Optimization-based regulation and control method and apparatus for distributed energy storage, device, and storage medium

Abstract

Disclosed in the present invention are an optimization-based regulation and control method and apparatus for distributed energy storage, a device, and a storage medium. The method comprises: on the basis of current weather parameters, constructing an extreme weather model; determining a plurality of faulted and out-of-service elements in which faults will occur in a power distribution network; then on the basis of the faulted and out-of-service elements, system parameters of a distributed energy storage system, and an operation parameter of the power distribution network, constructing a dual-objective optimization model taking load loss minimization and power failure time minimization as objectives; and finally, solving for the dual-objective optimization model to generate an optimal regulation and control scheme applied to the distributed energy storage system. Therefore, when a fault has occurred in the power distribution network, the distributed energy storage system is regulated and controlled by means of the optimal regulation and control scheme, such that power supply of the power distribution network is restored as soon as possible in extreme typhoon weather, and the transient power deficit of the power distribution network is made up promptly. Therefore, the present invention can effectively guarantee the safe operation of power distribution networks under the condition of extreme typhoon weather.

Description

Optimal regulation method, device and equipment based on distributed energy storage and storage medium TECHNICAL FIELD

[0001] The present application relates to the field of power systems and automation, and particularly relates to an optimal regulation method, device and equipment based on distributed energy storage and a storage medium. BACKGROUND

[0002] With global climate change, extreme weather disasters are occurring more and more frequently, causing large-scale power outages and huge economic losses and political and social impacts. Representative events include the 2021 Zhengzhou, Henan, 7.20 extreme rainstorm disaster, which caused 42 transformer substations, 1854 10kV and above power transmission and distribution lines, and 36800 power outages, with direct economic losses of 120.06 billion yuan. Extreme weather disasters such as rainstorms and typhoons have typical temporal and spatial random characteristics, and have a serious impact on the reliable operation of power distribution networks and power distribution lines. Because the power distribution network is directly serving the users, it is of great significance to maintain normal operation in extreme disaster conditions to protect people's production and life, resist disaster accidents, and promote social development.

[0003] Distributed energy storage has the advantages of fast response, high flexibility, stable output power, etc., and is mainly used in peak shaving, frequency modulation, emergency backup, etc. in power systems, and can provide new energy consumption, enhance power system stability, etc. and can basically be applied to all stages of source, network and load. In recent years, distributed energy storage participating in different operation scenarios of the system can produce different efficiency, can delay power grid upgrading and transformation, reduce network loss, etc. In the case of extreme disasters, distributed energy storage can serve as an emergency power supply to provide uninterrupted power supply for important users and support power load recovery, improving the resilience of the power distribution network. Therefore, it is very important to consider the support capability of distributed energy storage for the power distribution network in the case of extreme typhoon weather, and reasonable planning of distributed energy storage can ensure the safe operation of the power grid in the case of extreme typhoon weather.

[0004] However, in the current actual optimization and regulation of distributed energy storage, it is usually not possible to scientifically allocate the power in the distributed energy storage in the case of power distribution network failure, that is, it is not possible to recover power supply as soon as possible while making up for the power transient shortage of the power distribution network as soon as possible, so this not only causes the power distribution network to be unable to quickly recover normal operation, but also causes the power in the distributed energy storage to be wasted. Therefore, how to efficiently regulate the distributed energy storage and ensure the safe operation of the power grid in the case of extreme typhoon weather has become a problem to be solved. SUMMARY

[0005] The embodiment of the application provides a distributed energy storage-based optimal regulation method, device, equipment and storage medium, which can scientifically regulate the electric energy in the distributed energy storage, and ensures the safe operation of the power grid in the case of extreme typhoon weather.

[0006] An embodiment of the application provides a distributed energy storage-based optimal regulation method, comprising:

[0007] Obtaining typhoon parameters and constructing a corresponding extreme weather model;

[0008] According to the extreme weather model, the failure probability of the power distribution network element is calculated, and a plurality of failure outage elements are determined;

[0009] According to the failure outage element, the system parameters of the distributed energy storage system, and the operation parameters of the power distribution network, a double-objective optimization model is constructed; wherein the double-objective optimization model comprises: a first objective function for minimizing load loss, and a second objective function for minimizing power outage time;

[0010] The double-objective optimization model is solved by using a hierarchical sequence method, and an optimal regulation scheme applied to the distributed energy storage system is generated.

[0011] Further, the typhoon parameters include: the pressure difference between the peripheral pressure of a tropical cyclone and the central pressure of a typhoon, the typhoon moving speed, and the Coriolis force parameter of the earth rotation;

[0012] The typhoon parameters are obtained, and the corresponding extreme weather model is constructed, comprising:

[0013] According to the typhoon moving speed, the Coriolis force parameter of the earth rotation, and the pressure difference between the peripheral pressure of a tropical cyclone and the central pressure of a typhoon, the maximum wind speed of the typhoon and the maximum wind speed radius of the typhoon are calculated;

[0014] According to the maximum wind speed of the typhoon and the maximum wind speed radius of the typhoon, the extreme weather model is constructed.

[0015] Further, according to the extreme weather model, the failure probability of the power distribution network element is calculated, and a plurality of failure outage elements are determined, comprising:

[0016] According to the extreme weather model, the wind speed and direction on the power distribution network line are calculated, and then the wind load acting on the power distribution network element is determined;

[0017] According to the line strength of the power distribution network and the wind load, the failure probability of the power distribution network element is calculated;

[0018] According to the failure probability of the power distribution network element, a plurality of failure outage elements are determined by using Monte Carlo simulation.

[0019] Further, the double-objective optimization model further comprises a line flow constraint function, a node voltage constraint function, a distributed energy storage output constraint function, a power upper and lower limit constraint function, an energy storage available capacity constraint function, a rated energy storage power and rated capacity constraint function, an energy storage quantity constraint function, an energy storage charging and discharging power constraint function, an energy storage state of charge constraint function, and an important load constraint function.

[0020] Further, the double-objective optimization model is constructed according to the fault outage element, system parameters of the distributed energy storage system, and operation parameters of the power distribution network, and comprises:

[0021] A first objective function of the double-objective optimization model is constructed according to the fault outage element and the operation parameters of the power distribution network.

[0022] A second objective function of the double-objective optimization model is constructed according to the system parameters of the distributed energy storage system.

[0023] Further, the operation parameters of the power distribution network comprise first load data when the power distribution network is normally operated.

[0024] The first objective function of the double-objective optimization model is constructed according to the fault outage element and the operation parameters of the power distribution network, and comprises:

[0025] Second load data of the power distribution network after a fault is calculated according to the fault outage element.

[0026] A power distribution network load curve diagram is constructed according to the first load data and the second load data.

[0027] The first objective function is constructed according to the power distribution network load curve diagram.

[0028] wherein T1 is a first time node at which the power distribution network starts to fail, T4 is a second time node at which the power distribution network starts to recover to normal, L(t) is a first load curve formed by the first load data in the power distribution network load curve diagram, and L1(t) is a second load curve formed by the second load data in the power distribution network load curve diagram.

[0029] Further, the system parameters of the distributed energy storage system comprise a state of charge of each energy storage node, a maximum state of charge of each energy storage node, a number of movable modules of each energy storage node, a maximum state of charge of each movable module, a distance from each energy storage node to a line node on which each fault outage element is located, and a moving speed of the movable module.

[0030] The second objective function of the double-target optimization model is constructed according to the system parameters of the distributed energy storage system, and the second objective function comprises:

[0031] The available module quantity of each energy storage node is determined according to the state of charge of each energy storage node in the distributed energy storage system and the maximum state of charge of each energy storage module in each energy storage node.

[0032] The charging time is determined according to the available module quantity, the maximum module state of charge of each energy storage module, the maximum node state of charge of each energy storage node and the charging efficiency of each energy storage node.

[0033] The power supply time required by each energy storage node is determined according to the distance between each energy storage node and each line node of the fault outage element and the energy storage transmission speed.

[0034] The second objective function is constructed according to the charging time and the power supply time, and the second objective function is as follows:

[0035] f2 = min (maxT) ;

[0036] Wherein, T is the total time required by each energy storage node to supply power to each line node of the fault outage element.

[0037] Another embodiment of the present application provides an optimization control device based on distributed energy storage, comprising:

[0038] A weather model construction module is configured to obtain typhoon parameters and construct a corresponding extreme weather model.

[0039] A fault element determination module is configured to calculate the fault probability of the power distribution network element according to the extreme weather model and determine a plurality of fault outage elements.

[0040] An optimization model construction module is configured to construct a double-target optimization model according to the fault outage elements, the system parameters of the distributed energy storage system and the operation parameters of the power distribution network, wherein the double-target optimization model comprises a first objective function for minimizing load loss and a second objective function for minimizing power outage time.

[0041] A control scheme generation module is configured to solve the double-target optimization model by using a hierarchical sequence method and generate an optimal control scheme applied to the distributed energy storage system.

[0042] Another embodiment of the present application provides a device comprising a processor, a memory and a computer program stored in the memory and configured to be executed by the processor, wherein the processor implements an optimization control method based on distributed energy storage according to any one of the above embodiments when executing the computer program.

[0043] Another embodiment of the present application provides a storage medium comprising a stored computer program, wherein the computer readable storage medium is controlled to perform a distributed energy storage based optimal regulation method as described in any of the above embodiments when the computer program is run.

[0044] By implementing the present application, the following beneficial effects are achieved:

[0045] The present application discloses a distributed energy storage based optimal regulation method, device, equipment and storage medium. An extreme weather model is constructed according to current weather parameters, and then a number of fault outage elements that will fail in the distribution network in the case of extreme typhoon weather are determined. Then, a double-objective optimization model with the minimum load loss and the minimum power outage time as the objectives is constructed according to the fault outage elements, the system parameters of the distributed energy storage system, and the operation parameters of the distribution network. Finally, a hierarchical sequence method is used to solve the double-objective optimization model to generate an optimal regulation scheme for the distributed energy storage system. In the case of a fault, the optimal regulation scheme is used to regulate the distributed energy storage system, so that the distribution network can recover power supply as soon as possible and make up for the power transient shortage of the distribution network as soon as possible in extreme typhoon weather. Therefore, the present application can effectively ensure the safe operation of the distribution network in the case of extreme typhoon weather. BRIEF DESCRIPTION OF DRAWINGS

[0046] Fig. 1 is a flowchart of a distributed energy storage based optimal regulation method according to an embodiment of the present application.

[0047] Fig. 2 is a structural diagram of a distributed energy storage based optimal regulation device according to an embodiment of the present application.

[0048] Fig. 3 is a schematic diagram of a distribution network load curve according to an embodiment of the present application. DETAILED DESCRIPTION

[0049] The technical solutions in the embodiments of the present application will be described clearly and completely below with reference to the accompanying drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, rather than all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative labor fall within the scope of the present application.

[0050] Referring to Fig. 1, which is a flowchart of a distributed energy storage based optimal regulation method according to an embodiment of the present application, the method comprises the following steps:

[0051] S1, obtain typhoon parameters and construct a corresponding extreme weather model;

[0052] Preferably, the typhoon parameters include: a pressure difference between a peripheral pressure of a tropical cyclone and a central pressure of a typhoon, a typhoon moving speed, and a Coriolis force parameter of the earth rotation;

[0053] The typhoon parameters are acquired, and a corresponding extreme weather model is constructed, including:

[0054] S11, according to the typhoon moving speed, the Coriolis force parameter of the earth rotation, and the pressure difference between the peripheral pressure of the tropical cyclone and the central pressure of the typhoon, the maximum wind speed of the typhoon and the maximum wind speed radius of the typhoon are calculated;

[0055] In a preferred embodiment of the present application, the maximum wind speed radius of the typhoon is calculated by the following formula:

[0056] R max = exp (-0.1239Δp 0.6003 + 5.1034);

[0057] Wherein, R max is the maximum wind speed radius of the typhoon, and Δp is the pressure difference between the peripheral pressure of the tropical cyclone and the central pressure of the typhoon.

[0058] The maximum wind speed of the typhoon is calculated by the following formula:

[0059] V max = 0.865v gx + 0.5v T ;

[0060] Wherein, v gx is the maximum gradient wind speed of the typhoon, v gx is the typhoon moving speed, and V max is the maximum wind speed of the typhoon.

[0061] S12, according to the maximum wind speed of the typhoon and the maximum wind speed radius of the typhoon, the extreme weather model is constructed.

[0062] In a preferred embodiment of the present application, based on the advantage of fast operation speed of Batts model, the Batts model is used to quickly construct the extreme weather model corresponding to the current extreme typhoon weather according to the typhoon parameters, and then the wind speed during the typhoon process is determined, and the wind direction is determined as the counterclockwise tangential direction on the simulation circle:

[0063] Wherein, V is the wind speed.

[0064] S2, according to the extreme weather model, the failure probability of the power distribution network element is calculated, and a plurality of failure outage elements are determined;

[0065] Preferably, the method comprises the following steps of: S1, obtaining a weather model of a power distribution network; S2, calculating a failure probability of a power distribution network element according to the weather model, and determining a plurality of failure outage elements; S3, constructing a double-objective optimization model according to the failure outage elements, system parameters of a distributed energy storage system, and operation parameters of the power distribution network; S4, determining a plurality of optimal distributed energy storage system locations according to the double-objective optimization model.

[0066] S21, calculating a wind speed and a wind direction on a line of the power distribution network according to the weather model, and then determining a wind load acting on the power distribution network element;

[0067] In a preferred embodiment of the present application, the wind load acting on the power distribution network element by a typhoon is determined by the following formula:

[0068] wherein D is an outer diameter of a conductor of the power distribution network element, and θ is an included angle between the wind direction and the line.

[0069] S22, calculating the failure probability of the power distribution network element according to the line strength and the wind load;

[0070] In a preferred embodiment of the present application, the failure probability of the power distribution network element is calculated by the following formula based on the principle that the line strength obeys a normal distribution and the line will not fail when the wind load is less than the strength of the line itself:

[0071] wherein Φ is a normal distribution probability function, δ1 is a standard deviation of the line strength, and μ1 is a mean value of the line strength.

[0072] S23, determining a plurality of failure outage elements by Monte Carlo simulation according to the failure probability of the power distribution network element.

[0073] In a preferred embodiment of the present application, Monte Carlo simulation is used to simulate uncertain or random factors by random sampling, and a large number of random samples are generated to comprehensively analyze the problem. Therefore, the method of Monte Carlo simulation is used to extract the failure elements, and then a plurality of failure outage elements are determined.

[0074] S3, constructing a double-objective optimization model according to the failure outage elements, system parameters of a distributed energy storage system, and operation parameters of the power distribution network; wherein the double-objective optimization model comprises a first objective function of minimizing load loss and a second objective function of minimizing power outage time;

[0075] Preferably, the double-objective optimization model further comprises a line power flow constraint function, a node voltage constraint function, a distributed energy storage output constraint function, a power upper and lower limit constraint function, an energy storage available capacity constraint function, a rated energy storage power and rated capacity constraint function, an energy storage quantity constraint function, an energy storage charging and discharging power constraint function, an energy storage state of charge constraint function, and an important load constraint function.

[0076] In a preferred embodiment of the present application, the line flow constraint function is shown as follows:

[0077] where, P ij,t is the active power flow of branch ij; P mij is the upper limit of active power flow of branch ij; Q ij,t is the reactive power flow of branch ij; Q mij is the upper limit of reactive power flow of branch ij; P bi,t is the active power output of distributed energy storage; Q bi,t is the reactive power output of distributed energy storage; P li,t is the active power of load i at time t; Q li,t is the reactive power of load i at time t; x ij is the line 0-1 variable, which is 0 when the line is not faulted; Ω N is the node set of distribution system.

[0078] The node voltage constraint function is shown as follows:

[0079] where, U li is the lower limit of voltage amplitude of node i; U i,t is the voltage amplitude of node i at time t; U mi is the upper limit of voltage amplitude of node i.

[0080] The distributed energy storage output constraint function is shown as follows:

[0081] where, P mbi is the upper limit of active power output of distributed energy storage; Q mbi is the upper limit of reactive power output of distributed energy storage.

[0082] The power upper and lower limit constraint function is shown as follows:

[0083] P minij,t ≤ P k,ij,t ≤ P maxij,t ;

[0084] Q minij,t ≤ Q k,ij,t ≤ Q maxij,t ;

[0085] P Gmini,t ≤ P Gk,i,t ≤ P Gmaxi,t ;

[0086] Q Gmini,t ≤ Q Gk,i,t ≤ Q Gmaxi,t ;

[0087] where, P minij,tand P maxij,t and Q minij,t and P maxij,t and Q k,ij,t and P k,ij,t and P Gmini,t and P Gmaxi,t and Q Gmini,t and Q Gmaxi,t and Q

[0088] The energy storage available capacity constraint function is shown as follows:

[0089] where, N ess is the installation point of the distributed energy storage; β i,m represents whether the energy storage of the installation point i is moved to the node m, and is 0 if not; E essi,m is the energy storage capacity of the installation point i moved to the node m; T m is the minimum power supply time of the node m; P m is the power loss load of the node m.

[0090] The rated energy storage power and rated capacity constraint function is shown as follows:

[0091] where, and are the active power capacity of the energy storage installed at the node i; and are the maximum rated active power and rated capacity of the energy storage allowed to be installed at the node i; σ i is a 0-1 discrete variable, and is 1 if installed.

[0092] The energy storage number constraint function is shown as follows:

[0093] where, N ESS is the maximum number of energy storages allowed to be installed.

[0094] The energy storage charging and discharging power constraint function is shown as follows:

[0095] where, is the energy storage discharging power.

[0096] The energy storage state of charge constraint function is shown as follows:

[0097] wherein S min and S max are minimum and maximum state of charge values of the energy storage system, respectively; is the remaining energy of the energy storage system at time t for node i.

[0098] The important load constraint function is shown as follows:

[0099] wherein L Σ is the sum of load amounts of the important loads; and i is a load importance coefficient, which is divided into 1, 0.2, and 0.05 according to the importance.

[0100] Preferably, the double-objective optimization model is constructed according to the fault outage element, system parameters of the distributed energy storage system, and operation parameters of the power distribution network, and includes:

[0101] S31, constructing a first objective function of the double-objective optimization model according to the fault outage element and the operation parameters of the power distribution network;

[0102] Preferably, the operation parameters of the power distribution network include first load data when the power distribution network is normally operated.

[0103] The first objective function of the double-objective optimization model is constructed according to the fault outage element and the operation parameters of the power distribution network, and includes:

[0104] S311, calculating second load data of the power distribution network after a fault according to the fault outage element;

[0105] S312, constructing a power distribution network load curve diagram according to the first load data and the second load data;

[0106] S313, constructing the first objective function according to the power distribution network load curve diagram:

[0107] wherein T1 is a first time node at which the power distribution network starts to fail, T4 is a second time node at which the power distribution network starts to recover to normal, L(t) is a first load curve formed by the first load data in the power distribution network load curve diagram, and L1(t) is a second load curve formed by the second load data in the power distribution network load curve diagram.

[0108] In a preferred embodiment of the present application, when the power system is affected by an extreme typhoon, a large number of components of the power system fail, resulting in a large-area power outage. As shown in FIG. 3, T0 is the point at which the disaster occurs, T1 represents the time at which the power system begins to operate at a reduced capacity, T2 and T3 represent the beginning and end times of the power system operating at a reduced capacity, respectively, T3-T4 is the recovery stage of the power system, and the power system returns to normal operation after T4.

[0109] Further, under the same extreme weather, the fewer the fault outage components of the distribution network, the faster the power supply is restored, the less the load loss, the smaller the area of the load curve formed by the first load data and the second load data, and the better the transient power shortage support capability of the distribution network. Therefore, a first objective function is constructed to minimize the area of the load curve.

[0110] S32, constructing a second objective function of the double-objective optimization model according to system parameters of the distributed energy storage system.

[0111] Preferably, the system parameters of the distributed energy storage system include the state of charge of each energy storage node, the maximum state of charge of each energy storage node, the number of movable modules of each energy storage node, the maximum state of charge of each movable module, the distance from each energy storage node to each line node on which a fault outage component is located, and the moving speed of the movable module.

[0112] The second objective function of the double-objective optimization model is constructed according to the system parameters of the distributed energy storage system, and includes:

[0113] S321, determining the number of available modules of each energy storage node according to the state of charge of each energy storage node in the distributed energy storage system and the maximum state of charge of each energy storage module in each energy storage node;

[0114] In a preferred embodiment of the present application, the number of available modules of each energy storage node is calculated by the following formula:

[0115] wherein n i,t is the number of available modules of the energy storage node i at time t; SOC i,t is the state of charge of the energy storage node i at time t; SOC ess is the maximum state of charge of the energy storage module.

[0116] S322, determining the charging time according to the number of available modules, the maximum module state of charge of each energy storage module, the maximum node state of charge of each energy storage node, and the charging efficiency of each energy storage node;

[0117] In a preferred embodiment of the present application, the required charging time is calculated by the following formula:

[0118] wherein t c is the required charging time; η c is the charging efficiency of the energy storage node; SOC mi is the maximum state of charge of the i-th energy storage.

[0119] S323, determining the required power supply time of each energy storage node according to the distance between each energy storage node and the line node where each fault outage element is located and the energy storage transmission speed;

[0120] In a preferred embodiment of the present application, the required power supply time of each energy storage node is calculated by the following formula:

[0121] wherein t i,z is the required power supply time of the energy storage node i moving to the line node z where the fault outage element is located; d i,z is the distance to be passed through for the energy storage node i moving to the line node z where the fault outage element is located; and v is the energy storage transmission speed.

[0122] S324, constructing the second objective function according to the charging time and the power supply time:

[0123] f2 = min(maxT);

[0124] wherein T is the total time required for each energy storage node to supply power to the line node where each fault outage element is located.

[0125] In a preferred embodiment of the present application, the total time required for the energy storage node i moving to the line node z where the fault outage element is located is determined by the following formula:

[0126] wherein T is the total time required for each energy storage node to supply power to the line node where each fault outage element is located.

[0127] S4, solving the double-target optimization model by using a hierarchical sequence method to generate an optimal regulation scheme applied to the distributed energy storage system.

[0128] In a preferred embodiment of the present application, the hierarchical sequence method is used for solving, first, the optimal solution of the first objective function f1, that is, only the first objective function is considered, and all sets satisfying the optimal solution are found, and the optimal solution at this time is denoted as f1 * Then, f1 = f1 * is added to the original constraint condition, and the optimal solution of the second objective function f2 is calculated. It should be noted that when solving the optimization model problem, the YALMIP+CPLEX solver in MATLAB can be used for solving.

[0129] The embodiment of the present application provides a kind of optimization control method based on distributed energy storage, by current weather parameter, constructing extreme weather model, then determine the failure of several failure outage components in distribution network in the case of extreme typhoon weather, then according to failure outage component, the system parameter of distributed energy storage system and the operating parameter of distribution network, construct a double-objective optimization model with the minimum load loss and the minimum power outage time as target.Finally, hierarchical sequence method is used to solve the double-objective optimization model, and the optimal control scheme applied to the distributed energy storage system is generated. So that distribution network can restore power supply as soon as possible while making up power transient shortage of distribution network as soon as possible in extreme typhoon weather by the optimal control scheme for distributed energy storage system in the case of failure. Therefore, the present application can effectively ensure the safe operation of distribution network in the case of extreme typhoon weather.

[0130] Referring to Figure 2, it is a structure schematic diagram of an optimization control device based on distributed energy storage provided by an embodiment of the present application, comprising:

[0131] Weather model construction module, for obtaining typhoon parameter, constructing corresponding extreme weather model;

[0132] Failure component determination module, for calculating the failure probability of distribution network component according to the extreme weather model, and determining several failure outage components;

[0133] Optimization model construction module, for constructing a double-objective optimization model according to the failure outage component, the system parameter of distributed energy storage system and the operating parameter of distribution network;Wherein, the double-objective optimization model includes: the first objective function with the minimum load loss, and the second objective function with the minimum power outage time;

[0134] Control scheme generation module, for solving the double-objective optimization model by hierarchical sequence method, and generating the optimal control scheme applied to the distributed energy storage system.

[0135] The embodiment of the present application provides an optimal regulation and control device based on distributed energy storage, which constructs an extreme weather model according to current weather parameters, then determines a plurality of fault outage elements which will be faulty in a distribution network in the case of extreme typhoon weather, then constructs a double-target optimization model with the minimum load loss and the minimum power-off time as the targets according to the fault outage elements, system parameters of the distributed energy storage system and operation parameters of the distribution network. Finally, a hierarchical sequence method is adopted to solve the double-target optimization model, and an optimal regulation and control scheme applied to the distributed energy storage system is generated. In the case of failure, the optimal regulation and control scheme is used to regulate and control the distributed energy storage system, so that the distribution network can restore power supply as soon as possible and make up for the power transient shortage of the distribution network as soon as possible in the extreme typhoon weather. Therefore, the present application can effectively ensure the safe operation of the distribution network in the case of extreme typhoon weather.

[0136] It should be noted that the device embodiments described above are only schematic, wherein the units described as separate components can or can not be physically separate, and the components displayed as units can or can not be physical units, that is, they can be located in one place, or distributed on multiple network units. Part or all of the modules can be selected according to actual needs to achieve the purpose of the present embodiment. In addition, the connection relationship between the modules in the device embodiment provided by the present application indicates that there is a communication connection between them, which can be realized as one or more communication buses or signal lines. Those skilled in the art can understand and implement it without creative labor.

[0137] Those skilled in the art can clearly understand that, in order to facilitate and be brief, the specific working process of the above-described device can refer to the corresponding process in the foregoing method embodiments, which will not be described here.

[0138] Another embodiment of the present application provides a device comprising a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor, wherein the processor implements the optimal regulation and control method based on distributed energy storage according to any one of the above embodiments when executing the computer program. The device can be a desktop computer, a notebook, a palm computer, a cloud server and other computing devices. The device can include, but is not limited to, a processor and a memory.

[0139] The processor can be a central processing unit (CPU), and can also be other general-purpose processors, digital signal processors (DSP), application specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gates or transistor logic components, discrete hardware components, etc. The general-purpose processor can be a microprocessor or the processor can also be any conventional processor and the like, which is the control center of the device and connects all parts of the device through various interfaces and lines.

[0140] The memory can be used to store the computer program, and the processor realizes various functions of the device by running or executing the computer program stored in the memory and calling the data stored in the memory. The memory can mainly include a program storage area and a data storage area, wherein the program storage area can store an operating system, at least one application required by a function, etc.; and the data storage area can store data created according to the use of the mobile phone, etc. In addition, the memory can include a high-speed random access memory, and can also include a non-volatile memory, for example, a hard disk, a memory, a plug-in hard disk, a smart media card (SMC), a secure digital (SD) card, a flash card, at least one disk storage device, a flash memory device, or other volatile solid-state memory devices.

[0141] Another embodiment of the present application provides a storage medium, which is a computer readable storage medium, and a computer program is stored in the computer readable storage medium. The computer program can realize the steps of each method embodiment described above when executed by a processor. The computer program includes computer program code, which can be in the form of source code, object code, executable files or some intermediate forms, etc. The computer readable medium can include any entity or device capable of carrying the computer program code, recording medium, U disk, mobile hard disk, magnetic disk, optical disk, computer memory, read-only memory (ROM), random access memory (RAM), electrical carrier signals, telecommunication signals and software distribution medium, etc.

[0142] The above is the preferred embodiment of the present application, it should be pointed out that, for those skilled in the art, without departing from the principles of the present application, can also make a number of improvements and refinements, these improvements and refinements are also considered to be within the scope of the present application.

Claims

1. A method for optimal regulation based on distributed energy storage, characterized in that, The method comprises the following steps: acquiring typhoon parameters and constructing a corresponding extreme weather model; calculating the failure probability of power grid elements according to the extreme weather model, and determining a plurality of failure outage elements; constructing a double-objective optimization model according to the failure outage elements, system parameters of a distributed energy storage system, and operation parameters of the power grid; wherein the double-objective optimization model comprises a first objective function for minimizing load loss and a second objective function for minimizing power outage time; solving the double-objective optimization model by using a hierarchical sequence method to generate an optimal regulation scheme for the distributed energy storage system.

2. The method of claim 1, wherein, The typhoon parameters include the pressure difference between the peripheral pressure of a tropical cyclone and the central pressure of a typhoon, the moving speed of the typhoon, and the Coriolis force parameter of the earth rotation; The step of acquiring typhoon parameters and constructing a corresponding extreme weather model comprises the following steps: calculating the maximum wind speed of the typhoon and the maximum wind speed radius of the typhoon according to the moving speed of the typhoon, the Coriolis force parameter of the earth rotation, and the pressure difference between the peripheral pressure of a tropical cyclone and the central pressure of a typhoon; constructing the extreme weather model according to the maximum wind speed of the typhoon and the maximum wind speed radius of the typhoon.

3. The method of claim 2, wherein, The step of calculating the failure probability of power grid elements according to the extreme weather model, and determining a plurality of failure outage elements comprises the following steps: calculating the wind speed and direction on the lines of the power grid according to the extreme weather model, and then determining the wind load acting on the power grid elements; calculating the failure probability of the power grid elements according to the line strength of the power grid and the wind load; determining a plurality of failure outage elements by using Monte Carlo simulation according to the failure probability of the power grid elements.

4. The method of claim 1, wherein, The double-objective optimization model further comprises a line flow constraint function, a node voltage constraint function, a distributed energy storage output constraint function, a power upper and lower limit constraint function, an energy storage available capacity constraint function, a rated energy storage power and rated capacity constraint function, an energy storage quantity constraint function, an energy storage charging and discharging power constraint function, an energy storage state of charge constraint function, and an important load constraint function.

5. The method of claim 3, wherein the method further comprises: The step of constructing a double-objective optimization model according to the failure outage elements, system parameters of a distributed energy storage system, and operation parameters of the power grid comprises the following steps: constructing the first objective function of the double-objective optimization model according to the failure outage elements and the operation parameters of the power grid; constructing the second objective function of the double-objective optimization model according to the system parameters of the distributed energy storage system.

6. The method of claim 3, wherein the method further comprises: The operation parameters of the power grid include first load data when the power grid is normally operated; The step of constructing the first objective function of the double-objective optimization model according to the failure outage elements and the operation parameters of the power grid comprises the following steps: calculating second load data of the power grid after failure according to the failure outage elements; constructing a power grid load curve graph according to the first load data and the second load data; According to the power distribution network load curve, the first objective function is constructed: Wherein, T1 is the first time node when the power distribution network starts to fail, T4 is the second time node when the power distribution network starts to recover, L(t) is a first load curve formed by the first load data in the power distribution network load curve graph, and L1(t) is a second load curve formed by the second load data in the power distribution network load curve graph.

7. The method of claim 5, wherein the method further comprises: The system parameters of the distributed energy storage system include: state of charge of each energy storage node, maximum state of charge of each energy storage node, number of movable modules of each energy storage node, maximum state of charge of each movable module, distance from each energy storage node to a line node where each fault outage element is located, and moving speed of the movable module; The second objective function of the double-objective optimization model is constructed according to the system parameters of the distributed energy storage system, including: The number of available modules of each energy storage node is determined according to the state of charge of each energy storage node and the maximum state of charge of each energy storage module in each energy storage node; The charging time is determined according to the number of available modules, the maximum module state of charge of each energy storage module, the maximum node state of charge of each energy storage node, and the charging efficiency of each energy storage node; The power supply time required by each energy storage node is determined according to the distance from each energy storage node to a line node where each fault outage element is located and the energy storage transmission speed; The second objective function is constructed according to the charging time and the power supply time: f2 = min(maxT); Wherein, T is the total time required by each energy storage node to supply power to a line node where each fault outage element is located.

8. An optimal regulation device based on distributed energy storage, characterized in that, It includes: A weather model construction module is configured to obtain typhoon parameters and construct a corresponding extreme weather model; A fault element determination module is configured to calculate the failure probability of power distribution network elements according to the extreme weather model and determine a plurality of fault outage elements; An optimization model construction module is configured to construct a double-objective optimization model according to the fault outage elements, system parameters of the distributed energy storage system, and operation parameters of the power distribution network; wherein the double-objective optimization model includes a first objective function for minimizing load loss and a second objective function for minimizing power outage time; A regulation scheme generation module is configured to solve the double-objective optimization model by using a hierarchical sequence method and generate an optimal regulation scheme for the distributed energy storage system.

9. An apparatus, comprising: It includes a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor, and the processor implements the optimization regulation method based on the distributed energy storage according to any one of claims 1-7 when executing the computer program.

10. A storage medium, characterized by The storage medium includes a stored computer program, wherein the computer readable storage medium controls the device to execute the optimization regulation method based on the distributed energy storage according to any one of claims 1-7 when the computer program runs.

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