Distributed control method and system for heterogeneous energy storage system at any preset time

Abstract

The invention discloses a heterogeneous energy storage system distributed control method and system under any preset time, and belongs to the field of heterogeneous energy storage system distributed control. The method comprises the following steps: dividing the heterogeneous energy storage system into a battery cluster, a flexible load cluster and a building group according to electrical characteristics; each equipment unit is provided with a follower agent, and each cluster is provided with a leader agent. Parameters of an observer, a controller and a system are set, the state of the follower is set, and the state of a leader is initialized into the state of the first communication follower. The convergence time of the observer is set, and an observation value is initialized; after the system target power is issued, the leader judges the system state according to the positive and negative power, and distributes the total target power of each cluster. The observer monitors the error between the intelligent agent and the leader in real time, the follower updates the state and adjusts the equipment power accordingly, and the leader synchronously updates the own state. Through the cooperative control mechanism, the system realizes accurate distribution and regulation of target power within a preset time, and completes a multi-cluster cooperative power management task.

Description

Technical Field

[0001] The present invention belongs to the field of distributed control of heterogeneous energy storage systems, and in particular relates to a method and system for distributed control of heterogeneous energy storage systems under any preset time. Background Art

[0002] In recent years, with the rapid development of renewable energy, its environmental and sustainable characteristics have been widely recognized in urban distribution networks, leading to the integration of large quantities of renewable energy into the grid. However, the high proportion of renewable energy integrated into the grid and its associated instability can lead to increased grid power fluctuations, necessitating the strategic deployment of energy storage systems to maintain grid power balance. Previous studies have confirmed that the introduction of energy storage batteries can effectively mitigate the negative impact of renewable energy on the grid. These devices can proactively adjust their charge and discharge power by receiving dispatch commands, thereby smoothing grid power fluctuations. Furthermore, flexible loads in the grid can dynamically adjust their electricity consumption through demand response mechanisms, reducing demand during peak periods and increasing it during off-peak periods, thereby assisting in grid power regulation. Furthermore, some buildings with thermal storage capabilities can also serve as specialized energy storage units for grid operation. These diverse energy storage batteries, flexible loads, and buildings together constitute a heterogeneous energy storage system within the grid. Optimizing their coordinated control strategies can significantly enhance the grid's power regulation capabilities and ensure the stability and reliability of system operation.

[0003] It is worth noting that both traditional centralized and distributed control methods have significant drawbacks when managing heterogeneous energy storage systems. On the one hand, traditional centralized control methods rely on a high-cost central control unit, which must maintain global information and carries a high risk of single-point failure. A failure can cause the entire system to shut down. Furthermore, as system scale expands and communication topologies become increasingly complex, the adaptability of this control model becomes significantly insufficient. On the other hand, the convergence time of existing distributed control methods for heterogeneous energy storage systems depends on the system's initial values, resulting in long and unpredictable convergence times and increased controller energy consumption. Furthermore, power allocation strategies often ignore the individual characteristics of energy storage units and flexible loads, which can easily lead to overcharging and over-discharging, shortening equipment life and potentially causing secondary problems such as voltage over-limit and reduced energy utilization. Summary of the Invention

[0004] In view of the deficiencies in the prior art, the present invention aims to provide a distributed control method and system for a heterogeneous energy storage system under any preset time, thereby solving the problems in the prior art.

[0005] The purpose of the present invention can be achieved through the following technical solutions:

[0006] A distributed control method for a heterogeneous energy storage system under any preset time comprises the following steps:

[0007] Based on the electrical characteristics of each device unit in the heterogeneous energy storage system, it is divided into three clusters: battery cluster, flexible load cluster, and building cluster. Each device unit is equipped with a follower agent, and each cluster is equipped with a leader agent to communicate with the follower agents in the cluster.

[0008] Set the parameters of the observer and controller;

[0009] Setting system parameters and characteristic coefficients for equipment units in each cluster;

[0010] Initialize the states of follower agents in each cluster based on the device unit system parameters;

[0011] Set the control gain of each cluster leader agent and initialize the leader agent state according to the initial state of the follower agent;

[0012] Set the convergence time of the observer and controller, and set the time base generator function parameters;

[0013] Initialize the observer observation value;

[0014] When the system sets the target power P T Afterwards, the leader agent determines the system state based on the positive or negative power value and allocates the total target power of the battery cluster, flexible load and building group according to the initial state of each follower agent;

[0015] Based on the observer parameters, the time base generator function and the initial observation value of the observer, each observer completes the error observation between its follower agent and the leader agent within the observer convergence time according to the protocol;

[0016] Based on the total target power of the battery cluster, flexible load cluster, and building cluster, as well as the observer's observed errors, each follower agent in the cluster updates its own state and regulates the power of the equipment unit according to the characteristic coefficient. Simultaneously, based on the leader agent's control gain, the leader agent in each cluster updates its own state.

[0017] Complete the target power control task within the preset time.

[0018] Furthermore, the settings of the system parameters and characteristic coefficients include:

[0019] 1) For the i1th energy storage battery, set its system parameters as: in, are the battery capacity and coulombic efficiency, Its operating voltage, are its maximum input power and maximum output power respectively; its characteristic coefficient for:

[0020]

[0021] Where N1 is the number of batteries in the battery cluster;

[0022] 2) For the i2th flexible load, set its system parameters in The i2th is the flexible load capacity, For its maximum working power, set its characteristic coefficient for:

[0023]

[0024] Among them, N2 is the number of flexible loads in the flexible load cluster;

[0025] 3) For the i3th building, set its system parameters in and are the capacity and Coulomb efficiency of the i3th building, is the maximum operating power of the i3th building, is the indoor temperature difference of the i3th building, in Set the maximum and minimum temperatures for the i3th building indoors; set its characteristic coefficients for:

[0026]

[0027] Among them, N3 is the number of buildings in the building complex.

[0028] Furthermore, the step of initializing the states of follower agents in each cluster includes:

[0029] 1) Set the state of the i1th energy storage battery follower agent to in:

[0030]

[0031] in, is the state of the i1th energy storage battery follower agent, is the ratio of the power and characteristic coefficient of the i1th energy storage battery follower agent, and The state of charge and power of the i1th energy storage battery are set to their initial states. in and are the initial state of charge and initial power of its energy storage battery respectively;

[0032] 2) Set the state of the i2th flexible load agent to in:

[0033]

[0034] in, is the state of the i2th flexible load follower agent, is the ratio of the power and characteristic coefficient of the i2th flexible load follower agent, and The energy state and power of the i2th flexible load are set as the initial state. in and are the initial energy state and initial power of the i2th flexible load respectively;

[0035] 3) Set the initial state of the i3th building agent to in:

[0036]

[0037] in, is the state of the i3th building follower agent, The power and characteristic coefficient ratio of the i3th building follower agent; and The temperature state and working power of the i3th building are set at the initial time in and are the initial temperature state and initial power of the i3th building respectively.

[0038] Furthermore, the initialized observer observations include: and in: are the state error observer observation value and proportional error observer observation value of the i1th energy storage battery follower agent, respectively, and their initial states are set to are the state error observer observation value and proportional error observer observation value of the i2th flexible load follower agent, respectively, and their initial states are set to are the state error observer observation value and the proportional error observer observation value of the i3th building follower agent, respectively, and their initial states are set to

[0039] Furthermore, the total target power allocation for battery clusters, flexible loads, and building groups includes:

[0040] If the target power is positive, the system is in charging mode, and each leader agent will allocate the target power according to the adjustment capacity of each cluster; if it is negative, the system enters discharge mode, and only the battery cluster bears the power output, that is, in are the total target power of battery cluster, flexible load and building group respectively.

[0041] Furthermore, the protocol of each observer includes:

[0042] 1) Observation protocol of battery cluster observer:

[0043]

[0044]

[0045] Where η1(t) = (1 / 2λ1(Q1))H1(t), λ1(Q1) is the minimum eigenvalue of the matrix Q1, Q1 is the battery cluster enhanced Laplace matrix, H1(t) is the observer time base generator function, is the control input of the i1th energy storage battery follower agent. When t∈(0,t s2 ]hour,

[0046] 2) Observation protocol of flexible load cluster observer:

[0047]

[0048] Where η2(t) = (1 / 2λ1(Q2))H1(t), λ1(Q2) is the minimum eigenvalue of the matrix Q2, and Q2 is the flexible load cluster enhanced Laplace matrix. is the control input of the i2th flexible load follower agent, when t∈(0,t s2 ]hour,

[0049] 3) Observation protocol of building cluster observer:

[0050]

[0051] Where η3(t) = (1 / 2λ1(Q3))H1(t), λ1(Q3) is the minimum eigenvalue of the matrix Q3, and Q3 is the building complex enhanced Laplace matrix. is the control input of the i3th building follower agent, when t∈(0,t s2 ]hour,

[0052] Furthermore, when each follower agent in the cluster regulates the power of the device unit, it sets the power of the i1th energy storage battery The power of the i2th flexible load is The power of the i3th building is

[0053] A distributed control system for heterogeneous energy storage systems at any preset time, including:

[0054] Classification module: Based on the electrical characteristics of each device unit in the heterogeneous energy storage system, it is divided into three clusters: battery cluster, flexible load cluster, and building cluster. Each device unit is equipped with a follower intelligent agent, and each cluster is equipped with a leader intelligent agent to communicate with the follower intelligent agents in the cluster.

[0055] Observer controller parameter setting module: set the parameters of the observer and controller;

[0056] Equipment unit parameter setting module: sets system parameters and characteristic coefficients for equipment units in each cluster;

[0057] Follower agent initialization module: initializes the state of follower agents in each cluster based on the device unit system parameters;

[0058] Leader agent initialization module: sets the control gain of each cluster leader agent and initializes the leader agent state according to the initial state of the follower agent;

[0059] Convergence time setting module: sets the convergence time of the observer and controller, and sets the time base generator function parameters;

[0060] Observer initialization module: initializes the observer observation value;

[0061] Power allocation module: When the system sets the target power P T Afterwards, the leader agent judges the system state according to the positive or negative power value and allocates the total target power of the battery cluster, flexible load and building group according to the initial state of each follower agent;

[0062] Error Observation Module: Based on the observer parameters, time base generator function and the initial observation value of the observer, each observer completes the error observation between its follower agent and the leader agent within the observer convergence time according to the protocol;

[0063] Agent state update module: Based on the total target power of the battery cluster, flexible load cluster, and building cluster, as well as the observer's observation error, each follower agent in the cluster updates its own state and regulates the power of the equipment unit according to the characteristic coefficient. Simultaneously, based on the leader agent's control gain, the leader agent in each cluster updates its own state.

[0064] And, the control module: completes the target power control task within the preset time.

[0065] A computer storage medium stores a readable program, which, when executed by a processor, can execute the above-mentioned distributed control method for a heterogeneous energy storage system at any preset time.

[0066] An electronic device comprising: a processor, a memory, a communication interface, and a communication bus, wherein the processor, the memory, and the communication interface communicate with each other via the communication bus;

[0067] The memory is used to store at least one executable instruction, and the executable instruction enables the processor to perform operations corresponding to the above-mentioned heterogeneous energy storage system distributed control method under any preset time.

[0068] Beneficial effects of the present invention:

[0069] 1. The present invention uses the adjustment capacity of each device unit in the heterogeneous energy storage system to allocate target power, thereby preventing overcharging or over-discharging of each device unit, optimizing the power allocation scheme, and improving energy utilization efficiency.

[0070] 2. The present invention proposes a distributed control method for demand-side response of a heterogeneous energy storage system with preset time stability characteristics, which realizes that the charge state of each energy storage battery in the heterogeneous energy storage system reaches consistency at the preset time, and the energy state of each flexible load and the temperature state of each building reach consistency at the preset time, making the regulation time manually controllable, enhancing system manageability, reducing controller energy consumption, and further improving the coordinated operation capability of the heterogeneous energy storage system.

[0071] 3. This paper designs a distributed control strategy for heterogeneous energy storage systems suitable for demand-side response. This strategy utilizes a characteristic coefficient-based power allocation mechanism, comprehensively considering device aging characteristics and individual parameter differences, to allocate power to each device unit proportionally based on the characteristic coefficient. This control method effectively enhances system operational stability, optimizes power quality, and significantly improves the user experience on the distribution network side. BRIEF DESCRIPTION OF THE DRAWINGS

[0072] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, for ordinary technicians in this field, other drawings can be obtained based on these drawings without any creative work.

[0073] Figure 1 is a topological diagram of the communication network of the heterogeneous energy storage system of the present invention;

[0074] Figure 2 This is a flow chart of the distributed control method for demand-side response of a heterogeneous energy storage system according to the present invention;

[0075] Figure 3 It is a power trend diagram of each cluster device unit of the present invention;

[0076] Figure 4 It is a trend chart of the ratio of power and characteristic coefficient of each equipment unit in each cluster of the present invention;

[0077] Figure 5 It is a trend chart of the sum of the unit powers of the equipment of the present invention;

[0078] Figure 6 It is a status trend diagram of each cluster device unit of the present invention. DETAILED DESCRIPTION

[0079] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making any creative efforts shall fall within the scope of protection of the present invention.

[0080] Example 1

[0081] like Figure 2 As shown, the distributed control method of the heterogeneous energy storage system under any preset time includes the following steps:

[0082] S1, based on the electrical characteristics of each device unit in the heterogeneous energy storage system, is divided into three clusters: battery cluster, flexible load cluster, and building cluster. Each device unit is equipped with a follower agent, and each cluster is equipped with a leader agent to communicate with the follower agents in the cluster. The correlation matrix is set according to the intra-cluster communication topology.

[0083] In this embodiment, the heterogeneous energy storage system's equipment units are divided into three homogeneous clusters based on their electrical characteristics: a battery cluster, a flexible load cluster, and a building cluster. In this distributed architecture, each unit is assigned a follower agent, whose functions include status monitoring, error observation, and distributed communication. Each cluster has a leader agent responsible for collecting status information from followers within the cluster, regulating its own state, and enabling communication.

[0084] In each cluster, the number of energy storage batteries, flexible loads and buildings is set to N1, N2 and N3 respectively. For the communication topology structure within each cluster, the adjacency matrix A is defined as [a ij ], if there is a communication connection between the i-th agent and the j-th agent, then a ij =1, otherwise a ij = 0. Define the leader matrix B = diag{a i0}, if the i-th follower agent can obtain information from the leader agent, then a i0=1, otherwise a i0 = 0. Define the degree matrix D = diag{d i …d N},in Define the Laplace matrix L = DA and the matrix Q = L + B. Then the matrices of the battery cluster, flexible load cluster, and building cluster are: A1, B1, D1, L1, Q1; A2, B2, D2, L2, Q2; A3, B3, D3, L3, Q3.

[0085] S2, set the parameters of the observer and controller;

[0086] 1) Set the parameters of each energy storage battery follower agent observer Set the observer parameters of each flexible load follower agent Set the observer parameters for each building follower agent

[0087] 2) Set controller parameters: Set the controller parameter ρ1 of each energy storage battery follower intelligent agent, set the controller parameter ρ2 of each flexible load follower intelligent agent, and set the controller parameter ρ3 of each building follower intelligent agent.

[0088] S3, setting system parameters and characteristic coefficients for the equipment units in each cluster;

[0089] 1) For the i1th energy storage battery, set its system parameters as: in, are the battery capacity and coulombic efficiency, Its operating voltage, are its maximum input power and maximum output power respectively; its characteristic coefficient for:

[0090]

[0091] 2) For the i2th flexible load, set its system parameters in The i2th is the flexible load capacity, For its maximum working power, set its characteristic coefficient for:

[0092]

[0093] 3) For the i3th building, set its system parameters in and are the capacity and Coulomb efficiency of the i3th building, is the maximum operating power of the i3th building, is the indoor temperature difference of the i3th building, in Set the maximum and minimum temperatures for the i3th building indoors; set its characteristic coefficients for:

[0094]

[0095] S4, based on the system parameters of each device unit in S3, initialize the state of the follower agent in each cluster;

[0096] 1) Set the state of the i1th energy storage battery follower agent to in:

[0097]

[0098] in, is the state of the i1th energy storage battery follower agent, is the ratio of the power and characteristic coefficient of the i1th energy storage battery follower agent, and The state of charge and power of the i1th energy storage battery are set to their initial states. in and are the initial state of charge and initial power of its energy storage battery respectively;

[0099] 2) Set the state of the i2th flexible load agent to in:

[0100]

[0101] in, is the state of the i2th flexible load follower agent, is the ratio of the power and characteristic coefficient of the i2th flexible load follower agent, and The energy state and power of the i2th flexible load are set as the initial state. in and are the initial energy state and initial power of the i2th flexible load respectively;

[0102] 3) Set the initial state of the i3th building agent to in:

[0103]

[0104] in, is the state of the i3th building follower agent, is the ratio of the power and characteristic coefficient of the i3th building follower agent; and The temperature state and working power of the i3th building are set at the initial time in and are the initial temperature state and initial power of the i3th building respectively.

[0105] S5, set the control gain of each cluster leader agent and initialize the leader agent state according to the follower agent state of each cluster in S4;

[0106] 1) Set the control gain K of each cluster leader agent BESS , K FL , K BBESS , where K BESS is the control gain of the leader agent of the battery cluster, K FL is the control gain of the leader agent in the flexible load cluster, K BBESS is the control gain of the leader agent of the building group, and K BESS , K FL , K BBESS are all constants greater than 0.

[0107] 2) Initialize the leader agent state to the state of the first follower agent it communicates with; that is, the initial states of the leader agent in the battery cluster are:

[0108]

[0109] in, is the initial state of the leader agent of the battery cluster, is the ratio of the power and characteristic coefficient of the leader agent of the battery cluster at the initial moment, is the initial power of the leader agent of the battery cluster, is the characteristic coefficient of the leader agent of the battery cluster.

[0110] The initial states of the leader agent in the flexible load cluster are:

[0111]

[0112] in, is the initial state of the leader agent of the flexible load cluster, is the ratio of the power and characteristic coefficient of the leader agent of the flexible load cluster at the initial moment, is the initial power of the leader agent in the flexible load cluster, is the characteristic coefficient of the leader agent in the flexible load cluster.

[0113] The initial states of the building cluster leader agents are:

[0114]

[0115] in, is the initial state of the building group leader agent, is the ratio of the power and characteristic coefficient of the leader agent of the building group at the initial moment, is the initial power of the leader agent of the building group, is the characteristic coefficient of the building complex leader intelligent agent.

[0116] S6, set the convergence time of the observer and controller, and set the time base generator function parameters;

[0117] 1) Set the observer convergence time t s1 , t s2 , set the observer time base generator function H1(t):

[0118] When t∈(0,t s1 ], H1(t)=h1(t), when t∈(t s1 ,t s2 ], H1(t)=h2(t), when t>t s2 hour,

[0119] H1(t)=0, where

[0120]

[0121] where t s1 is the convergence time of the proportional error observer of the i-th power and characteristic coefficient (hereinafter referred to as proportional error observer), β i is the observation value of the observer, that is o1 is an arbitrarily small positive constant, where is the proportional error between the follower agent and the leader agent; t s2 is the convergence time of the i-th state error observer, α i is the observation value of the observer, that is o2 is an arbitrarily small positive constant, is the state error between the follower agent and the leader agent, ε1, ε2 are the observer time base generators, k1, k2 are the observer time base generator function gains, and δ1, δ2 are arbitrarily small positive constants.

[0122] 2) Set the controller convergence time t s3 , t s4 , set the controller time base generator function H2(t):

[0123] When t∈(t s2 ,t s3 ], H2(t)=h3(t), when t∈(t3,t s4 ], H2(t)=h4(t), when t>t s4 When H2(t)=0,

[0124]

[0125] where t s3 is the time for the ratio of the i-th follower agent to the leader agent to converge, i.e., limt→t s3 |v i -v0|≤o3, o3 is an arbitrarily small normal number, t s4 is the state convergence time of the i-th follower agent and the leader agent, that is, when limt→t s4 |r i -r0|≤o4, o4 is an arbitrarily small positive constant, ε3, ε4 are the controller time base generator, k1, k2 are the controller time base generator function gains, δ1, δ2 are arbitrarily small positive constants, and i=N1+N2+N3.

[0126] S7, initialize the observer observation value

[0127] initialization and in: are the state error observer observation value and proportional error observer observation value of the i1th energy storage battery follower agent, respectively, and their initial states are set to are the state error observer observation value and proportional error observer observation value of the i2th flexible load follower agent, respectively, and their initial states are set to are the state error observer observation value and the proportional error observer observation value of the i3th building follower agent, respectively, and their initial states are set to

[0128] S8, when the system sets the target power P T Afterwards, the leader agent judges the system state according to the positive or negative power value and allocates the total target power of the battery cluster, flexible load and building group according to the initial state of each follower agent in S4;

[0129] If the target power is positive, the system is in charging mode, and each leader agent will allocate the target power according to the adjustment capacity of each cluster; if it is negative, the system enters discharge mode, and only the battery cluster bears the power output, that is, in are the total target power of battery cluster, flexible load and building group respectively.

[0130] The leader agent's strategy for allocating target power based on the regulation capacity of each cluster includes:

[0131]

[0132]

[0133] Where z is the regulation capacity of each device unit in the heterogeneous energy storage system; is the regulation capacity of the i1th energy storage battery, is the regulation capacity of the i2th flexible load, is the regulation capacity of the i3th building;

[0134] is the maximum state of charge of the i1th energy storage battery, is the maximum energy state of the i2th flexible load; is the maximum temperature state of the i3th building, Δt is the total time of primary power action; N1, N2 and N3 are the number of energy storage batteries, flexible loads and buildings in each cluster respectively.

[0135] S9, based on the observer parameters set in S2, the observer time base generator function set in S6, and the initial observation values of each observer in S7, each observer completes the error observation between its follower intelligent agent and the leader intelligent agent within the observer convergence time set in S6 according to the protocol.

[0136] The protocols for each observer include:

[0137] 1) Observation protocol of battery cluster observer:

[0138]

[0139] Where η1(t)=(1 / 2λ1(Q1))H1(t), λ1(Q1) is the minimum eigenvalue of the matrix Q1, is the control input of the i1th energy storage battery follower agent. When t∈(0,t s2 ]hour,

[0140] 2) Observation protocol of flexible load cluster observer:

[0141]

[0142]

[0143] Where η2(t)=(1 / 2λ1(Q2))H1(t), λ1(Q2) is the minimum eigenvalue of the matrix Q2, is the control input of the i2th flexible load follower agent, when t∈(0,t s2 ]hour,

[0144] 3) Observation protocol of building cluster observer:

[0145]

[0146] Where η3(t)=(1 / 2λ1(Q3))H1(t), λ1(Q3) is the minimum eigenvalue of the matrix Q3, is the control input of the i3th building follower agent, when t∈(0,t s2 ]hour,

[0147] In step S10, based on the total target power of the battery cluster, flexible load cluster, and building cluster obtained in step S8 and the error observed by the observer in step S9, each follower agent in the cluster updates its own state and regulates the power of the equipment unit according to the characteristic coefficient set in step S2. At the same time, based on the control gain of the leader agent set in step S5, the leader agent in each cluster updates its own state.

[0148] The rules for each cluster leader agent to update its own status include:

[0149] 1) The state update rule of the battery cluster leader agent is:

[0150]

[0151] in, It can be directly measured by the intelligent agent of each device unit in the battery cluster, K BESS Control gains for the cluster leader agent.

[0152] 2) The state update rule of the flexible load leader agent is:

[0153]

[0154] Among them, K FL is the control gain of the leader agent of the battery cluster, It can be directly measured by the intelligent entities of each equipment unit of the flexible load cluster.

[0155] 3) The update rule of the building group leader agent is:

[0156]

[0157] Among them, K BBESSControl gain for the leader agent of the building group, It can be directly measured by each intelligent agent in the building complex.

[0158] The rules for each follower agent in the cluster to update its own state include:

[0159] 1) The update rule of the battery cluster follower agent is:

[0160]

[0161] in is the sliding surface of the controller of the i1th energy storage battery follower agent,

[0162] 2) The update rule of the flexible load follower agent is:

[0163]

[0164]

[0165] in is the sliding surface of the controller of the i2-th flexible load follower agent,

[0166]

[0167] 3) The update rules of the building group follower agent are:

[0168]

[0169] in is the sliding surface of the i3-th building follower agent controller,

[0170]

[0171] Controlling the power of each device unit includes:

[0172] Set the power of the i1th energy storage battery The power of the i2th flexible load is The power of the i3th building is S11, completing the target power control task within a preset time.

[0173] Among them, the target power control task is:

[0174] 1) Target power tracking:

[0175]

[0176] 2) Within the preset time, each device unit in the heterogeneous energy storage system distributes power according to the characteristic coefficient ratio:

[0177]

[0178] 3) Balance the status of each equipment unit within the preset time:

[0179]

[0180] Based on similar inventive concepts, an embodiment of the present invention further provides a computer storage medium storing a readable program, which, when executed by a processor, can execute the above-mentioned distributed control method for a heterogeneous energy storage system at any preset time.

[0181] Based on similar inventive concepts, an embodiment of the present invention provides an electronic device, comprising: a processor, a memory, a communication interface, and a communication bus, wherein the processor, the memory, and the communication interface communicate with each other via the communication bus;

[0182] The memory is used to store at least one executable instruction, and the executable instruction enables the processor to perform operations corresponding to the above-mentioned distributed control method for a heterogeneous energy storage system under any preset time.

[0183] Based on similar inventive concepts, an embodiment of the present invention further provides a computer program product, including computer instructions, which instruct a computing device to execute operations corresponding to the above-mentioned distributed control method for a heterogeneous energy storage system at any preset time.

[0184] Example 2

[0185] The technical solution of the present invention is described below through specific examples;

[0186] In this embodiment, it is assumed that there are three different clusters in a heterogeneous energy storage system, where the number of device units and the communication topology of the three clusters are as follows: Figure 1 The specific implementation steps are as shown in Figure 2 shown.

[0187] (1) Set up the communication network topology according to the communication status of the intelligent agents in each cluster, such as Figure 1 The adjacency matrices of the three clusters shown are:

[0188]

[0189] The three leadership matrices are:

[0190]

[0191] The three degree matrices are:

[0192]

[0193] The remaining matrices can be obtained through calculation.

[0194] (2) Set the parameters of each observer to Set the controller parameters to ρ1 = ρ2 = ρ3 = 0.5.

[0195] (3) Set the parameters and characteristic coefficients of the heterogeneous energy storage system: Set the parameters of each energy storage battery in the battery cluster as follows: V1=1v, V2=1v, V3=1.5v, V4=1.5v, The characteristic coefficients of energy storage batteries are: Set the flexible load parameters as follows: Set the maximum operating power of all flexible loads to 150kw, and the flexible load characteristic coefficients are: Set the building parameters as follows: ΔT1=2℃, ΔT2=2℃, ΔT3=2℃, ΔT4=2℃, The characteristic coefficients of each building are:

[0196] (4) Set the initial state of charge of each energy storage battery in the battery cluster to SOC(0) = [55, 55.3, 55.4, 55.5] T , the highest state of charge is SOC max =[80,80,85,85] T , set the initial power of each energy storage battery to P BESS (0) = [26, 23, 24, 25], initialize the battery cluster agent to the initial state r BESS (0)=SOC(0)=[55,55.3,55.4,55.5] T , Set the initial energy state of the flexible load cluster to SOE(0) = [31.5, 30, 30.2, 31.2, 31] T , the highest energy state is SOE max =[80,80,90,90,90] T , set the initial power of each flexible load to P FL (0) = [34, 30, 31, 32, 33], initialize the flexible load cluster agent to the initial state r FL (0)=SOE(0)=[31.5,30,30.2,30.5,31] T, Set the initial temperature state of the building complex to SOT(0)=[41,40.2,40.8,40] T , the highest temperature state is SOT max =[85,85,90,90] T , the initial power is P BBESS (0) = [38, 36, 37, 35], initialize the state of the building complex agent r BBESS (0)=SOT(0)=[41,40.2,40.8,40] T ,

[0197] (5) Set the leader control gain: K BESS =0.001, K FL =0.001, K BBESS =0.001. Initialize the state of each cluster leader to

[0198] (6) Set the observer convergence time t s1 =2s,t s2 =4s, set the controller convergence time t s1 =10s, t s1 =16s, set the total time of power action Δt = 30s. Set the time base generator parameters δ1 = 0.01, δ2 = 0.01, δ3 = 0.02, δ4 = 0.02, set the time base generator gains k1 = 2, k2 = 2, k3 = 4, k4 = 4. (7) Initialize the states of each observer as:

[0199] (8) The system operator gives the target power signal P T =1000kw, the power signal is greater than 0, the system is in the charging state, and each cluster distributes the power according to the adjustment capacity, that is,

[0200] (9), at t∈(0,t s2 ], the cluster agent observer observes the error between its agent and the leader agent according to the following protocol:

[0201]

[0202] The flexible load cluster agent observer observes the error between its agents and the leader agent according to the following protocol:

[0203]

[0204]

[0205] The swarm agent observer observes the error between its agents and the leader agent according to the following protocol:

[0206]

[0207] (10), at t∈(t s2 ,t s4 ], the battery cluster agent updates its own state according to the following rules:

[0208]

[0209] Each intelligent agent regulates the energy storage battery power as follows: The leader agent updates its state according to the following rules:

[0210]

[0211] The flexible load cluster agent updates its state according to the following rules:

[0212]

[0213] The power of flexible load controlled by each intelligent agent is:

[0214] The leader agent updates its state according to the following rules:

[0215]

[0216] The building complex agent updates its status according to the following rules:

[0217]

[0218] The building load power controlled by each intelligent agent is:

[0219] The leader agent updates its state according to the following rules:

[0220]

[0221] (11) Within the preset controller convergence time, the system completes the target power control task.

[0222] In order to verify the effectiveness of the present invention, a simulation experiment was carried out.

[0223] Figure 3 This is the power trend chart of each cluster equipment unit, Figure 4The power of each device unit and the ratio of the characteristic coefficient are shown in the trend chart. It can be seen that in this embodiment, the power of each device unit strictly complies with its maximum power limit requirement during regulation, and the regulation can be completed within the preset time and the power is allocated strictly according to the ratio of its characteristic coefficient, meeting the requirements for safe and scientific regulation of power resources.

[0224] Figure 5 This is a trend chart of the sum of the equipment unit powers. It can be seen that the proposed power allocation method can track the operator's scheduling target power well and meet the power supply and demand balance requirements.

[0225] Figure 6 The state trend diagram of each cluster device unit is shown in Figure 2. It can be seen that under the proposed distributed control strategy, the state of each cluster device unit can converge to the same state within the preset time to meet the system requirements.

[0226] In summary, this invention ensures that each device unit in a heterogeneous energy storage system allocates power according to its adjustable capacity based on its own parameters and component aging, and precisely regulates power within a preset timeframe, following the characteristic coefficient ratio, thereby preventing overcharging or over-discharging in the system. This solution improves system management efficiency, achieves optimal regulation of power resources, and ensures the stable and safe operation of the heterogeneous energy storage system.

[0227] Example 3

[0228] Based on the distributed control method for a heterogeneous energy storage system at any preset time proposed in Example 1, this embodiment proposes a distributed control system for a heterogeneous energy storage system at any preset time, specifically including:

[0229] Classification module: Based on the electrical characteristics of each device unit in the heterogeneous energy storage system, it is divided into three clusters: battery cluster, flexible load cluster, and building cluster. Each device unit is equipped with a follower intelligent agent, and each cluster is equipped with a leader intelligent agent to communicate with the follower intelligent agents in the cluster.

[0230] Observer controller parameter setting module: set the parameters of the observer and controller;

[0231] Equipment unit parameter setting module: sets system parameters and characteristic coefficients for equipment units in each cluster;

[0232] Follower agent initialization module: initializes the state of follower agents in each cluster based on the device unit system parameters;

[0233] Leader agent initialization module: sets the control gain of each cluster leader agent and initializes the leader agent state according to the initial state of the follower agent;

[0234] Convergence time setting module: sets the convergence time of the observer and controller, and sets the time base generator function parameters;

[0235] Observer initialization module: initializes the observer observation value;

[0236] Power allocation module: When the system sets the target power P T Afterwards, the leader agent judges the system state according to the positive or negative power value and allocates the total target power of the battery cluster, flexible load and building group according to the initial state of each follower agent;

[0237] Error Observation Module: Based on the observer parameters, time base generator function and the initial observation value of the observer, each observer completes the error observation between its follower agent and the leader agent within the observer convergence time according to the protocol;

[0238] Agent state update module: Based on the total target power of the battery cluster, flexible load cluster, and building cluster, as well as the observer's observation error, each follower agent in the cluster updates its own state and regulates the power of the equipment unit according to the characteristic coefficient. Simultaneously, based on the leader agent's control gain, the leader agent in each cluster updates its own state.

[0239] And, the control module: completes the target power control task within the preset time.

[0240] The method of the present invention can be implemented in hardware, firmware, or as software or computer code that can be stored in a recording medium (such as a CDROM, RAM, floppy disk, hard disk or magneto-optical disk), or as computer code that is originally stored in a remote recording medium or a non-temporary machine-readable medium downloaded over a network and will be stored in a local recording medium, so that the method described herein can be stored in such software processing on a recording medium using a general-purpose computer, a special-purpose processor or programmable or special-purpose hardware (such as an ASIC or FPGA). It will be understood that a computer, a processor, a microprocessor controller or programmable hardware includes a storage component (e.g., RAM, ROM, flash memory, etc.) that can store or receive software or computer code, and when the software or computer code is accessed and executed by a computer, a processor or hardware, the method described herein is implemented. In addition, when a general-purpose computer accesses the code for implementing the method shown here, the execution of the code converts the general-purpose computer into a special-purpose computer for executing the method shown here.

[0241] The basic principles, main features, and advantages of the present invention are shown and described above. Those skilled in the art should understand that the present invention is not limited to the above embodiments. The above embodiments and descriptions are merely illustrative of the principles of the present invention. Various changes and modifications may be made to the present invention without departing from the spirit and scope of the present invention, and such changes and modifications fall within the scope of the invention as claimed.

Claims

1. A distributed control method for heterogeneous energy storage systems under any preset time, characterized in that: The following steps are involved: Based on the electrical characteristics of each device unit in the heterogeneous energy storage system, it is divided into three clusters: battery cluster, flexible load cluster, and building cluster. Each device unit is equipped with a follower agent, and each cluster is equipped with a leader agent to communicate with the follower agents in the cluster. Set the parameters of the observer and controller; Setting system parameters and characteristic coefficients for equipment units in each cluster; Initialize the states of follower agents in each cluster based on the device unit system parameters; Set the control gain of each cluster leader agent and initialize the leader agent state according to the initial state of the follower agent; Set the convergence time of the observer and controller, and set the time base generator function parameters; Initialize the observer observation value; When the system sets the target power P T Afterwards, the leader agent determines the system state based on the positive or negative power value and allocates the total target power of the battery cluster, flexible load and building group according to the initial state of each follower agent; Based on the observer parameters, the time base generator function and the initial observation value of the observer, each observer completes the error observation between its follower agent and the leader agent within the observer convergence time according to the protocol; Based on the total target power of the battery cluster, flexible load cluster, and building cluster, as well as the observer's observed errors, each follower agent in the cluster updates its own state and regulates the power of the equipment unit according to the characteristic coefficient. Simultaneously, based on the leader agent's control gain, the leader agent in each cluster updates its own state. Complete the target power control task within the preset time.

2. The distributed control method for heterogeneous energy storage systems at any preset time according to claim 1, characterized in that: The settings of the system parameters and characteristic coefficients include: 1) For the i1th energy storage battery, set its system parameters as: in, are the battery capacity and coulombic efficiency, Its operating voltage, are its maximum input power and maximum output power respectively; its characteristic coefficient for: Where N1 is the number of batteries in the battery cluster; 2) For the i2th flexible load, set its system parameters in The i2th is the flexible load capacity, For its maximum working power, set its characteristic coefficient for: Among them, N2 is the number of flexible loads in the flexible load cluster; 3) For the i3th building, set its system parameters in and are the capacity and Coulomb efficiency of the i3th building, is the maximum operating power of the i3th building, is the indoor temperature difference of the i3th building, in Set the maximum and minimum temperatures for the i3th building indoors; set its characteristic coefficients for: Among them, N3 is the number of buildings in the building complex.

3. The distributed control method for heterogeneous energy storage systems at any preset time according to claim 2, characterized in that: The steps of initializing the states of follower agents in each cluster include: 1) Set the state of the i1th energy storage battery follower agent to in: in, is the state of the i1th energy storage battery follower agent, is the ratio of the power and characteristic coefficient of the i1th energy storage battery follower agent, and The state of charge and power of the i1th energy storage battery are set to their initial states. in and are the initial state of charge and initial power of its energy storage battery respectively; 2) Set the state of the i2th flexible load agent to in: in, is the state of the i2th flexible load follower agent, is the ratio of the power and characteristic coefficient of the i2th flexible load follower agent, and The energy state and power of the i2th flexible load are set as the initial state. in and are the initial energy state and initial power of the i2th flexible load respectively; 3) Set the initial state of the i3th building agent to in: in, is the state of the i3th building follower agent, The power and characteristic coefficient ratio of the i3th building follower agent; and The temperature state and working power of the i3th building are set at the initial time in and are the initial temperature state and initial power of the i3th building respectively.

4. The distributed control method for heterogeneous energy storage systems at any preset time according to claim 3, characterized in that: The initialized observer observations include: and in: are the state error observer observation value and proportional error observer observation value of the i1th energy storage battery follower agent, respectively, and their initial states are set to are the state error observer observation value and proportional error observer observation value of the i2th flexible load follower agent, respectively, and their initial states are set to are the state error observer observation value and the proportional error observer observation value of the i3th building follower agent, respectively, and their initial states are set to 5. The distributed control method for heterogeneous energy storage systems at any preset time according to claim 4, characterized in that: The total target power allocation for battery clusters, flexible loads, and buildings includes: If the target power is positive, the system is in charging mode, and each leader agent will allocate the target power according to the adjustment capacity of each cluster; if it is negative, the system enters discharge mode, and only the battery cluster bears the power output, that is, in are the total target power of battery cluster, flexible load and building group respectively.

6. The distributed control method for heterogeneous energy storage systems at any preset time according to claim 4, characterized in that: The protocols for each observer include: 1) Observation protocol of battery cluster observer: Where η1(t) = (1 / 2λ1(Q1))H1(t), λ1(Q1) is the minimum eigenvalue of the matrix Q1, Q1 is the battery cluster enhanced Laplace matrix, H1(t) is the observer time base generator function, is the control input of the i1th energy storage battery follower agent. When t∈(0,t s2 ]hour, 2) Observation protocol of flexible load cluster observer: Where η2(t) = (1 / 2λ1(Q2))H1(t), λ1(Q2) is the minimum eigenvalue of the matrix Q2, and Q2 is the flexible load cluster enhanced Laplace matrix. is the control input of the i2th flexible load follower agent, when t∈(0,t s2 ]hour, 3) Observation protocol of building cluster observer: Where η3(t) = (1 / 2λ1(Q3))H1(t), λ1(Q3) is the minimum eigenvalue of the matrix Q3, and Q3 is the building complex enhanced Laplace matrix. is the control input of the i3th building follower agent, when t∈(0,t s2 ]hour, 7. The distributed control method for heterogeneous energy storage systems at any preset time according to claim 3, characterized in that: When each follower agent in the cluster regulates the power of the equipment unit, it sets the power of the i1th energy storage battery The power of the i2th flexible load is The power of the i3th building is 8. A distributed control system for heterogeneous energy storage systems at any preset time, characterized in that: include: Classification module: Based on the electrical characteristics of each device unit in the heterogeneous energy storage system, it is divided into three clusters: battery cluster, flexible load cluster, and building cluster. Each device unit is equipped with a follower intelligent agent, and each cluster is equipped with a leader intelligent agent to communicate with the follower intelligent agents in the cluster. Observer controller parameter setting module: set the parameters of the observer and controller; Equipment unit parameter setting module: sets system parameters and characteristic coefficients for equipment units in each cluster; Follower agent initialization module: initializes the state of follower agents in each cluster based on the device unit system parameters; Leader agent initialization module: sets the control gain of each cluster leader agent and initializes the leader agent state according to the initial state of the follower agent; Convergence time setting module: sets the convergence time of the observer and controller, and sets the time base generator function parameters; Observer initialization module: initializes the observer observation value; Power allocation module: When the system sets the target power P T Afterwards, the leader agent judges the system state according to the positive or negative power value and allocates the total target power of the battery cluster, flexible load and building group according to the initial state of each follower agent; Error Observation Module: Based on the observer parameters, time base generator function and the initial observation value of the observer, each observer completes the error observation between its follower agent and the leader agent within the observer convergence time according to the protocol; Agent state update module: Based on the total target power of the battery cluster, flexible load cluster, and building cluster, as well as the observer's observation error, each follower agent in the cluster updates its own state and regulates the power of the equipment unit according to the characteristic coefficient. Simultaneously, based on the leader agent's control gain, the leader agent in each cluster updates its own state. And, the control module: completes the target power control task within the preset time.

9. A computer storage medium storing a readable program, characterized in that: When the program is executed by the processor, it can execute the distributed control method of the heterogeneous energy storage system at any preset time as described in any one of claims 1 to 7.

10. An electronic device, characterized in that: include: A processor, a memory, a communication interface and a communication bus, wherein the processor, the memory and the communication interface communicate with each other via the communication bus; The memory is used to store at least one executable instruction, and the executable instruction enables the processor to perform operations corresponding to the distributed control method for a heterogeneous energy storage system at any preset time according to any one of claims 1 to 7.

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