of the target substation, and control the operation of the target substation based on the dispatching optimization result when a comparison result between the historical dispatching data and the dispatching optimization result is lower than a preset threshold.

8. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 6 are implemented.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 6 are implemented.

10. A computer program product, comprising a computer program, characterized in that When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 6 are implemented.

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