A distributed power supply site selection and capacity configuration optimization method, system and device

Abstract

The application discloses a distributed power supply distribution point and capacity configuration optimization method, system and equipment, belongs to the power supply field, and aims to solve the problems that in the prior art, optimization indexes reach the optimum through capacity optimization, but the solving speed is slow and the solving precision is low. By giving an increment, a small change of the i-th point capacity is evaluated for the influence on the overall income, that is, the sensitivity of the i-th point capacity to the income is determined. Among the N point positions, which one increases the income most, the maximum value is taken to change the actual capacity. By increasing the capacity increment ΔP, the maximum increment of the income is still small (smaller than the threshold value), so it is proved that the optimization space is already small, and the optimization cycle can be ended. Through the gradient increment calculation of the capacity, the iteration cycle is performed, so that the distributed power supply distribution point and capacity configuration optimization are realized, and the method has the advantages of fast solving speed, high and controllable solving precision.

Description

Technical Field

[0001] This invention belongs to the field of power supply and relates to a method, system and device for optimizing the deployment and capacity configuration of distributed power sources. Background Technology

[0002] Distributed power generation: Distributed power generation devices refer to small, modular, environmentally compatible, independent power sources with capacities ranging from several kilowatts to 50MW. These power sources are owned by power companies, power users, or third parties to meet specific requirements of the power system and users, such as peak shaving, supplying power to remote users or commercial and residential areas, saving on transmission and transformation investment, and improving power supply reliability. They mainly include distributed wind power and distributed photovoltaic (PV). Distributed wind power: Small-scale, distributed wind turbine units. Distributed PV: Small-scale, distributed photovoltaic modules.

[0003] Distributed power generation planning refers to optimizing regional revenue indicators (which can be economic, carbon emission, or other indicators) by planning the capacity and distribution of distributed generation units in a region, while ensuring power balance. Current distributed power generation planning typically involves solving abstract optimization problems and optimizing capacity to achieve the optimal indicators, but this approach suffers from slow solution speed and low accuracy. Summary of the Invention

[0004] The purpose of this invention is to solve the problems in the prior art and provide a method, system and device for optimizing the deployment and capacity configuration of distributed power sources. It aims to solve the problems of slow solution speed and low solution accuracy in the prior art when optimizing capacity to achieve the best optimization index.

[0005] To achieve the above objectives, the present invention employs the following technical solution:

[0006] The present invention proposes a method for optimizing the deployment and capacity configuration of distributed power sources, comprising the following steps:

[0007] Step 1: Define a backup point for the distributed power source as node i, where node i can be 1 to N. Determine the initial configuration capacity based on the number of backup points for the distributed power source; where N is a positive integer.

[0008] Step 2: Obtain the original regional revenue based on the initial configuration capacity; Given a capacity increment of the distributed power source of the i-th node, obtain the regional revenue at the given increment; Obtain the i-th gradient revenue increment of the regional revenue based on the regional revenue at the given increment and the original regional revenue;

[0009] Step 3: After traversing N nodes, record the maximum and minimum revenue increments. Adjust the capacity of the nodes corresponding to the maximum and minimum revenue increments. If the maximum revenue increment is greater than a preset threshold, continue to execute Step 2 based on the adjusted node power capacity obtained in Step 3. If the maximum revenue increment is less than or equal to the preset threshold, obtain the distributed power optimization capacity results for nodes 1 to N.

[0010] Preferably, the threshold value ε is 1% of the total fixed capacity P0 of the distributed power source.

[0011] Preferably, the initial configuration capacity of N nodes is set as P10 = P20 = ... = PN0 = P0 / N;

[0012] Where P0 is the total fixed capacity of distributed power sources.

[0013] Preferably, a loop is started for node i from 1 to N. In the i-th loop, a capacity increment ΔP is given to the distributed power source of the i-th node, denoted as Pi, and the revenue S(P1, P2, ..., PN) of the region is obtained when the increment is given.

[0014] The gain configuration capacity of N nodes is P1=P2=…PN=P0 / N;

[0015] The capacity increment ΔP is 0.1 times P0 / N.

[0016] Preferably, a revenue calculation method is used to obtain regional revenue.

[0017] Preferably, the calculation of the i-th gradient income increment ΔSi of the region is as shown in formula (1):

[0018] ΔSi=S(P1,P2,…,PN)-S(P10,P20,…,PN0) (1);

[0019] Where S(P1, P2, ..., PN) is the revenue of the region when given an increment; S(P10, P20, ..., PN0) is the revenue of the original region.

[0020] Preferably, the specific steps for adjusting the capacity of the nodes corresponding to the maximum revenue increment ΔSimax and the minimum revenue increment ΔSimin are as follows:

[0021] Increase the capacity increment ΔP of the node corresponding to the maximum revenue increment ΔSimax.

[0022] For the node corresponding to the minimum revenue increment ΔSimin, decrease the capacity increment ΔP.

[0023] Among them, the capacity increment ΔP is 0.1 times P0 / N.

[0024] The system for optimizing distributed power supply deployment and capacity configuration proposed in this invention includes:

[0025] The data setting unit is used to set the alternative points of the distributed power supply as node i, where node i takes the value from 1 to N, and to determine the initial configuration capacity based on the set number of alternative points of the distributed power supply.

[0026] A gradient increment acquisition unit is used to acquire the original regional revenue based on the initial configured capacity; acquire the regional revenue at a given increment given a capacity increment of the distributed power source of the i-th node; and acquire the i-th gradient increment of the regional revenue based on the regional revenue at the given increment and the original regional revenue.

[0027] The distributed power optimization capacity result acquisition unit is used to traverse N nodes, record the maximum and minimum revenue increments, adjust the capacity of the nodes corresponding to the maximum and minimum revenue increments, and if the maximum revenue increment is greater than a preset threshold value, continue to execute step 2 based on the adjusted node power capacity obtained in step 3; if the maximum revenue increment is less than or equal to the preset threshold value, acquire the distributed power optimization capacity result of nodes 1 to N.

[0028] A computer device includes a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the steps of a method for optimizing distributed power supply deployment and capacity configuration.

[0029] A computer-readable storage medium storing a computer program, which, when executed by a processor, implements the steps of a method for optimizing distributed power supply deployment and capacity configuration.

[0030] Compared with the prior art, the present invention has the following beneficial effects:

[0031] This invention proposes a method for optimizing the deployment and capacity configuration of distributed power sources. By calculating the gradient increment of capacity and performing iterative loops, it achieves optimization of distributed power source deployment and capacity configuration, offering advantages such as fast solution speed, high accuracy, and controllability. The increment is used to evaluate the impact of a small change in the capacity of the i-th location on the overall revenue, based on the current capacity allocation; that is, to determine the sensitivity of the i-th location's capacity to revenue. Among the N locations, the one that increases revenue the most is compared, and the maximum value is used for the actual capacity change. If increasing the capacity increment ΔP results in a maximum revenue increment that is still relatively small (smaller than the threshold value), then the optimization space is limited, and the optimization loop can be terminated. The optimization method proposed in this invention is applicable to any distributed power source, including distributed wind power and distributed photovoltaics, offering fast calculation speed, high accuracy, and controllability. It allows for arbitrary setting of the revenue calculation function, satisfying different revenue measurement standards according to the actual needs of the region.

[0032] Furthermore, by setting the same configuration capacity on average for each distributed point, the optimal capacity value can be found more quickly and effectively during subsequent optimization processes.

[0033] Furthermore, the capacity increment ΔP is generally taken as 0.1 times P0 / N. The larger ΔP is, the faster the calculation speed, but the optimization accuracy will decrease, and vice versa.

[0034] The present invention proposes a system for optimizing the deployment and capacity configuration of distributed power sources. By dividing the system into a data setting unit, a gradient increment acquisition unit, and a distributed power source optimization capacity result acquisition unit, the modular approach makes each module independent of the others, facilitating unified management of each module. Attached Figure Description

[0035] To more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings used in the embodiments will be briefly introduced below. It should be understood that the following drawings only show some embodiments of the present invention and should not be regarded as a limitation on the scope. For those skilled in the art, other related drawings can be obtained based on these drawings without creative effort.

[0036] Figure 1 This is a flowchart of the distributed power supply deployment and capacity configuration method of the present invention.

[0037] Figure 2 This is a detailed flowchart illustrating the distributed power supply deployment and capacity configuration method of the present invention.

[0038] Figure 3 This is a system diagram of the distributed power supply deployment and capacity configuration of the present invention. Detailed Implementation

[0039] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. The components of the embodiments of the present invention described and shown in the accompanying drawings can generally be arranged and designed in various different configurations.

[0040] Therefore, the following detailed description of the embodiments of the invention provided in the accompanying drawings is not intended to limit the scope of the claimed invention, but merely to illustrate selected embodiments of the invention. All other embodiments obtained by those skilled in the art based on the embodiments of the invention without inventive effort are within the scope of protection of the invention.

[0041] It should be noted that similar labels and letters in the following figures indicate similar items. Therefore, once an item is defined in one figure, it does not need to be further defined and explained in subsequent figures.

[0042] In the description of the embodiments of the present invention, it should be noted that if terms such as "upper," "lower," "horizontal," or "inner" indicate the orientation or positional relationship based on the orientation or positional relationship shown in the accompanying drawings, or the orientation or positional relationship commonly used when the product of the invention is in use, they are only for the convenience of describing the present invention and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation, and therefore should not be construed as a limitation of the present invention. Furthermore, terms such as "first" and "second" are only used to distinguish descriptions and should not be construed as indicating or implying relative importance.

[0043] Furthermore, the use of the term "horizontal" does not imply that the component must be absolutely horizontal, but rather that it can be slightly tilted. For example, "horizontal" simply means that its direction is more horizontal than "vertical," and does not mean that the structure must be completely horizontal, but can be slightly tilted.

[0044] In the description of the embodiments of the present invention, it should also be noted that, unless otherwise explicitly specified and limited, the terms "set," "install," "connect," and "link" should be interpreted broadly. For example, they can refer to a fixed connection, a detachable connection, or an integral connection; they can refer to a mechanical connection or an electrical connection; they can refer to a direct connection or an indirect connection through an intermediate medium; and they can refer to the internal connection of two components. Those skilled in the art can understand the specific meaning of the above terms in the present invention according to the specific circumstances.

[0045] The present invention will now be described in further detail with reference to the accompanying drawings:

[0046] This invention proposes a method for optimizing the deployment and capacity configuration of distributed power sources, such as... Figure 1 The process includes the following steps:

[0047] Step 1: Set the alternative point of the distributed power source as node i, where node i can be 1 to N. Determine the initial configuration capacity based on the number of alternative points of the distributed power source.

[0048] Step 2: Obtain the original regional revenue based on the initial configuration capacity; Given a capacity increment of the distributed power source of the i-th node, obtain the regional revenue at the given increment; Obtain the i-th gradient increment of regional revenue based on the regional revenue at the given increment and the original regional revenue;

[0049] Step 3: After traversing N nodes, record the maximum and minimum revenue increments. Adjust the capacity of the nodes corresponding to the maximum and minimum revenue increments. If the maximum revenue increment is greater than a preset threshold, continue to execute Step 2 based on the adjusted node power capacity obtained in Step 3. If the maximum revenue increment is less than or equal to the preset threshold, obtain the distributed power optimization capacity results for nodes 1 to N.

[0050] like Figure 2 The diagram shown is a schematic representation of the specific process for optimizing the deployment and capacity configuration of distributed power sources.

[0051] Specifically, the distributed power supply deployment and capacity configuration process includes the following steps:

[0052] 1) Define the alternative point of the distributed power source as node i, where node i takes the value from 1 to N;

[0053] Set the initial configuration capacity of N nodes as P10 = P20 = ... = PN0 = P0 / N; the gain configuration capacity of N nodes is P1 = P2 = ... PN = P0 / N; where P0 is the fixed total capacity of the distributed power source; and N is a positive integer.

[0054] Initial configuration capacity refers to the initial value of the capacity of each power point set before capacity configuration. Subsequent optimization based on this value can make the optimization speed faster and more likely to reach the optimal point.

[0055] Generally, let P1 = P2 = ... PN = P10 = P20 = ... = PN0 = P0 / N. For example, if there are 10 power supply points with a total capacity of 10MW, then the initial capacity can be set to 1MW for each power supply point.

[0056] 2) Based on the initial configuration capacity P10=P20=…=PN0=P0 / N, calculate the original regional revenue S(P10,P20,…,PN0);

[0057] Initiate a loop from i to N. In the i-th loop, give an increment ΔP to the distributed power capacity of the i-th node, i.e., Pi = Pi0 + ΔP (the capacity increment ΔP is 0.1 times P0 / N). Calculate the revenue S(P1, P2, ..., PN) of the region when the increment is given. Compare the revenue of the region without the increment, i.e., the original region revenue S(P10, P20, ..., PN0), with the revenue of the region with the increment to calculate the i-th gradient revenue increment ΔSi of the region.

[0058] The calculation of the incremental return ΔSi of the i-th gradient of regional returns is shown in Equation (1):

[0059] ΔSi=S(P1,P2,…,PN)-S(P10,P20,…,PN0) (1)

[0060] The capacity increment ΔP is generally taken as 0.1 times P0 / N. The larger the capacity increment ΔP, the faster the calculation speed, but the optimization accuracy will decrease, and vice versa.

[0061] Regional revenue is obtained using a revenue calculation method.

[0062] 3) After the loop is completed, record the maximum profit increment ΔSi (corresponding to i = imax) i.e., ΔSimax and the minimum profit increment ΔSi (corresponding to i = imin) i.e., ΔSimin. Increase the capacity of the imax node by the capacity increment ΔP, and decrease the capacity of the imin node by the capacity increment ΔP.

[0063] 4) Determine whether the maximum revenue increment ΔSi in this cycle is less than or equal to the threshold value ε (generally taken as 1% of the total investment in distributed power). Adjust the capacity of the nodes corresponding to the maximum revenue increment ΔSimax and the minimum revenue increment ΔSimin: If the maximum revenue increment ΔSi is greater than or equal to the threshold value ε, then continue to step 2) based on the node power capacity that has been corrected in step 3); if the maximum revenue increment ΔSi is less than or equal to the threshold value ε, then determine that the calculation is over and output P10~PN0 at this time as the final distributed power optimization capacity result of nodes 1~N.

[0064] The distributed power capacity of the current nodes 1-N is used as the optimized capacity result. Since the maximum revenue increment is less than or equal to the preset threshold, the space for further iterative optimization is very small, so the optimization can be terminated.

[0065] This invention proposes a system for optimizing the deployment and capacity configuration of distributed power sources, such as... Figure 3 As shown, it includes:

[0066] The data setting unit is used to set the alternative points of the distributed power supply as node i, where node i takes the value from 1 to N, and to determine the initial configuration capacity based on the set number of alternative points of the distributed power supply.

[0067] A gradient increment acquisition unit is used to acquire the original regional revenue based on the initial configured capacity; acquire the regional revenue at a given increment given a capacity increment of the distributed power source of the i-th node; and acquire the i-th gradient increment of the regional revenue based on the regional revenue at the given increment and the original regional revenue.

[0068] The distributed power optimization capacity result acquisition unit is used to traverse N nodes, record the maximum and minimum revenue increments, adjust the capacity of the nodes corresponding to the maximum and minimum revenue increments, and if the maximum revenue increment is greater than a preset threshold value, continue to execute step 2 based on the adjusted node power capacity obtained in step 3; if the maximum revenue increment is less than or equal to the preset threshold value, acquire the distributed power optimization capacity result of nodes 1 to N.

[0069] An embodiment of the present invention provides a terminal device comprising: a processor, a memory, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, it implements the steps in the various method embodiments described above. Alternatively, when the processor executes the computer program, it implements the functions of each module / unit in the various device embodiments described above.

[0070] The computer program can be divided into one or more modules / units, which are stored in the memory and executed by the processor to complete the present invention.

[0071] The terminal device may be a desktop computer, laptop, handheld computer, or cloud server, etc. The terminal device may include, but is not limited to, a processor and a memory.

[0072] The processor may be a central processing unit (CPU), or other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc.

[0073] The memory can be used to store the computer program and / or module. The processor implements various functions of the terminal device by running or executing the computer program and / or module stored in the memory and calling the data stored in the memory.

[0074] If the modules / units integrated into the terminal device are implemented as software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, all or part of the processes in the methods of the above embodiments can also be implemented by a computer program instructing related hardware. The computer program can be stored in a computer-readable storage medium, and when executed by a processor, it can implement the steps of the various method embodiments described above. The computer program includes computer program code, which can be in the form of source code, object code, executable files, or certain intermediate forms. The computer-readable medium can include: any entity or device capable of carrying the computer program code, recording media, USB flash drives, portable hard drives, magnetic disks, optical disks, computer memory, read-only memory (ROM), random access memory (RAM), electrical carrier signals, telecommunication signals, and software distribution media, etc. It should be noted that the content included in the computer-readable medium can be appropriately added or removed according to the requirements of legislation and patent practice in the jurisdiction. For example, in some jurisdictions, according to legislation and patent practice, computer-readable media do not include electrical carrier signals and telecommunication signals.

[0075] This invention proposes a method, system, and equipment for optimizing the deployment and capacity configuration of distributed power sources. The core of this invention lies in the process flow and the definition of all variables within the distributed power source deployment and capacity configuration method. This invention has the following advantages: 1) It can be applied to any distributed power source, including distributed wind power and distributed photovoltaic power. 2) The method of this invention has fast calculation speed, high calculation accuracy, and is controllable. 3) The method of this invention allows for the arbitrary setting of the revenue calculation function S, satisfying different revenue measurement standards according to the actual needs of the region.

[0076] The above are merely preferred embodiments of the present invention and are not intended to limit the present invention. Various modifications and variations can be made to the present invention by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

Claims

1. A method for optimizing the deployment and capacity configuration of distributed power sources, characterized in that, Includes the following steps: Step 1: Define the alternative points for distributed power sources as nodes. ,node Values The initial configuration capacity is determined based on the set number of distributed power backup points; where N is a positive integer. Step 2: Obtain the original region revenue based on the initial configuration capacity; given the first... The distributed power supply with 1 node obtains the revenue of the region at a given increment based on a capacity increment; and obtains the region revenue based on the revenue of the region at the given increment and the original region revenue. Gradient reward increment; Step 3: Complete traversal After each node, record the maximum and minimum revenue increments. Adjust the capacity of the nodes corresponding to the maximum and minimum revenue increments. If the maximum revenue increment is greater than a preset threshold, continue with step 2 based on the adjusted node power capacity obtained in step 3; if the maximum revenue increment is less than or equal to the preset threshold, obtain the node... The results of distributed power source capacity optimization; For the maximum incremental return and minimum profit increment The specific steps for adjusting the capacity of the corresponding node are as follows: For the maximum incremental return The corresponding node's capacity increases by the capacity increment. ; For minimum incremental revenue The corresponding node's capacity decreases and capacity increases. ; Among them, capacity increment It is 0.1 times P0 / N; where, The gain configuration capacity of each node is P1=P2=…PN=P0 / N, where P0 is the total fixed capacity of the distributed power source; N is a positive integer.

2. The method for optimizing the deployment and capacity configuration of distributed power sources according to claim 1, characterized in that, The threshold value ε is 1% of the total fixed capacity P0 of the distributed power source.

3. The method for optimizing the deployment and capacity configuration of distributed power sources according to claim 1, characterized in that, set up The initial configuration capacity of each node is P10=P20=…=PN0=P0 / N; Where P0 is the total fixed capacity of distributed power sources.

4. The method for optimizing the deployment and capacity configuration of distributed power sources according to claim 3, characterized in that, Start Node From 1 to The cycle, in the first Within the loop, given the first... A distributed power supply with one node and a capacity increment. Let Pi be the revenue S(P1, P2, ..., PN) for a given increment in the region. The gain configuration capacity of each node is P1=P2=…PN=P0 / N; Capacity increment It is 0.1 times P0 / N.

5. The method for optimizing the deployment and capacity configuration of distributed power sources according to claim 4, characterized in that, Regional revenue is obtained using a revenue calculation method.

6. The method for optimizing the deployment and capacity configuration of distributed power sources according to claim 4, characterized in that, Regional revenue Gradient Incremental Returns The calculation is shown in formula (1): （1）； Where S(P1, P2, ..., PN) is the revenue of the region when given an increment; S(P10, P20, ..., PN0) is the revenue of the original region.

7. A system employing the distributed power supply deployment and capacity configuration optimization method according to any one of claims 1 to 6, characterized in that, include: The data setting unit is used to set the alternative points of the distributed power source as nodes. ,node Values The initial configuration capacity is determined based on the number of backup distributed power sources set. Gradient increment acquisition unit, the gradient increment acquisition unit is used to obtain the original region benefit based on the initial configuration capacity; given the first The distributed power supply with 1 node obtains the revenue of the region at a given increment based on a capacity increment; and obtains the region revenue based on the revenue of the region at the given increment and the original region revenue. Gradient increment; Distributed power source optimization capacity result acquisition unit, the distributed power source optimization capacity result acquisition unit is used to traverse the entire After each node, record the maximum and minimum revenue increments. Adjust the capacity of the nodes corresponding to the maximum and minimum revenue increments. If the maximum revenue increment is greater than a preset threshold, continue with step 2 based on the adjusted node power capacity obtained in step 3; if the maximum revenue increment is less than or equal to the preset threshold, obtain the node... The results of distributed power source capacity optimization.

8. A computer device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that, When the processor executes a computer program, it implements the steps of the distributed power supply deployment and capacity configuration optimization method according to any one of claims 1 to 6.

9. A computer-readable storage medium storing a computer program, characterized in that, When the computer program is executed by the processor, it implements the steps of the distributed power supply deployment and capacity configuration optimization method according to any one of claims 1 to 6.

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