A method, device and medium for optimizing main transformer

Abstract

The present invention relates to a method, device and medium for optimizing a main transformer. By making constraint judgments in the optimization method, resource utilization, reliability of the power system and new energy consumption are considered when deriving an objective function, the objective function is continuously iterated to obtain the transformer capacity and the planned new energy power output power that minimize the objective function, thereby achieving the purpose of optimization. In the iterative process, the GRG algorithm and the projected gradient method are combined to calculate the optimal result, so that the capacity selection of the main transformer can improve the resource utilization of the power grid while ensuring reliability and new energy consumption capacity. Compared with the prior art, the present invention has the advantages of high resource utilization, high reliability and high flexibility.

Description

Technical Field

[0001] The present invention relates to the field of power systems, and in particular to a method, equipment and medium for optimizing a main transformer. Background Art

[0002] With the continuous growth of economy and the advancement of urbanization, the demand for electricity is growing day by day. As the key equipment for power transmission and distribution, the reasonable selection of the capacity of 110kV / 10kV main transformer is very important.

[0003] From the perspective of construction resource allocation, the traditional method of selecting the capacity of the main transformer often lacks detailed analysis, which may lead to the utilization rate of various resources being far lower than the reasonable utilization rate required to meet the demand. Power supply reliability is one of the core requirements of the power system. If the capacity of the main transformer is not properly selected, stable power supply may not be guaranteed during peak power consumption or sudden failures, which will have a serious impact on industrial production, commercial activities and residents' lives. At the same time, with the rapid development of new energy technologies, the proportion of distributed power sources in the power system has gradually increased. New energy distributed power sources have the advantages of clean and renewable, but they also bring new challenges to the operation and management of the power system. The reasonable selection of the capacity of the main transformer is crucial to absorbing the output of new energy distributed power sources. If the capacity is too small, it may not be able to fully absorb the power generation of distributed power sources, resulting in wind and light abandonment, and wasting renewable energy; if the capacity is too large, it will increase unnecessary investment and operating costs. Therefore, how to improve resource utilization, reliability, and absorption capacity when selecting the main transformer has become a technical problem that needs to be solved. Summary of the invention

[0004] The purpose of the present invention is to provide a main transformer optimization method, device and medium in order to overcome the defects of the above-mentioned prior art. By considering resource utilization, reliability of the power system and new energy consumption in the objective function, the objective function is continuously iterated to obtain the transformer capacity and the planned additional new energy power output that minimize the objective function, thereby achieving the purpose of optimization.

[0005] The purpose of the present invention can be achieved by the following technical solutions:

[0006] According to one aspect of the present invention, a method for optimizing a main transformer is provided, comprising the following steps:

[0007] S1. Obtain fixed value parameters to obtain the maximum output of distributed renewable energy power sources;

[0008] S2. Set the initial value of the transformer capacity and the planned additional output power of the new energy power source;

[0009] S3, judging whether the initial value satisfies the constraint condition, if yes, executing S4, otherwise, adjusting the initial value until the constraint condition is satisfied;

[0010] S4. According to the current transformer capacity and the planned new energy power output, as well as the maximum output of the distributed new energy power, an objective function value is calculated based on the objective function, wherein the objective function is a weighted sum of the first objective function of the main transformer, the reliability objective function, and the new energy distributed power consumption objective function;

[0011] S5, judging whether the objective function value converges to the minimum value, if yes, executing step S6, otherwise, updating the transformer capacity and the planned new energy power output, and returning to S4;

[0012] S6. Output the minimum objective function value, as well as the current transformer capacity and the planned new energy power output.

[0013] Furthermore, the fixed value parameters of S1 include the predicted minimum load of the power supply area, the total capacity of distributed renewable energy power sources, the maximum output coefficient of distributed renewable energy power sources, the upper limit of budget resources, the area of ​​the power supply area, the type of power supply area, the transformer construction cost coefficient and the maintenance and operation cost coefficient, the transformer design life and expected use cycle and the objective function weight coefficient.

[0014] Furthermore, the constraint condition of S3 includes the constraint condition of new energy consumption, which is expressed as:

[0015] minutes { P ( t )- P ne ( t )} ≥ 0,

[0016] in, P ( t ) is the function of load changing with time, P ne ( t ) is the function of renewable energy output changing with time.

[0017] The constraint conditions also include resource usage constraint conditions, which are expressed as follows:

[0018] C t ≤ C max ,

[0019] in, C max is the upper limit of resource consumption, C tThe total resource consumption for main transformer construction is expressed as:

[0020] C t =ω 1 S n 2 +oh 2 S n +oh 3 +c × minutes { L 1 , L 2},

[0021] in, oh 1 , oh 2 , oh 3 is the resource consumption coefficient for transformer construction, S n is the transformer capacity, c To maintain the resource consumption coefficient, L 1 Design life of transformer, L 2 The expected usage period.

[0022] The constraint conditions also include reliability constraints, that is, the maximum load rate of the main transformer in the normal operation state should not exceed 80% of the heavy load standard level, and the expression is:

[0023] ,

[0024] in, S n is the transformer capacity, S t is the area of ​​power supply, s It is the maximum value of saturated load density corresponding to the power supply area type.

[0025] Furthermore, the normalized expression of the combined power supply reliability objective function and the main transformer objective function of S4 is:

[0026] ,

[0027] Among them, is the transformer capacity, is the weight coefficient of resource consumption in the objective function, is the total resource consumption for main transformer construction, and is the upper limit of resource consumption;

[0028] The objective function expression obtained by normalizing the objective function of the new energy distributed power consumption is:

[0029] ,

[0030] Among them, △P ne The output power of the planned new energy power source is β is the weight coefficient of the new energy consumption capacity in the objective function, P min is the predicted minimum load for the power supply area, P ne,max It is the maximum output of distributed new energy power source.

[0031] The weighted sum of the objective functions is the objective function of the 110kV / 10kV main transformer capacity multi-objective optimization model, and the expression is:

[0032] ,

[0033] in, S n is the transformer capacity, △P ne It is the output power of the planned new energy power source.

[0034] Furthermore, in S5, after updating the transformer capacity and the planned new energy power output, a new objective function value is obtained through iteration, wherein the iterative algorithms include the GRG iterative algorithm and the projected gradient method.

[0035] According to a second aspect of the present invention, there is provided an electronic device, comprising a memory and a processor, wherein a computer program is stored in the memory, and the method described above is implemented when the processor executes the program.

[0036] According to a third aspect of the present invention, there is provided a computer-readable storage medium having a computer program stored thereon, wherein the program implements the method described above when executed by a processor.

[0037] Compared with the prior art, the present invention has the following beneficial effects:

[0038] (1) Improving resource utilization: By comprehensively considering the consumption of construction resources and operation and maintenance resources, the transformer capacity with high utilization rate is selected. By promoting the consumption of new energy distributed power sources, the phenomenon of wind and solar power abandonment is reduced, and the utilization rate of renewable energy is improved.

[0039] (2) Improving the reliability of the power system: Through precise load forecasting and new energy power output forecasting, a stable power supply can be ensured under various operating conditions, which improves power supply reliability and reduces economic losses and social impacts caused by unstable power supply.

[0040] (3) Enhance the flexibility and adaptability of grid planning: By calculating and selecting the appropriate transformer capacity, the transformer capacity of distributed power sources and traditional power sources can be flexibly planned, making grid planning more flexible and adaptable to actual working conditions. BRIEF DESCRIPTION OF THE DRAWINGS

[0041] Figure 1 Flowchart for optimizing the selection of main transformer capacity. DETAILED DESCRIPTION

[0042] 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 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 should fall within the scope of protection of the present invention.

[0043] Example 1

[0044] like Figure 1 As shown, a method for optimizing the main transformer is provided, comprising the following steps:

[0045] S1. Obtain fixed value parameters to obtain the maximum output of distributed renewable energy power sources;

[0046] S2. Set the initial value of the transformer capacity and the planned additional output power of the new energy power source;

[0047] S3, judging whether the initial value satisfies the constraint condition, if yes, executing S4, otherwise, adjusting the initial value until the constraint condition is satisfied;

[0048] S4. According to the current transformer capacity and the planned new energy power output, as well as the maximum output of the distributed new energy power, an objective function value is calculated based on the objective function, wherein the objective function is a weighted sum of the first objective function of the main transformer, the reliability objective function, and the new energy distributed power consumption objective function;

[0049] S5, judging whether the objective function value converges to the minimum value, if yes, executing step S6, otherwise, updating the transformer capacity and the planned new energy power output, and returning to S4;

[0050] S6. Output the minimum objective function value, as well as the current transformer capacity and the planned new energy power output.

[0051] The constant parameters of S1 include the predicted minimum load of the power supply area, the total capacity of distributed renewable energy power sources, the maximum output coefficient of distributed renewable energy power sources, the upper limit of budget resources, the area of ​​the power supply area, the type of power supply area, the transformer construction cost coefficient and the maintenance and operation cost coefficient, the transformer design life and expected use cycle and the objective function weight coefficient.

[0052] The constraints of S3 include the constraints of new energy consumption, which are expressed as follows:

[0053] minutes { P ( t )- P ne ( t )} ≥ 0,

[0054] in, P ( t ) is the function of load changing with time, P ne ( t ) is the function of renewable energy output changing with time.

[0055] Constraints also include resource constraints, expressed as:

[0056] C t ≤ C max ,

[0057] in, C max is the upper limit of resource consumption, C t The total resource consumption for main transformer construction is expressed as:

[0058] C t =ω 1 S n 2 +oh 2 S n +oh 3 +c × minutes { L 1 , L 2},

[0059] in, oh 1 , oh 2 , oh 3 is the resource consumption coefficient for transformer construction, S n is the transformer capacity, c To maintain the resource consumption coefficient, L 1 Design life of transformer, L 2 The expected usage period.

[0060] The constraints also include reliability constraints, that is, the maximum load rate of the main transformer in normal operation should not exceed 80% of the heavy load standard level, expressed as:

[0061] ,

[0062] in, S n is the transformer capacity, α is the weight coefficient of resource consumption in the objective function, C t Total resource consumption for main transformer construction, C max The upper limit of resource consumption;

[0063] The normalized expression of the combined power supply reliability objective function of S4 and the main transformer objective function is:

[0064] ,

[0065] in, S n is the transformer capacity, α is the weight coefficient of resource consumption in the objective function, C t Total resource consumption for main transformer construction, C max The upper limit of resource consumption;

[0066] The objective function expression of the new energy distributed power consumption is obtained by normalizing the objective function:

[0067] ,

[0068] Among them, △ P ne The output power of the planned new energy power source is β is the weight coefficient of the new energy consumption capacity in the objective function, P min The predicted minimum load of the area, P ne，max Maximum output of energy source.

[0069] The weighted sum of objective functions is the objective function of the multi-objective optimization model for 110kV / 10kV main transformer capacity, and the expression is:

[0070] ,

[0071] in, S n is the transformer capacity, △P ne It is the output power of the planned new energy power source.

[0072] S5 iterates the objective function value through the GRG iterative algorithm and the projected gradient method. The specific steps of the GRG iterative algorithm include:

[0073] S501, selecting a single initial point or randomly selecting multiple initial points according to specific working conditions;

[0074] S502, performing constraint condition judgment on the initial point value, and calculating the objective function result if the condition is met;

[0075] S503, calculating the gradient function of the objective function with respect to the variable. In this embodiment, the gradient expression of the objective function of the 110kV / 10kV main transformer capacity optimization selection method model is:

[0076] ,

[0077] ,

[0078] Get the gradient value ( at this point. In this embodiment, the minimum value of the objective function is sought, so an iterative search should be performed along the gradient descent direction, so ( should be taken as the search direction;

[0079] S504, setting an iterative search step in the search direction, the specific steps include:

[0080] S5041, preliminarily determine the approximate range of the step length, which is set between 1 and 10 in the embodiment;

[0081] S5042, proceed along the search direction to obtain new variable values, evaluate the constraints of the initially determined step length, and if the constraint function is satisfied, take the maximum value of the step length range or further increase the step length range to speed up the iteration speed; if the constraint function is not satisfied, reduce the step length or optimize the search direction;

[0082] S5043, to speed up the iteration and set up convergence conditions;

[0083] S5044. Adjust the initial value based on the search direction and step size, and perform iterative updates in the descending direction until the change in the objective function value meets the convergence condition or the update of the variable value does not meet the constraint condition; at this time, methods should be taken to adjust the exploration direction and optimize the iteration process.

[0084] The projected gradient method is used to adjust the search direction. Based on the gradient data of the objective function, if the variable does not meet the constraint conditions after adjusting the direction and step update, it should be considered whether the boundary conditions are the optimal solution and whether the projected gradient method is used to adjust the search direction to try to further iterate for a better solution. The gradient of the constraint function is calculated. When the variable cannot meet the convergence condition after being substituted into the constraint function, the boundary value of the constraint function corresponding to the boundary point of the constraint function corresponding to the variable in the original search direction is obtained. The equation of this boundary value and the constraint function is the boundary tangent plane of this constraint condition. Project the objective function onto the tangent plane, and the expression is:

[0085] ;

[0086] in, is the gradient after projection, is the original objective function gradient, is the normal vector of the tangent plane of the feasible domain. The new gradient projected on the feasible domain is used as the search direction and the iteration continues.

[0087] The minimum specification of the transformer capacity value selected when minimizing the output value of the objective function is selected in the specification standard as the final selected main transformer capacity optimization selection result. Table 1 is a comparative reference of the main transformer capacity selection values ​​obtained using the traditional method and this embodiment. It can be seen that this embodiment gives full play to the coupling regulation effect of the distributed power source on the load, achieving the effect of reducing the maximum load and improving the power grid transfer capacity.

[0088] Table 1 Comparison reference of the main transformer capacity selection values ​​obtained using the traditional method and this embodiment

[0089]

[0090] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working process of the described module can refer to the corresponding process in the aforementioned method embodiment, and will not be repeated here.

[0091] Example 2

[0092] The electronic device of the present invention includes a central processing unit (CPU), which can perform various appropriate actions and processes according to computer program instructions stored in a read-only memory (ROM) or loaded from a storage unit into a random access memory (RAM). In the RAM, various programs and data required for the operation of the device can also be stored. The CPU, ROM and RAM are connected to each other through a bus. An input / output (I / O) interface is also connected to the bus.

[0093] Multiple components in the device are connected to the I / O interface, including: input units, such as keyboards, mice, etc.; output units, such as various types of displays, speakers, etc.; storage units, such as disks, optical disks, etc.; and communication units, such as network cards, modems, wireless communication transceivers, etc. The communication unit allows the device to exchange information / data with other devices through computer networks such as the Internet and / or various telecommunication networks. The processing unit performs the various methods and processes described above, such as the method of the present invention. For example, in some embodiments, the method of the present invention may be implemented as a computer software program, which is tangibly contained in a machine-readable medium, such as a storage unit. In some embodiments, part or all of the computer program may be loaded and / or installed on the device via ROM and / or a communication unit. When the computer program is loaded into RAM and executed by the CPU, one or more steps of the method of the present invention described above may be performed. Alternatively, in other embodiments, the CPU may be configured to perform the method of the present invention in any other appropriate manner (e.g., by means of firmware).

[0094] The functions described above herein may be performed at least in part by one or more hardware logic components. For example, without limitation, exemplary types of hardware logic components that may be used include: field programmable gate arrays (FPGAs), application specific integrated circuits (ASICs), application specific standard products (ASSPs), systems on chips (SOCs), complex programmable logic devices (CPLDs), and the like.

[0095] The program code for implementing the method of the present invention can be written in any combination of one or more programming languages. These program codes can be provided to a processor or controller of a general-purpose computer, a special-purpose computer or other programmable data processing device, so that the program code, when executed by the processor or controller, enables the functions / operations specified in the flow chart and / or block diagram to be implemented. The program code can be executed entirely on the machine, partially on the machine, partially on the machine as a stand-alone software package and partially on a remote machine, or entirely on a remote machine or server.

[0096] In the context of the present invention, a machine-readable medium may be a tangible medium that may contain or store a program for use by an instruction execution system, device or equipment or used in combination with an instruction execution system, device or equipment. A machine-readable medium may be a machine-readable signal medium or a machine-readable storage medium. A machine-readable medium may include, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, devices or equipment, or any suitable combination of the foregoing. A more specific example of a machine-readable storage medium may include an electrical connection based on one or more lines, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

[0097] The above is only a specific embodiment of the present invention, but the protection scope of the present invention is not limited thereto. Any technician familiar with the technical field can easily think of various equivalent modifications or replacements within the technical scope disclosed by the present invention, and these modifications or replacements should be included in the protection scope of the present invention. Therefore, the protection scope of the present invention shall be based on the protection scope of the claims.

Claims

1. A method for optimizing a main transformer, characterized in that: The following steps are involved: S1. Obtain fixed value parameters to obtain the maximum output of distributed renewable energy power sources; S2. Set the initial value of the transformer capacity and the planned additional output power of the new energy power source; S3, judging whether the initial value satisfies the constraint condition, if yes, executing S4, otherwise, adjusting the initial value until the constraint condition is satisfied; S4. According to the current transformer capacity and the planned new energy power output, as well as the maximum output of the distributed new energy power, the objective function value is calculated based on the objective function, wherein the objective function is the weighted sum of the first objective function of the main transformer, the reliability objective function and the objective function of the new energy distributed power consumption, specifically including: The reliability objective function is combined with the first objective function of the main transformer and normalized, and the expression is: ， in, S n is the transformer capacity, α is the weight coefficient of resource consumption in the objective function, C t Total resource consumption for main transformer construction, C max The upper limit of resource consumption; The objective function of the new energy distributed power consumption is normalized and expressed as follows: ， Among them, △ P ne The output power of the planned new energy power source is β is the weight coefficient of the new energy consumption capacity in the objective function, P min is the predicted minimum load in the power supply area, P ne,max It is the maximum output of distributed new energy power source; The expression of the objective function is obtained as follows: ， in, S n is the transformer capacity, △ P ne The output power of the planned new energy power source; S5, judging whether the objective function value converges to the minimum value, if yes, executing S6, otherwise, updating the transformer capacity and the planned new energy power output, and returning to S4; S6. Output the minimum objective function value, as well as the current transformer capacity and the planned new energy power output.

2. A main transformer optimization method according to claim 1, characterized in that: The fixed value parameters of S1 include the predicted minimum load of the power supply area, the total capacity of distributed renewable energy power sources, the maximum output coefficient of distributed renewable energy power sources, the upper limit of budget resources, the area of ​​the power supply area, the type of power supply area, the resource consumption coefficient of transformer construction and the resource consumption coefficient of maintenance and operation, the design life of the transformer and the expected use cycle and the weight coefficient of the objective function.

3. A main transformer optimization method according to claim 1, characterized in that: The constraints of S3 include new energy consumption constraints, expressed as: min { P ( t )- P ne ( t )} ≥0， in, P ( t ) is the function of load changing with time, P ne ( t ) is the function of renewable energy output changing with time.

4. A method for selecting a main transformer according to claim 3, characterized in that: The constraints of S3 also include resource usage constraints, expressed as: C t ≤ C max ， in, C max is the upper limit of resource consumption, C t The total resource consumption for main transformer construction is expressed as: C t =ω 1 S n 2 +ω 2 S n +ω 3 +c × min { L 1 ， L 2}， in, ω 1 , ω 2 , ω 3 is the resource consumption coefficient for transformer construction, S n is the transformer capacity, c To maintain the resource consumption coefficient, L 1 Design life of transformer, L 2 The expected usage period.

5. A method for selecting a main transformer according to claim 4, characterized in that: The constraint condition of S3 also includes a reliability constraint condition, that is, the maximum load rate of the main transformer in the normal operation state should not exceed 80% of the heavy load standard level, and the expression is: ， in, S n is the transformer capacity, S t is the area of ​​power supply, σ It is the maximum value of saturated load density corresponding to the power supply area type.

6. A method for selecting a main transformer according to claim 1, characterized in that: In S5, after updating the transformer capacity and the planned new energy power output, a new objective function value is obtained through iteration, wherein the iterative algorithms include the GRG iterative algorithm and the projected gradient method.

7. An electronic device comprising a memory and a processor, wherein a computer program is stored in the memory, wherein: When the processor executes the program, the method according to any one of claims 1 to 6 is implemented.

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

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