Micro-grid operation regulation and control method and device based on information physical cooperation, and storage medium

Abstract

The invention discloses a micro-grid operation regulation and control method and device based on information physical cooperation and a storage medium, and belongs to the technical field of micro-grid control, and the method comprises the steps: executing a consistency regulation and control mechanism when the communication quality of an information layer is good; when the information layer has transmission uncertainty, triggering an information layer communication enhancement and physical layer disturbance suppression strategy; when the power generation unit is continuously fully loaded, a virtual cost adjustment mechanism is triggered; according to the consistency regulation and control mechanism, marginal costs of all power generation units in the micro-grid system tend to be the same based on a virtual leader-following consistency algorithm; a virtual cost adjustment mechanism: introducing a virtual cost parameter to correct the marginal cost of the output overrun power generation unit; a physical layer disturbance suppression strategy: starting a sliding mode controller, and suppressing disturbance through a switching control law; and an information layer communication enhancement strategy: evaluating the importance level of the data and preferentially transmitting the data with high importance through a low-risk communication path. And safe, stable and energy-saving operation of the micro-grid system is realized.

Description

Technical Field

[0001] This invention belongs to the field of microgrid control technology, and in particular relates to a cyber-physical collaborative microgrid operation control method, device and storage medium. Background Technology

[0002] Currently, islanded microgrids (IMGs) are evolving towards greater intelligence, gradually transforming into cyber-physical microgrids (CPMGs). CPMGs will be structured as cyber-physical systems (CPS), encompassing both physical and information devices, and will achieve electrical data transmission and interaction through communication networks. Their operational control effectiveness essentially depends on the close cooperation between the information and physical layers. Cyber-physical microgrids and islanded microgrids are not opposing or parallel concepts, but rather microgrid types defined from different dimensions. From a "technical architecture" perspective, cyber-physical microgrids are characterized by intelligent system control achieved through deep integration of the information and physical layers. Islanded microgrids, from an "operational mode" perspective, are characterized by being independent of the main power grid (or disconnected from it), relying on their own energy sources for independent power supply. Furthermore, the two can be deeply integrated: isolated microgrids often need to leverage the technological advantages of collaborative cyber-physical microgrids (such as real-time data acquisition and dynamic optimization control) to improve the overall observability and controllability of the system, and to carry out voltage, frequency, and power regulation operations to maintain stable power supply. In addition, cyber-physical microgrids can also operate in "grid-connected" or "islanded" modes, with islanded mode being just one of its suitable operating scenarios. In the context of cyber-physical convergence, how to efficiently utilize communication data to achieve the safe, stable, and economical operation of CPMGs is a key research focus.

[0003] Because IMGs (Integrated Power Grids) lack the support of a large power grid, they face more challenges and have greater research value. Currently, one of the core research topics is how to efficiently regulate the active power output of distributed energy resources (DERs) within IMGs. The research goal is to achieve optimal active power allocation through the design of cooperative control strategies. In most studies, droop control is a commonly used method, which can generate output commands based on capacity ratios. However, using droop control for active power allocation has significant shortcomings: on the one hand, allocating active power based on capacity ratios often fails to guarantee the energy utilization rate of the microgrid; on the other hand, under the influence of mismatched line inductance parameters, droop control struggles to achieve precise proportional allocation of active power. Furthermore, due to the droop characteristics, the output voltage and frequency of the controlled DER cannot be adjusted to their rated values. Therefore, how to achieve optimal active power allocation still requires further research. Under the premise that each DER has a production margin, master-slave control models have attracted considerable attention from scholars. In this mode, the system voltage and frequency are stabilized by the main power supply (usually a large-capacity energy storage), while each slave power supply adjusts its output according to given output commands. This reduces the risk of voltage and frequency instability and ensures the supply and demand balance of the CPMG. Given this context, considering the physical interference (such as external disturbances and model parameter perturbations) and information interference (such as packet loss and bandwidth limitations) that can occur during CPMG operation, how to maintain the safe, stable, and energy-efficient operation of the CPMG under the superposition of these two types of interference requires further research. Summary of the Invention

[0004] The purpose of this invention is to overcome the shortcomings of the prior art and provide a cyber-physical coordination microgrid operation control method, device and storage medium that executes corresponding control strategies according to the information layer operation status, achieving energy-saving operation while taking into account the microgrid communication quality and resistance to physical disturbances.

[0005] To achieve the above objectives, the present invention is implemented using the following technical solution:

[0006] In a first aspect, the present invention provides a cyber-physical coordinated microgrid operation and control method, wherein the pre-constructed control strategy for the cyber-physical coordinated control of the microgrid includes:

[0007] Physical layer active power optimization strategy: Consistency control mechanism: Based on the virtual leader-follower consensus algorithm, the marginal costs of each distributed generation unit in the microgrid system are made to converge. The output command of the generation unit is controlled by the converged marginal cost, and the marginal cost reference value of the virtual leader is determined by the Lagrange multiplier method; Virtual cost adjustment mechanism: For the generation unit whose output command reaches the output limit, the virtual cost parameter is introduced to correct its marginal cost, so that it exits the full load operation state and is maintained within the preset safe output boundary;

[0008] Physical layer disturbance suppression strategy: When there is an uncertain disturbance in the local control loop of the power generation unit, the sliding mode controller is activated to couple the virtual leader-follower consensus algorithm, and the disturbance is suppressed by switching the control law;

[0009] Information layer communication enhancement strategies: Data importance assessment mechanism: assess the importance level of data based on the control coupling strength between power generation units; Transmission path reconfiguration mechanism: prioritize the transmission of high-importance data through low-risk communication paths;

[0010] The microgrid operation and control method includes: executing a consistency control mechanism when the information layer communication quality is good; triggering an information layer communication enhancement strategy when transmission uncertainty occurs in the information layer; triggering a physical layer disturbance suppression strategy when there is uncertainty disturbance in the physical layer; and triggering a virtual cost adjustment mechanism when the power generation unit is continuously fully loaded.

[0011] Optionally, when the microgrid physical layer implements the consistency control mechanism, the formula for calculating the generation cost of the i-th generation unit is as follows:

[0012] ,

[0013] Where i is the index of the power generation unit. Let be the power generation cost of the i-th power generation unit; , and All are power generation cost coefficients; Let be the active power output of the i-th power generation unit;

[0014] The optimization objective of the microgrid physical layer's consistent control mechanism is to minimize the total generation cost while satisfying the energy balance constraint. The expressions for the optimization objective and the constraint are as follows:

[0015] ,

[0016] ,

[0017] in, The objective function for implementing the consistency control mechanism at the physical layer is n, where n is the number of power generation units. This represents the load demand.

[0018] Optionally, determining the marginal cost reference value of the virtual leader using the Lagrange multiplier method includes:

[0019] Based on the optimization objective and constraints of the consistency control mechanism, the Lagrange parametric equations are constructed as follows:

[0020] ,

[0021] in, Let be the marginal cost of the i-th power generation unit. The Lagrange parameter equation relating the power output and marginal cost of a generating unit is given.

[0022] The extremum conditions of the Lagrange parametric equations are solved using the following formula:

[0023] ,

[0024] The marginal cost reference value is obtained based on the extreme value condition and the constraints of the consistency control mechanism, and the calculation formula is as follows:

[0025] ,

[0026] when hour, ,

[0027] in, The marginal cost reference value after convergence; j is the non-i index of the power generation unit; This is a continuous multiplication operation.

[0028] Optionally, the virtual cost parameter is dynamically generated by a proportional-integral controller, and the calculation formula is as follows:

[0029] ,

[0030] in, Let be the virtual cost of the i-th power generation unit; and This is the preset safe output boundary for the i-th power generation unit; and This refers to the gain of the proportional-integral controller.

[0031] Optionally, when the microgrid physical layer executes the consensus control mechanism, a consensus controller is constructed as follows:

[0032] ,

[0033] in, h is the marginal cost consistency controller for the i-th controlled generation unit, where i is the index of the controlled generation unit; h is the non-i index of the controlled generation unit. Let i be the set of adjacent power generation units of the i-th controlled power generation unit; For the coefficients of the consistency controller; This represents the controlled state of the h-th controlled power generation unit; This represents the controlled state of the i-th controlled power generation unit; Let be the connection weights of the i-th controlled power generation unit and the h-th controlled power generation unit; The status of the virtual leader power generation unit; The connection weights between the i-th controlled power generation unit and the virtual leader power generation unit;

[0034] Based on the aforementioned consistency controller, when the controlled power generation unit is subjected to disturbance, an anti-disturbance consistency controller is constructed as follows:

[0035] ,

[0036] in, For the i-th controlled power generation unit, there is a disturbance rejection consistency controller; Let h be the disturbance received by the h-th controlled power generation unit; Let be the disturbance received by the i-th controlled power generation unit; The disturbance received by the virtual leader power generation unit; and These are the parameters for the consistency controller; , , All are data packet loss rates;

[0037] Based on the disturbance-resistant consistency controller, the marginal cost tracking deviation equation for the i-th controlled power generation unit after a disturbance is obtained as follows:

[0038] ,

[0039] in, The marginal cost tracking deviation after the i-th controlled power generation unit is disturbed;

[0040] The marginal cost tracking deviation equation of the disturbed controlled power generation unit is converged to 0, and the optimal output command of the disturbed controlled power generation unit is obtained.

[0041] Optionally, when the disturbance experienced by the controlled power generation unit changes, a sliding mode-consistency controller is constructed based on the marginal cost tracking deviation as follows:

[0042] Sliding surface: ,

[0043] Switching control laws: ,

[0044] Lyapunov function: ,

[0045] in, It is a sliding surface; and The coefficient of the sliding surface; To switch control laws; This is the switching gain; Sgn(.) is the sign function; It is a Lyapunov function.

[0046] Optionally, methods for assessing the importance level of data include:

[0047] The sensitivity of the marginal contribution of each power generation unit's output to the total output of the microgrid system is quantified by the following formula:

[0048] ,

[0049] The coupling strength of the marginal costs of adjacent power generation units is quantified, and the calculation formula is as follows:

[0050] ,

[0051] ,

[0052] ,

[0053] in, For the i-th controlled power generation unit, the marginal cost consistency controller is used. Let $\mathbf{j}$ be the marginal cost of the $j$-th controlled power generation unit.

[0054] The importance ranking of power generation units is obtained based on the quantified sensitivity and coupling strength.

[0055] The importance of the data transmitted by the power generation units is mapped according to the importance ranking of the power generation units.

[0056] Optionally, the optimization objective of the information layer's transmission path reconstruction mechanism is to maximize the reliability of important data transmission. The constraints include: the number of connected channels must be greater than the number of power generation units; there must be at most one connection channel between two routers; the controlled power generation unit is connected to only one input channel; and the virtual leader power generation unit is connected to at least one output channel and not connected to any input channel. The expressions for the optimization objective and constraints are as follows:

[0057] Optimization goal: ,

[0058] Constraints: ,

[0059] ,

[0060] ,

[0061] ,

[0062] in, The number of router nodes; Sgn(.) is the sign function; For from router To router The probability of communication channel risk; For from router To router Communication channels; For router The set of adjacent connectable routers; The number of controllable power generation units; For from router To router Communication channels; From Virtual Leader Generator Unit to Router Communication channels; For from router Communication channel to the virtual leader power generation unit; The set of neighboring routers for the virtual leader generator unit.

[0063] Secondly, the present invention provides a cyber-physical coordinated microgrid operation control device, comprising: a pre-constructed control strategy for cyber-physical coordinated control of the microgrid, including:

[0064] Physical layer active power optimization strategy: Consistency control mechanism: Based on the virtual leader-follower consensus algorithm, the marginal costs of each distributed generation unit in the microgrid system are made to converge. The output command of the generation unit is controlled by the converged marginal cost, and the marginal cost reference value of the virtual leader is determined by the Lagrange multiplier method; Virtual cost adjustment mechanism: For the generation unit whose output command reaches the output limit, the virtual cost parameter is introduced to correct its marginal cost, so that it exits the full load operation state and is maintained within the preset safe output boundary;

[0065] Physical layer disturbance suppression strategy: When there is an uncertain disturbance in the local control loop of the power generation unit, the sliding mode controller is activated to couple the virtual leader-follower consensus algorithm, and the disturbance is suppressed by switching the control law;

[0066] Information layer communication enhancement strategies: Data importance assessment mechanism: assess the importance level of data based on the control coupling strength between power generation units; Transmission path reconfiguration mechanism: prioritize the transmission of high-importance data through low-risk communication paths;

[0067] The microgrid operation control device includes:

[0068] Consistency control module: Used to execute consistency control mechanisms when the information layer communication quality is good;

[0069] Communication enhancement module: used to trigger information layer communication enhancement strategies when transmission uncertainties occur at the information layer;

[0070] Anti-disturbance module: used to trigger physical layer disturbance suppression strategies when there are uncertain disturbances in the physical layer;

[0071] Virtual cost adjustment module: used to trigger the execution of the virtual cost adjustment mechanism when the power generation unit is continuously at full load.

[0072] Thirdly, the present invention provides a computer storage medium having a computer program stored thereon, wherein when the computer program is executed by a processor, the cyber-physical cooperative microgrid operation and control method as described in any of the first aspects is implemented.

[0073] Compared with existing technologies, the beneficial effects achieved by this invention are as follows: The designed sliding mode control + consistency control allows the power generation units to collaboratively execute control commands even in the presence of disturbances, enhancing the uncertainty resistance of the controlled system and ensuring that the DER executes the optimal output command. Furthermore, distributed active power control through virtual cost and virtual leader-follower consistency algorithms can reduce system power generation costs and improve energy utilization, while avoiding equipment overheating and lifespan damage caused by long-term full-load operation of some resources. In the information layer, data importance classification + path reconstruction mechanism ensures the transmission of key control commands. Transmission performance remains unaffected. Under the physical-information collaborative control strategy, the physical layer and information layer combine to form a closed-loop anti-interference system, which will further enhance the robustness of the controlled microgrid system. By designing a sliding mode control switching controller, the system state is forced to run along a preset sliding surface, thereby effectively suppressing external disturbances. At the same time, its disturbance suppression response speed is fast, and even if the controlled variable suddenly encounters an external disturbance, the system abnormal state can be quickly corrected through the switching controller. In the information layer, the path reconstruction method can prioritize the transmission of key data to the destination, reducing the impact of data transmission performance on latency, packet loss, and bandwidth waste, and meeting real-time control requirements. Attached Figure Description

[0074] Figure 1 The diagram shown is a flowchart of a cyber-physical collaborative microgrid operation and control method in one embodiment of the present invention.

[0075] Figure 2 The diagram shown is a cyber-physical cooperative active power control architecture in one embodiment of the present invention.

[0076] Figure 3 The diagram shown is a schematic representation of the topology between a virtual leader and followers under physical layer perturbation in one embodiment of the present invention. Detailed Implementation

[0077] The present invention will be further described below with reference to the accompanying drawings. The following embodiments are only used to more clearly illustrate the technical solution of the present invention, and should not be used to limit the scope of protection of the present invention.

[0078] Example 1

[0079] This embodiment provides a cyber-physical collaborative microgrid operation and control method. By designing a cyber-physical collaborative active power control architecture and a collaborative control strategy, it enables collaborative control of the microgrid's information and physical layers. Specifically, it includes the following steps:

[0080] I. Cyber-physical Co-operation Active Power Control Architecture

[0081] In IMG, physical disturbances, triggered by external disturbances or parameter perturbations, pose a threat to system stability. Therefore, a coupled model incorporating external disturbances is needed to reveal their propagation mechanisms. Packet loss, often stemming from network unreliability, can lead to unstable active power flow and even threaten system security. More importantly, there is a dynamic coupling between information and physical layer issues: power fluctuations caused by physical disturbances need to be suppressed through real-time communication feedback, while packet loss delays control response and amplifies the uncertainty of transient processes. Therefore, it is necessary to consider and address both types of uncertainty simultaneously to improve cross-layer anti-interference capabilities.

[0082] like Figure 2 As shown, the control architecture is divided into two parts: the physical layer and the information layer. The composition and functions of each layer are described below. The information layer includes the following two parts: (1) Communication network: composed of sensors, routers, gateways and channels, responsible for realizing data interaction between controlled DERs; (2) Path update module: used to adjust the transmission path of key data in a timely manner to suppress information uncertainty. The physical layer consists of the following parts: (1) DER and its inverter control loop (including LC filter, Park conversion unit, power calculation unit, current loop and PWM signal generator); (2) Sliding mode-consistency controller: used to solve the problem of physical layer control signal disturbance and ensure that the controlled DER executes the optimal output command; (3) Virtual cost module: used to avoid some DERs from running at full load for a long time and reduce the risk of overheating and damage to their service life.

[0083] Based on the above architecture, the execution process of cyber-physical collaborative control is as follows:

[0084] S1: The output information and power generation cost parameters of each DER will be uploaded to the gateway, and the marginal cost (IC) function will be generated using the Lagrange multiplier method;

[0085] S2: Based on the obtained IC function and the tracking consensus algorithm with virtual leader, a distributed cooperative active power controller is designed to obtain the optimal output command of DER;

[0086] S3: When there is an uncertain disturbance in the control loop, the sliding mode controller is activated to suppress it;

[0087] S4: When the DER is operating at full load, by designing virtual costs and incorporating them into the active power control process, the DER output is limited and a margin is reserved to avoid the DER operating at full load;

[0088] S5: When uncertainties occur in the information layer (such as packet loss), the path update strategy is initiated to update the transmission path, ensuring data transmission quality and power control effectiveness.

[0089] In summary, the control of CPMG is achieved through the coordinated operation of the information layer and the physical layer.

[0090] Graph Theory: The topology of a multi-agent system (MAS) can usually be represented as a directed graph. It can be viewed as a series of nodes: If there exists a directed path connecting all nodes, this path is called a "directed tree". Its adjacency matrix is ​​defined as: If the j-th node is connected to the i-th node, ;otherwise, In CPMG, each DER is considered an intelligent agent.

[0091] Consistency Theory: In MAS, assume there are n agents, where the controlled state of the i-th agent is... If there exists a unique agent that can propagate its state along a directed tree to all other agents, that agent is called the "leader." The leader's state is defined as follows: Other intelligent agents are referred to as "followers". In the above case, when there is a directed spanning tree with the leader as the root node and the controller is designed as in formula (1), the following will be achieved: .

[0092] (1)

[0093] in, Let j be the consistency controller for the i-th controlled DER, where i is the index of the controlled DER and j is the non-i index of the controlled DER. Let be the set of adjacent controlled DERs of the i-th controlled DER; For the consistency controller coefficient; This represents the controlled state of the j-th controlled DER; This represents the controlled state of the i-th controlled DER. Let be the connection weights between the i-th controlled DER and the j-th controlled DER; The status of the virtual leader (DER); Let be the connection weight between the i-th controlled DER and the virtual leader DER. If the two are directly related, then... ,otherwise .

[0094] A. Distributed Active Power Control Strategy Based on Virtual Leader-Follower Consensus Algorithm Using Virtual Cost

[0095] In CPMG, the power generation cost of the i-th controlled DER is:

[0096] (2)

[0097] Where i is the index of the controlled DER. Let be the power generation cost of the i-th controlled DER; , and All are power generation cost coefficients; Let be the active power output of the i-th controlled DER;

[0098] The optimization objective of the microgrid physical layer's consistent control mechanism is to minimize the total generation cost while satisfying the energy balance constraint.

[0099] The objective function of the system is: In CPMG, energy balance constraints must also be satisfied:

[0100] (3)

[0101] in, The objective function for implementing the consistency control mechanism at the physical layer is n, where n is the number of controlled DERs. This represents the load demand.

[0102] By using Lagrange parameters Adding it to the above formula, we get:

[0103] (4)

[0104] in, Let i be the marginal cost of the i-th controlled DER. The Lagrange parameter equations relate the power output and marginal cost of a generating unit.

[0105] By taking the partial derivative of (4), the necessary condition for the existence of an extremum is:

[0106] (5)

[0107] According to the Lagrange multiplier method, when When the conditions are consistent, IMG's power generation cost is the lowest. Based on the constraints of equation (3), we can obtain:

[0108] (6)

[0109] when At that time, there were:

[0110] (7)

[0111] in, Here, j represents the converged marginal cost reference value, and j is the non-i index of the power generation unit. This is a continuous multiplication operation.

[0112] Since IC will eventually converge to a unified value, a Virtual Leader (VL) consensus algorithm can be used to design the corresponding controller, where the value of VL is set as shown in equation (7). Based on equation (1), the design form of the IC consensus controller is as follows:

[0113] (8)

[0114] in, and These are parameters in the IC conformance controller.

[0115] In VL-follower consensus control, each DER is typically required to be interconnected, forming a directed spanning tree, with the leader serving as the root node. A virtual leader refers to a role in consensus control where information can be directly transmitted to the controlled DER, or a separate communication device can be designated as the leader, eliminating the need to select an actual DER in the microgrid. This significantly simplifies system control complexity. Under this regulation, the IC of each DER will be adjusted. Assume the i-th controlled DER... Adjusted to Then the corresponding power It can be represented as:

[0116] (9)

[0117] However, considering that the output capability of DER is limited, its active power output command is:

[0118] (10)

[0119] If the output power of some DERs exceeds the limit, they will adjust their output according to the maximum or minimum output constraints. However, if these DERs operate at full load for a long time, they are prone to exiting the consistency control process, thus affecting the overall control effect of the system. To avoid this situation, a virtual cost is designed to adjust the output power of the DERs. When a virtual cost parameter is added to the i-th controlled DER, its... Will be adjusted to Furthermore, the study found that if... With proper selection, the i-th DER will not exit the consistency control process. When the power exceeds the limit, the designed... for:

[0120] (11)

[0121] in, Let be the virtual cost of the i-th controlled DER; and This is the preset safety output boundary for the i-th controlled DER; and This refers to the gain of the proportional-integral controller.

[0122] In MG, the output power of each DER is limited. Therefore, the magnitude of the virtual cost can be determined by setting the output power of the DER. For example, in this embodiment, when the optimal power of the i-th controlled DER exceeds the maximum (or minimum) value, it can be pulled back to 90% by incorporating virtual costs. (90% of the rated maximum power) or 110% (110% of the rated minimum power). Under the above conditions, the following exists:

[0123] or (12)

[0124] in, Let be the updated IC value of the i-th controlled DER under virtual cost intervention. Therefore, the two gain parameters in the virtual cost are constrained by equation (12). In economic regulation, the VL-follower consistency control algorithm will be adopted to make the IC of each DER reach consistency, and the final convergence value will be provided by the virtual leader DER. In this process, the virtual cost will be added to the IC of some DERs, thereby reducing their power generation pressure and preventing these DERs from operating at full load for a long time.

[0125] B. Analyze the collaborative relationship between information layer data communication and physical layer physical devices.

[0126] 1. Communication paths in the network layer can be adjusted according to the priority of physical data.

[0127] For the various types of data involved in decision-making / control operations, it is necessary to assess the importance of this data and assign it corresponding transmission priorities to ensure that important data is transmitted via paths with low packet loss rates. In the event of sudden network congestion, a dynamic path reconstruction function will be activated to reconstruct the data transmission path according to the order of data importance from high to low, thereby ensuring the real-time performance and accuracy of critical data transmission.

[0128] 2. The control effectiveness of the physical layer is related to the defense capabilities provided by the information layer.

[0129] When severe congestion occurs at the network layer, the controlled resources at the physical layer will be regulated through a combination of sliding mode controllers and consensus algorithms. The sliding mode controller will be used to suppress disturbances in the control links, ensuring the effectiveness of the distributed active power control method. When the network layer transmission performance is stable, the physical layer will directly use a consensus algorithm to design the controller and generate active power control commands based on the IC information of adjacent DERs transmitted over the network.

[0130] II. Solutions to Physical Layer Uncertainty Problems

[0131] A. Analyze the impact of uncertainty

[0132] To address physical disturbances, a sliding mode-consensus algorithm is designed based on sliding mode control theory and consensus algorithms to enhance the uncertainty resistance of the controlled system and ensure that the DER executes the optimal control command, ultimately improving operational economy. When physical disturbances exist during the control process, the controller is designed as follows:

[0133] (13)

[0134] in, h is the disturbance-resistant consistency controller for the i-th controlled DER; h is the non-i, non-j index of the controlled DER; This represents the controlled state of the h-th controlled DER. Let h be the disturbance amount experienced by the h-th controlled DER; Let be the disturbance amount experienced by the i-th controlled DER; The disturbance quantity experienced by the virtual leader DER; Let h be the connection weight between the h-th controlled DER and the i-th controlled DER; and These are the parameters for the consistency controller.

[0135] When physical uncertainties are introduced during the control process, the follower's state cannot remain consistent with the VL data. This invention uses any two DERs in the CPMG as the analysis object, and its topology is as follows: Figure 3 As shown, these two DERs are denoted as the i-th DER and the j-th DER, respectively.

[0136] Based on equations (1) and (13), the disturbance rejection consistency controllers for the i-th DER and the j-th DER are respectively:

[0137] (14)

[0138] in, For the j-th controlled DER, a disturbance-resistant consistency controller is used. Let be the connection weights between the i-th controlled DER and the j-th controlled DER; The connection weight between the h-th controlled DER and the j-th controlled DER; Let be the connection weight between the j-th controlled DER and the virtual leader DER.

[0139] By performing a Laplace transform on the above equation, we can obtain:

[0140] (15)

[0141] After processing the above equation, we get:

[0142] (16)

[0143] For the i-th controlled DER, the deviation between its state variable and the set value of the virtual leader DER is:

[0144] (17)

[0145] Applying the Laplace transform to the above equation, we get:

[0146] (18)

[0147] According to Laplace's final value theorem, we can obtain:

[0148] (19)

[0149] in, Using the same method, the deviation between the controlled variables of the j-th controlled DER and the state variables of the virtual leader DER can be obtained as follows:

[0150] (20)

[0151] Through the design of the controller, the final result is achieved. and Each DER's controlled variable will be kept consistent with the VL value, ultimately resulting in the optimal output instruction.

[0152] B. Design of Sliding Mode-Conformity Controller

[0153] According to formula (14), if the disturbance amount , and Changes will lead to steady-state errors between the controlled variables of each DER and the VL command value. Furthermore, if the fluctuation range of the disturbance is too large, it will cause severe overshoot during the control process. Considering the above problems, to further overcome the influence of physical disturbances, the patent proposes a sliding mode-consistency control method. Taking the i-th DER as an example, its controlled deviation equation is as follows:

[0154] (twenty one)

[0155] The sliding surface is designed as follows:

[0156] (twenty two)

[0157] in, It is a sliding surface; and This is the coefficient of the sliding surface.

[0158] The sliding mode controller is designed as Based on the above design, we can conclude that:

[0159] (twenty three)

[0160] make and Then we get:

[0161] (twenty four)

[0162] This is the solution to formula (24). If the Lyapunov function is designed as... Its derivative is:

[0163] (25)

[0164] To ensure that the function satisfies the stability condition of the Lyapunov function, and considering... Then we have:

[0165] (26)

[0166] in, To switch control laws; This is the conversion function for the sliding mode controller.

[0167] During operation, sliding mode control can be designed to switch control laws to make the system state run along a preset sliding surface, which can effectively suppress external disturbances. At the same time, it has a fast response speed in terms of disturbance suppression: even if an external disturbance is suddenly introduced, the system state can be quickly corrected through instantaneous state switching to avoid the expansion of control deviation caused by the disturbance.

[0168] In summary, by designing a sliding mode controller, deviations caused by physical disturbances can be suppressed to zero. Based on this, by executing a consistency control algorithm, it can be ensured that the ICs of each DER synchronously approach the command value of the virtual leader, ultimately obtaining the optimal control command.

[0169] III. Solutions to the Information Layer Uncertainty Problem

[0170] A. The impact of information layer uncertainty

[0171] To address network interference issues, this invention, based on data importance assessment and dynamic path reconstruction methods, designs a transmission path update strategy to enhance the anti-interference capability of communication networks and ensure data transmission quality. The specific design process is as follows: This invention primarily models the impact of network uncertainty as a packet loss problem, and... Figure 2 Taking the scenario shown as an example, by comprehensively considering the uncertainties in the control process, the disturbance rejection and consistency controller is updated as follows:

[0172] (27)

[0173] in, , , , , , All figures represent packet loss rates.

[0174] From the above formula, we can obtain and The Laplace equation is: (28)

[0175] The deviation between the state variable of the i-th controlled DER and the set value of the virtual leader DER is:

[0176] (29)

[0177] The deviation between the state variable of the j-th controlled DER and the set value of the virtual leader DER is:

[0178] (30)

[0179] in, Comparing equations (27) and (30), it can be seen that when network uncertainty is introduced into a controlled system, the ICs of each controlled DER will fail to converge to the set value. Therefore, it is necessary to study measures to address network uncertainty in order to improve communication reliability.

[0180] B. Data Importance Assessment

[0181] The network layer should be able to know the data transmission performance requirements of the physical layer (such as transmission rate, data accuracy, etc.) in order to determine a suitable data transmission scheme. First, it is necessary to model the correlation between active power control services and transmitted data; second, based on the importance analysis method, the impact of changes in data transmission rate and accuracy on the control effect is quantified, and then the importance of different data is determined. Since this patent mainly considers the economic issues of system operation and the service execution time scale is relatively long, the requirement for data transmission rate is low, and the main focus is on improving data transmission accuracy. In the economic regulation scenario, the process mainly includes two steps: (1) IC management, that is, adjusting the IC of the controlled DER to the reference value given by VL; (2) Output management of each DER, that is, solving the optimal output value of each DER based on the IC. The specific steps are as follows:

[0182] First, sort the DERs by importance. The output of the j-th DER is... The output of all controlled DERs in the system is... And its partial derivative with respect to the IC of the j-th DER is:

[0183] (31)

[0184] Therefore, by calculation This allows us to determine the importance of each DER.

[0185] Secondly, the importance of each adjacent DER involved in regulating the DER is prioritized. For the i-th DER, its controller is shown in Equation (27). The partial derivative between this controller and the IC of the j-th DER is:

[0186] (32)

[0187] in, For the i-th controlled DER, the marginal cost consistency controller; Let $\mathbf{j}$ be the marginal cost of the $j$-th controlled DER.

[0188] Based on the quantified sensitivity and coupling strength, the importance ranking of DER is obtained; the importance ranking of DER transmitted data is then sorted according to the importance order of DER; finally, path reconstruction is performed based on the importance ranking results and the principle of "matching data importance with path transmission performance".

[0189] C. Path Restructuring

[0190] For DER (Distributed Grid Management) to support the execution of distributed control strategies, it is necessary to ensure the existence of a directed spanning tree (DSG) in the network (this differs from the network matching strategy of microgrids). Therefore, this invention relies on routers to perform dynamic path optimization to form a path update strategy. It is assumed that a DSG exists in the network topology. If there are 1 router node, then the optimization objective is:

[0191] (33)

[0192] Where max(.) is the maximization function; Sgn(.) is the sign function; For from router To router The probability of communication channel risk; For from router To router The communication channel; if the first The router can send its own information to the first router. One router, otherwise ; For router The set of adjacent connectable routers.

[0193] In addition, the following restrictions must also be met:

[0194] (1) The number of connected channels must be greater than the number of DERs.

[0195] (34)

[0196] in, The number of controllable DERs; This represents the number of router nodes.

[0197] (2) There is at most one connection channel between two routers.

[0198] (35)

[0199] in, For from router To router The communication channel.

[0200] (3) The controlled DER has only one input channel.

[0201] (36)

[0202] (4) VL has at least one output channel and no input channel.

[0203] (37)

[0204] in, From Virtual Leader DER to Router Communication channels; For from router Communication channels to the virtual leader DER; This is the set of neighboring routers of the virtual leader DER.

[0205] In summary, this patent designs a corresponding method to solve the problem in scenarios where cyber-physical uncertainties are superimposed and interfere with each other. For example... Figure 1 As shown, the specific process is as follows: (1) If the information layer communication quality is good and there is no external interference in the physical control loop, then based on the sparse network between DERs, each controlled DER will generate and execute power regulation instructions locally through the virtual leader-follower consensus algorithm; (2) If the communication network has packet loss problems, the data transmission path will be updated according to the network status, and a new path will be used to ensure that the data is reliably transmitted to the destination; (3) If there is external interference in the local control loop of each DER, a sliding mode controller should be designed to quickly suppress the interference and ensure the normal generation and execution of power regulation instructions; (4) If there is a DER whose power regulation instructions reach the output limit, a virtual cost parameter is introduced to correct its marginal cost, so that it exits the full load operation state and is maintained within the preset safe output boundary.

[0206] Example 2

[0207] This embodiment provides a cyber-physical coordinated microgrid operation control device, including: a pre-constructed control strategy for cyber-physical coordinated control of the microgrid, comprising:

[0208] Physical layer active power optimization strategy: Consistency control mechanism: Based on the virtual leader-follower consensus algorithm, the marginal costs of each DER in the microgrid system are made to converge. The output command of the DER is adjusted by the converged marginal cost, and the reference value of the marginal cost of the virtual leader is determined by the Lagrange multiplier method; Virtual cost adjustment mechanism: For DERs whose output command reaches the output limit, virtual cost parameters are introduced to correct their marginal costs, so that they exit the full load operation state and are maintained within the preset safe output boundary;

[0209] Physical layer disturbance suppression strategy: When there is an uncertain disturbance in the DER local control loop, the sliding mode controller is enabled to couple the virtual leader-follower consensus algorithm to suppress the disturbance by switching the control law;

[0210] Information layer communication enhancement strategies: Data importance assessment mechanism: assessing data importance level based on the control coupling strength between DERs; Transmission path reconstruction mechanism: prioritizing the transmission of high-importance data through low-risk communication paths;

[0211] The microgrid operation control device includes:

[0212] Consistency control module: Used to execute consistency control mechanisms when the information layer communication quality is good;

[0213] Communication enhancement module: used to trigger information layer communication enhancement strategies when transmission uncertainties occur at the information layer;

[0214] Anti-disturbance module: used to trigger physical layer disturbance suppression strategies when there are uncertain disturbances in the physical layer;

[0215] Virtual cost adjustment module: used to trigger the execution of the virtual cost adjustment mechanism when DER is continuously at full load.

[0216] The device provided in this embodiment can execute the cyber-physical collaborative microgrid operation and control method provided in any step of Embodiment 1, and has the corresponding functional modules and beneficial effects of the execution method.

[0217] Example 3

[0218] This embodiment provides a computer storage medium storing a computer program. When the computer program is executed by a processor, it implements the cyber-physical coordination microgrid operation and control method as provided in any step of Embodiment 1.

[0219] Those skilled in the art will understand that embodiments of this application can be provided as methods, systems, or computer program products. Therefore, this application can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, this application can take the form of a computer program product embodied on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0220] This application is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this application. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart... Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0221] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0222] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0223] The embodiments of the present invention have been described above with reference to the accompanying drawings. However, the present invention is not limited to the specific embodiments described above. The specific embodiments described above are merely illustrative and not restrictive. Those skilled in the art can make many other forms under the guidance of the present invention without departing from the spirit and scope of the claims. All of these forms are within the protection scope of the present invention.

Claims

1. A cyber-physical collaborative microgrid operation and control method, characterized in that, For cyber-physical coordinated control of microgrids, pre-constructed control strategies include: Physical layer active power optimization strategy: Consistency control mechanism: Based on the virtual leader-follower consensus algorithm, the marginal costs of each distributed generation unit in the microgrid system are made to converge. The output command of the generation unit is controlled by the converged marginal cost, and the marginal cost reference value of the virtual leader is determined by the Lagrange multiplier method; Virtual cost adjustment mechanism: For the generation unit whose output command reaches the output limit, the virtual cost parameter is introduced to correct its marginal cost, so that it exits the full load operation state and is maintained within the preset safe output boundary; Physical layer disturbance suppression strategy: When there is an uncertain disturbance in the local control loop of the power generation unit, the sliding mode controller is activated to couple the virtual leader-follower consensus algorithm, and the disturbance is suppressed by switching the control law; Information layer communication enhancement strategies: Data importance assessment mechanism: assess the importance level of data based on the control coupling strength between power generation units; Transmission path reconfiguration mechanism: prioritize the transmission of high-importance data through low-risk communication paths; The microgrid operation and control method includes: executing a consistency control mechanism when the information layer communication quality is good; triggering an information layer communication enhancement strategy when transmission uncertainty occurs in the information layer; triggering a physical layer disturbance suppression strategy when there is uncertainty disturbance in the physical layer; and triggering a virtual cost adjustment mechanism when the power generation unit is continuously fully loaded.

2. The cyber-physical collaborative microgrid operation and control method according to claim 1, characterized in that, When the microgrid physical layer implements the consistency control mechanism, the formula for calculating the generation cost of the i-th generation unit is as follows: ， Where i is the index of the power generation unit. Let be the power generation cost of the i-th power generation unit; , and All are power generation cost coefficients; Let be the active power output of the i-th power generation unit; The optimization objective of the microgrid physical layer's consistent control mechanism is to minimize the total generation cost while satisfying the energy balance constraint. The expressions for the optimization objective and the constraint are as follows: ， ， in, The objective function for implementing the consistency control mechanism at the physical layer is n, where n is the number of power generation units. This represents the load demand.

3. The cyber-physical collaborative microgrid operation and control method according to claim 2, characterized in that, The determination of the marginal cost reference value of the virtual leader using the Lagrange multiplier method includes: Based on the optimization objective and constraints of the consistency control mechanism, the Lagrange parametric equations are constructed as follows: ， in, Let be the marginal cost of the i-th power generation unit. The Lagrange parameter equation relating the power output and marginal cost of a generating unit is given. The extremum conditions of the Lagrange parametric equations are solved using the following formula: ， The marginal cost reference value is obtained based on the extreme value condition and the constraints of the consistency control mechanism, and the calculation formula is as follows: ， when hour, , in, The marginal cost reference value after convergence; j is the non-i index of the power generation unit; This is a continuous multiplication operation.

4. The cyber-physical collaborative microgrid operation and control method according to claim 1, characterized in that, The virtual cost parameter is dynamically generated by a proportional-integral controller, and the calculation formula is as follows: ， in, Let be the virtual cost of the i-th power generation unit; and This is the preset safe output boundary for the i-th power generation unit; and This refers to the gain of the proportional-integral controller.

5. The cyber-physical collaborative microgrid operation and control method according to claim 2, characterized in that, When the microgrid physical layer executes the consensus control mechanism, the consensus controller is constructed as follows: ， in, h is the marginal cost consistency controller for the i-th controlled generation unit, where i is the index of the controlled generation unit; h is the non-i index of the controlled generation unit. Let i be the set of adjacent power generation units of the i-th controlled power generation unit; For the coefficients of the consistency controller; This represents the controlled state of the h-th controlled power generation unit; This represents the controlled state of the i-th controlled power generation unit; Let be the connection weights of the i-th controlled power generation unit and the h-th controlled power generation unit; The status of the virtual leader power generation unit; The connection weights between the i-th controlled power generation unit and the virtual leader power generation unit; Based on the aforementioned consistency controller, when the controlled power generation unit is subjected to disturbance, an anti-disturbance consistency controller is constructed as follows: ， in, For the i-th controlled power generation unit, there is a disturbance rejection consistency controller; Let h be the disturbance received by the h-th controlled power generation unit; Let be the disturbance received by the i-th controlled power generation unit; The disturbance received by the virtual leader power generation unit; and These are the parameters for the consistency controller; , , All are data packet loss rates; Based on the disturbance-resistant consistency controller, the marginal cost tracking deviation equation for the i-th controlled power generation unit after a disturbance is obtained as follows: ， in, The marginal cost tracking deviation after the i-th controlled power generation unit is disturbed; The marginal cost tracking deviation equation of the disturbed controlled power generation unit is converged to 0, and the optimal output command of the disturbed controlled power generation unit is obtained.

6. The cyber-physical collaborative microgrid operation and control method according to claim 5, characterized in that, When the disturbance experienced by the controlled power generation unit changes, a sliding mode-consistency controller is constructed based on the marginal cost tracking deviation as follows: Sliding surface: , Switching control laws: , Lyapunov function: , in, It is a sliding surface; and The coefficient of the sliding surface; To switch control laws; This is the switching gain; Sgn(.) is the sign function; It is a Lyapunov function.

7. The cyber-physical collaborative microgrid operation and control method according to claim 5, characterized in that, Methods for assessing the importance level of data include: The sensitivity of the marginal contribution of each power generation unit's output to the total output of the microgrid system is quantified by the following formula: ， The coupling strength of the marginal costs of adjacent power generation units is quantified, and the calculation formula is as follows: ， ， ， in, For the i-th controlled power generation unit, the marginal cost consistency controller is used. Let $\mathbf{j}$ be the marginal cost of the $j$-th controlled power generation unit. The importance ranking of power generation units is obtained based on the quantified sensitivity and coupling strength. The importance of the data transmitted by the power generation units is mapped according to the importance ranking of the power generation units.

8. The cyber-physical collaborative microgrid operation and control method according to claim 7, characterized in that, The optimization objective of the information layer's transmission path reconstruction mechanism is to maximize the reliability of important data transmission. The constraints include: the number of connected channels must be greater than the number of power generation units; there must be at most one connection channel between two routers; each controlled power generation unit is connected to only one input channel; and each virtual leader power generation unit is connected to at least one output channel and has no input channel connected. The expressions for the optimization objective and constraints are as follows: Optimization goal: , Constraints: , ， ， ， in, The number of router nodes; Sgn(.) is the sign function; For from router To router The probability of communication channel risk; For from router To router Communication channels; For router The set of adjacent connectable routers; The number of controllable power generation units; For from router To router Communication channels; From Virtual Leader Generator Unit to Router Communication channels; For from router Communication channel to the virtual leader power generation unit; The set of neighboring routers for the virtual leader generator unit.

9. A cyber-physical collaborative microgrid operation and control device, characterized in that, include: For cyber-physical coordinated control of microgrids, pre-constructed control strategies include: Physical layer active power optimization strategy: Consistency control mechanism: Based on the virtual leader-follower consensus algorithm, the marginal costs of each distributed generation unit in the microgrid system are made to converge. The output command of the generation unit is controlled by the converged marginal cost, and the marginal cost reference value of the virtual leader is determined by the Lagrange multiplier method; Virtual cost adjustment mechanism: For the generation unit whose output command reaches the output limit, the virtual cost parameter is introduced to correct its marginal cost, so that it exits the full load operation state and is maintained within the preset safe output boundary; Physical layer disturbance suppression strategy: When there is an uncertain disturbance in the local control loop of the power generation unit, the sliding mode controller is activated to couple the virtual leader-follower consensus algorithm, and the disturbance is suppressed by switching the control law; Information layer communication enhancement strategies: Data importance assessment mechanism: assess the importance level of data based on the control coupling strength between power generation units; Transmission path reconfiguration mechanism: prioritize the transmission of high-importance data through low-risk communication paths; The microgrid operation control device includes: Consistency control module: Used to execute consistency control mechanisms when the information layer communication quality is good; Communication enhancement module: used to trigger information layer communication enhancement strategies when transmission uncertainties occur at the information layer; Anti-disturbance module: used to trigger physical layer disturbance suppression strategies when there are uncertain disturbances in the physical layer; Virtual cost adjustment module: used to trigger the execution of the virtual cost adjustment mechanism when the power generation unit is continuously at full load.

10. A computer storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements the cyber-physical cooperative microgrid operation and control method as described in any one of claims 1-8.

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