A direct current power distribution network distributed energy storage planning method and device

Abstract

The application discloses a kind of direct current distribution network distributed energy storage planning method and device, method includes: through the random distribution model of load and distributed power output power, a certain number of random scenarios is generated using Monte Carlo simulation method, and the power flow data of random scenario is calculated;With the lowest planning cost of distributed energy storage in direct current distribution network as the goal, the charge and discharge power of energy storage, the power flow control parameter of converter in direct current distribution network as optimization variable, combined with the preset constraint condition, the planning model of distributed energy storage in direct current distribution network is constructed;Based on random scenario, the planning model of the distributed energy storage in direct current distribution network is solved using genetic algorithm, and the programmable charge and discharge power corresponding to each energy storage in direct current distribution network is obtained.The power flow distribution of system is determined by the optimization of converter power flow control parameter, and on this basis, the characteristics of multi-terminal power supply of direct current distribution network are comprehensively considered, so as to ensure the rationality and accuracy of energy storage planning.

Description

Technical Field

[0001] This invention relates to the field of DC distribution network safety technology, and in particular to a method and device for planning distributed energy storage in DC distribution networks. Background Technology

[0002] Compared to AC distribution networks, DC distribution networks based on voltage source converters can improve the acceptance of distributed power sources and electric vehicles, reduce system power losses, increase transmission capacity, and enhance the flexibility of the distribution network. However, random variations in load and renewable energy output may cause node voltage to exceed limits; therefore, real-time and effective voltage control is particularly important for ensuring system voltage levels.

[0003] While distributed energy storage plays a vital role in maintaining voltage levels in distribution networks by effectively regulating voltage exceedances caused by load and renewable energy output changes through adjusting charging and discharging power, the current optimized configuration of distributed energy storage in DC distribution networks has not fully considered the characteristics of multi-terminal power supply in DC distribution networks, as well as the coordination relationship between distributed energy storage and converters. Summary of the Invention

[0004] This invention provides a method and device for planning distributed energy storage in DC distribution networks, which comprehensively considers the characteristics of multi-terminal power supply in DC distribution networks, as well as the coordination relationship between distributed energy storage and converters, thus ensuring the rationality of energy storage planning.

[0005] In a first aspect, the present invention provides a distributed energy storage planning method for DC distribution networks, comprising:

[0006] Obtain the converter capacity of the DC distribution network, and the power flow control parameters of the converters in the DC distribution network in each time period;

[0007] Using a stochastic distribution model of load and distributed power output, a certain number of stochastic scenarios are generated using the Monte Carlo simulation method, and power flow data of the stochastic scenarios are calculated.

[0008] With the goal of minimizing the planning cost of distributed energy storage in the DC distribution network, and taking the charging and discharging power of energy storage and the power flow control parameters of the converter in the DC distribution network as optimization variables, and combined with preset constraints, a planning model for distributed energy storage in the DC distribution network is constructed.

[0009] Based on the aforementioned random scenario, a genetic algorithm is used to solve the planning model of distributed energy storage in the DC distribution network, thereby obtaining the planarable charging and discharging power corresponding to each energy storage in the DC distribution network.

[0010] Optionally, the converter capacity of the DC distribution network and the power flow control parameters of the converters in the DC distribution network for each time period are obtained, including:

[0011] Obtain the converter capacity, historical load parameters, and distributed power source parameters;

[0012] The maximum value of the historical load parameters and the maximum value of the output power among the distributed power generation parameters are taken. With the goal of minimizing network loss, a genetic algorithm is used to determine the power flow control parameters of the converter in the DC distribution network for each time period.

[0013] Optionally, based on the random scenario, a genetic algorithm is used to solve the planning model of distributed energy storage in the DC distribution network to obtain the planarable charging and discharging power corresponding to each energy storage in the DC distribution network, including:

[0014] Based on the aforementioned random scenario, a genetic algorithm is used to solve the planning model of distributed energy storage in the DC distribution network, thereby determining the charging and discharging power corresponding to each energy storage in the DC distribution network.

[0015] The maximum absolute value of the charging and discharging power is taken as the planarable charging and discharging power corresponding to each energy storage.

[0016] Optionally, the constraints include: node voltage constraints, converter power constraints, line power constraints, energy storage charging and discharging power constraints, and converter control parameter adjustment constraints.

[0017] The node voltage constraint is:

[0018] U min ≤U i,m ≤U max ;

[0019] The power constraint of the converter is:

[0020] -P x,cmax ≤P x,m ≤P x,cmax ;

[0021] The line power constraint is:

[0022] -P l,max ≤P l,m ≤P l,max ;

[0023] The charging and discharging power constraint of the energy storage is:

[0024] -P n,max ≤P n,m ≤P n,max ;

[0025] The adjustment constraints for the converter control parameters are as follows:

[0026] ΔU max ≤ΔU≤ΔU max ;

[0027] -ΔP y,max ≤ΔP y ≤ΔP y,max ;

[0028] Among them, U i,m Let U be the voltage at node i in scene m. min U is the minimum node voltage. max This represents the maximum node voltage. Px,m Let P be the converter power at node x in scenario m. x,cmax P represents the maximum converter power at node x. l,m Let P be the transmission power of line l in scenario m. l,max P is the maximum transmission power of line l. n,m Let P be the charging and discharging power of the nth energy storage unit in scenario m. n,max The maximum charging and discharging power of the nth energy storage unit, ΔU is the voltage control parameter adjustment of the constant voltage control converter, ΔU max ΔP represents the maximum value of the voltage control parameter adjustment for a constant voltage controlled converter. x Let ΔP be the power control parameter adjustment for the y-th constant power control converter. x,max This represents the maximum value of the power control parameter adjustment for the y-th constant power control converter.

[0029] Secondly, the present invention also provides a distributed energy storage planning device for DC distribution networks, comprising:

[0030] The acquisition module is used to acquire the converter capacity of the DC distribution network and the power flow control parameters of the converters in the DC distribution network in each time period.

[0031] The power flow data calculation module is used to generate a certain number of random scenarios using the Monte Carlo simulation method through a random distribution model of load and distributed power output power, and to calculate the power flow data of the random scenarios.

[0032] The model building module is used to construct a planning model for distributed energy storage in the DC distribution network with the goal of minimizing the planning cost of distributed energy storage in the DC distribution network, and with the charging and discharging power of energy storage and the power flow control parameters of the converter in the DC distribution network as optimization variables, combined with preset constraints.

[0033] The planable charge and discharge power determination module is used to solve the planning model of distributed energy storage in the DC distribution network based on the random scenario using a genetic algorithm, so as to obtain the planable charge and discharge power corresponding to each energy storage in the DC distribution network.

[0034] Optionally, the acquisition module includes:

[0035] The acquisition submodule is used to acquire the converter capacity, historical load parameters, and distributed power source parameters;

[0036] The power flow control parameter determination submodule is used to take the maximum value of the historical load parameters and the maximum value of the output power among the distributed power parameters, and use a genetic algorithm to determine the power flow control parameters of the converter in the DC distribution network for each time period with the goal of minimizing network loss.

[0037] Optionally, the plannable charge / discharge power determination module includes:

[0038] The charging and discharging power determination submodule is used to solve the planning model of the distributed energy storage in the DC distribution network based on the random scenario and determine the charging and discharging power corresponding to each energy storage in the DC distribution network.

[0039] The planable charge / discharge power determination submodule is used to take the maximum value of the absolute value of the charge / discharge power as the planable charge / discharge power corresponding to each energy storage.

[0040] Optionally, the constraints include: node voltage constraints, converter power constraints, line power constraints, energy storage charging and discharging power constraints, and converter control parameter adjustment constraints.

[0041] The node voltage constraint is:

[0042] U min ≤U i,m ≤U max ;

[0043] The power constraint of the converter is:

[0044] -P x,cmax ≤P x,m ≤P x,cmax ;

[0045] The line power constraint is:

[0046] -P l,max ≤P l,m ≤P l,max ;

[0047] The charging and discharging power constraint of the energy storage is:

[0048] -P n,max ≤P n，m ≤P n,max ;

[0049] The adjustment constraints for the converter control parameters are as follows:

[0050] -ΔU max ≤ΔU≤ΔUmax ;

[0051] -ΔP y,max ≤ΔP y ≤ΔP y,max ;

[0052] Among them, U i,m Let U be the voltage at node i in scene m. min U is the minimum node voltage. max P represents the maximum node voltage. x,m Let P be the converter power at node x in scenario m. x,cmax P represents the maximum converter power at node x. l,m Let P be the transmission power of line l in scenario m. l,max P is the maximum transmission power of line l. n,m Let P be the charging and discharging power of the nth energy storage unit in scenario m. n,max The maximum charging and discharging power of the nth energy storage unit, ΔU is the voltage control parameter adjustment of the constant voltage control converter, ΔU max ΔP represents the maximum value of the voltage control parameter adjustment for a constant voltage controlled converter. x Let ΔP be the power control parameter adjustment for the y-th constant power control converter. x,max This represents the maximum value of the power control parameter adjustment for the y-th constant power control converter.

[0053] A third aspect of this application provides an electronic device, the device including a processor and a memory;

[0054] The memory is used to store program code and transmit the program code to the processor;

[0055] The processor is used to execute the DC distribution network distributed energy storage planning method described in the first aspect according to the instructions in the program code.

[0056] A fourth aspect of this application provides a computer-readable storage medium for storing program code for executing the DC distribution network distributed energy storage planning method described in the first aspect.

[0057] As can be seen from the above technical solutions, the present invention has the following advantages:

[0058] This invention obtains the converter capacity of a DC distribution network and the power flow control parameters of the converters in the DC distribution network for each time period. Using a random distribution model of load and distributed power output, a number of random scenarios are generated using Monte Carlo simulation, and the power flow data of these random scenarios is calculated. With the goal of minimizing the planning cost of distributed energy storage in the DC distribution network, and using the charging and discharging power of the energy storage and the power flow control parameters of the converters in the DC distribution network as optimization variables, combined with preset constraints, a planning model for distributed energy storage in the DC distribution network is constructed. Based on the random scenarios, a genetic algorithm is used to solve the planning model for distributed energy storage in the DC distribution network, obtaining the planarable charging and discharging power corresponding to each energy storage unit in the DC distribution network. By optimizing the power flow control parameters of the converters, the power flow distribution of the system is determined, and the characteristics of multi-terminal power supply in the DC distribution network are fully considered, thereby ensuring the rationality and accuracy of energy storage planning. Attached Figure Description

[0059] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0060] Figure 1 This is a flowchart illustrating the steps of a first embodiment of the distributed energy storage planning method for DC distribution networks according to the present invention.

[0061] Figure 2 This is a flowchart illustrating the steps of a second embodiment of the distributed energy storage planning method for DC distribution networks according to the present invention.

[0062] Figure 3 This is a structural block diagram of an embodiment of a distributed energy storage planning device for DC distribution networks according to the present invention. Detailed Implementation

[0063] This invention provides a method and apparatus for planning distributed energy storage in DC distribution networks, which comprehensively considers the characteristics of multi-terminal power supply in DC distribution networks, as well as the coordination relationship between distributed energy storage and converters, thus ensuring the rationality of energy storage planning.

[0064] To make the objectives, features, and advantages of this invention more apparent and understandable, the technical solutions of the embodiments of this invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the embodiments described below are only some embodiments of this invention, and not all embodiments. Based on the embodiments of this invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this invention.

[0065] Please see Figure 1 , Figure 1 This is a flowchart illustrating the steps of a distributed energy storage planning method for DC distribution networks according to an embodiment of the present invention, which may specifically include the following steps:

[0066] S101, Obtain the converter capacity of the DC distribution network and the power flow control parameters of the converter in the DC distribution network in each time period;

[0067] S102, using a random distribution model of load and distributed power output, a certain number of random scenarios are generated by Monte Carlo simulation, and the power flow data of the random scenarios are calculated.

[0068] S103, with the goal of minimizing the planning cost of distributed energy storage in the DC distribution network, and with the charging and discharging power of energy storage and the power flow control parameters of the converter in the DC distribution network as optimization variables, and combined with preset constraints, a planning model for distributed energy storage in the DC distribution network is constructed.

[0069] S104. Based on the random scenario, a genetic algorithm is used to solve the planning model of distributed energy storage in the DC distribution network to obtain the planarable charging and discharging power corresponding to each energy storage in the DC distribution network.

[0070] This invention obtains the converter capacity of a DC distribution network and the power flow control parameters of the converters in the DC distribution network for each time period. Using a random distribution model of load and distributed power output, a certain number of random scenarios are generated using Monte Carlo simulation, and the power flow data of these random scenarios is calculated. With the goal of minimizing the planning cost of distributed energy storage in the DC distribution network, and using the charging and discharging power of the energy storage and the power flow control parameters of the converters in the DC distribution network as optimization variables, combined with preset constraints, a planning model for distributed energy storage in the DC distribution network is constructed. Based on the random scenarios, a genetic algorithm is used to solve the planning model for distributed energy storage in the DC distribution network, obtaining the planarable charging and discharging power corresponding to each energy storage unit in the DC distribution network. By optimizing the converter power flow control parameters, the power flow distribution of the system is determined, and the characteristics of multi-terminal power supply in the DC distribution network are fully considered, thereby ensuring the rationality and accuracy of energy storage planning.

[0071] Please see Figure 2 The flowchart below shows the steps of a second embodiment of the distributed energy storage planning method for DC distribution networks according to the present invention, specifically including:

[0072] Step S201: Obtain the converter capacity, historical load parameters, and distributed power source parameters;

[0073] Step S202: Take the maximum value of the historical load parameters and the maximum value of the output power in the distributed power parameters, and use a genetic algorithm to determine the power flow control parameters of the converter in the DC distribution network for each time period with the goal of minimizing network loss.

[0074] In this embodiment of the invention, the DC power distribution system adopts a droop control mode, therefore the power flow control parameter of the converter is the operating reference power of the droop control. When using a genetic algorithm, the operating reference power of the converter is used as the optimization variable. The minimum network loss of the system is used as the fitness value in the genetic algorithm. For individuals that do not meet the operating constraints, their fitness value is set to 100 times their network loss. Through the selection, crossover, and mutation operations of the genetic algorithm, the operating reference power of the converter is optimized and obtained.

[0075] In practical implementation, the maximum value of the load at each node and the maximum value of the output power of the distributed power source are taken from the historical load parameters. The minimum network loss of the system is used as the objective function for optimizing the power flow control parameters of the converter, that is:

[0076]

[0077] l ij For a line with starting node i and ending node j; T L For the set of all lines in a DC distribution network; g ij P is the line admittance with starting node i and ending node j; ij Let f1 be the active power of the line with starting node i and ending node j, and f1 be the network loss of the DC distribution network. j Let be the voltage value at the terminal node j.

[0078] In addition, there are constraints on the optimization of converter power flow control parameters, including:

[0079] (1) Node voltage constraint, that is, the node voltage must be between the allowable minimum voltage value and the maximum voltage value, as shown below:

[0080] U min ≤U i,d ≤U max ;

[0081] Among them, U min For the minimum allowable voltage, U i,dFor deterministic load and renewable energy output power, the voltage value U at node i is... max This represents the maximum permissible voltage.

[0082] (2) Converter power constraint, that is, the transmission power of the converter is less than the converter capacity, as shown below:

[0083] -P x,cmax ≤P x,d ≤P x,cmax ;

[0084] Among them, P x,cmax P represents the converter capacity located at node x. x,d The converter power at node x is given by deterministic load and renewable energy output power values.

[0085] (3) Line power constraint, that is, the transmission power of the line is less than the line capacity, as shown below:

[0086] -P l,max ≤P l,d ≤P l,cmax ;

[0087] Among them, P l,cmax P represents the maximum transmission capacity of line l. l,d The transmission power of line l under deterministic load and new energy output power values.

[0088] Step S203: Using a random distribution model of load and distributed power output, a certain number of random scenarios are generated using the Monte Carlo simulation method, and the power flow data of the random scenarios are calculated.

[0089] It should be noted that the Monte Carlo method, also known as statistical simulation or statistical experimentation, is a numerical simulation method that takes probabilistic phenomena as its research object. It is a calculation method that uses sampling surveys to obtain statistical values ​​to estimate unknown characteristic quantities, and is suitable for computational simulation experiments on discrete systems. In computational simulation, by constructing a probabilistic model that approximates the system's performance and conducting random experiments on a digital computer, the stochastic characteristics of the system can be simulated.

[0090] In this embodiment of the invention, a normal distribution is used to represent the random distribution model of the load and the output power of the distributed power source. The mean of the normal distribution model describing the output power of the load or the distributed power source is the maximum value in its historical data. The variance of the normal distribution model is set to 10% of the mean. A certain number of random scenarios are generated using the Monte Carlo simulation method. The values ​​of the load and the output power of the distributed power source in the random scenarios are generated by Monte Carlo sampling.

[0091] Step S204: With the goal of minimizing the planning cost of distributed energy storage in the DC distribution network, and taking the charging and discharging power of energy storage and the power flow control parameters of the converter in the DC distribution network as optimization variables, and combined with preset constraints, a planning model for distributed energy storage in the DC distribution network is constructed.

[0092] The constraints include: node voltage constraints, converter power constraints, line power constraints, energy storage charging and discharging power constraints, and converter control parameter adjustment constraints.

[0093] The node voltage constraint is:

[0094] U min ≤U i,m ≤U max ;

[0095] The power constraint of the converter is:

[0096] -P x,cmax ≤P x,m ≤P x,cmax ;

[0097] The line power constraint is:

[0098] -P l,max ≤P l,m ≤P l,max ;

[0099] The charging and discharging power constraint of the energy storage is:

[0100] -P n,max ≤P n,m ≤P n,max ;

[0101] The adjustment constraints for the converter control parameters are as follows:

[0102] ΔU max ≤ΔU≤ΔU max ;

[0103] -ΔP y,max ≤ΔP y ≤ΔP y,max ;

[0104] Among them, U i,m Let U be the voltage at node i in scene m. min U is the minimum node voltage. max P represents the maximum node voltage. x,m Let P be the converter power at node x in scenario m. x,cmax P represents the maximum converter power at node x. l,m Let P be the transmission power of line l in scenario m. l,maxP is the maximum transmission power of line l. n,m Let P be the charging and discharging power of the nth energy storage unit in scenario m. n,max The maximum charging and discharging power of the nth energy storage unit, ΔU is the voltage control parameter adjustment of the constant voltage control converter, ΔU max ΔP represents the maximum value of the voltage control parameter adjustment for a constant voltage controlled converter. x Let ΔP be the power control parameter adjustment for the y-th constant power control converter. x,max This represents the maximum value of the power control parameter adjustment for the y-th constant power control converter.

[0105] In this embodiment of the invention, the objective function is to minimize the planning cost of distributed energy storage in the DC distribution network, expressed as:

[0106]

[0107] Where Ness is the total number of energy storage units, f2 is the planning cost of distributed energy storage in the DC distribution network, and P n,max η represents the maximum charge / discharge power of the nth energy storage device. n This is the cost coefficient corresponding to the maximum charge / discharge power of the nth energy storage unit.

[0108] Step S205: Based on the random scenario, a genetic algorithm is used to solve the planning model of distributed energy storage in the DC distribution network to determine the charging and discharging power corresponding to each energy storage in the DC distribution network.

[0109] Step S206: The maximum value of the absolute value of the charging and discharging power is taken as the planarable charging and discharging power corresponding to each energy storage.

[0110] In this embodiment of the invention, a genetic algorithm is used to solve the planning model of distributed energy storage in DC distribution network for the obtained random scenario set, so as to obtain the charging and discharging power corresponding to each energy storage. The maximum value of the absolute value of the charging and discharging power of each energy storage in all scenarios is the maximum charging and discharging power of the energy storage planning design.

[0111] The distributed energy storage planning method for DC distribution networks provided in this embodiment of the invention obtains the converter capacity of the DC distribution network and the power flow control parameters of the converters in the DC distribution network for each time period; generates a certain number of random scenarios using Monte Carlo simulation based on a random distribution model of load and distributed power output, and calculates the power flow data of the random scenarios; aims to minimize the planning cost of distributed energy storage in the DC distribution network, and constructs a planning model for distributed energy storage in the DC distribution network using the charging and discharging power of energy storage and the power flow control parameters of the converters in the DC distribution network as optimization variables, combined with preset constraints; based on the random scenarios, uses a genetic algorithm to solve the planning model for distributed energy storage in the DC distribution network, obtaining the planable charging and discharging power corresponding to each energy storage in the DC distribution network. By optimizing the power flow control parameters of the converters, the power flow distribution of the system is determined, and the characteristics of multi-terminal power supply in the DC distribution network are fully considered, thereby ensuring the rationality and accuracy of energy storage planning.

[0112] Please see Figure 3 The diagram illustrates a structural block diagram of an embodiment of a distributed energy storage planning device for a DC distribution network, comprising the following modules:

[0113] The acquisition module 301 is used to acquire the converter capacity of the DC distribution network and the power flow control parameters of the converter in the DC distribution network in each time period.

[0114] The power flow data calculation module 302 is used to generate a certain number of random scenarios using the Monte Carlo simulation method through a random distribution model of load and distributed power output power, and to calculate the power flow data of the random scenarios.

[0115] The model building module 303 is used to construct a planning model for distributed energy storage in the DC distribution network with the goal of minimizing the planning cost of distributed energy storage in the DC distribution network, and with the charging and discharging power of energy storage and the power flow control parameters of the converter in the DC distribution network as optimization variables, combined with preset constraints.

[0116] The planable charge and discharge power determination module 304 is used to solve the planning model of the distributed energy storage in the DC distribution network based on the random scenario using a genetic algorithm, so as to obtain the planable charge and discharge power corresponding to each energy storage in the DC distribution network.

[0117] In an optional embodiment, the acquisition module includes:

[0118] The acquisition submodule is used to acquire the converter capacity, historical load parameters, and distributed power source parameters;

[0119] The power flow control parameter determination submodule is used to take the maximum value of the historical load parameters and the maximum value of the output power among the distributed power parameters, and use a genetic algorithm to determine the power flow control parameters of the converter in the DC distribution network for each time period with the goal of minimizing network loss.

[0120] In an optional embodiment, the plannable charge / discharge power determination module includes:

[0121] The charging and discharging power determination submodule is used to solve the planning model of the distributed energy storage in the DC distribution network based on the random scenario and determine the charging and discharging power corresponding to each energy storage in the DC distribution network.

[0122] The planable charge / discharge power determination submodule is used to take the maximum value of the absolute value of the charge / discharge power as the planable charge / discharge power corresponding to each energy storage.

[0123] In an optional embodiment, the constraints include: node voltage constraints, converter power constraints, line power constraints, energy storage charging and discharging power constraints, and converter control parameter adjustment constraints.

[0124] The node voltage constraint is:

[0125] U min ≤U i,m ≤U max ;

[0126] The power constraint of the converter is:

[0127] -P x,cmax ≤P x,m ≤P x,cmax ;

[0128] The line power constraint is:

[0129] -P l,max ≤P l,m ≤P l,max ;

[0130] The charging and discharging power constraint of the energy storage is:

[0131] -P n,max ≤P n,m ≤P n,max ;

[0132] The adjustment constraints for the converter control parameters are as follows:

[0133] ΔU max ≤ΔU≤ΔU max ;

[0134] -ΔP y,max ≤ΔPy ≤ΔP y,max ;

[0135] Among them, U i,m Let U be the voltage at node i in scene m. min U is the minimum node voltage. max P represents the maximum node voltage. x,m Let P be the converter power at node x in scenario m. x,cmax P represents the maximum converter power at node x. l,m Let P be the transmission power of line l in scenario m. l,max P is the maximum transmission power of line l. n,m Let P be the charging and discharging power of the nth energy storage unit in scenario m. n,max The maximum charging and discharging power of the nth energy storage unit, ΔU is the voltage control parameter adjustment of the constant voltage control converter, ΔU max ΔP represents the maximum value of the voltage control parameter adjustment for a constant voltage controlled converter. x Let ΔP be the power control parameter adjustment for the y-th constant power control converter. x,max This represents the maximum value of the power control parameter adjustment for the y-th constant power control converter.

[0136] This application also provides an electronic device, which includes a processor and a memory;

[0137] The memory is used to store program code and transfer the program code to the processor;

[0138] The processor is used to execute the DC distribution network distributed energy storage planning method in the above method embodiment according to the instructions in the program code.

[0139] This application also provides a computer-readable storage medium for storing program code for executing the DC distribution network distributed energy storage planning method in the above method embodiments.

[0140] In the several embodiments provided in this application, it should be understood that the disclosed apparatus and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces; the indirect coupling or communication connection between apparatuses or units may be electrical, mechanical, or other forms.

[0141] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0142] Furthermore, the functional units in the various embodiments of this application can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.

[0143] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions for executing all or part of the steps of the methods described in the various embodiments of this application through a computer device (which may be a personal computer, server, or network device, etc.). The aforementioned storage medium includes: USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, optical disks, and other media capable of storing program code.

[0144] The above-described embodiments are only used to illustrate the technical solutions of this application, and are not intended to limit them. Although this application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of this application.

Claims

1. A method for planning distributed energy storage in a DC distribution network, characterized in that, include: Obtain the converter capacity of the DC distribution network, and the power flow control parameters of the converters in the DC distribution network in each time period; Using a stochastic distribution model of load and distributed power output, a certain number of stochastic scenarios are generated using the Monte Carlo simulation method, and power flow data of the stochastic scenarios are calculated. With the goal of minimizing the planning cost of distributed energy storage in the DC distribution network, and taking the charging and discharging power of energy storage and the power flow control parameters of the converter in the DC distribution network as optimization variables, and combined with preset constraints, a planning model for distributed energy storage in the DC distribution network is constructed. Based on the aforementioned random scenario, a genetic algorithm is used to solve the planning model of distributed energy storage in the DC distribution network, thereby obtaining the planarable charging and discharging power corresponding to each energy storage in the DC distribution network. Obtain the converter capacity of the DC distribution network, and the power flow control parameters of the converters in the DC distribution network for each time period, including: Obtain the converter capacity, historical load parameters, and distributed power source parameters; The maximum value of the historical load parameters and the maximum value of the output power among the distributed power generation parameters are taken. With the goal of minimizing network loss, a genetic algorithm is used to determine the power flow control parameters of the converter in the DC distribution network for each time period.

2. The distributed energy storage planning method for DC distribution networks according to claim 1, characterized in that, Based on the aforementioned random scenario, a genetic algorithm is used to solve the planning model of distributed energy storage in the DC distribution network, obtaining the planarable charging and discharging power corresponding to each energy storage unit in the DC distribution network, including: Based on the aforementioned random scenario, a genetic algorithm is used to solve the planning model of distributed energy storage in the DC distribution network, thereby determining the charging and discharging power corresponding to each energy storage in the DC distribution network. The maximum absolute value of the charging and discharging power is taken as the planarable charging and discharging power corresponding to each energy storage.

3. The distributed energy storage planning method for DC distribution networks according to claim 1, characterized in that, The constraints include: node voltage constraints, converter power constraints, line power constraints, energy storage charging and discharging power constraints, and converter control parameter adjustment constraints. The node voltage constraint is: ； The power constraint of the converter is: ； The line power constraint is: ； The charging and discharging power constraint of the energy storage is: ； The adjustment constraints for the converter control parameters are as follows: ； ； in, In the scene Next node voltage, This represents the minimum node voltage. This represents the maximum node voltage. In the scene Next node The converter power, For nodes The maximum power of the converter, In the scene Downline Transmission power, For the line Maximum transmission power In the scene Next The charging and discharging power of an energy storage device. No. The maximum charge and discharge power of each energy storage unit The voltage control parameter adjustment amount for a constant voltage controlled converter. This refers to the maximum value of the voltage control parameter adjustment for a constant voltage control converter. For the first The power control parameter adjustment amount of a constant power control converter. For the first The maximum value of the power control parameter adjustment for a constant power control converter.

4. A distributed energy storage planning device for DC distribution networks, characterized in that, include: The acquisition module is used to acquire the converter capacity of the DC distribution network and the power flow control parameters of the converters in the DC distribution network in each time period. The power flow data calculation module is used to generate a certain number of random scenarios using the Monte Carlo simulation method through a random distribution model of load and distributed power output power, and to calculate the power flow data of the random scenarios. The model building module is used to construct a planning model for distributed energy storage in the DC distribution network with the goal of minimizing the planning cost of distributed energy storage in the DC distribution network, and with the charging and discharging power of energy storage and the power flow control parameters of the converter in the DC distribution network as optimization variables, combined with preset constraints. The planable charge and discharge power determination module is used to solve the planning model of the distributed energy storage in the DC distribution network based on the random scenario using a genetic algorithm, so as to obtain the planable charge and discharge power corresponding to each energy storage in the DC distribution network. The acquisition module includes: The acquisition submodule is used to acquire the converter capacity, historical load parameters, and distributed power source parameters; The power flow control parameter determination submodule is used to take the maximum value of the historical load parameters and the maximum value of the output power among the distributed power parameters, and use a genetic algorithm to determine the power flow control parameters of the converter in the DC distribution network for each time period with the goal of minimizing network loss.

5. The DC distribution network distributed energy storage planning device according to claim 4, characterized in that, The plannable charge / discharge power determination module includes: The charging and discharging power determination submodule is used to solve the planning model of the distributed energy storage in the DC distribution network based on the random scenario and determine the charging and discharging power corresponding to each energy storage in the DC distribution network. The planable charge / discharge power determination submodule is used to take the maximum value of the absolute value of the charge / discharge power as the planable charge / discharge power corresponding to each energy storage.

6. The DC distribution network distributed energy storage planning device according to claim 4, characterized in that, The constraints include: node voltage constraints, converter power constraints, line power constraints, energy storage charging and discharging power constraints, and converter control parameter adjustment constraints. The node voltage constraint is: ； The power constraint of the converter is: ； The line power constraint is: ； The charging and discharging power constraint of the energy storage is: ； The adjustment constraints for the converter control parameters are as follows: ； ； in, In the scene Next node voltage, This represents the minimum node voltage. This represents the maximum node voltage. In the scene Next node The converter power, For nodes The maximum power of the converter, In the scene Downline Transmission power, For the line Maximum transmission power In the scene Next The charging and discharging power of an energy storage device. No. The maximum charge and discharge power of each energy storage unit The voltage control parameter adjustment amount for a constant voltage controlled converter. This refers to the maximum value of the voltage control parameter adjustment for a constant voltage control converter. For the first The power control parameter adjustment amount of a constant power control converter. For the first The maximum value of the power control parameter adjustment for a constant power control converter.

7. An electronic device, characterized in that, It includes a processor and a memory, the memory storing computer-readable instructions that, when executed by the processor, perform the method as described in any one of claims 1-3.

8. A storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by this processor, it performs the method as described in any one of claims 1-3.

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