Distributed energy storage locating and sizing method and system based on teaching optimization algorithm

Abstract

The invention discloses a distributed energy storage locating and sizing method and system based on a teaching optimization algorithm, and relates to the technical field of distributed energy storage system optimization, and the method comprises the steps: building a radial power distribution network model according to the deployment condition of each clean energy power generation power supply in a power distribution network, the method comprises the following steps: establishing a distributed energy storage system addressing and sizing optimization model by taking minimization of peak load fluctuation and network loss of a radial power distribution network model as a target, and solving the distributed energy storage system addressing and sizing optimization model based on a teaching optimization algorithm. And obtaining the deployment address of each energy storage device of the distributed energy storage system in the power distribution network and the deployment capacity of each energy storage device when the peak load fluctuation and the network loss of the radial power distribution network model are minimum. According to the invention, more sufficient reference can be provided for power distribution network planning in reality, and the problems of output intermittency and volatility of the distributed power supply and electric energy loss of the power distribution network can be solved synchronously through energy storage configuration.

Description

Technical Field

[0001] The present application relates to the technical field of distributed energy storage system optimization, and in particular to a distributed energy storage site selection and capacity determination method and system based on a teaching optimization algorithm. Background Art

[0002] Under the trend of greenhouse gas emission reduction, in order to meet the growing electricity load and ensure energy security, it is necessary to provide more diversified energy supply, especially to promote the integration of renewable energy. However, the intermittent and volatile output of distributed energy has brought serious obstacles to the large-scale integration of renewable energy. Therefore, the efficient utilization of renewable energy power generation is highly dependent on the deployment of energy storage facilities. In the distribution network system, there are many types of energy storage technologies available, such as electrochemical energy storage, mechanical energy storage, and electromagnetic energy storage. However, the electrochemical energy storage system in the form of battery cells is the most widely used energy storage technology in the integration of distribution network and distributed energy, which helps to meet the growing electricity load and reduce the intermittent and volatile output of distributed power sources and the impact of load fluctuations on the distribution network. The impact of distributed energy storage on the distribution network is directly related to its access location and capacity. Therefore, how to scientifically select the site and capacity of distributed energy storage will help further improve the benefits it brings to the distribution network.

[0003] Current research on methods for site selection and sizing of distributed energy storage usually adopts a single optimization objective, and the starting point of optimization is usually the economy of energy storage facilities, or a single performance indicator such as the voltage deviation of the distribution network. However, the performance benefits of distributed energy storage in distribution networks are also reflected in reducing peak load fluctuations and reducing network losses, which cannot be taken into account under a single optimization objective. In addition, many studies on site selection and sizing of energy storage in distribution networks focus on a single type of distributed new energy distribution and storage problem. Energy storage only serves simple distribution networks that deploy distributed wind power or photovoltaics, and has limited reference value for energy storage configuration in complex distribution networks. In addition, the sophistication and complexity of the distributed energy storage site selection and sizing model are often restricted by the solution algorithm.

[0004] Therefore, how to overcome the above-mentioned shortcomings of the existing distributed energy storage site selection and capacity determination methods and improve the benefits brought by distributed energy storage systems to distribution networks is an extremely important direction for improvement. Summary of the invention

[0005] The purpose of this application is to provide a distributed energy storage site selection and sizing method and system based on a teaching optimization algorithm, which can provide a more adequate reference for actual distribution network planning, and is conducive to synchronously solving the intermittent and volatility of distributed power output and the power loss problems of the distribution network through energy storage configuration.

[0006] To achieve the above objectives, this application provides the following solutions:

[0007] In a first aspect, the present application provides a distributed energy storage site selection and capacity determination method based on a teaching optimization algorithm, comprising the following steps:

[0008] According to the deployment of each clean energy power generation source in the distribution network, a radial distribution network model is established; the radial distribution network model includes multiple clean energy power generation sources.

[0009] With the goal of minimizing the peak load fluctuation and network loss of the radial distribution network model, an optimization model for site selection and sizing of distributed energy storage systems is established.

[0010] The site selection and sizing optimization model of the distributed energy storage system is solved based on the teaching optimization algorithm to obtain the site selection and sizing results of the distributed energy storage system; the site selection and sizing results include the deployment address of each energy storage device of the distributed energy storage system in the distribution network and the deployment capacity of each energy storage device.

[0011] Optionally, with the goal of minimizing the peak load fluctuation and network loss of the radial distribution network model, a distributed energy storage system site selection and capacity optimization model is established, which specifically includes the following steps:

[0012] According to the operating characteristics of the distributed energy storage system and the radial distribution network model, the constraint conditions of the distributed energy storage system site selection and sizing optimization model are determined; the constraint conditions of the distributed energy storage system site selection and sizing optimization model include the power limit constraint of the energy storage device, the charging state constraint of the energy storage device, the flow constraint of the radial distribution network model and the voltage constraint of the radial distribution network model.

[0013] With the goal of minimizing the peak load fluctuation of the radial distribution network model, the first objective function of the distributed energy storage system site selection and sizing optimization model is determined.

[0014] With the goal of minimizing the network loss of the radial distribution network model, the second objective function of the distributed energy storage system site selection and sizing optimization model is determined.

[0015] According to the constraints of the distributed energy storage system site selection and sizing optimization model, the first objective function of the distributed energy storage system site selection and sizing optimization model and the second objective function of the distributed energy storage system site selection and sizing optimization model, a distributed energy storage system site selection and sizing optimization model is established; the comprehensive objective function of the distributed energy storage system site selection and sizing optimization model is the sum of the first objective function and the second objective function.

[0016] Optionally, the power limit constraint of the energy storage device can be expressed by the following formula:

[0017]

[0018] in, is the lower limit of the charging power of the energy storage device, is the upper limit of the charging power of the energy storage device, P char (t) is the charging power of the energy storage device at time t, is the lower limit of the discharge power of the energy storage device, is the upper limit of the discharge power of the energy storage device, P dischar (t) is the discharge power of the energy storage device at time t.

[0019] The charge state constraint of the energy storage device can be expressed as follows:

[0020] SOC min <SOC(t)<SOC max .

[0021]

[0022] Among them, SOC min is the lower limit of the charging state of the energy storage device, SOC(t) is the charging state of the energy storage device, SOC max is the upper limit of the charging state of the energy storage device, ξ is the self-discharge rate of the energy storage device, Δt is the change time, η char is the charging efficiency of the energy storage device, E capR is the maximum rated capacity of the energy storage device, η dischar is the discharge efficiency of the energy storage device.

[0023] The power flow constraint of the radial distribution network model can be expressed as follows:

[0024]

[0025] Among them, P l is the power flow flux of line l, is the maximum power allowed to pass through line l.

[0026] The voltage constraint of the radial distribution network model can be expressed as follows:

[0027]

[0028] Among them, V b is the voltage at node b, is the lower voltage limit of node b, is the upper voltage limit of node b.

[0029] Optionally, the first objective function of the distributed energy storage system site selection and capacity optimization model is as follows:

[0030]

[0031] Where obf1 is the first objective function value, t is the time label, b is the node label, P a,b,tis the peak load fluctuation of node b at time t.

[0032] The second objective function of the distributed energy storage system site selection and capacity optimization model is as follows:

[0033]

[0034] Where obf2 is the second objective function value, l is the line number, I l,t is the current of line l at time t, R l is the resistance of line l.

[0035] The comprehensive objective function of the distributed energy storage system site selection and capacity optimization model is as follows:

[0036] obf=obf1+obf2.

[0037] Among them, obf is the comprehensive objective function value.

[0038] Optionally, according to the deployment of each clean energy power source in the distribution network, an improved IEEE 33-node system is used to establish a radial distribution network model; multiple clean energy power sources are connected to different nodes of the radial distribution network model, and the output characteristics of each clean energy power source are clarified.

[0039] Optionally, solving the distributed energy storage system site selection and capacity determination optimization model based on the teaching optimization algorithm to obtain the site selection and capacity determination result of the distributed energy storage system specifically includes the following steps:

[0040] An initial solution set is generated and used as a current solution set; the initial solution set includes a plurality of solutions; the solutions include the deployment addresses of each energy storage device of the distributed energy storage system in the distribution network and the deployment capacity of each energy storage device.

[0041] For each solution in the current solution set, the objective function value of the solution is calculated, and a current optimal solution is determined in the current solution set.

[0042] If the current number of iterations does not reach the preset number of iterations, the other solutions in the current solution set are updated according to the current optimal solution to obtain the current teacher solution set.

[0043] A solution is randomly selected from the current teacher solution set as a reference solution.

[0044] For any solution in the current teacher solution set, the solution is updated according to the objective function value of the reference solution and the objective function value of the solution to obtain the current student solution set.

[0045] The current student solution set is taken as the current solution set, and the process jumps to the step: for each solution in the current solution set, the objective function value of the solution is calculated, and the current optimal solution is determined in the current solution set.

[0046] If the current number of iterations reaches the preset number of iterations, the current optimal solution is used as the site selection and sizing result of the distributed energy storage system.

[0047] Optionally, other solutions in the current solution set are updated according to the current optimal solution, as shown in the following formula:

[0048] X′ i =X i +R1(X best -T f ·X mean ).

[0049] Among them, X' i is the solution updated according to the current optimal solution, is the solution in the current teacher solution set, and X i is the other solution in the current solution set, R1 is a random value between [0,1], X best is the current optimal solution, T f is the teacher factor, X mean is the average value of the current solution set.

[0050] Optionally, the solution is updated according to the objective function value of the reference solution and the objective function value of the solution, as shown in the following formula:

[0051]

[0052] Among them, X″ i is the updated solution based on the reference solution, X' i is the solution in the current teacher solution set, F i is the objective function value of the solution in the current teacher solution set, R1 is a random value between [0,1], X R is the reference solution, F R is the objective function value of the reference solution.

[0053] Optionally, the distributed energy storage site selection and capacity determination method based on the teaching optimization algorithm further includes:

[0054] The operating peak load and network loss of the radial distribution network model after storage is allocated according to the site selection and sizing results are analyzed to clarify the impact of the site selection and sizing results on reducing the output fluctuation of the radial distribution network model and reducing the energy loss of the radial distribution network model.

[0055] In the second aspect, the present application provides a distributed energy storage site selection and capacity determination system based on a teaching optimization algorithm, comprising the following modules:

[0056] The radial distribution network model establishment module is used to establish a radial distribution network model according to the deployment of each clean energy power generation source in the distribution network; the radial distribution network model includes multiple clean energy power generation sources.

[0057] The site selection and sizing optimization module is used to establish a distributed energy storage system site selection and sizing optimization model with the goal of minimizing the peak load fluctuation and network loss of the radial distribution network model.

[0058] The site selection and sizing optimization model solving module is used to solve the site selection and sizing optimization model of the distributed energy storage system based on the teaching optimization algorithm to obtain the site selection and sizing results of the distributed energy storage system; the site selection and sizing results include the deployment address of each energy storage device of the distributed energy storage system in the distribution network and the deployment capacity of each energy storage device.

[0059] According to the specific embodiments provided in this application, this application discloses the following technical effects:

[0060] The present application provides a method and system for site selection and sizing of distributed energy storage based on a teaching optimization algorithm, the method comprising the following steps: according to the deployment of each clean energy power generation source in the distribution network, a radial distribution network model is established, with the goal of minimizing the peak load fluctuation and network loss of the radial distribution network model, a distributed energy storage system site selection and sizing optimization model is established, and the distributed energy storage system site selection and sizing optimization model is solved based on the teaching optimization algorithm to obtain the deployment address of each energy storage device of the distributed energy storage system in the distribution network and the deployment capacity of each energy storage device when the peak load fluctuation and network loss of the radial distribution network model are minimized. The above-mentioned scheme of the present application, aimed at a complex distribution network system in which different clean energy power generation sources are deployed in multiple locations in the distribution network, can provide a more sufficient reference for the actual distribution network planning, and at the same time, minimizing peak load fluctuation and minimizing network loss are taken as optimization goals, which is conducive to synchronously solving the intermittent and volatility of the output of distributed power sources and the power loss problems of the distribution network through energy storage configuration. BRIEF DESCRIPTION OF THE DRAWINGS

[0061] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings required for use in the embodiments 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 drawings can be obtained based on these drawings without paying creative work.

[0062] Figure 1 A flowchart of a distributed energy storage site selection and capacity determination method based on a teaching optimization algorithm is provided in accordance with an embodiment of the present application.

[0063] Figure 2A schematic diagram of a radial distribution network model in a distributed energy storage site selection and sizing method based on a teaching optimization algorithm provided in one embodiment of the present application.

[0064] Figure 3 A schematic diagram of 24-hour power generation output curves of two distributed power sources in a distributed energy storage site selection and sizing method based on a teaching optimization algorithm provided in one embodiment of the present application.

[0065] Figure 4 A flowchart of step A3 of a distributed energy storage site selection and capacity determination method based on a teaching optimization algorithm provided in one embodiment of the present application.

[0066] Figure 5 A schematic diagram of comparing operating peak load fluctuation curves with and without configuration of site selection and sizing results in a distributed energy storage site selection and sizing method based on a teaching optimization algorithm provided in one embodiment of the present application.

[0067] Figure 6 A schematic diagram of comparing network loss curves of whether or not the site selection and sizing results are configured in a distributed energy storage site selection and sizing method based on a teaching optimization algorithm provided in one embodiment of the present application.

[0068] Figure 7 A schematic diagram of the structure of a computer device provided in one embodiment of the present application. DETAILED DESCRIPTION

[0069] The following will be combined with the drawings in the embodiments of the present application to clearly and completely describe the technical solutions in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

[0070] In order to make the above-mentioned objects, features and advantages of the present application more obvious and easy to understand, the present application is further described in detail below with reference to the accompanying drawings and specific implementation methods.

[0071] In an exemplary embodiment, Figure 1 As shown, a distributed energy storage site selection and capacity determination method based on a teaching optimization algorithm is provided, comprising the following steps:

[0072] A1. According to the deployment of each clean energy power source in the distribution network, a radial distribution network model is established; the radial distribution network model includes multiple clean energy power sources. In this embodiment, according to the deployment of each clean energy power source in the distribution network, an improved IEEE 33-node system is used to establish a radial distribution network model; multiple clean energy power sources are connected to different nodes of the radial distribution network model, and the output characteristics of each clean energy power source are clarified.

[0073] Specifically, clean energy power sources include distributed wind power and photovoltaic power sources. In a specific example, an improved IEEE 33-node system is used to establish a radial distribution network model. The model structure is as follows: Figure 2 In this distribution network model, a distributed photovoltaic power generation source is deployed at node 10, with a rated capacity of 108.15kW; four distributed wind power generation sources are deployed at nodes 31, 15, 8, and 25, with rated capacities of 1109.055kW, 672.25kW, 848.48kW, and 783.21KW, respectively; the 24-hour power generation output curves of the two distributed power sources are shown in Figure 3 .

[0074] A2. With the goal of minimizing the peak load fluctuation and network loss of the radial distribution network model, a distributed energy storage system site selection and sizing optimization model is established.

[0075] A3. Solve the site selection and sizing optimization model of the distributed energy storage system based on the teaching optimization algorithm to obtain the site selection and sizing results of the distributed energy storage system; the site selection and sizing results include the deployment address of each energy storage device of the distributed energy storage system in the distribution network and the deployment capacity of each energy storage device.

[0076] Specifically in this embodiment, step A2 includes the following steps:

[0077] A21. According to the operating characteristics of the distributed energy storage system and the radial distribution network model, determine the constraints of the distributed energy storage system site selection and sizing optimization model; the constraints of the distributed energy storage system site selection and sizing optimization model include the power limit constraint of the energy storage device, the charge state constraint of the energy storage device, the flow constraint of the radial distribution network model, and the voltage constraint of the radial distribution network model. Specifically, the power limit constraint of the energy storage device can be expressed by the following formula:

[0078]

[0079] in, is the lower limit of the charging power of the energy storage device, is the upper limit of the charging power of the energy storage device, P char (t) is the charging power of the energy storage device at time t, is the lower limit of the discharge power of the energy storage device, is the upper limit of the discharge power of the energy storage device, P dischar (t) is the discharge power of the energy storage device at time t, which means that the charge and discharge power of the energy storage device must always be kept between the upper and lower limits of the allowable charge and discharge power.

[0080] The charge state constraint of the energy storage device can be expressed as follows:

[0081] SOC min <SOC(t)<SOC max .

[0082]

[0083] Among them, SOC min is the lower limit of the charging state of the energy storage device, SOC(t) is the charging state of the energy storage device, SOC max is the upper limit of the charging state of the energy storage device, ξ is the self-discharge rate of the energy storage device, Δt is the change time, η char is the charging efficiency of the energy storage device, E capR is the maximum rated capacity of the energy storage device, in kWh, η dischar is the discharge efficiency of the energy storage device. It represents the charging state of the energy storage device at time t. The charging state needs to be kept between its upper and lower limits at all times. The upper limit SOC of the charging state of the energy storage device is max Take 90%, lower limit SOC min Take 20%, the charging and discharging efficiency of the energy storage device η char and η dischar The average value is 90%.

[0084] The power flow constraint of the radial distribution network model can be expressed as follows:

[0085]

[0086] Among them, P l is the power flow flux of line l, is the maximum power allowed to pass through line l. represents the power flow flux P of each line l The maximum power allowed should be Within the range.

[0087] The voltage constraint of the radial distribution network model can be expressed as follows:

[0088]

[0089] Among them, V b is the voltage at node b, is the lower voltage limit of node b, is the upper voltage limit of node b.

[0090] A22. With the goal of minimizing the peak load fluctuation of the radial distribution network model, determine the first objective function of the distributed energy storage system site selection and sizing optimization model.

[0091] Specifically, in order to minimize the impact of the output fluctuation of distributed power sources on the hourly peak load fluctuation of the distribution network during the optimization process of distributed energy storage site selection and capacity determination, the first objective function of the optimization model is defined as the sum of the hourly peak load fluctuations of each node. Specifically, there are 24 time periods that need to be accumulated within 1 day, that is, the first objective function is shown as follows:

[0092]

[0093] Where obf1 is the first objective function value, t is the time label, b is the node label, P a,b,t is the peak load fluctuation of node b at time t.

[0094] A23. With the goal of minimizing the network loss of the radial distribution network model, determine the second objective function of the distributed energy storage system site selection and sizing optimization model. Specifically, in order to minimize the power loss of the distribution network in the energy storage site selection and sizing, the second objective function is defined as the sum of the network loss of each line in each time period, that is, the second objective function is shown as follows:

[0095]

[0096] Where obf2 is the second objective function value, l is the line number, I l,t is the current of line l at time t, R l is the resistance of line l.

[0097] A24. According to the constraints of the distributed energy storage system site selection and sizing optimization model, the first objective function of the distributed energy storage system site selection and sizing optimization model and the second objective function of the distributed energy storage system site selection and sizing optimization model, a distributed energy storage system site selection and sizing optimization model is established; the comprehensive objective function of the distributed energy storage system site selection and sizing optimization model is the sum of the first objective function and the second objective function. Specifically, the comprehensive objective function of the distributed energy storage system site selection and sizing optimization model is as follows:

[0098] obf=obf1+obf2.

[0099] Among them, obf is the comprehensive objective function value.

[0100] The teaching optimization algorithm adopted in this embodiment is a two-layer optimization algorithm. The upper layer simulates the teacher to optimize, and the lower layer simulates the student to optimize. Specifically in this embodiment, Figure 4 As shown, step A3 includes the following steps:

[0101] A31. Generate an initial solution set and use the initial solution set as the current solution set; the initial solution set includes several solutions; the solutions include the deployment addresses of each energy storage device of the distributed energy storage system in the distribution network and the deployment capacity of each energy storage device.

[0102] A32. For each solution in the current solution set, calculate the objective function value of the solution, and determine the current optimal solution in the current solution set.

[0103] If the current number of iterations does not reach the preset number of iterations, execute step A33; otherwise, execute step A37.

[0104] A33, updating the other solutions in the current solution set according to the current optimal solution to obtain the current teacher solution set. Specifically, updating the other solutions in the current solution set according to the current optimal solution is as shown in the following formula:

[0105] X′ i =X i +R1(X best -T f ·X mean ).

[0106] Among them, X' i is the solution updated according to the current optimal solution, is the solution in the current teacher solution set, and X i is the other solution in the current solution set, R1 is a random value between [0,1], X best is the current optimal solution, T f is the teacher factor, a constant with a value of 1 or 2, X mean is the average value of the current solution set.

[0107] A34. Randomly select a solution from the current teacher solution set as a reference solution.

[0108] A35. For any solution in the current teacher solution set, the solution is updated according to the objective function value of the reference solution and the objective function value of the solution to obtain the current student solution set. Specifically, the solution is updated according to the objective function value of the reference solution and the objective function value of the solution, as shown in the following formula:

[0109]

[0110] Among them, X″ i is the updated solution based on the reference solution, X' i is the solution in the current teacher solution set, F iis the objective function value of the solution in the current teacher solution set, R1 is a random value between [0,1], X R is the reference solution, F R is the objective function value of the reference solution.

[0111] A36. Take the current student solution set as the current solution set, and jump to step A32: for each solution in the current solution set, calculate the objective function value of the solution, and determine the current optimal solution in the current solution set.

[0112] A37. Use the current optimal solution as the site selection and sizing result of the distributed energy storage system.

[0113] In order to facilitate understanding of the effect of the site selection and capacity determination results obtained by the method of this embodiment, in an exemplary embodiment, the distributed energy storage site selection and capacity determination method based on the teaching optimization algorithm further includes the following steps:

[0114] A4. Analyze the operating peak load and network loss of the radial distribution network model after storage according to the site selection and sizing results, and clarify the effect of the site selection and sizing results on reducing the output fluctuation of the radial distribution network model and reducing the energy loss of the radial distribution network model.

[0115] Table 1 gives the site selection and sizing results of the distributed energy storage system, and Table 2 compares the changes in total operating peak load fluctuations and network losses before and after storage.

[0116] Table 1 Site selection and capacity determination results of distributed energy storage system

[0117]

[0118] Table 2 Changes in total operating peak load fluctuation and network loss before and after storage

[0119] Whether to store Total operating peak load (MW) Network loss (kW) no 33.15 1890.63 yes 32.51 1852.654

[0120] The peak load fluctuation and network loss curves of the site selection and capacity determination results are shown in Figure 5 and Figure 6 ,from Figure 5 It can be seen that the configuration of the distributed energy storage system reduces the impact of the intermittent and volatile output of distributed power sources on the hourly peak load curve fluctuations. Figure 6 It can be seen that the reasonable configuration of distributed energy storage systems also reduces the overall network loss level of the distribution network.

[0121] The above-mentioned scheme provided in this embodiment is aimed at a complex distribution network system in which different clean energy power generation sources are deployed in multiple locations of the distribution network, and can provide a more sufficient reference for the actual distribution network planning. At the same time, minimizing peak load fluctuations and minimizing network losses are taken as optimization goals, which is conducive to synchronously solving the intermittent and volatility of distributed power output and the power loss problems of the distribution network through energy storage configuration. By using the teaching optimization algorithm to solve, the distributed energy storage can be optimally located and capacity determined, and the peak load fluctuations and network losses of the distribution network operation can be minimized.

[0122] Based on the same inventive concept, the embodiment of the present application also provides a system for implementing the above-mentioned distributed energy storage site selection and capacity determination method based on the teaching optimization algorithm. In an exemplary embodiment, the distributed energy storage site selection and capacity determination system based on the teaching optimization algorithm includes the following modules:

[0123] The radial distribution network model establishment module is used to establish a radial distribution network model according to the deployment of each clean energy power generation source in the distribution network; the radial distribution network model includes multiple clean energy power generation sources.

[0124] The site selection and sizing optimization module is used to establish a distributed energy storage system site selection and sizing optimization model with the goal of minimizing the peak load fluctuation and network loss of the radial distribution network model.

[0125] The site selection and sizing optimization model solving module is used to solve the site selection and sizing optimization model of the distributed energy storage system based on the teaching optimization algorithm to obtain the site selection and sizing results of the distributed energy storage system; the site selection and sizing results include the deployment address of each energy storage device of the distributed energy storage system in the distribution network and the deployment capacity of each energy storage device.

[0126] In an exemplary embodiment, a computer device is provided. The computer device may be a server or a terminal. The internal structure diagram thereof may be as follows: Figure 7As 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 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, the steps of a distributed energy storage site selection and capacity determination method based on a teaching optimization algorithm provided in the above embodiment are implemented.

[0127] Those skilled in the art will understand that Figure 7 The 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.

[0128] In an exemplary embodiment, a computer device is further provided, including a memory and a processor, wherein a computer program is stored in the memory, and the processor implements the steps in the above-mentioned method embodiments when executing the computer program.

[0129] In an exemplary embodiment, a computer-readable storage medium is provided, storing a computer program, and when the computer program is executed by a processor, the steps in the above method embodiments are implemented.

[0130] In an exemplary embodiment, a computer program product is provided, including a computer program, and when the computer program is executed by a processor, the steps in the above method embodiments are implemented.

[0131] 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.

[0132] Those of ordinary skill 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 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. By way of illustration and not limitation, RAM may be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM).

[0133] The database involved in each embodiment provided in this application may include at least one of a relational database and a non-relational database. The non-relational database may include a distributed database based on blockchain, etc., but is not limited thereto. 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, etc., but is not limited thereto.

[0134] 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 specification.

[0135] This article uses specific examples to illustrate the principles and implementation methods of this application. The description of the above embodiments is only used to help understand the method and core ideas of this application. At the same time, for those skilled in the art, according to the ideas of this application, there will be changes in the specific implementation methods and application scope. In summary, the content of this specification should not be understood as limiting this application.

Claims

1. A distributed energy storage site selection and capacity determination method based on a teaching optimization algorithm, characterized in that: include: According to the deployment of each clean energy power source in the distribution network, a radial distribution network model is established; The radial distribution network model includes a variety of clean energy power generation sources; To minimize the peak load fluctuation and network loss of the radial distribution network model, a distributed energy storage system site selection and capacity optimization model is established; The site selection and sizing optimization model of the distributed energy storage system is solved based on the teaching optimization algorithm to obtain the site selection and sizing results of the distributed energy storage system; the site selection and sizing results include the deployment address of each energy storage device of the distributed energy storage system in the distribution network and the deployment capacity of each energy storage device.

2. The distributed energy storage site selection and capacity determination method based on the teaching optimization algorithm according to claim 1 is characterized in that: With the goal of minimizing the peak load fluctuation and network loss of the radial distribution network model, a distributed energy storage system site selection and capacity optimization model is established, which includes: According to the operating characteristics of the distributed energy storage system and the radial distribution network model, the constraint conditions of the distributed energy storage system site selection and sizing optimization model are determined; the constraint conditions of the distributed energy storage system site selection and sizing optimization model include the power limit constraint of the energy storage device, the charging state constraint of the energy storage device, the flow constraint of the radial distribution network model and the voltage constraint of the radial distribution network model; With the goal of minimizing the peak load fluctuation of the radial distribution network model, the first objective function of the distributed energy storage system site selection and capacity optimization model is determined; Taking minimizing the network loss of the radial distribution network model as the goal, the second objective function of the distributed energy storage system site selection and capacity optimization model is determined; According to the constraints of the distributed energy storage system site selection and sizing optimization model, the first objective function of the distributed energy storage system site selection and sizing optimization model and the second objective function of the distributed energy storage system site selection and sizing optimization model, a distributed energy storage system site selection and sizing optimization model is established; the comprehensive objective function of the distributed energy storage system site selection and sizing optimization model is the sum of the first objective function and the second objective function.

3. The distributed energy storage site selection and capacity determination method based on the teaching optimization algorithm according to claim 2 is characterized in that: The power limit constraint of the energy storage device can be expressed as follows: in, is the lower limit of the charging power of the energy storage device, is the upper limit of the charging power of the energy storage device, P char (t) is the charging power of the energy storage device at time t, is the lower limit of the discharge power of the energy storage device, is the upper limit of the discharge power of the energy storage device, P dischar (t) is the discharge power of the energy storage device at time t; The charge state constraint of the energy storage device can be expressed as follows: SOC min <SOC(t)<SOC max ; Among them, SOC min is the lower limit of the charging state of the energy storage device, SOC(t) is the charging state of the energy storage device, SOC max is the upper limit of the charging state of the energy storage device, ξ is the self-discharge rate of the energy storage device, Δt is the change time, η char is the charging efficiency of the energy storage device, E capR is the maximum rated capacity of the energy storage device, η dischar is the discharge efficiency of the energy storage device; The power flow constraint of the radial distribution network model can be expressed as follows: Among them, P l is the power flow flux of line l, is the maximum power allowed to pass through line l; The voltage constraint of the radial distribution network model can be expressed as follows: Among them, V b is the voltage at node b, is the lower voltage limit of node b, is the upper voltage limit of node b.

4. The distributed energy storage site selection and capacity determination method based on the teaching optimization algorithm according to claim 2 is characterized in that: The first objective function of the distributed energy storage system site selection and capacity optimization model is as follows: Where obf1 is the first objective function value, t is the time label, b is the node label, P a,b,t is the peak load fluctuation of node b at time t; The second objective function of the distributed energy storage system site selection and capacity optimization model is as follows: Where obf2 is the second objective function value, l is the line number, I l,t is the current of line l at time t, R l is the resistance of line l; The comprehensive objective function of the distributed energy storage system site selection and capacity optimization model is as follows: obf = obf1 + obf2; Among them, obf is the comprehensive objective function value.

5. The distributed energy storage site selection and capacity determination method based on the teaching optimization algorithm according to claim 1 is characterized in that: According to the deployment of each clean energy power source in the distribution network, an improved IEEE 33-node system is used to establish a radial distribution network model; multiple clean energy power sources are connected to different nodes of the radial distribution network model, and the output characteristics of each clean energy power source are clarified.

6. The distributed energy storage site selection and capacity determination method based on the teaching optimization algorithm according to claim 1 is characterized in that: The distributed energy storage system site selection and capacity optimization model is solved based on the teaching optimization algorithm to obtain the site selection and capacity determination results of the distributed energy storage system, which specifically include: Generate an initial solution set, and use the initial solution set as the current solution set; the initial solution set includes a plurality of solutions; the solution includes the deployment address of each energy storage device of the distributed energy storage system in the distribution network and the deployment capacity of each energy storage device; For each solution in the current solution set, calculating the objective function value of the solution, and determining a current optimal solution in the current solution set; If the current number of iterations does not reach the preset number of iterations, then the other solutions in the current solution set are updated according to the current optimal solution to obtain the current teacher solution set; Randomly select a solution from the current teacher solution set as a reference solution; For any solution in the current teacher solution set, the solution is updated according to the objective function value of the reference solution and the objective function value of the solution to obtain the current student solution set; The current student solution set is taken as the current solution set, and the process jumps to the step: for each solution in the current solution set, the objective function value of the solution is calculated, and the current optimal solution is determined in the current solution set; If the current number of iterations reaches the preset number of iterations, the current optimal solution is used as the site selection and sizing result of the distributed energy storage system.

7. The distributed energy storage site selection and capacity determination method based on the teaching optimization algorithm according to claim 6 is characterized in that: Update other solutions in the current solution set according to the current optimal solution, as shown in the following formula: X′ i =X i +R1(X best -T f ·X mean ) Among them, X' i is the solution updated according to the current optimal solution, is the solution in the current teacher solution set, and X i is the other solution in the current solution set, R1 is a random value between [0,1], X best is the current optimal solution, T f is the teacher factor, X mean is the average value of the current solution set.

8. The distributed energy storage site selection and capacity determination method based on the teaching optimization algorithm according to claim 6 is characterized in that: According to the objective function value of the reference solution and the objective function value of the solution, the solution is updated as shown in the following formula: Among them, X i is the updated solution based on the reference solution, X' i is the solution in the current teacher solution set, F i is the objective function value of the solution in the current teacher solution set, R1 is a random value between [0,1], X R is the reference solution, F R is the objective function value of the reference solution.

9. The distributed energy storage site selection and capacity determination method based on the teaching optimization algorithm according to claim 1 is characterized in that: Also includes: The operating peak load and network loss of the radial distribution network model after storage is allocated according to the site selection and sizing results are analyzed to clarify the impact of the site selection and sizing results on reducing the output fluctuation of the radial distribution network model and reducing the energy loss of the radial distribution network model.

10. A distributed energy storage site selection and capacity determination system based on a teaching optimization algorithm, characterized in that: include: A radial distribution network model establishment module is used to establish a radial distribution network model according to the deployment of each clean energy power generation source in the distribution network; the radial distribution network model includes multiple clean energy power generation sources; A site selection and sizing optimization module is established to establish a distributed energy storage system site selection and sizing optimization model with the goal of minimizing peak load fluctuations and network losses in a radial distribution network model; The site selection and sizing optimization model solving module is used to solve the site selection and sizing optimization model of the distributed energy storage system based on the teaching optimization algorithm to obtain the site selection and sizing results of the distributed energy storage system; the site selection and sizing results include the deployment address of each energy storage device of the distributed energy storage system in the distribution network and the deployment capacity of each energy storage device.

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