Multi-agent distributed energy storage cooperative control method and system

Abstract

The invention discloses a multi-agent distributed energy storage cooperative control method and system, and the method comprises the steps: obtaining an agent technology and a distributed energy storage technology, combining the agent technology with the distributed energy storage technology, and constructing a distributed energy storage multi-agent cooperative control architecture; determining a multi-agent active control strategy according to the distributed energy storage multi-agent cooperative control architecture, and obtaining control logic of distributed energy storage agents; based on the control logic, setting a multi-agent distributed energy storage cooperative control algorithm to obtain a target function; and solving the objective function by using an optimization algorithm to obtain an optimal solution, and providing effective frequency and voltage support for the power system.

Description

Technical Field

[0001] The present invention relates to the technical field of energy storage systems, and in particular to a multi-agent distributed energy storage collaborative control method and system. Background Art

[0002] In modern power systems, with the increasing penetration of renewable energy (such as wind power and solar energy), power grids are facing more and more challenges, such as power fluctuations, load imbalance and power quality issues. In order to meet these challenges, energy storage systems (ESS) have gradually become a key component of power systems. Energy storage systems can not only balance supply and demand and stabilize power grids, but also improve the utilization rate of renewable energy. However, traditional energy storage control methods are usually managed in a centralized manner, with problems such as single point failures, communication delays, and uneven resource utilization, and cannot fully cope with the complexity and dynamics of modern power grids. Large-scale centralized energy storage facilities are restricted by geography and resource environment, and the construction cost is relatively high; while distributed energy storage facilities are flexible to install and can be freely combined, but there are problems such as dispersed locations, unequal capacities, and different forms. Effectively controlling the coordinated actions of multi-point distributed energy storage and giving full play to its regulation potential have become research difficulties.

[0003] In order to overcome these problems, Multi-Agent System (MAS) has been gradually introduced into the control of energy storage systems. A Multi-Agent System is a distributed artificial intelligence system in which multiple agents work together to complete complex tasks through autonomy and collaboration. In distributed energy storage systems, the multi-agent approach can achieve collaborative control between multiple nodes and improve the reliability and flexibility of the system. Each agent can independently perceive the state of its area and make local decisions without relying on a central controller. This approach is particularly suitable for energy storage management in large-scale distributed energy systems. Summary of the invention

[0004] In view of the above existing problems, the present invention is proposed.

[0005] Therefore, the present invention provides a multi-agent distributed energy storage collaborative control method and system to solve the problems that traditional energy storage control methods are usually centralized management, with single point failures, communication delays, and uneven resource utilization. Large-scale centralized energy storage facilities are restricted by geography and resource environment, and have high construction costs. Distributed energy storage facilities are flexible to install and can be freely combined, but have problems such as dispersed locations, unequal capacities, and different shapes.

[0006] In order to solve the above technical problems, the present invention provides the following technical solutions:

[0007] In a first aspect, the present invention provides a multi-agent distributed energy storage collaborative control method, comprising:

[0008] Acquire intelligent agent technology and distributed energy storage technology, combine the intelligent agent technology and distributed energy storage technology, and build a distributed energy storage multi-agent collaborative control architecture;

[0009] Determine the multi-agent active control strategy according to the distributed energy storage multi-agent collaborative control architecture to obtain the control logic of the distributed energy storage agent;

[0010] Based on the control logic, a multi-agent distributed energy storage collaborative control algorithm is set to obtain an objective function;

[0011] The objective function is solved using an optimization algorithm to obtain an optimal solution.

[0012] As a preferred solution of the multi-agent distributed energy storage collaborative control method described in the present invention, the distributed energy storage multi-agent collaborative control architecture includes:

[0013] Acquire several terminal agents, and control the energy storage converter to actively respond to frequency and voltage disturbances through a preset active control strategy;

[0014] If active control fails to eliminate the disturbance, the terminal agent feeds back relevant information to the coordination agent, requesting collaborative control;

[0015] The intelligent agent selects the optimal interval through a preset algorithm and distributes the power grid dispatching instructions to the terminal intelligent agent.

[0016] As a preferred solution of the multi-agent distributed energy storage collaborative control method described in the present invention, the multi-agent distributed energy storage collaborative control algorithm includes:

[0017] Set the total control target value of the energy storage coordination agent according to the grid dispatching requirements;

[0018] Obtain the energy storage charge state of the battery pack of the energy storage terminal agent at the last moment. If the charge state of the terminal agent is discharge, the initial value of the discharge power distribution calculated by the terminal agent is:

[0019] The ratio of the energy storage state of charge of the energy storage terminal intelligent body battery pack at the last moment to the sum of the energy storage state of charge of all terminal intelligent body battery packs at the last moment is multiplied by the total control target value of the energy storage coordination intelligent body.

[0020] As a preferred solution of the multi-agent distributed energy storage collaborative control method of the present invention, it also includes:

[0021] Combined with the maximum charge and discharge power allowed by the energy storage battery power conversion system, the power reference value of the energy storage terminal intelligent body is:

[0022] The product of the first power adjustment coefficient and the initial value of the discharge power distribution calculated by the terminal intelligent body plus the maximum allowable charge and discharge power of the energy storage battery power conversion system of the terminal intelligent body and the sum of the maximum allowable charge and discharge power of the energy storage battery power conversion systems of all terminal intelligent bodies multiplied by the second power adjustment coefficient, to obtain a calculation result, and the calculation result is multiplied by the total control target value of the energy storage coordination intelligent body;

[0023] A first judgment is performed between the initial power allocation value of the terminal intelligent body and the maximum allowable charging and discharging power to obtain a first power adjustment coefficient and a second power adjustment coefficient.

[0024] As a preferred solution of the multi-agent distributed energy storage collaborative control method described in the present invention, the objective function includes:

[0025] Performing a second judgment on the initial value of power allocation and the maximum allowable charge and discharge power;

[0026] If the initial value of power allocation is greater than the maximum allowed charge and discharge power, the total power deviation is obtained;

[0027] If the initial value of power allocation is less than the maximum allowed charge and discharge power, the power margin of the terminal agent is obtained.

[0028] As a preferred solution of the multi-agent distributed energy storage collaborative control method described in the present invention, the objective function includes:

[0029] The total charge and discharge power of the energy storage system has the smallest deviation from the target value of the total regulation command;

[0030] The adjusted state of charge of each distributed energy storage system has the smallest deviation from the preset state of charge.

[0031] As a preferred solution of the multi-agent distributed energy storage collaborative control method of the present invention, solving the objective function includes:

[0032] Initialize the position of the population;

[0033] For each individual in the population, the fitness value is calculated according to the objective function;

[0034] Each individual determines its growth direction based on its fitness value and random disturbance;

[0035] Move the individual according to the determined growth direction to obtain a new position, update the population, and retain individuals with high fitness;

[0036] If the maximum number of iterations is reached or the fitness value converges, the algorithm terminates and outputs the optimal solution.

[0037] In a second aspect, the present invention provides a multi-agent distributed energy storage collaborative control system, comprising:

[0038] An architecture building module, used to obtain intelligent agent technology and distributed energy storage technology, combine the intelligent agent technology and distributed energy storage technology, and build a distributed energy storage multi-agent collaborative control architecture;

[0039] A strategy determination module is used to determine the multi-agent active control strategy according to the distributed energy storage multi-agent collaborative control architecture to obtain the control logic of the distributed energy storage agent;

[0040] An algorithm setting module, used to set a multi-agent distributed energy storage collaborative control algorithm based on the control logic to obtain an objective function;

[0041] The function solving module is used to solve the objective function using an optimization algorithm to obtain an optimal solution.

[0042] In a third aspect, the present invention provides a computing device, comprising:

[0043] Memory and processor;

[0044] The memory is used to store computer executable instructions, and the processor is used to execute the computer executable instructions. When the computer executable instructions are executed by the processor, the steps of the multi-agent distributed energy storage collaborative control method are implemented.

[0045] In a fourth aspect, the present invention provides a computer-readable storage medium storing computer-executable instructions, which, when executed by a processor, implement the steps of the multi-agent distributed energy storage collaborative control method.

[0046] Compared with the prior art, the present invention has the following beneficial effects: the present invention uses a multi-agent distributed energy storage collaborative control method to control the energy storage system, which can significantly improve the dynamic response and stability of the system. When a disturbance occurs in the system, each energy storage terminal intelligent agent provides rapid frequency support by adjusting the active power output, achieving rapid response of the energy storage system, and thus providing effective frequency and voltage support for the power system. BRIEF DESCRIPTION OF THE DRAWINGS

[0047] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings required for use in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other accompanying drawings can be obtained based on these accompanying drawings without paying creative work.

[0048] Figure 1A schematic diagram of the overall process logic of a multi-agent distributed energy storage collaborative control method according to an embodiment of the present invention;

[0049] Figure 2 A control logic diagram of a multi-agent distributed energy storage collaborative control method according to an embodiment of the present invention;

[0050] Figure 3 A power system simulation system for multi-point distributed energy storage of a multi-agent distributed energy storage collaborative control method according to an embodiment of the present invention;

[0051] Figure 4 It is a diagram showing the energy storage output and the frequency and voltage change of line 4 during the control process under scenario 1 in the multi-agent distributed energy storage cooperative control method according to an embodiment of the present invention;

[0052] Figure 5 This is a diagram of system frequency and voltage changes before and after system control in scenario 2 in a multi-agent distributed energy storage collaborative control method described in an embodiment of the present invention. DETAILED DESCRIPTION

[0053] In order to make the above-mentioned purposes, features and advantages of the present invention more obvious and easy to understand, the specific implementation methods of the present invention are described in detail below in conjunction with the drawings of the specification. Obviously, the described embodiments are part of the embodiments of the present invention, but not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary persons in the art without creative work should fall within the scope of protection of the present invention.

[0054] Example 1

[0055] Reference Figure 1-Figure 2 , is an embodiment of the present invention, and provides a multi-agent distributed energy storage collaborative control method, comprising:

[0056] S10)0): Acquire intelligent agent technology and distributed energy storage technology, combine the intelligent agent technology and distributed energy storage technology, and build a distributed energy storage multi-agent collaborative control architecture;

[0057] S200: Determine a multi-agent active control strategy according to a distributed energy storage multi-agent collaborative control architecture, and obtain a control logic of the distributed energy storage agent;

[0058] Specifically, based on the distributed energy storage multi-agent collaborative control architecture, a multi-agent active control strategy is formulated, including frequency control strategy and voltage control strategy.

[0059] By setting the virtual damping and inertia, the frequency modulation control equation of the photovoltaic virtual synchronous generator is expressed as:

[0060]

[0061] Where ω is the electrical angular velocity, θ is the electrical angle, ω N is the synchronous electrical angular velocity, J is the virtual inertia, D is the damping coefficient, P ref is the target power given value, P e is the output power;

[0062] The reactive voltage control of the photovoltaic virtual synchronous generator is expressed as:

[0063]

[0064] Among them, U n is the voltage amplitude, U o is the output voltage amplitude, D q is the reactive-voltage coefficient, Q ref is the target reactive power, Q e is the output reactive power, K is the reactive voltage integral coefficient, and s is the complex frequency variable.

[0065] The control logic of distributed energy storage intelligent body is as follows Figure 2 As shown, information flow ① includes voltage, current, state of charge and other information collected by the battery management system (BMS) from the battery body; information flow ② includes real-time information and historical data of the battery body, collaborative control information, etc. collected by the battery management system; information flow ③ includes relevant information in the operation control of the power conversion system (PCS); information flow ④ includes active control information of the power conversion system, etc.; information flow ⑤ includes real-time battery information, active control deviation information, coordinated control deviation information, etc.; information flow ⑥ includes energy storage output power demand information, collaborative control request information, etc.; information flow ⑦ includes power demand information of the power grid dispatching center, etc.

[0066] S300: Based on the control logic, set the multi-agent distributed energy storage collaborative control algorithm to obtain the objective function;

[0067] S400: solving the objective function using an optimization algorithm to obtain an optimal solution;

[0068] It should be noted that it can quickly respond to dynamic changes in the power grid and improve the stability of the power system. When disturbances occur, the energy storage terminal intelligent body can quickly adjust the output active power, provide rapid frequency support, reduce frequency fluctuations, and maintain the stability of the power grid frequency. Through the collaborative work between intelligent bodies, it can more effectively balance power fluctuations and load imbalances in the power grid and improve the quality of power.

[0069] In the embodiment of the present application, the above step S100 includes the following sub-steps A1-A3;

[0070] In A1: a number of terminal agents are obtained, and the energy storage converter is controlled to actively respond to frequency and voltage disturbances through a preset active control strategy;

[0071] In A2: If active control fails to eliminate the disturbance, the terminal agent feeds back relevant information to the coordination agent and requests collaborative control;

[0072] In A3: The agent selects the optimal interval through a preset algorithm and distributes the power grid dispatching instructions to the terminal agent.

[0073] Specifically, the distributed energy storage multi-agent collaborative control architecture transforms and upgrades the strategies of each distributed battery energy storage management system, making it a terminal intelligent entity with active control and response capabilities.

[0074] Each terminal agent controls the energy storage converter to actively respond to frequency and voltage disturbances through a preset active control strategy. If the active control fails to eliminate the disturbance, the terminal agent feeds back the relevant information to the coordination agent and requests collaborative control.

[0075] The coordination agent selects the optimal interval through a preset algorithm and distributes the grid dispatching instructions to each energy storage terminal agent; on the other hand, it receives requests from the terminal agents and selects the appropriate terminal agent for collaborative control.

[0076] It should be noted that by transforming and upgrading the distributed battery energy storage management system into a terminal intelligent entity with active control and response functions, the system can respond more flexibly to frequency and voltage disturbances in the power grid, improving the reliability of the system. The terminal intelligent entity can actively control the energy storage inverter to respond to frequency and voltage disturbances in the power grid. The active control strategy helps to quickly alleviate the instantaneous imbalance of the power grid.

[0077] In the embodiment of the present application, the above step S300 includes the following sub-steps B1-B3:

[0078] In B1: the total control target value of the energy storage coordination agent is set according to the grid dispatching requirements;

[0079] In B2: the energy storage charge state of the battery pack of the energy storage terminal intelligent body at the last moment is obtained. If the charge state of the terminal intelligent body is discharge, the initial value of the discharge power distribution calculated by the terminal intelligent body is:

[0080] In B3: the ratio of the energy storage state of charge of the energy storage terminal intelligent body battery pack at the last moment to the sum of the energy storage state of charge of all terminal intelligent body battery packs at the last moment is multiplied by the total control target value of the energy storage coordination intelligent body.

[0081] Specifically, when each energy storage terminal agent calculates the initial value of power allocation, it needs to know the information such as the charge state of the energy storage battery group of each terminal agent at the previous moment. When the power demand is positive, power generation is required. At this time, the initial value of power allocation P calculated by the i-th terminal agent is i (t) is:

[0082]

[0083] Where i = 1, 2, 3…, n is the number of terminal agents, S i (t-1) is the energy storage charge state of the battery group of the i-th energy storage terminal intelligent agent at the previous moment, and P0(t) is the total control target value of the energy storage coordination intelligent agent.

[0084] In the embodiment of the present application, after completing steps B1-B3 in the above step S300, the following sub-steps B4-B6 are also included;

[0085] In B4: Combined with the maximum charge and discharge power allowed by the energy storage battery power conversion system, the power reference value of the energy storage terminal intelligent body is:

[0086] In B5: the product of the first power adjustment coefficient and the initial value of the discharge power distribution calculated by the terminal intelligent body plus the sum of the maximum allowable charge and discharge power of the energy storage battery power conversion system of the terminal intelligent body and the maximum allowable charge and discharge power of the energy storage battery power conversion systems of all terminal intelligent bodies multiplied by the second power adjustment coefficient, to obtain a calculation result, and the calculation result is multiplied by the total control target value of the energy storage coordination intelligent body;

[0087] In B6: a first judgment is made between the initial power allocation value of the terminal intelligent body and the maximum allowed charging and discharging power to obtain a first power adjustment coefficient and a second power adjustment coefficient.

[0088] Specifically, combined with the maximum charge and discharge power allowed by the energy storage battery power conversion system, the power reference value of the energy storage terminal intelligent body is expressed as:

[0089]

[0090] in, is the maximum allowed charging and discharging power of the i-th energy storage terminal intelligent entity PCS, and α and β are the first and second power adjustment coefficients.

[0091] The first judgment includes comparing the initial power allocation value of the energy storage terminal intelligent body with the maximum allowable charge and discharge power. If the initial power allocation value is greater than the maximum allowable charge and discharge power, the first power adjustment coefficient α is 0 and the second power adjustment coefficient β is 1; if the initial power allocation value is less than the maximum allowable charge and discharge power, the first power adjustment coefficient α is 1 and the second power adjustment coefficient β is 0, which is expressed as:

[0092]

[0093] It should be noted that by setting the total control target value of the energy storage coordination agent according to the grid dispatching requirements, it is ensured that the entire energy storage system distributes power according to the grid requirements. Each energy storage terminal agent calculates the initial value of the discharge power distribution according to its charge state at the previous moment, so that the power distribution is more in line with the actual energy storage state. By combining the energy storage charge state with the total control target value, the utilization of energy storage resources is optimized and the efficiency of the energy storage system is improved.

[0094] In the embodiment of the present application, after completing steps B1-B3 in the above step S300, the following sub-steps B7-B9 are also included;

[0095] In B7: a second judgment is made on the initial value of power allocation and the maximum allowed charge and discharge power;

[0096] In B8: if the initial value of the power allocation is greater than the maximum allowable charge and discharge power, the total power deviation is obtained;

[0097] In B9: if the initial value of power allocation is less than the maximum allowed charge and discharge power, the power margin of the terminal agent is obtained;

[0098] Specifically, the output power target value of the i-th energy storage terminal intelligent agent is There are two possibilities: if the initial value of power allocation is greater than the maximum allowable charge and discharge power, the output of the energy storage terminal intelligent agent has reached the limit, but in order to reach the initial value of allocation, a certain power deviation occurs. At this time, the total power deviation P dev It is expressed as:

[0099]

[0100] If the initial power allocation value is less than the maximum allowable charge and discharge power, the output target value of the energy storage terminal agent reaches the initial allocation value of the coordination agent and there is still margin for additional adjustment. The power margin of the energy storage terminal agent is expressed as:

[0101]

[0102] For energy storage terminal agents that still have power margin and whose state of charge has not reached the limit, the additional power redistributed is added on the original basis, and the total control target value of the energy storage coordination agent is set to the total power deviation, and each value is updated;

[0103] In the embodiment of the present application, after completing steps B1-B3 in the above step S300, the following sub-steps B10-B11 are also included;

[0104] In B10: the total charge and discharge power of the energy storage system has the smallest deviation from the target value of the total regulation command;

[0105] In B11: the deviation between the adjusted state of charge of each distributed energy storage system and the preset state of charge is minimal;

[0106] Specifically, the objective function is expressed as:

[0107] G=min(ω 1 F 1 +ω 2 F 2 )

[0108]

[0109]

[0110] Among them, F 1 F is the power difference between the charging and discharging power output of the energy storage power station and the total power regulation instruction; 2 The difference between the state of charge after adjustment of the energy storage power station and the preset state of charge; is the current charging and discharging power of the i-th energy storage terminal intelligent agent; S r is the preset state of charge of the battery pack; Δt is the adjustment time; C i is the current charging and discharging power of the i-th energy storage terminal intelligent agent; ω 1 ,ω 2 is the weight coefficient, and the sum of the two is 1.

[0111] It should be noted that by accurately calculating the initial power allocation value of each energy storage terminal intelligent body, it is ensured that the power allocation is closely corresponding to the actual needs of the power grid, and the response speed and control accuracy of the system are improved. Taking into account the charge state and maximum charge and discharge power limits of each energy storage terminal intelligent body, the use of energy storage resources is optimized, over-charging and discharging is avoided, and battery life is extended. By dynamically adjusting the power allocation, the system can adapt to changes in power grid demand, thereby improving the flexibility and adaptability of the system.

[0112] In the embodiment of the present application, the above step S400 includes the following sub-steps C1-C5;

[0113] In C1: Initialize the position of the population;

[0114] In C2: For each individual in the population, the fitness value is calculated according to the objective function;

[0115] In C3: Each individual determines the growth direction based on the fitness value and random disturbance;

[0116] In C4: move the individuals according to the determined growth direction to obtain new positions, update the population, and retain individuals with high fitness;

[0117] In C5: If the maximum number of iterations is reached or the fitness value converges, the algorithm terminates and outputs the optimal solution.

[0118] In an optional embodiment, the optimization algorithm may be an ivy optimization algorithm, a genetic algorithm, or a particle swarm optimization algorithm;

[0119] Genetic algorithms optimize problems by simulating natural selection and genetics principles through operations such as selection, crossover, and mutation; particle swarm optimization algorithms simulate the social behavior of bird flocks or fish schools, and particles move in the search space to find the optimal solution;

[0120] In a preferred embodiment, the present application preferably uses the ivy optimization algorithm;

[0121] Specifically, N individuals are randomly generated in the solution space, each individual represents a possible solution, and its position can be expressed as:

[0122] X i =[x i,1 , x i,2 , …, x i,d ]

[0123] Where i=1,2,..., N represents the i-th individual, d is the number of digits of the solution, and during initialization, the value of each dimension can be randomly generated within the range defined by the problem:

[0124] x i,j =x min,j +rand×(x max，j -x min,j )

[0125] Among them, rand is a random number in [0, 1], x max，j and x min，j are the minimum and maximum values ​​of the j-th dimension respectively;

[0126] For each individual, the fitness value is calculated according to the objective function of the optimization problem. The fitness value f i It is expressed as:

[0127] f i =f(X i )

[0128] Among them, f(X i ) is the value of the objective function, which measures the individual X i The quality of the solution.

[0129] Each individual determines the growth direction according to the fitness value and random disturbance. The choice of growth direction is expressed as:

[0130] D i =X best -X i +randn·σ

[0131] Among them, individual X i According to the determined direction D i Move a distance, including extension and climbing;

[0132] The expansion is large in the early stages of optimization, enhancing the global search capability;

[0133] Climbing is to reduce the expansion amplitude and enhance the local search capability in the later stage of optimization.

[0134] The new position update is represented by:

[0135]

[0136] Among them, α is the step size factor, which controls the degree of expansion and climbing. In order to make the algorithm gradually transition from global search to local search, α is designed to be a value that decreases with the number of iterations t, expressed as:

[0137] α=α initial exp(-β t / MaxIter)

[0138] Among them, α initial is the initial step size factor, β is the attenuation coefficient, and MaxIter is the maximum number of iterations;

[0139] At the end of each iteration, the population is updated, retaining individuals with higher fitness;

[0140] Determine whether the convergence conditions are met, such as reaching the maximum number of iterations or fitness value convergence. If the conditions are met, the algorithm terminates and outputs the current optimal solution.

[0141] It should be noted that the algorithm achieves a balance between global search and local search through the two modes of expansion and crawling. The step size factor decreases with the increase in the number of iterations, allowing the algorithm to dynamically adjust the search step size during the search process, thereby quickly exploring the solution space in the early stage and searching for the optimal solution in the later stage. Each individual determines the growth direction based on the fitness value and random disturbance, which helps the individual move towards a better solution while maintaining the diversity of the search.

[0142] The above is a schematic scheme of a multi-agent distributed energy storage collaborative control method of this embodiment. It should be noted that the technical scheme of the multi-agent distributed energy storage collaborative control system and the technical scheme of the multi-agent distributed energy storage collaborative control method described above belong to the same concept. The details of the technical scheme of the multi-agent distributed energy storage collaborative control system not described in detail in this embodiment can be referred to the description of the technical scheme of the multi-agent distributed energy storage collaborative control method described above.

[0143] In this embodiment, the multi-agent distributed energy storage collaborative control system includes:

[0144] An architecture building module, used to obtain intelligent agent technology and distributed energy storage technology, combine the intelligent agent technology and distributed energy storage technology, and build a distributed energy storage multi-agent collaborative control architecture;

[0145] A strategy determination module is used to determine the multi-agent active control strategy according to the distributed energy storage multi-agent collaborative control architecture to obtain the control logic of the distributed energy storage agent;

[0146] An algorithm setting module, used to set a multi-agent distributed energy storage collaborative control algorithm based on the control logic to obtain an objective function;

[0147] The function solving module is used to solve the objective function using an optimization algorithm to obtain an optimal solution.

[0148] This embodiment also provides a computing device, which is applicable to the coordinated control of multi-agent distributed energy storage, including:

[0149] Memory and processor; the memory is used to store computer executable instructions, and the processor is used to execute computer executable instructions to implement the multi-agent distributed energy storage collaborative control method proposed in the above embodiment.

[0150] This embodiment also provides a storage medium on which a computer program is stored. When the program is executed by a processor, the method for realizing the coordinated control of multi-agent distributed energy storage as proposed in the above embodiment is implemented.

[0151] The storage medium proposed in this embodiment and the method for realizing multi-agent distributed energy storage collaborative control proposed in the above embodiment belong to the same inventive concept. The technical details not fully described in this embodiment can be referred to the above embodiment, and this embodiment has the same beneficial effects as the above embodiment.

[0152] Through the above description of the implementation methods, the technicians in the relevant field can clearly understand that the present invention can be implemented by means of software and necessary general hardware, and of course can also be implemented by hardware. Based on such an understanding, the technical solution of the present invention is essentially or the part that contributes to the prior art can be embodied in the form of a software product, and the computer software product can be stored in a computer-readable storage medium, such as a computer floppy disk, read-only memory (ReadOnly, Memory, ROM), random access memory (Random Access Memory, RAM), flash memory (FLASH), hard disk or optical disk, etc., including a number of instructions for a computer device (which can be a personal computer, server, or network device, etc.) to perform the methods of various embodiments of the present invention.

[0153] Example 2

[0154] Reference Figure 3-Figure 5 This embodiment is different from the first embodiment in that it provides a verification test of a multi-agent distributed energy storage collaborative control method to verify and illustrate the technical effects used in this method.

[0155] Use DIgSILENT simulation software to establish a power system simulation system including multi-point distributed energy storage. Figure 3 As shown;

[0156] Two scenarios are set to simulate the effect of the proposed control method under external disturbances:

[0157] Scenario 1: The circuit breaker at node 8 trips at 1 second and no subsequent action is taken.

[0158] Scenario 2: A three-phase short circuit fault occurs at node 8 at 1 second, and the fault is cleared after 0.1 second.

[0159] In scenario 1, the proposed multi-agent distributed energy storage collaborative control method is used for control. The energy storage output during the control process and the frequency and voltage changes of line 4 are shown in Figure 4 and Figure 5 As shown;

[0160] from Figure 4As shown, it can be seen that in scenario 1, the active power of energy storage 1, 2, and 3 increases rapidly by about 0.006MW in 1 second, and recovers to the initial value after about 3 seconds. When the system is disturbed, each energy storage terminal intelligent agent actively adjusts the output active power to provide rapid frequency support. The frequency of the system oscillates and decreases in 1 second, and the frequency value at 10 seconds is 0.0001Hz lower than the initial value. When the proposed algorithm is used for control, the system frequency fluctuation range is smaller than when it is not controlled and a certain degree of stability is guaranteed. The voltage at Bus4 rises rapidly to 1.0017pu after 1 second, and stabilizes after about 8 seconds. The stabilized voltage is 0.002pu higher than the original voltage.

[0161] from Figure 5 As shown in the figure, in scenario 2, the output power of energy storage 1 does not change, and the output power of energy storage 2 and 3 increases rapidly by about 0.0074MW in 1 second, and returns to the initial value after about 4 seconds. When the system is disturbed, each energy storage terminal intelligent agent actively adjusts the output active power to provide rapid frequency support. After the control is applied, the frequency of the system oscillates after 1 second, and stabilizes after about 8 seconds. The stable value is approximately 0.000002pu higher than the initial value, and the frequency fluctuation range is greatly reduced compared to when it is not controlled. The voltage at Bus4 drops rapidly to 0.05pu after 1 second, and returns to stability after the fault is cleared. The stabilized voltage is 0.0038pu lower than the original voltage. After control is performed, the value of the system voltage after it returns to stability is closer to the initial value and no abnormal values ​​will appear in the whole process.

[0162] In both scenarios, the energy storage system can respond quickly to grid disturbances and provide fast frequency support by adjusting active power output, showing good dynamic response capabilities. According to this method, the frequency fluctuation range of the system is significantly reduced, and it can quickly recover to a stable state close to the initial value after the disturbance, improving the frequency stability of the system. In both scenarios, the system voltage can recover quickly after the disturbance and reach a stable state after a certain period of time. Especially in scenario 2, the value of the system voltage after control is closer to the initial value after it recovers and stabilizes, showing good voltage recovery ability.

[0163] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit the present invention. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical solutions of the present invention, which should all be included in the scope of the claims of the present invention.

Claims

1. A multi-agent distributed energy storage collaborative control method, characterized in that: include: Acquire intelligent agent technology and distributed energy storage technology, combine the intelligent agent technology and distributed energy storage technology, and build a distributed energy storage multi-agent collaborative control architecture; Determine the multi-agent active control strategy according to the distributed energy storage multi-agent collaborative control architecture to obtain the control logic of the distributed energy storage agent; Based on the control logic, a multi-agent distributed energy storage collaborative control algorithm is set to obtain an objective function; The objective function is solved using an optimization algorithm to obtain an optimal solution.

2. The multi-agent distributed energy storage collaborative control method according to claim 1, characterized in that: The distributed energy storage multi-agent collaborative control architecture includes: Acquire several terminal agents, and control the energy storage converter to actively respond to frequency and voltage disturbances through a preset active control strategy; If active control fails to eliminate the disturbance, the terminal agent feeds back relevant information to the coordination agent, requesting collaborative control; The intelligent agent selects the optimal interval through a preset algorithm and distributes the power grid dispatching instructions to the terminal intelligent agent.

3. The multi-agent distributed energy storage collaborative control method according to claim 2, characterized in that: The multi-agent distributed energy storage collaborative control algorithm includes: Set the total control target value of the energy storage coordination agent according to the grid dispatching requirements; Obtain the energy storage charge state of the battery pack of the energy storage terminal agent at the last moment. If the charge state of the terminal agent is discharge, the initial value of the discharge power distribution calculated by the terminal agent is: The ratio of the energy storage state of charge of the energy storage terminal intelligent body battery pack at the last moment to the sum of the energy storage state of charge of all terminal intelligent body battery packs at the last moment is multiplied by the total control target value of the energy storage coordination intelligent body.

4. The multi-agent distributed energy storage collaborative control method according to claim 2 or 3, characterized in that: Also includes: Combined with the maximum charge and discharge power allowed by the energy storage battery power conversion system, the power reference value of the energy storage terminal intelligent body is: The product of the first power adjustment coefficient and the initial value of the discharge power distribution calculated by the terminal intelligent body plus the sum of the maximum allowable charge and discharge power of the energy storage battery power conversion system of the terminal intelligent body and the maximum allowable charge and discharge power of the energy storage battery power conversion system of all terminal intelligent bodies multiplied by the second power adjustment coefficient, to obtain a calculation result, and the calculation result is multiplied by the total control target value of the energy storage coordination intelligent body; A first judgment is performed between the initial power allocation value of the terminal intelligent body and the maximum allowable charging and discharging power to obtain a first power adjustment coefficient and a second power adjustment coefficient.

5. The multi-agent distributed energy storage collaborative control method according to claim 4, characterized in that: The objective function includes: Performing a second judgment on the initial value of power allocation and the maximum allowable charge and discharge power; If the initial value of power allocation is greater than the maximum allowed charge and discharge power, the total power deviation is obtained; If the initial value of power allocation is less than the maximum allowed charge and discharge power, the power margin of the terminal agent is obtained.

6. The multi-agent distributed energy storage collaborative control method according to claim 5, characterized in that: The objective function includes: The total charge and discharge power of the energy storage system has the smallest deviation from the target value of the total regulation command; The adjusted state of charge of each distributed energy storage system has the smallest deviation from the preset state of charge.

7. The multi-agent distributed energy storage collaborative control method according to claim 6, characterized in that: Solving the objective function includes: Initialize the position of the population; For each individual in the population, the fitness value is calculated according to the objective function; Each individual determines its growth direction based on its fitness value and random disturbance; Move the individual according to the determined growth direction to obtain a new position, update the population, and retain individuals with high fitness; If the maximum number of iterations is reached or the fitness value converges, the algorithm terminates and outputs the optimal solution.

8. A system using the multi-agent distributed energy storage collaborative control method according to any one of claims 1 to 7, characterized in that: include: An architecture building module, used to obtain intelligent agent technology and distributed energy storage technology, combine the intelligent agent technology and distributed energy storage technology, and build a distributed energy storage multi-agent collaborative control architecture; A strategy determination module is used to determine the multi-agent active control strategy according to the distributed energy storage multi-agent collaborative control architecture to obtain the control logic of the distributed energy storage agent; An algorithm setting module, used to set a multi-agent distributed energy storage collaborative control algorithm based on the control logic to obtain an objective function; The function solving module is used to solve the objective function using an optimization algorithm to obtain an optimal solution.

9. An electronic device, comprising: Memory and processor; The memory is used to store computer-executable instructions, and the processor is used to execute the computer-executable instructions. When the computer-executable instructions are executed by the processor, the steps of the multi-agent distributed energy storage collaborative control method described in any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium storing computer-executable instructions, which, when executed by a processor, implement the steps of the multi-agent distributed energy storage collaborative control method described in any one of claims 1 to 7.

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