CN116885795A - Microgrid power grid load storage collaborative dispatching method, device, equipment and storage medium - Google Patents

Abstract

The embodiment of the disclosure provides a micro-grid source network load storage collaborative scheduling method, device and equipment and a storage medium, which are applied to the technical field of micro-grid operation analysis. The method comprises the following steps: acquiring historical operation data of a micro-grid; solving a micro-grid source network load storage collaborative scheduling model according to historical operation data by adopting a war strategy optimization algorithm; generating a micro-grid source network load storage collaborative scheduling strategy according to the solving result; and carrying out source network load storage collaborative scheduling on the micro-grid according to the micro-grid source network load storage collaborative scheduling strategy. In this way, the method can generate the source-network-charge-storage cooperative scheduling strategy of the micro-grid under the cooperative action of the source-network-charge-storage multi-energy elements based on the historical operation data of the micro-grid, and perform source-network-charge-storage cooperative scheduling on the micro-grid on the basis, so that the efficient consumption of renewable energy sources is promoted to the greatest extent, the carbon emission is reduced, and the electricity quality of users is ensured.

Description

Microgrid power grid load storage collaborative dispatching method, device, equipment and storage medium

Technical field

The present disclosure relates to the technical field of microgrid operation analysis, and in particular to a microgrid source, network, load and storage collaborative dispatching method, device, equipment and storage medium.

Background technique

With the development of new power systems and the advancement of dual-carbon goals, a high proportion of wind and solar power has penetrated into the grid, and the original grid topology has become more complex. Microgrid is a small source-grid-load-storage system that combines distributed energy, loads and energy storage devices. It is an effective means to absorb distributed energy such as wind power and photovoltaics and reduce carbon emissions. Therefore, it is of great significance to study the coordinated dispatch of microgrid power grid, load and storage, including new energy and carbon emissions, to reduce the operating costs of the distribution system and promote the implementation of dual carbon goals. However, at present, the collaborative dispatching efficiency of microgrid power grid, load and storage at home and abroad generally suffers from poor efficiency. Therefore, how to improve the efficiency of collaborative dispatching of microgrid power grid, load and storage has become an urgent technical problem that needs to be solved.

Contents of the invention

Embodiments of the present disclosure provide a microgrid power grid load-storage collaborative scheduling method, device, equipment and storage medium.

In the first aspect, embodiments of the present disclosure provide a microgrid power grid load-storage collaborative dispatching method, which method includes:

Obtain historical operating data of microgrid;

The war strategy optimization algorithm is used to solve the microgrid power grid load-storage collaborative dispatch model based on historical operating data;

Generate a microgrid power grid load-storage collaborative dispatching strategy based on the solution results;

According to the microgrid source, grid, load and storage collaborative dispatching strategy, the microgrid is subject to source, grid, load and storage collaborative dispatching.

In some possible implementation methods of the first aspect, the historical operating data includes: historical line operating data, historical wind power output data, historical photovoltaic output data, historical micro gas turbine output data, and historical battery data.

Among some possible implementation methods of the first aspect, the war strategy optimization algorithm is used to solve the microgrid power grid load-storage collaborative dispatch model based on historical operating data, including:

Based on the historical operating data, the microgrid power grid load-storage collaborative dispatch model is initially solved and the initial solution is obtained;

The war strategy optimization algorithm is used to continuously iteratively optimize the initial solution to obtain the optimal solution.

Among some possible implementation methods of the first aspect, when using the war strategy optimization algorithm to continuously iteratively optimize the initial solution, the weak soldier update strategy of the war strategy optimization algorithm is:

In the early stage of iterative optimization, weak soldiers are randomly updated;

In the middle stage of iterative optimization, while randomly updating weak soldiers, the weak soldiers will gradually move to the middle of the battlefield;

In the later stage of iterative optimization, place weak soldiers in the middle of the battlefield;

Iterative optimization passed early stage Indicates that the iterative optimization is passed in the middle stage/> Indicates that it will be passed in the later stage of iterative optimization/> means, t represents the number of iterations, and T represents the total number of iterations.

Among some possible implementation methods of the first aspect, the microgrid power grid load-storage collaborative dispatch model includes: a microgrid system model and a multi-objective optimization model.

In some implementable ways of the first aspect, the microgrid system model includes: photovoltaic power generation model, wind power generation model, micro gas turbine power generation model, and battery model;

The multi-objective optimization model takes minimizing the operating cost of the microgrid system, minimizing the operating voltage deviation of the microgrid system, and minimizing the carbon emission cost of the microgrid system as objective functions, and takes the microgrid power balance constraints, micro gas turbine ramping constraints, and distributed power sources as the objective functions. Power constraints, energy interaction constraints between microgrid and large grid, battery charging and discharging constraints, and node voltage constraints are the constraints.

In the second aspect, embodiments of the present disclosure provide a microgrid power grid load-storage coordinated dispatching device, which includes:

The acquisition module is used to obtain historical operating data of the microgrid;

The solving module is used to use the war strategy optimization algorithm to solve the microgrid power grid load-storage collaborative dispatch model based on historical operating data;

The generation module is used to generate the microgrid power grid load-storage collaborative dispatching strategy based on the solution results;

The dispatching module is used to perform source-grid-load-storage coordinated dispatching on the microgrid according to the microgrid source-grid-load-storage coordinated dispatching strategy.

In some implementable ways of the second aspect, the solving module is specifically used for:

Based on the historical operating data, the microgrid power grid load-storage collaborative dispatch model is initially solved and the initial solution is obtained;

The war strategy optimization algorithm is used to continuously iteratively optimize the initial solution to obtain the optimal solution.

In a third aspect, embodiments of the present disclosure provide an electronic device, which includes: at least one processor; and a memory communicatively connected to the at least one processor; the memory stores instructions that can be executed by the at least one processor, The instructions are executed by at least one processor to enable at least one processor to perform the above-described method.

In a fourth aspect, embodiments of the present disclosure provide a non-transitory computer-readable storage medium storing computer instructions, the computer instructions being used to cause a computer to perform the above-described method.

In embodiments of the present disclosure, a microgrid source-grid-load-storage coordinated dispatching strategy can be generated based on the historical operating data of the microgrid and under the synergy of source-grid-load-storage multiple energy elements, and on this basis, the microgrid can be The power grid performs coordinated dispatching of sources, grids, and loads to maximize the efficient consumption of renewable energy, reduce carbon emissions, and ensure the quality of electricity for users.

It should be understood that what is described in this summary is not intended to identify key or important features of the embodiments of the disclosure, nor to limit the scope of the disclosure. Other features of the present disclosure will become apparent from the description below.

Description of the drawings

The above and other features, advantages and aspects of various embodiments of the present disclosure will become more apparent with reference to the following detailed description taken in conjunction with the accompanying drawings. The drawings are used to better understand the present solution and do not constitute a limitation of the present disclosure. In the drawings, the same or similar reference numbers represent the same or similar elements, where:

Figure 1 shows a flow chart of a microgrid power grid load-storage coordinated dispatching method provided by an embodiment of the present disclosure;

Figure 2 shows a structural diagram of a microgrid power grid load-storage coordinated dispatching device provided by an embodiment of the present disclosure;

3 shows a block diagram of an exemplary electronic device capable of implementing embodiments of the present disclosure.

Detailed ways

In order to make the purpose, technical solutions and advantages of the embodiments of the present disclosure clearer, the technical solutions in the embodiments of the present disclosure will be clearly and completely described below in conjunction with the accompanying drawings in the embodiments of the present disclosure. Obviously, the description The embodiments are part of the embodiments of the present disclosure, rather than all of them. Based on the embodiments in this disclosure, all other embodiments obtained by those of ordinary skill in the art without creative efforts fall within the scope of protection of this disclosure.

In addition, the term "and/or" in this article is only an association relationship that describes related objects, indicating that there can be three relationships. For example, A and/or B can mean: A alone exists, and A and B exist simultaneously. There are three cases of B alone. In addition, the character "/" in this article generally indicates that the related objects are an "or" relationship.

In view of the problems that arise in the background technology, embodiments of the present disclosure provide a microgrid power grid load-storage collaborative scheduling method, device, equipment and storage medium. Specifically, the historical operating data of the microgrid is obtained; the war strategy optimization algorithm is used to solve the microgrid power grid load-storage collaborative dispatching model based on the historical operating data; the microgrid power grid load-storage collaborative dispatching strategy is generated based on the solution results; according to the microgrid The power grid source, grid, load and storage coordinated dispatching strategy implements source, grid, load and storage coordinated dispatching for the microgrid.

In this way, based on the historical operating data of the microgrid, under the synergy of the source-grid-load-storage multiple energy elements, a microgrid source-grid-load-storage collaborative dispatching strategy can be generated, and on this basis, the source-grid management of the microgrid can be carried out. Load-storage coordinated dispatching can promote the efficient consumption of renewable energy to the greatest extent, reduce carbon emissions, and ensure the quality of electricity for users.

The microgrid power grid load-storage coordinated dispatching method, device, equipment and storage medium provided by embodiments of the present disclosure will be described in detail below with reference to the accompanying drawings through specific embodiments.

Figure 1 shows a flow chart of a microgrid power grid, load and storage collaborative scheduling method provided by an embodiment of the present disclosure. As shown in Figure 1, the microgrid power grid, load and storage collaborative scheduling method 100 may include the following steps:

S110: Obtain historical operating data of the microgrid.

Among them, historical operating data can include: historical line operating data, historical wind power output data, historical photovoltaic output data, historical micro gas turbine output data, and historical battery data.

S120, use the war strategy optimization algorithm to solve the microgrid power grid load-storage collaborative dispatch model based on historical operating data.

Among them, the microgrid power grid load-storage collaborative dispatch model can include: microgrid system model and multi-objective optimization model.

The microgrid system model can include: photovoltaic power generation model, wind power generation model, micro gas turbine power generation model, and battery model, as shown below:

(1) Photovoltaic power generation model

Affected by the position of the sun, the longitude and latitude of the power station, altitude, meteorological factors, etc., PV output is volatile and random. Its maximum output power is:

P _Pv,max =E _max Aη (1)

Among them, P _pv,max is the maximum output power of PV in the time period; E _max is the maximum light intensity in the time period; A and eta are the PV array area and photoelectric conversion efficiency respectively.

According to the PV output characteristics, Beta distribution is used to model it:

Among them, P _pv is the actual output power of PV; Γ is the Gamma function; α and β are the shape parameters of the Beta distribution respectively.

(2) Wind power generation model

Considering the intermittency and randomness of wind energy, the basis of WT output power prediction is its cumulative probability distribution. A large amount of weather data shows that the randomness of wind energy can be described by the Weibull distribution. The Weibull probability density function of wind speed is:

Among them, v is the actual wind speed; b is the shape parameter, ranging from 1.8 to 2.8; a is the scale parameter, which reflects the average wind speed in a certain time period.

The functional relationship between the active power output of WT and wind speed is as follows:

Among them, P _WT is the active power output of WT; v _r , _ve , and _ve are the cut-in, cut-out and rated speed of wind speed respectively; P _e is the maximum power output by WT.

(3) Micro gas turbine power generation model

Micro gas turbines have high efficiency, low pollution, and are dispatchable micro sources. Their actual output power is affected by many factors, such as the low calorific value of the fuel. The relationship between its operating efficiency and output power is as follows:

Among them, eta _MT and P _MT are the operating efficiency and output power of the micro gas turbine respectively.

The relationship between the output power P _MT of the micro gas turbine and the operation and management cost C _MT is as follows:

Among them, K _MT.OM is the unit operation and maintenance cost coefficient of micro gas turbine; T is a dispatch cycle, which is 24 hours.

(4)Battery model

When the new energy power generation meets the load demand, the battery is used to store the remaining power and serve as a power supply together with other equipment during peak periods, reducing wind and light curtailment. The battery state of charge in each period is:

Among them, S _OC (t) is the state of charge of the battery at time t; S _OC (t-1) is the state of charge of the battery at time t-1; λ is the self-discharge coefficient, which is 0.001; P _cha is the charging power; P _dis is the discharge power; Δt is the time period; μ _cha is the charging efficiency, which is taken as 0.95; μ _dis is the discharge efficiency, which is taken as 0.95; C _t is the capacity of the battery at time t.

The multi-objective optimization model takes minimizing the operating cost of the microgrid system, minimizing the operating voltage deviation of the microgrid system, and minimizing the carbon emission cost of the microgrid system as objective functions, and takes the microgrid power balance constraints, micro gas turbine ramping constraints, and distributed power sources as the objective functions. Power constraints, energy interaction constraints between microgrid and large grid, battery charging and discharging constraints, and node voltage constraints are the constraints.

Minimizing the operating cost of the microgrid system, minimizing the operating voltage deviation of the microgrid system, and minimizing the carbon emission cost of the microgrid system can be as follows:

(1) Minimizing the operating cost of the microgrid system

The objective function is to minimize the operating cost of the microgrid system, including fuel costs, operating costs of each unit of the microgrid, green certificate-carbon transaction costs, electricity transaction costs between microgrids and large grids, wind and light abandonment costs, and pollutant emission penalties. Cost, specifically as follows:

Among them, F is the operating cost of the microgrid system; T is a dispatching period, which is taken as 24h; P _nt is the power generated by WT, PV, MT, BAT charging and discharging power, the power traded with the large power grid, and the power consumed by P2G and CCUS; C _{on, i} is the operation and maintenance coefficient of microgrid equipment; C _gas , are the price of natural gas and the natural gas _{consumed by micro gas turbines} respectively; Photoelectricity.

(2) Minimize the operating voltage deviation of the microgrid system

The high penetration rate of distributed power supply connected to the microgrid is conducive to increasing the node voltage, reducing the voltage deviation between nodes, and stabilizing the voltage level. When carrying out the operation planning and parallel planning of the "source-grid-load-storage" microgrid, it is necessary to make the operating voltage deviation of the microgrid system as small as possible to improve the voltage quality. In engineering practice, the average deviation between the actual voltage amplitude of the node and the rated value can be used to represent the operating voltage deviation of the microgrid system:

Among them, ΔU is the node average voltage deviation, representing the operating voltage deviation level of the microgrid system; N is the number of nodes in the microgrid system; U _i and U _iref are respectively the actual voltage amplitude and rated voltage of node i after the distributed power supply is connected. Amplitude, U _iref is generally 1.0pu.

(3) Minimizing carbon emission costs of microgrid systems

For traditional energy units, it will incur additional carbon trading costs,

Among them, F _c is the carbon emission cost of the microgrid system; T is a dispatching period, which is 24 hours; P _t is the transaction power between the microgrid and the large power grid; C is the carbon emission price per unit of electricity in the large power grid; S is the actual carbon emissions; M is the free carbon emission allowance; c is the unit carbon trading price.

It is worth noting that when minimizing the operating cost of the microgrid system, minimizing the operating voltage deviation of the microgrid system, and minimizing the carbon emission cost of the microgrid system as the objective functions, each objective function needs to be standardized. For example, it is necessary to Take the maximum value of the microgrid system operating cost, microgrid system operating voltage deviation, and microgrid system carbon emission cost in the iterative process as the benchmark value, and normalize them, that is:

The unit value is:

Among them, the subscript B indicates the base value, and the subscript * indicates the unit value.

The microgrid power balance constraints, micro gas turbine ramping constraints, distributed power supply power constraints, microgrid and large grid energy interaction constraints, battery charge and discharge constraints, and node voltage constraints can be as follows:

(1) Microgrid power balance constraints

P _WT +P _PV +P _MT +P _ESS +P _gird =P _L (13)

Among them, P _WT , P _PV , P _MT , P _ESS , and _PL are the output powers of WT, PV, MT, fuel cell, and battery respectively.

(2) Micro gas turbine climbing constraints

R _i,min Δt≤P _i,t -P _i,t-1 ≤R _i,max Δt (14)

Among them, R _i,min and R _i,max are the upper and lower limits of the output active power respectively.

(3) Distributed power supply power constraints

P _i,min ≤P _i ≤P _i,max (15)

Among them, Pi _,min and Pi _,max are the minimum and maximum power of the i-th distributed power supply respectively.

(4) Energy interaction constraints between microgrid and large grid

P _gird,min ≤P _gird ≤P _gird,max (16)

Among them, P _gird,min and P _gird,max are the minimum and maximum interactive power between the microgrid and the large grid respectively.

(5) Battery charging and discharging constraints

S _min ≤S _t ≤S _max

S _o = S _T

X _t ·Y _t =0

0≤P _cha,t ≤0.2E _b,n X _t

0≤P _dis,t ≤0.2E _b,n Y _t

Among them, S _t , S _max , S _min are the battery state of charge and its upper and lower limits respectively; S _o and S _T are the initial and final state of charge of the battery on the day respectively; X _T and Y _t are the battery charge and discharge states respectively, where, _t ∈{0,1}, Y _t ∈{0,1}; P _cha,t and P _dis,t are the charging and discharging power of the battery at time t respectively; E _b,n is the battery capacity; N ₁ and N ₂ are the battery Maximum number of charge and discharge times.

(6) Node voltage constraints

V _i,min ≤V _i ≤V _i,max (18)

Among them, V _i,min and V _i,max are the minimum allowable voltage amplitude value and the maximum allowable voltage amplitude value of node i respectively.

In some embodiments, the microgrid power grid load-storage cooperative dispatch model can be initially solved based on historical operating data to obtain the initial solution. Then a war strategy optimization algorithm is used to continuously iteratively optimize the initial solution to obtain the optimal solution, which is about to The initial solution is used as the army in the war strategy optimization algorithm, and it is continuously optimized through iteration to quickly obtain the optimal solution.

As an example, the war strategy optimization algorithm used here can look like this:

(1) Offensive strategy

Soldiers adjust their positions according to the positions of the king and commander. This is the main strategy for soldier position update. The details can be as follows:

X _i (t+1)＝X _i (t)+2·rand·(C-King)+rand·(W _i ·King-X _i (t)) (19)

_{Among them ,} The weight of the position.

(2) Military rank and weight update

Soldiers give priority to better positions on the battlefield. Soldiers' ranks increase as their combat effectiveness increases. The details can be as follows:

X _i (t+1)＝(X _i (t+1))·(F _n ≥F _p )+(X _i (t))·(F _n <F _p )

R _i =(R _i +1)·(F _n ≥F _p )+(R _i )·(F _n <F _p ) (20)

Among them, F _n is the combat effectiveness of the soldier in the new position, F _p is the combat effectiveness of the soldier in the old position, and R _i is the rank of the i-th soldier.

To rank soldiers according to their effectiveness, the updated weights can be as follows:

W _i =W _i ·(1-R _i /T) ^α (21)

Among them, T is the total number of iterations, and α is the exponential factor.

The weight factor _Wi is closely related to the search ability of the algorithm. In order to improve the global search ability in the early stage and the convergence ability in the later stage, the exponential factor is set to be dynamic. The details can be as follows:

α=2(1-e ^-t/T ) (22)

Among them, t is the number of iterations of the algorithm.

(3) Defense strategy

The soldier measures the distance between him and the king to protect the king's safety during the war. The details can be as follows:

X _i (t+1)＝X _i (t)+2·rand·(King-X _rand (t))+rand·W _i ·(CX _i (t)) (23)

Among them, X _rand (t) is the random position of the soldier in the t-th iteration.

(4) Weak soldier update strategy (i.e. weak soldier replacement/repositioning strategy)

Two strategies are used to update the position of weak soldiers. One is to randomly generate new soldiers through formula (24), and the other is to place weak soldiers in the middle of the entire battlefield through formula (25):

X _w (t+1)＝Lb+rand·(Ub-Lb) (24)

X _w (t+1)＝-(1-randn)·(X _w (t)-median(X))+King (25)

Among them, X _w (t+1) is the soldier replaced in the t+1 iteration, and Ub and Lb represent the upper limit and lower limit of the search space. randn is a random number uniformly distributed between 0 and 1, and median(X) represents the median performance.

It is worth noting that in order to improve the optimization capability of the war strategy optimization algorithm, another new weak soldier update strategy is proposed, specifically:

In the early stage of iterative optimization, weak soldiers are randomly updated;

In the middle stage of iterative optimization, while randomly updating weak soldiers, the weak soldiers will gradually move to the middle of the battlefield;

In the later stages of iterative optimization, place weak soldiers in the middle of the battlefield.

Among them, iterative optimization passed in the early stage Indicates that the iterative optimization is passed in the middle stage/> Indicates that it will be passed in the later stage of iterative optimization/> means, t represents the number of iterations, and T represents the total number of iterations.

For example, it can be shown as formula (26):

Among them, β is the time-weighted search factor,

S130: Generate a microgrid power grid load-storage coordinated dispatching strategy based on the solution results.

S140. According to the microgrid source, grid, load and storage collaborative dispatching strategy, perform source, grid, load and storage collaborative dispatching on the microgrid.

For example, the microgrid source, grid, load and storage coordinated dispatching strategy can be sent to the intelligent dispatching center to which the microgrid belongs, and the intelligent dispatching center performs the source, grid, load and storage coordinated dispatching of the microgrid.

In embodiments of the present disclosure, a microgrid source-grid-load-storage coordinated dispatching strategy can be generated based on the historical operating data of the microgrid and under the synergy of source-grid-load-storage multiple energy elements, and on this basis, the microgrid can be The power grid performs coordinated dispatching of sources, grids, and loads to maximize the efficient consumption of renewable energy, reduce carbon emissions, and ensure the quality of electricity for users.

It should be noted that for the sake of simple description, the foregoing method embodiments are expressed as a series of action combinations. However, those skilled in the art should know that the present disclosure is not limited by the described action sequence. Because in accordance with the present disclosure, certain steps may be performed in other orders or simultaneously. Secondly, those skilled in the art should also know that the embodiments described in the specification are optional embodiments, and the actions and modules involved are not necessarily necessary for the present disclosure.

The above is an introduction to the method embodiments. The solutions described in the present disclosure will be further described below through device embodiments.

Figure 2 shows a structural diagram of a microgrid power grid, load and storage coordinated dispatching device provided by an embodiment of the present disclosure. As shown in Figure 2, the microgrid power grid, load and storage coordinated dispatching device 200 may include:

The acquisition module 210 is used to acquire historical operating data of the microgrid;

The solving module 220 is used to use the war strategy optimization algorithm to solve the microgrid power grid load-storage collaborative dispatch model based on the historical operating data;

The generation module 230 is used to generate a microgrid power grid load-storage coordinated dispatching strategy based on the solution results;

The scheduling module 240 is configured to perform source-network-load-storage collaborative scheduling on the microgrid according to the microgrid source-grid-load-storage collaborative scheduling strategy.

In some embodiments, the solution module 220 is specifically configured to:

Perform an initial solution to the microgrid power grid load-storage collaborative dispatch model based on the historical operating data to obtain an initial solution;

The war strategy optimization algorithm is used to continuously iteratively optimize the initial solution to obtain the optimal solution.

It can be understood that each module/unit in the microgrid power grid load-storage coordinated dispatching device 200 shown in Figure 2 has the function of realizing each step in the microgrid power grid load-storage coordinated dispatching method 100 shown in Figure 1, And can achieve its corresponding technical effects. For the sake of simplicity, they will not be repeated here.

3 shows a block diagram of an exemplary electronic device capable of implementing embodiments of the present disclosure. Electronic device 300 is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. Electronic device 300 may also represent various forms of mobile devices, such as personal digital assistants, cellular phones, smart phones, wearable devices, and other similar computing devices. The components shown herein, their connections and relationships, and their functions are examples only and are not intended to limit implementations of the disclosure described and/or claimed herein.

As shown in FIG. 3 , the electronic device 300 may include a computing unit 301 that may be configured according to a computer program stored in a read-only memory (ROM) 302 or loaded from a storage unit 308 into a random access memory (RAM) 303 . to perform various appropriate actions and processing. In the RAM 303, various programs and data required for the operation of the electronic device 300 can also be stored. Computing unit 301, ROM 302 and RAM 303 are connected to each other via bus 304. An input/output (I/O) interface 305 is also connected to bus 304.

Multiple components in the electronic device 300 are connected to the I/O interface 305, including: an input unit 306, such as a keyboard, a mouse, etc.; an output unit 307, such as various types of displays, speakers, etc.; a storage unit 308, such as a magnetic disk, an optical disk, etc. etc.; and communication unit 309, such as network card, modem, wireless communication transceiver, etc. The communication unit 309 allows the electronic device 300 to exchange information/data with other devices through a computer network such as the Internet and/or various telecommunications networks.

Computing unit 301 may be a variety of general and/or special purpose processing components having processing and computing capabilities. Some examples of the computing unit 301 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various dedicated artificial intelligence (AI) computing chips, various computing units that run machine learning model algorithms, digital signal processing processor (DSP), and any appropriate processor, controller, microcontroller, etc. Computing unit 301 performs various methods and processes described above, such as method 100 . For example, in some embodiments, method 100 may be implemented as a computer program product, including a computer program, tangibly embodied in a computer-readable medium, such as storage unit 308. In some embodiments, part or all of the computer program may be loaded and/or installed onto device 300 via ROM 302 and/or communication unit 309. When the computer program is loaded into RAM 303 and executed by computing unit 301, one or more steps of method 100 described above may be performed. Alternatively, in other embodiments, computing unit 301 may be configured to perform method 100 in any other suitable manner (eg, by means of firmware).

Various implementations described above may be implemented in digital electronic circuit systems, integrated circuit systems, field programmable gate arrays (FPGAs), application specific integrated circuits (ASICs), application specific standard products (ASSPs), systems on a chip (SOCs), loads Implemented in programmable logic devices (CPLDs), computer hardware, firmware, software, and/or combinations thereof. These various embodiments may include implementation in one or more computer programs executable and/or interpreted on a programmable system including at least one programmable processor, the programmable processor The processor, which may be a special purpose or general purpose programmable processor, may receive data and instructions from a storage system, at least one input device, and at least one output device, and transmit data and instructions to the storage system, the at least one input device, and the at least one output device. An output device.

Program code for implementing the methods of the present disclosure may be written in any combination of one or more programming languages. These program codes may be provided to a processor or controller of a general-purpose computer, special-purpose computer, or other programmable data processing device, such that the program codes, when executed by the processor or controller, cause the functions specified in the flowcharts and/or block diagrams/ The operation is implemented. The program code may execute entirely on the machine, partly on the machine, as a stand-alone software package, partly on the machine and partly on a remote machine or entirely on the remote machine or server.

In the context of this disclosure, computer-readable media may be tangible media that may contain or store a program for use by or in connection with an instruction execution system, apparatus, or device. The computer-readable medium may be a computer-readable signal medium or a computer-readable storage medium. Computer-readable media may include, but are not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, devices or devices, or any suitable combination of the foregoing. More specific examples of computer readable storage media would include electrical connections based on one or more wires, laptop disks, hard drives, random access memory (RAM), read only memory (ROM), erasable programmable read only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage device, magnetic storage device, or any suitable combination of the above.

It should be noted that the present disclosure also provides a non-transitory computer-readable storage medium storing computer instructions, wherein the computer instructions are used to cause the computer to execute the method 100 and achieve the corresponding results achieved by the embodiments of the present disclosure when executing the method. The technical effects are briefly described and will not be repeated here.

In addition, the present disclosure also provides a computer program product, which includes a computer program that implements the method 100 when executed by a processor.

In order to provide interaction with the user, the embodiments described above may be implemented on a computer having: a display device (for example, a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user; and a keyboard and pointing device (eg, a mouse or trackball) through which a user can provide input to the computer. Other kinds of devices may also be used to provide interaction with the user; for example, the feedback provided to the user may be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and may be provided in any form, including Acoustic input, voice input or tactile input) to receive input from the user.

The embodiments described above may be implemented in a computing system that includes back-end components (e.g., as a data server), or a computing system that includes middleware components (e.g., an application server), or a computing system that includes front-end components (e.g., with a graphics server). A user's computer with a user interface or web browser through which the user can interact with implementations of the systems and technologies described herein), or that includes such backend components, middleware components, or front-ends Any combination of components in a computing system. The components of the system may be interconnected by any form or medium of digital data communication (eg, a communications network). Examples of communication networks include: local area network (LAN), wide area network (WAN), and the Internet.

Computer systems may include clients and servers. Clients and servers are generally remote from each other and typically interact over a communications network. The relationship of client and server is created by computer programs running on corresponding computers and having a client-server relationship with each other. The server can be a cloud server, a distributed system server, or a server combined with a blockchain.

It should be understood that various forms of the process shown above may be used, with steps reordered, added or deleted. For example, each step described in the present disclosure can be executed in parallel, sequentially, or in a different order. As long as the desired results of the technical solutions disclosed in the present disclosure can be achieved, there is no limitation here.

The above-mentioned specific embodiments do not constitute a limitation on the scope of the present disclosure. It will be understood by those skilled in the art that various modifications, combinations, sub-combinations and substitutions are possible depending on design requirements and other factors. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of this disclosure shall be included in the protection scope of this disclosure.

Claims (10)

1. A microgrid power grid load-storage collaborative dispatching method, characterized in that the method includes: Obtain historical operating data of microgrid; The war strategy optimization algorithm is used to solve the microgrid power grid load-storage collaborative dispatch model based on the historical operating data; Generate a microgrid power grid load-storage collaborative dispatching strategy based on the solution results; According to the microgrid source, grid, load and storage coordinated dispatching strategy, the microgrid is subject to source, grid, load and storage coordinated dispatching. 2. The method according to claim 1, characterized in that the historical operation data includes: historical line operation data, historical wind power output data, historical photovoltaic output data, historical micro gas turbine output data and historical battery data. 3. The method according to claim 1, characterized in that the war strategy optimization algorithm is used to solve the microgrid power grid load-storage collaborative dispatch model based on the historical operating data, including: Perform an initial solution to the microgrid power grid load-storage collaborative dispatch model based on the historical operating data to obtain an initial solution; The war strategy optimization algorithm is used to continuously iteratively optimize the initial solution to obtain the optimal solution. 4. The method according to claim 3, characterized in that when a war strategy optimization algorithm is used to continuously iteratively optimize the initial solution, the weak soldier update strategy of the war strategy optimization algorithm is: In the early stage of iterative optimization, weak soldiers are randomly updated; In the middle stage of iterative optimization, while randomly updating weak soldiers, the weak soldiers will gradually move to the middle of the battlefield; In the later stage of iterative optimization, place weak soldiers in the middle of the battlefield; The iterative optimization passed in the early stage Indicates that the iterative optimization is passed in the middle stage/> Indicates that the iterative optimization later passes/> means, t represents the number of iterations, and T represents the total number of iterations. 5. The method according to claim 1, characterized in that the microgrid power grid load storage coordinated dispatch model includes: a microgrid system model and a multi-objective optimization model. 6. The method according to claim 5, characterized in that the microgrid system model includes: a photovoltaic power generation model, a wind power generation model, a micro gas turbine power generation model, and a storage battery model; The multi-objective optimization model takes minimizing the operating cost of the microgrid system, minimizing the operating voltage deviation of the microgrid system, and minimizing the carbon emission cost of the microgrid system as objective functions, and takes the microgrid power balance constraint, the micro gas turbine ramping constraint, and the distribution The constraint conditions include power supply power constraints, energy interaction constraints between the microgrid and the large grid, battery charging and discharging constraints, and node voltage constraints. 7. A microgrid power grid load-storage coordinated dispatching device, characterized in that the device includes: The acquisition module is used to obtain historical operating data of the microgrid; A solving module used to use the war strategy optimization algorithm to solve the microgrid power grid load-storage collaborative dispatch model based on the historical operating data; The generation module is used to generate the microgrid power grid load-storage collaborative dispatching strategy based on the solution results; A scheduling module, configured to perform source-grid-load-storage coordinated dispatching on the microgrid according to the microgrid source-grid-load-storage coordinated dispatching strategy. 8. The device according to claim 7, characterized in that the solving module is specifically used to: Perform an initial solution to the microgrid power grid load-storage collaborative dispatch model based on the historical operating data to obtain an initial solution; The war strategy optimization algorithm is used to continuously iteratively optimize the initial solution to obtain the optimal solution. 9. An electronic device, characterized in that the electronic device includes: at least one processor; and a memory communicatively connected to the at least one processor; wherein, The memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to perform any one of claims 1-6. Methods. 10. A non-transitory computer-readable storage medium storing computer instructions, characterized in that the computer instructions are used to cause a computer to execute the method according to any one of claims 1-6.