Active control method, system, device and storage medium of intelligent power distribution cabinet cluster

Abstract

The present invention discloses an active control method, system, device and storage medium of an intelligent distribution cabinet cluster, and relates to the technical field of electric power dispatching. When the electric power data and energy storage information of a first area where any load area exists meet the condition of overpower supply, an energy storage optimization model is constructed based on load layout information, power supply information, regional electric power data and energy storage information; the energy storage optimization model is solved to obtain an energy storage control scheme; when the electric power data and power supply information of a second area where any load area exists meet the condition of insufficient power supply, a scheduling optimization model is constructed based on load layout information, power supply information, regional electric power data and energy storage information; the scheduling optimization model is solved to obtain a load control scheme; the role of cross-regional distribution cabinet clusters and energy storage equipment is brought into play to a greater extent, and the utilization efficiency of electric power resources is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of electric power dispatching, and in particular to an active control method, system, device and storage medium for an intelligent power distribution cabinet cluster. Background Art

[0002] With the acceleration of urbanization, energy consumption is increasing, and the rational allocation and utilization of power resources has become an urgent issue. The traditional centralized power supply mode has disadvantages such as imbalance between supply and demand, power grid fluctuations, and high line losses, which makes it difficult to meet the requirements of modern cities for efficient, stable and intelligent power systems. Therefore, it is particularly important to establish an intelligent distribution cabinet cluster control system to realize the active allocation and optimal utilization of power resources.

[0003] In the existing technology, a passive dispatching method is usually adopted, that is, the power distribution between the distribution cabinet clusters is carried out according to the unified dispatching instructions of the power supply company. This passive dispatching has several major shortcomings: 1) delayed response, it is difficult to respond to changes in power demand in a timely manner; 2) lack of autonomous optimization, unable to fully utilize local energy storage and load response resources; 3) limited dispatching scope, unable to achieve cross-regional power balance; 4) high line loss, dispatching decisions do not fully consider line loss.

[0004] To this end, the present invention proposes an active control method, system, device and storage medium for an intelligent power distribution cabinet cluster. Summary of the invention

[0005] The present invention aims to solve at least one of the technical problems existing in the prior art. To this end, the present invention proposes an active control method, system, device and storage medium of an intelligent power distribution cabinet cluster, which greatly exerts the role of cross-regional power distribution cabinet clusters and energy storage devices and improves the utilization efficiency of power resources.

[0006] To achieve the above purpose, an active control method of an intelligent power distribution cabinet cluster is proposed, comprising the following steps:

[0007] Step 1: Collect load layout information, power supply information of the distribution cabinet cluster corresponding to each load area, and collect energy storage information of each distribution cabinet cluster;

[0008] Step 2: Collect energy storage data and energy consumption data of each load area as first area power data;

[0009] Step 3: If the first regional power data and energy storage information of any load region satisfy the oversupply condition, then an energy storage optimization model is constructed based on the load layout information, power supply information, regional power data and energy storage information; the energy storage optimization model is solved to obtain an energy storage control plan;

[0010] Step 4: After the energy storage control decision-making scheme is regulated, the energy storage data and energy consumption data of each load area are collected again as the second area power data;

[0011] Step 5: If the second area power data and power supply information of any load area meet the power shortage condition, a scheduling optimization model is constructed based on the load layout information, power supply information, regional power data and energy storage information, and the scheduling optimization model is solved to obtain a load control solution;

[0012] The method of collecting the energy storage data and energy consumption data of each load area is as follows:

[0013] For each load area, the load energy consumption of the load area is collected as energy consumption data;

[0014] Collect the remaining stored power of the energy storage devices of the power distribution cabinet cluster corresponding to the load area;

[0015] The excess power supply condition is that the remaining stored power in the energy storage data is greater than or equal to the maximum capacity in the energy storage information, and the total power supply capacity in the power supply information is greater than the remaining stored power of the energy storage device;

[0016] The method of constructing the energy storage optimization model based on load layout information, power supply information, regional power data and energy storage information is:

[0017] The set of all load areas that meet the oversupply condition is marked as I0, the set of all remaining load areas is marked as I1, the numbers of the load areas in the set I0 are marked as i0, and the numbers of the load areas in the set I1 are marked as I1;

[0018] The maximum capacity and remaining storage power of the energy storage device of the i1th distribution cabinet cluster are marked as Max_i1 and Mi1 respectively;

[0019] The load energy consumption of the i0th load area is marked as Fi0, and the total power supply capacity of the i0th distribution cabinet cluster is marked as Zi0;

[0020] The load energy consumption of the i1th load area is marked as Fi1, and the total power supply capacity of the i1th distribution cabinet cluster is marked as Zi1;

[0021] The length of the power line between the i0th distribution cabinet cluster and the i1th distribution cabinet cluster is marked as Li0i1;

[0022] A first power transmission variable xi0i1 is set for the i0th distribution cabinet cluster and the i1th distribution cabinet cluster to represent the excess power transmitted from the i0th distribution cabinet cluster to the i1th distribution cabinet cluster;

[0023] The transmission loss function is marked as S(x, L); wherein the transmission loss function is the power loss value caused by the resistance of the power line during the power dispatching process, x is the transmission amount, and L is the length of the power line;

[0024] Based on the transmission loss function S(x, L), the first transmission quantity variable xi0i1, the maximum capacity Max_i1 of the energy storage device, the remaining storage power Mi1, the load energy consumption Fi0 and Fi1, the total power supply capacity Zi0 and Zi1, and the line length Li0i1, the energy storage optimization objective function and the energy storage scheduling constraint set are constructed;

[0025] Taking minimizing the energy storage optimization objective function as the optimization goal and the energy storage scheduling constraint set as the constraint, a convex optimization problem is constructed, which is the energy storage optimization model.

[0026] The method for obtaining the energy storage control scheme is:

[0027] The result of solving the energy storage optimization model is marked as X0, and the variable value of the power transmitted from the i0th distribution cabinet cluster to the i1th distribution cabinet cluster in X0 is marked as , then the energy storage control scheme is:

[0028] Set the amount of power delivered from the i0th distribution cabinet cluster to the i1th distribution cabinet cluster as ;

[0029] The insufficient power supply condition is that the sum of the remaining stored power in the energy storage data and the total power supply capacity in the power supply information is less than the load energy consumption in the energy consumption data;

[0030] The method of constructing the dispatch optimization model based on load layout information, power supply information, regional power data and energy storage information is:

[0031] The set of all load areas that meet the power shortage condition is marked as I2, the set of all remaining load areas is marked as I3, the numbers of the load areas in the set I2 are marked as i2, and the numbers of the load areas in the set I3 are marked as I3;

[0032] The remaining stored power of the energy storage device in the i3th distribution cabinet cluster is marked as Mi3;

[0033] The load energy consumption of the i2th load area is marked as Fi2, and the total power supply capacity of the i2th distribution cabinet cluster is marked as Zi2;

[0034] The load energy consumption of the i3th load area is marked as Fi3, and the total power supply capacity of the i3th distribution cabinet cluster is marked as Zi3;

[0035] The length of the power line between the i2th distribution cabinet cluster and the i3th distribution cabinet cluster is marked as Li2i3;

[0036] A second power transmission variable yi2i3 is set for the i2th distribution cabinet cluster and the i3th distribution cabinet cluster to represent the required power transmitted from the i3th distribution cabinet cluster to the i2th distribution cabinet cluster;

[0037] Based on the transmission loss function S(x, L), the second transmission quantity variable yi2i3, the remaining storage power Mi3, the load energy consumption Fi2 and Fi3, the total power supply capacity Zi2 and Zi3, and the line length Li2i3, the scheduling optimization objective function and the scheduling constraint set are constructed;

[0038] Taking minimizing the scheduling optimization objective function as the optimization goal and the scheduling constraint set as the constraint, a convex optimization problem is constructed, which is the scheduling optimization model.

[0039] The method for obtaining the load regulation solution is:

[0040] The result of solving the scheduling optimization model is marked as Y1, and the variable value of the power transmitted from the i3th distribution cabinet cluster to the i2th distribution cabinet cluster in Y1 is marked as , then the load control scheme is:

[0041] Set the power delivered from the i3th distribution cabinet cluster to the i2th distribution cabinet cluster to .

[0042] An active control system for an intelligent power distribution cabinet cluster is proposed, comprising a basic data collection module, an energy storage control module and a load control module; wherein each module is electrically connected;

[0043] The basic data collection module collects load layout information, power supply information of the distribution cabinet cluster corresponding to each load area, and energy storage information of each distribution cabinet cluster, and collects energy storage data and energy consumption data of each load area as the first area power data, and sends the load layout information, power supply information, energy storage information and first area power data to the energy storage control module and the load control module;

[0044] The energy storage control module constructs an energy storage optimization model based on the load layout information, power supply information, regional power data and energy storage information when the first regional power data and energy storage information of any load region meet the overpower supply condition; solves the energy storage optimization model, obtains the energy storage control plan, and sends the energy storage control plan to the load control module;

[0045] The load control module, after being regulated by the energy storage control decision-making scheme, re-collects the energy storage data and energy consumption data of each load area as the second area power data. When the second area power data and power supply information of any load area meet the power shortage condition, a scheduling optimization model is constructed based on the load layout information, power supply information, regional power data and energy storage information, and the scheduling optimization model is solved to obtain the load control plan.

[0046] An active control device comprises: a processor and a memory, wherein the memory stores a computer program that can be called by the processor; the processor executes the above-mentioned active control method, system, device and storage medium of an intelligent distribution cabinet cluster by calling the computer program stored in the memory.

[0047] A computer-readable storage medium stores instructions. When the instructions are executed on a computer, the computer executes the above-mentioned active control method, system, device and storage medium of an intelligent power distribution cabinet cluster.

[0048] Compared with the prior art, the present invention has the following beneficial effects:

[0049] The present invention divides the power distribution network into multiple load areas, each area is powered by a distribution cabinet cluster and has a certain energy storage capacity; collects power supply information, energy storage status and energy consumption data of each area, and constructs an optimization model describing regional power flow; when there is an oversupply in a certain area, the optimal storage plan for the excess power is calculated through the energy storage optimization model; when there is an undersupply in a certain area, the optimal inter-regional power allocation plan is calculated through the scheduling optimization model; the goal of the optimization model is to minimize line losses and meet various operating constraints, and solve to obtain specific energy storage and scheduling control strategies. The regulating role of energy storage facilities can be brought into play to a greater extent, and mutual assistance and sharing of power between regions can be achieved, thereby improving the overall power supply reliability and economy of the power grid and realizing efficient configuration of power resources. BRIEF DESCRIPTION OF THE DRAWINGS

[0050] Figure 1 This is a flow chart of an active control method for an intelligent power distribution cabinet cluster in Example 1 of the present invention;

[0051] Figure 2 This is a module connection relationship diagram of an active control system of an intelligent power distribution cabinet cluster in Example 2 of the present invention;

[0052] Figure 3 Schematic diagram of the structure of the active control device in Example 3 of the present invention;

[0053] Figure 4 This is a schematic diagram of the computer-readable storage medium structure in Example 4 of the present invention. DETAILED DESCRIPTION

[0054] The technical solution of the present invention will be clearly and completely described below in conjunction with the embodiments. Obviously, the described embodiments are only part of the embodiments of the present invention, rather than all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0055] Example 1

[0056] like Figure 1 As shown, a method for actively controlling a cluster of intelligent power distribution cabinets includes the following steps:

[0057] Step 1: Collect load layout information, power supply information of the distribution cabinet cluster corresponding to each load area, and collect energy storage information of each distribution cabinet cluster;

[0058] Step 2: Collect energy storage data and energy consumption data of each load area as first area power data;

[0059] Step 3: If the first regional power data and energy storage information of any load region satisfy the oversupply condition, then an energy storage optimization model is constructed based on the load layout information, power supply information, regional power data and energy storage information; the energy storage optimization model is solved to obtain an energy storage control plan;

[0060] Step 4: After the energy storage control decision-making scheme is regulated, the energy storage data and energy consumption data of each load area are collected again as the second area power data;

[0061] Step 5: If the second area power data and power supply information of any load area meet the power shortage condition, a scheduling optimization model is constructed based on the load layout information, power supply information, regional power data and energy storage information, and the scheduling optimization model is solved to obtain a load control solution;

[0062] The method of collecting load layout information is as follows:

[0063] Collect the area range and spatial relationship of each functional area in the area to be regulated, and generate a distribution map of each functional area according to the area range and spatial relationship as load layout information, where each functional area is a load area;

[0064] It is understandable that in the distribution map, the distances between different functional areas, the length of the power transmission lines between each functional area, etc. can be marked;

[0065] The functional areas are several areas divided according to the different functions of the power supply objects. In the general urban power supply system, the functional areas include but are not limited to industrial power supply areas, agricultural power supply areas, office power supply areas, and residential power supply areas. The peak and valley time periods of power consumption in different power supply areas are different. Therefore, generally, a power supply area is powered by a group of distribution cabinet clusters;

[0066] Furthermore, the power supply information of the power distribution cabinet cluster corresponding to each load area includes the total power supply capacity of each power distribution cabinet cluster;

[0067] Specifically, a distribution cabinet cluster consists of several distribution cabinets. The power supply capacity of each distribution cabinet is usually measured in kVA (kilovolt-ampere) or MVA (megavolt-ampere), which refers to its maximum transmission capacity. The total power supply capacity of a distribution cabinet cluster can be regarded as the sum of the power supply capacities of all the distribution cabinets in its composition.

[0068] It can be further understood that since the power consumption of functional areas generally presents peak-valley fluctuation characteristics, there may be insufficient power supply during peak power consumption, and power waste during low power consumption. Therefore, a group of energy storage devices are generally provided for each distribution cabinet cluster to discharge during peak power consumption and charge during low power consumption, so as to achieve the purpose of peak shaving and valley filling.

[0069] Furthermore, the energy storage information of each power distribution cabinet cluster includes the maximum capacity of the energy storage device configured for the power distribution cabinet cluster;

[0070] Furthermore, the method of collecting the energy storage data and energy consumption data of each load area is as follows:

[0071] For each load area, the load energy consumption of the load area is collected as energy consumption data;

[0072] Collect the remaining stored power of the energy storage devices of the power distribution cabinet cluster corresponding to the load area;

[0073] It should be noted that, in this embodiment, the energy consumption data adopts the energy consumption collected in real time, which can be read from the electric energy meter responsible for the area within the load area;

[0074] In more embodiments, the energy consumption data may also be a predicted value of the future power consumption of the load area, so as to achieve early prediction and early power dispatch, thereby ensuring a smooth transition of power supply to electrical equipment in each load area;

[0075] In more embodiments, the energy consumption data may also be the total power consumption within a period of time in the load area, such as the power consumption during the peak power consumption period or the power consumption during the low power consumption period. By predicting the total power consumption within a period of time, the power dispatching plan within the period of time may be planned in advance and then evenly divided into each unit time, so as to avoid frequent calculation and excessive regulation, resulting in line power fluctuations and grid instability.

[0076] Furthermore, the oversupply condition is that the remaining stored power in the energy storage data is greater than or equal to the maximum capacity in the energy storage information, and the total power supply capacity in the power supply information is greater than the remaining stored power of the energy storage device; that is, the amount of power that the energy storage device can store has reached its limit, and the energy consumption of the load area has not yet consumed the power supply of the distribution cabinet cluster corresponding to the load area, which will lead to power waste of the distribution cabinet cluster, and the excess power supply needs to be dispatched to other load areas or other distribution cabinet clusters of energy storage devices;

[0077] Furthermore, the method of constructing the energy storage optimization model based on the load layout information, power supply information, regional power data and energy storage information is:

[0078] The set of all load areas that meet the oversupply condition is marked as I0, the set of all remaining load areas is marked as I1, the numbers of the load areas in the set I0 are marked as i0, and the numbers of the load areas in the set I1 are marked as I1;

[0079] The maximum capacity and remaining storage power of the energy storage device of the i1th distribution cabinet cluster are marked as Max_i1 and Mi1 respectively;

[0080] The load energy consumption of the i0th load area is marked as Fi0, and the total power supply capacity of the i0th distribution cabinet cluster is marked as Zi0;

[0081] The load energy consumption of the i1th load area is marked as Fi1, and the total power supply capacity of the i1th distribution cabinet cluster is marked as Zi1;

[0082] The length of the power line between the i0th distribution cabinet cluster and the i1th distribution cabinet cluster is marked as Li0i1;

[0083] A first power transmission variable xi0i1 is set for the i0th distribution cabinet cluster and the i1th distribution cabinet cluster to represent the excess power transmitted from the i0th distribution cabinet cluster to the i1th distribution cabinet cluster;

[0084] The transmission loss function is marked as S(x, L); wherein the transmission loss function is the power loss value caused by the resistance of the power line during the power dispatching process, x is the transmission amount, and L is the length of the power line;

[0085] Specifically, based on the principle of electricity, the transmission loss function can generally be set as ; Where R is the resistance of the power line and U is the transmission voltage;

[0086] Based on the transmission loss function S(x, L), the first transmission quantity variable xi0i1, the maximum capacity Max_i1 of the energy storage device, the remaining storage power Mi1, the load energy consumption Fi0 and Fi1, the total power supply capacity Zi0 and Zi1, and the line length Li0i1, the energy storage optimization objective function and the energy storage scheduling constraint set are constructed;

[0087] Taking minimizing the energy storage optimization objective function as the optimization goal and the energy storage scheduling constraint set as the constraint, a convex optimization problem is constructed, which is the energy storage optimization model.

[0088] Specifically, the energy storage optimization objective function is constructed as follows:

[0089] The energy storage optimization objective function is labeled as H(X); where X is the set of all first transmission quantity variables;

[0090] Then the expression of energy storage optimization objective function is:

[0091] ;

[0092] It can be understood that the energy storage optimization objective function expresses the total loss of transmitted power in the line;

[0093] Furthermore, the constraints in the energy storage scheduling constraint set include:

[0094] ;

[0095] in, The power of the distribution station cluster used to limit each load area that meets the oversupply condition is transmitted as much as possible without wasting it. It should be noted that since the optimization goal is to minimize the total loss in the line, the optimization goal will reduce the transmission of power as much as possible. Therefore, when optimizing the solution, it will satisfy = This boundary condition thus ensures the power supply to the load area; The power received by the distribution substation cluster for limiting each load area that does not meet the oversupply condition can be completely consumed or stored, thereby avoiding power waste;

[0096] It is understandable that in the actual dispatching process, the total power supply of all distribution cabinets and energy storage equipment will be greater than the total power consumption of the load area, resulting in This condition cannot be fully met, resulting in an unsolvable energy storage optimization model. Therefore, in more preferred embodiments, the total power supply of all distribution cabinets and energy storage devices can also be calculated in advance. , and calculate the total power consumption ;like ,but The constraint remains unchanged, otherwise, the constraint is replaced by , that is, the power consumption of each load area is proportionally expanded to ensure that the energy storage optimization model has a solution;

[0097] The method for solving the energy storage optimization model is to solve it through an optimization solving tool, which can be a tool library such as CVXPY, or an intelligent optimization algorithm such as a genetic algorithm and an ant colony algorithm;

[0098] The method for obtaining the energy storage control scheme is:

[0099] The result of solving the energy storage optimization model is marked as X0, and the variable value of the power transmitted from the i0th distribution cabinet cluster to the i1th distribution cabinet cluster in X0 is marked as , then the energy storage control scheme is:

[0100] Set the amount of power delivered from the i0th distribution cabinet cluster to the i1th distribution cabinet cluster as ;

[0101] Furthermore, the power shortage condition is that the sum of the remaining stored power in the energy storage data and the total power supply capacity in the power supply information is less than the load energy consumption in the energy consumption data; that is, the power required to be consumed in the load area is greater than the total power supply of the power distribution cabinet cluster and the energy storage device, and the power supply shortage phenomenon occurs;

[0102] The method of constructing the dispatch optimization model based on load layout information, power supply information, regional power data and energy storage information is:

[0103] The set of all load areas that meet the power shortage condition is marked as I2, the set of all remaining load areas is marked as I3, the numbers of the load areas in the set I2 are marked as i2, and the numbers of the load areas in the set I3 are marked as I3;

[0104] The remaining stored power of the energy storage device in the i3th distribution cabinet cluster is marked as Mi3;

[0105] The load energy consumption of the i2th load area is marked as Fi2, and the total power supply capacity of the i2th distribution cabinet cluster is marked as Zi2;

[0106] The load energy consumption of the i3th load area is marked as Fi3, and the total power supply capacity of the i3th distribution cabinet cluster is marked as Zi3;

[0107] The length of the power line between the i2th distribution cabinet cluster and the i3th distribution cabinet cluster is marked as Li2i3;

[0108] A second power transmission variable yi2i3 is set for the i2th distribution cabinet cluster and the i3th distribution cabinet cluster to represent the required power transmitted from the i3th distribution cabinet cluster to the i2th distribution cabinet cluster;

[0109] Based on the transmission loss function S(x, L), the second transmission quantity variable yi2i3, the remaining storage power Mi3, the load energy consumption Fi2 and Fi3, the total power supply capacity Zi2 and Zi3, and the line length Li2i3, the scheduling optimization objective function and the scheduling constraint set are constructed;

[0110] Taking minimizing the scheduling optimization objective function as the optimization goal and the scheduling constraint set as the constraint, a convex optimization problem is constructed, which is the scheduling optimization model.

[0111] Specifically, the scheduling optimization objective function is constructed as follows:

[0112] The dispatch optimization objective function is marked as D(Y); Y is the set of all second transmission quantity variables yi2i3;

[0113] Then the expression of the scheduling optimization objective function is:

[0114] ;

[0115] Furthermore, the constraints in the scheduling constraint set include:

[0116] ;

[0117] in, The power distribution substation cluster used to limit the power transmission to the outside can ensure the power consumption of the local load area after the power is transmitted. The distribution station cluster used to limit power shortage can fully supply the local load area after receiving the transmitted power; similarly, when the total power supply of all distribution cabinets and energy storage equipment is less than the total power consumption of the load area, the total power consumption is calculated. , there will be If the restriction cannot be met, replace it with , that is, the power supply of the i2th distribution cabinet in the formula is increased proportionally (the actual power supply remains unchanged) to reduce the amount of power being transmitted, thereby ensuring that the scheduling optimization model has a solution;

[0118] In more preferred embodiments, some distribution cabinet clusters may meet the condition of insufficient power supply while another part of distribution cabinet clusters meet the condition of excess power supply. Therefore, it is necessary to deliver sufficient power to the distribution cabinet clusters with insufficient power supply and to fully deliver the excess power of the distribution cabinet clusters with excess power supply. Therefore, it can be understood that at this time, the I0 set (the distribution cabinet clusters with excess power supply) is a subset of the I3 set (the set of power delivered outward);

[0119] Therefore, the scheduling constraint set also includes the constraint conditions: ;

[0120] The method for obtaining the load regulation solution is:

[0121] The result of solving the scheduling optimization model is marked as X1, and the variable value of the power transmitted from the i3th distribution cabinet cluster to the i2th distribution cabinet cluster in X1 is marked as , then the load control scheme is:

[0122] Set the power delivered from the i3th distribution cabinet cluster to the i2th distribution cabinet cluster to .

[0123] Example 2

[0124] like Figure 2 As shown, an active control system of an intelligent power distribution cabinet cluster includes a basic data collection module, an energy storage control module and a load control module; wherein each module is electrically connected;

[0125] The basic data collection module collects load layout information, power supply information of the distribution cabinet cluster corresponding to each load area, and energy storage information of each distribution cabinet cluster, and collects energy storage data and energy consumption data of each load area as the first area power data, and sends the load layout information, power supply information, energy storage information and first area power data to the energy storage control module and the load control module;

[0126] The energy storage control module constructs an energy storage optimization model based on the load layout information, power supply information, regional power data and energy storage information when the first regional power data and energy storage information of any load region meet the overpower supply condition; solves the energy storage optimization model, obtains the energy storage control plan, and sends the energy storage control plan to the load control module;

[0127] The load control module, after being regulated by the energy storage control decision-making scheme, re-collects the energy storage data and energy consumption data of each load area as the second area power data. When the second area power data and power supply information of any load area meet the power shortage condition, a scheduling optimization model is constructed based on the load layout information, power supply information, regional power data and energy storage information, and the scheduling optimization model is solved to obtain the load control plan.

[0128] Example 3

[0129] Figure 3 Schematic diagram of the structure of an active control device provided by an embodiment of the present application. Figure 3 As shown, according to another aspect of the present application, an active control device 100 is also provided. The active control device 100 may include one or more processors and one or more memories. The memory stores a computer readable code, and when the computer readable code is executed by one or more processors, the active control method of the intelligent power distribution cabinet cluster as described above can be executed.

[0130] The method or device according to the embodiment of the present application can also be used by Figure 3 The active control device shown in the figure is implemented. Figure 3 As shown, the active control device 100 may include a bus 101, one or more CPUs 102, ROM 103, RAM 104, a communication port 105 connected to a network, an input / output component 106, a hard disk 107, etc. The storage device in the active control device 100, such as ROM 103 or hard disk 107, may store the active control method of the intelligent power distribution cabinet cluster provided in the present application.

[0131] Furthermore, the active control device 100 may also include a user interface 108. Figure 3 The architecture shown is only exemplary and can be omitted according to actual needs when implementing different devices. Figure 3 One or more components of an active control device are shown.

[0132] Example 4

[0133] Figure 4 Schematic diagram of a computer-readable storage medium structure provided by an embodiment of the present application. Figure 4As shown, a computer-readable storage medium 200 according to an embodiment of the present application is shown. Computer-readable instructions are stored on the computer-readable storage medium 200. When the computer-readable instructions are executed by the processor, the active control method of the intelligent distribution cabinet cluster according to the embodiment of the present application described with reference to the above figures can be executed. The computer-readable storage medium 200 includes, but is not limited to, for example, volatile memory and / or non-volatile memory. Volatile memory may include, for example, random access memory (RAM) and cache memory (cache), etc. Non-volatile memory may include, for example, read-only memory (ROM), hard disk, flash memory, etc.

[0134] In addition, according to the implementation of the present application, the process described above with reference to the flowchart can be implemented as a computer software program. For example, the present application provides a non-transitory machine-readable storage medium, the non-transitory machine-readable storage medium stores machine-readable instructions, the machine-readable instructions can be run by a processor to execute instructions corresponding to the method steps provided by the present application, and when the computer program is executed by a central processing unit (CPU), the above functions defined in the method of the present application are executed.

[0135] The methods, apparatuses, and devices of the present application may be implemented in many ways. For example, the methods, apparatuses, and devices of the present application may be implemented by software, hardware, firmware, or any combination of software, hardware, and firmware. The above order of steps for the method is for illustration only, and the steps of the method of the present application are not limited to the order specifically described above unless otherwise specified. In addition, in some embodiments, the present application may also be implemented as programs recorded in a recording medium, which include machine-readable instructions for implementing the method according to the present application. Therefore, the present application also covers recording media storing programs for executing the method according to the present application.

[0136] In addition, the parts of the above-mentioned technical solutions provided in the embodiments of the present application that are consistent with the implementation principles of the corresponding technical solutions in the prior art are not described in detail to avoid excessive redundancy.

[0137] The specific implementation modes as described above further describe the purpose, technical solutions and beneficial effects of the present invention in detail. It should be understood that the above description is only a specific implementation mode of the present invention and is not intended to limit the present invention. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention shall be included in the protection scope of the present invention.

[0138] The above preset parameters or preset thresholds are all set by technicians in this field according to actual conditions or obtained through large-scale data simulation.

[0139] The above embodiments are only used to illustrate the technical method of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical method of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical method of the present invention.

Claims

1. An active control method for an intelligent power distribution cabinet cluster, characterized in that: The following steps are involved: Step 1: Collect load layout information, power supply information of the distribution cabinet cluster corresponding to each load area, and collect energy storage information of each distribution cabinet cluster; Step 2: Collect energy storage data and energy consumption data of each load area as first area power data; Step 3: If the first regional power data and energy storage information of any load region satisfy the oversupply condition, then construct an energy storage optimization model based on the load layout information, power supply information, regional power data and energy storage information; Solve the energy storage optimization model and obtain the energy storage control plan; Step 4: After the energy storage control decision-making scheme is regulated, the energy storage data and energy consumption data of each load area are collected again as the second area power data; Step 5: If the second area power data and power supply information of any load area meet the power shortage condition, a scheduling optimization model is constructed based on the load layout information, power supply information, regional power data and energy storage information, and the scheduling optimization model is solved to obtain a load control solution; The method of constructing the energy storage optimization model based on load layout information, power supply information, regional power data and energy storage information is: The set of all load areas that meet the oversupply condition is marked as I0, the set of all remaining load areas is marked as I1, the numbers of the load areas in the set I0 are marked as i0, and the numbers of the load areas in the set I1 are marked as I1; The maximum capacity and remaining storage power of the energy storage device of the i1th distribution cabinet cluster are marked as Max_i1 and Mi1 respectively; The load energy consumption of the i0th load area is marked as Fi0, and the total power supply capacity of the i0th distribution cabinet cluster is marked as Zi0; The load energy consumption of the i1th load area is marked as Fi1, and the total power supply capacity of the i1th distribution cabinet cluster is marked as Zi1; The length of the power line between the i0th distribution cabinet cluster and the i1th distribution cabinet cluster is marked as Li0i1; A first power transmission variable xi0i1 is set for the i0th distribution cabinet cluster and the i1th distribution cabinet cluster to represent the excess power transmitted from the i0th distribution cabinet cluster to the i1th distribution cabinet cluster; The transmission loss function is marked as S(x, L); wherein the transmission loss function is the power loss value caused by the resistance of the power line during the power dispatching process, x is the transmission amount, and L is the length of the power line; Based on the transmission loss function S(x, L), the first transmission quantity variable xi0i1, the maximum capacity Max_i1 of the energy storage device, the remaining storage power Mi1, the load energy consumption Fi0 and Fi1, the total power supply capacity Zi0 and Zi1, and the line length Li0i1, the energy storage optimization objective function and the energy storage scheduling constraint set are constructed; Taking minimizing the energy storage optimization objective function as the optimization goal and the energy storage scheduling constraint set as the constraint, a convex optimization problem is constructed, which is the energy storage optimization model.

2. The active control method of an intelligent power distribution cabinet cluster according to claim 1 is characterized in that: The method of collecting the energy storage data and energy consumption data of each load area is as follows: For each load area, the load energy consumption of the load area is collected as energy consumption data; Collect the remaining stored power of the energy storage devices of the distribution cabinet cluster corresponding to the load area.

3. The active control method of an intelligent power distribution cabinet cluster according to claim 2 is characterized in that: The method for obtaining the energy storage control scheme is: The result of solving the energy storage optimization model is marked as X0, and the variable value of the power transmitted from the i0th distribution cabinet cluster to the i1th distribution cabinet cluster in X0 is marked as , then the energy storage control scheme is: Set the amount of power delivered from the i0th distribution cabinet cluster to the i1th distribution cabinet cluster as .

4. The active control method of an intelligent power distribution cabinet cluster according to claim 3 is characterized in that: The method of constructing the dispatch optimization model based on load layout information, power supply information, regional power data and energy storage information is: The set of all load areas that meet the power shortage condition is marked as I2, the set of all remaining load areas is marked as I3, the numbers of the load areas in the set I2 are marked as i2, and the numbers of the load areas in the set I3 are marked as I3; The remaining stored power of the energy storage device in the i3th distribution cabinet cluster is marked as Mi3; The load energy consumption of the i2th load area is marked as Fi2, and the total power supply capacity of the i2th distribution cabinet cluster is marked as Zi2; The load energy consumption of the i3th load area is marked as Fi3, and the total power supply capacity of the i3th distribution cabinet cluster is marked as Zi3; The length of the power line between the i2th distribution cabinet cluster and the i3th distribution cabinet cluster is marked as Li2i3; A second power transmission variable yi2i3 is set for the i2th distribution cabinet cluster and the i3th distribution cabinet cluster to represent the required power transmitted from the i3th distribution cabinet cluster to the i2th distribution cabinet cluster; Based on the transmission loss function S(x, L), the second transmission quantity variable yi2i3, the remaining storage power Mi3, the load energy consumption Fi2 and Fi3, the total power supply capacity Zi2 and Zi3, and the line length Li2i3, the scheduling optimization objective function and the scheduling constraint set are constructed; Taking minimizing the scheduling optimization objective function as the optimization goal and the scheduling constraint set as the constraint, a convex optimization problem is constructed, which is the scheduling optimization model.

5. The active control method of an intelligent power distribution cabinet cluster according to claim 4 is characterized in that: The method for obtaining the load regulation solution is: The result of solving the scheduling optimization model is marked as Y1, and the variable value of the power transmitted from the i3th distribution cabinet cluster to the i2th distribution cabinet cluster in Y1 is marked as , then the load control scheme is: Set the power delivered from the i3th distribution cabinet cluster to the i2th distribution cabinet cluster to .

6. The active control method of an intelligent power distribution cabinet cluster according to claim 5 is characterized in that: The excess power supply condition is that the remaining stored power in the energy storage data is greater than or equal to the maximum capacity in the energy storage information, and the total power supply capacity in the power supply information is greater than the remaining stored power of the energy storage device; The insufficient power supply condition is that the sum of the remaining stored power in the energy storage data and the total power supply capacity in the power supply information is less than the load energy consumption in the energy consumption data.

7. An active control system for a smart power distribution cabinet cluster, which is used to implement the active control method for a smart power distribution cabinet cluster as described in any one of claims 1 to 6, characterized in that: It includes a basic data collection module, an energy storage control module and a load control module; wherein each module is electrically connected; The basic data collection module collects load layout information, power supply information of the distribution cabinet cluster corresponding to each load area, and energy storage information of each distribution cabinet cluster, and collects energy storage data and energy consumption data of each load area as the first area power data, and sends the load layout information, power supply information, energy storage information and first area power data to the energy storage control module and the load control module; The energy storage control module constructs an energy storage optimization model based on the load layout information, power supply information, regional power data and energy storage information when the first regional power data and energy storage information of any load region meet the overpower supply condition; solves the energy storage optimization model, obtains the energy storage control plan, and sends the energy storage control plan to the load control module; The load control module, after being regulated by the energy storage control decision-making scheme, re-collects the energy storage data and energy consumption data of each load area as the second area power data. When the second area power data and power supply information of any load area meet the power shortage condition, a scheduling optimization model is constructed based on the load layout information, power supply information, regional power data and energy storage information, and the scheduling optimization model is solved to obtain the load control plan.

8. An active control device, characterized in that: include: A processor and a memory, wherein the memory stores a computer program that can be called by the processor; The processor executes the active control method of an intelligent distribution cabinet cluster according to any one of claims 1 to 6 by calling the computer program stored in the memory.

9. A computer-readable storage medium, characterized in that: Instructions are stored, and when the instructions are executed on a computer, the computer executes an active control method for an intelligent distribution cabinet cluster as described in any one of claims 1 to 6.

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