Operation scheduling method and system for power distribution cyber physical system

Abstract

This invention discloses an operation and scheduling method and system for distribution network cyber-physical systems, relating to the field of distribution network operation control and scheduling technology. It acquires physical operation status information of the distribution network and operation status information of the information system. Based on the radial topology of the distribution network, a linearized DistFlow model is used to describe branch power balance and node voltage constraints. Combined with the operation status information of the information system, control executability constraints are constructed to form a dynamic state evolution model. With the objective of minimizing the sum of operational economy, operational deviation, and information-constrained risk penalties, a scheduling optimization model is constructed and solved to obtain candidate scheduling control vectors. These candidate scheduling control vectors are processed through gating correction and amplitude contraction mechanisms to obtain the final scheduling control vector. Dynamic correction and rolling updates are performed based on real-time state feedback. This invention significantly improves the scheduling reliability and economy of distribution systems in high-penetration renewable energy scenarios through strong cyber-physical coupling modeling and rolling optimization mechanisms.

Description

Technical Field

[0001] This invention relates to the field of power distribution network operation control and dispatching technology, specifically to operation and dispatching methods and systems for power distribution cyber-physical systems. Background Technology

[0002] With the large-scale integration of distributed power sources, energy storage devices, controllable loads, and automation terminals into distribution networks, the operational mode of distribution networks has gradually evolved from a traditional unidirectional power supply structure to a complex system with multiple sources, multiple nodes, and high interaction. At the same time, the dependence of distribution network operation on information systems is constantly increasing. Functions such as state perception, communication transmission, remote control, and dispatch decision-making all need to be completed in collaboration with information systems. Distribution networks are gradually exhibiting the characteristics of a cyber-physical system with deep coupling between information systems and power systems.

[0003] Against this backdrop, the operating state of the distribution network is no longer solely determined by the power equipment itself, but is also significantly influenced by the operating state of the information system. For example, issues such as communication link interruptions, data delays, incomplete sensing information, or unreachable control commands can all prevent the effective execution of distribution network dispatching strategies, thereby affecting the system's security and reliability. Especially in complex operating environments or emergency recovery phases, factors such as the distribution network's topology, load levels, and distributed generation output often change continuously over time, resulting in the system exhibiting significant dynamic evolution characteristics.

[0004] Most existing power distribution network operation and dispatching methods are based on relatively static or quasi-static system models, typically assuming that the information system operates reliably and its state can be fully acquired, and making centralized or time-sharing dispatching decisions based on this. These methods have certain applicability under normal operating conditions, but as the coupling between the information system and the power system deepens and the operating state changes frequently, it is difficult to accurately depict the dynamic evolution process of the power distribution cyber-physical system, and deviations easily occur between the dispatching results and the actual operating state.

[0005] Furthermore, while some existing technologies have introduced phased scheduling, rolling correction, or uncertainty handling mechanisms, most focus on modeling and optimizing the physical layer of the power system. They fail to fully consider the impact of changes in the operating state of the information system on scheduling feasibility and operational reliability, and lack a unified description and coordinated control of the co-evolutionary relationship between the information system and the power system. When the information system is constrained or its operating state changes, the relevant scheduling methods often struggle to adjust their strategies in a timely manner, easily leading to increased system operational risks.

[0006] Therefore, there is an urgent need for an operation and scheduling method to achieve safe, reliable and efficient operation of the distribution network in complex operating environments. Summary of the Invention

[0007] The technical problem to be solved by this invention is how to address the shortcomings of existing power distribution network operation and scheduling methods in effectively characterizing the dynamic evolution of system state under conditions of deep coupling between information systems and power systems, and the insufficient matching between scheduling strategies and actual operating states.

[0008] This invention solves the above-mentioned technical problems through the following technical means: an operation and scheduling method for a power distribution cyber-physical system, comprising:

[0009] S1. Obtain the physical operating status information of the distribution network and the operating status information of the information system. The physical operating status information of the distribution network includes node load power, distributed power output, line connection relationship and line parameters. The operating status information of the information system includes communication link connectivity, data packet arrival rate, control command issuance success rate and communication delay. S2. Based on the radial topology of the distribution network, a linearized DistFlow power flow model is used to describe the branch power balance relationship and node voltage constraints. Combined with the information system operation status information, control executability constraints are constructed to form a dynamic state evolution model. S3. Based on the aforementioned dynamic state evolution model, construct an operation scheduling optimization model with the objective of minimizing the sum of operational economic costs, operational deviation penalty terms, and information-limited risk penalty terms, and solve the operation scheduling optimization model to obtain candidate scheduling control vectors; according to the information system operation status information, perform gating correction and amplitude contraction on the candidate scheduling control vectors through control gating and amplitude contraction mechanisms to obtain scheduling control vectors; S4. Based on the real-time acquired physical operation status information and information system operation status feedback information, the scheduling control vector is dynamically corrected and continuously updated.

[0010] Furthermore, collecting the physical operating status information of the distribution network includes: During the scheduling period Within the system, active and reactive power data of loads at each node of the distribution network are collected to form a load vector. (1) (2) In the formula, and This represents the active power vector and reactive power vector of the load at each node in the distribution network. and This represents the active and reactive power of the load at the first node. and This represents the active and reactive power of the load at the second node. and Let T represent the active and reactive power of the load at the Nth node, and T denote the transpose. Collect active and reactive power data from distributed generation sources to form distributed generation source injection vectors: (3) (4) In the formula, and This represents the active power vector and reactive power vector of all distributed power sources in the system. and This represents the active and reactive power injected into or absorbed by the first distributed power source into the distribution network. and This represents the active and reactive power injected into or absorbed by the second distributed power source into the distribution network. and This represents the active and reactive power injected or absorbed by the Gth distributed power source into the distribution network, and T represents transpose; Based on the distribution network topology, construct a mapping matrix between distributed power sources and nodes. Its elements are defined as: (5) In the formula, , ∈ indicates belonging to; Based on equations (1), (2), (3), (4), and (5), and according to the nodal power balance relationship, the active and reactive power injection vectors at the distribution network node level are constructed as follows: (6) (7) In the formula, This represents the net active power injection vector of a node. This represents the net reactive power injection vector of the node.

[0011] Furthermore, collecting the physical operating status information of the distribution network also includes: collecting the connection relationships and parameters of the distribution network lines, numbering the nodes and branches of the distribution network, defining the direction of each branch, and constructing a node-branch association matrix. The node-branch association matrix The behavior is the distribution network node, and the column is the distribution network branch; for any branch, the matrix element corresponding to the starting point of the branch is 1, the matrix element corresponding to the ending point of the branch is -1, and the matrix element corresponding to the other nodes is 0. Based on the line parameters, the equivalent resistance and equivalent reactance of each branch are extracted, and the branch resistance diagonal matrix and branch reactance diagonal matrix are constructed respectively: (8) In the formula, This is a diagonal matrix composed of branch resistance parameters. Represents a node With nodes The equivalent resistance of the branch between them, This is a diagonal matrix composed of branch reactance parameters. Represents a node With nodes The equivalent reactance of the branch between them.

[0012] Furthermore, collecting the operational status information of the information system includes: The system collects operational status information related to power distribution network operation and scheduling. This operational status information includes at least communication link connectivity, data packet arrival rate, control command issuance success rate, and communication latency. Communication link connectivity is obtained based on the online status of communication nodes and link handshake results. The data packet arrival rate is calculated as the ratio of the number of successfully received data packets to the total number of sent data packets within a preset time window. The control command issuance success rate is calculated as the ratio of the number of successfully issued and received execution confirmation control commands to the total number of issued control commands within a preset time window. Communication latency is obtained as the difference between the sending and receiving times of data packets between the control center and field terminals. Construct an information system operation status index vector based on information system operation status information: (9) In the formula, This represents a vector of information system operational status indicators. This represents the first information system operation status indicator. This represents the second information system operational status indicator. Indicates the first Each information system's operational status indicators; Combining equations (6), (7), (8), and (9) forms a scheduling optimization model for the scheduling cycle. Unified input set : (10).

[0013] Furthermore, S2 includes: Based on the radial topology of the distribution network, a linearized DistFlow power flow model is used to describe the relationship between active power, reactive power, and voltage in the distribution network. The branch power balance relationship is expressed as follows: (11) (12) In the formula, and They represent nodes respectively To the node The branch lines have contributing and non-contributing currents. Represents a node The set of downstream nodes, and They represent nodes respectively Pointing to its downstream node The active and passive currents, and Representing nodes respectively Net active and reactive power injection; The node voltage drop relationship is expressed as follows: (13) In the formula, Represents a node voltage amplitude, Represents a node voltage amplitude, Represents a node With nodes The equivalent resistance of the branch between them, Represents a node With nodes The equivalent reactance of the branches between them; Node voltage vector Represented as: (14) In the formula, This is a node-branch association matrix. This is a diagonal matrix composed of branch resistance parameters. This is a diagonal matrix composed of branch reactance parameters. This represents the net active power injection vector of a node. This represents the net reactive power injection vector at the node. Reference node voltage; Introducing branch state variables ,when When the branch is in operation, it indicates that the branch is in operation. The time indicates that the branch is open; the corresponding voltage constraint is expressed as: (15) In the formula, It is a constant; Information system operation status index vector based on equation (9) Based on this, define a set of control instruction executability constraints. , This represents the actual executable scheduling and control vector under the constraints of the current information system operating state. This represents the executable control domain, which is jointly determined by communication link connectivity, data packet arrival rate, control command issuance success rate, and communication latency. The state evolution of the power distribution cyber-physical system within adjacent scheduling cycles is represented as follows: (16) In the formula, Indicates the scheduling period The overall operational status of the power distribution information physical system.

[0014] Furthermore, the operation scheduling optimization model in S3 includes: By scheduling cycle t With the goal of minimizing the overall operating cost and operational risk of the internal system, an objective function is constructed for the operation scheduling optimization model. : (17) in, (18) (19) (20) In the formula, Indicates the economic cost of operation. g Represents a collection of distributed power sources. Indicates the first The weight of the operating cost corresponding to the active power output of each distributed power source unit. Indicates the first Active power output of a distributed power source Represents a collection of energy storage units. Indicates the first The weight of the operating cost per unit charge / discharge power of each energy storage unit. Indicates the first The charging and discharging power of each energy storage unit Represents the set of nodes in the distribution network. Indicates the first d The penalty weight corresponding to the load reduction of each node. Indicates the first d Load reduction per node This indicates the penalty for operational deviation. This represents the voltage deviation weighting coefficient. Represents a node voltage amplitude, Represents a node The reference voltage, This indicates a penalty item for information restriction risks. Indicates the first The weighting coefficients of each information system's operational status indicators in the comprehensive risk assessment. Indicates the first The values ​​of the operational status indicators of an information system. .

[0015] Furthermore, the operation scheduling optimization model satisfies node voltage constraints, distributed power output constraints, energy storage state of charge (SOC) constraints, and control executability constraints, as follows: (twenty one) In the formula, and Representing nodes respectively The minimum and maximum values ​​of the voltage amplitude, and They represent the first The minimum and maximum active power output of a distributed power source. Indicates the first The state of charge of each energy storage unit and They represent the first The minimum and maximum values ​​of the state of charge of each energy storage unit.

[0016] Furthermore, S3 introduces a control gating and amplitude contraction mechanism to obtain candidate scheduling control vectors based on the running scheduling objective function and its constraints. Based on the current information system operation status indicator vector Constructing a control gating matrix The candidate scheduling control vector is then gating and amplitude-shrinking to obtain the scheduling control vector. Its expression is: (twenty two) In the formula, It is a diagonal matrix, and the values ​​of each diagonal element are in the range of 1. .

[0017] Furthermore, S4 includes: The scheduling control vector obtained in S3 The command is sent to the power distribution information physical system. Under the condition that the communication link connectivity, data packet arrival rate, control command issuance success rate and communication latency meet the preset execution requirements, the corresponding control operations are executed, including distributed power generation output adjustment, energy storage charging and discharging control and network operation mode adjustment. Based on the scheduling execution results and combined with real-time measurement information, the system state vector is updated, and the system operating state for the next scheduling cycle is obtained according to equation (16). Entering the next scheduling cycle Beforehand, re-collect or assess the operational status of the information system and update the information system operational status indicator vector. Based on this, the set of control instruction executability constraints in the runtime scheduling optimization model is revised: (twenty three) Based on the updated integrated operation status of the power distribution cyber-physical system and information system operating status As input, S3 is executed repeatedly to continuously solve the runtime scheduling optimization model for the next scheduling cycle, thereby obtaining new candidate scheduling control vectors. and scheduling control vector This enables adaptive updates of operation scheduling decisions as the physical and information system operating states change.

[0018] This invention also provides an operation scheduling system for a power distribution cyber-physical system, comprising: The physical and information system status acquisition module is used to acquire physical operation status information of the distribution network and information system operation status information. The physical operation status information of the distribution network includes node load power, distributed power output, line connection relationship and line parameters. The information system operation status information includes communication link connectivity, data packet arrival rate, control command issuance success rate and communication latency. The state dynamic evolution model construction module is used to describe the branch power balance relationship and node voltage constraints based on the radial topology of the distribution network using a linearized DistFlow power flow model, and to construct control executability constraints by combining the information system operation status information to form a state dynamic evolution model. The scheduling control vector generation module is used to construct an operation scheduling optimization model based on the state dynamic evolution model, with the goal of minimizing the sum of the operation economic cost, operation deviation penalty term, and information limitation risk penalty term, and solve the operation scheduling optimization model to obtain candidate scheduling control vectors; according to the information system operation status information, the candidate scheduling control vectors are gating and amplitude shrinking through control gating and amplitude shrinking mechanisms to obtain the scheduling control vector; The dynamic correction and rolling update module is used to dynamically correct and roll update the scheduling control vector based on the real-time acquired physical operation status information and information system operation status feedback information.

[0019] The advantages of this invention are: (1) This invention constructs a state description and dynamic evolution model for a power distribution cyber-physical system, which collaboratively models the operating states of the power system, such as node voltage and power injection, with the operating states of the information system, such as communication connectivity, data packet arrival rate, and control command issuance success rate. This achieves a unified characterization of the multi-dimensional state evolution process of the power distribution cyber-physical system over time. It breaks through the limitation of traditional scheduling models that only focus on the physical layer of the power system, enabling operation and scheduling decisions to better reflect the impact of changes in the operating state of the information system on scheduling executability and system operation security, thereby helping to improve the matching between scheduling strategies and actual operating states.

[0020] (2) This invention introduces information system operating status constraints and information limitation risk penalty mechanisms into the operation scheduling optimization model, explicitly incorporating the information system operating status into the scheduling objective function in the form of risk costs. During the scheduling optimization process, it comprehensively weighs operational economy, operational safety, and information limitation risks. When communication conditions are limited or the information system operating status declines, the operation scheduling optimization model can reduce its reliance on high-precision control and frequent adjustments, guiding the operation strategy towards a more robust mode. This effectively avoids system operation risks caused by unreachable or failed scheduling commands, improving the safety and reliability of the distribution network in complex operating environments.

[0021] (3) This invention proposes an information state-driven control gating and amplitude contraction mechanism. By constructing a control gating matrix determined by the information system's operating state indicators, the scheduling control vector is adaptively modified. When the information system is operating well, the complete execution of the scheduling strategy is guaranteed, and the control adjustment amplitude is automatically reduced when communication is limited. This mechanism transforms the abstract information system executability constraints into explicit control mapping relationships, significantly enhancing the executability and engineering feasibility of the scheduling control strategy under information-limited conditions, and avoiding the problem in traditional methods where the scheduling results are theoretically feasible but difficult to execute in practice.

[0022] (4) This invention adopts a scheduling mechanism that combines rolling time-domain operation scheduling with emergency mode switching. During the actual operation of the system, the scheduling strategy is dynamically corrected according to the real-time changes in the system status and the information system operation status. When a voltage over-limit trend, equipment operation abnormality, or significant decline in the information system operation status is detected, the emergency operation mode can be triggered in a timely manner. By adjusting the objective function weight, the basic operation stability of the system is ensured. As communication conditions and system operation status gradually recover, the scheduling strategy can gradually return to the normal operation mode, thereby realizing the continuous, safe and efficient operation of the distribution network in complex operating environments. Attached Figure Description

[0023] Figure 1 This is a flowchart of the operation scheduling method for a power distribution cyber-physical system according to Embodiment 1 of the present invention; Figure 2 This is a graph showing the voltage variation of the lowest node under normal communication conditions in Embodiment 1 of the present invention. Figure 3 This is a graph showing the change in the gating coefficient under normal communication conditions in Embodiment 1 of the present invention. Figure 4 This is a diagram showing the coordinated scheduling results of distributed power sources, energy storage devices, and load reduction under normal communication conditions in Embodiment 1 of the present invention. Figure 5 This is a graph showing the change in the state of charge of the energy storage device under normal communication conditions in Embodiment 1 of the present invention. Figure 6 This is a graph showing the changes in operating costs and information risk costs under normal communication conditions in Embodiment 1 of the present invention; Figure 7 This is a comparison diagram of the voltage of each node under the maximum load during normal communication conditions in Embodiment 1 of the present invention; Figure 8 This is a graph showing the voltage change of the lowest node under the condition of decreased communication status in Embodiment 1 of the present invention. Figure 9 This is a graph showing the changes in the information system's operating status indicators and the gating coefficient under the condition of declining communication status in Embodiment 1 of the present invention. Figure 10 This is a comparison chart of the candidate output and actual output of the distributed power source under the condition of decreased communication status in Embodiment 1 of the present invention. Figure 11 This is a comparison chart of the candidate discharge power and the actual discharge power of the energy storage device under the condition of decreased communication status in Embodiment 1 of the present invention. Figure 12 This is a comparison chart of the candidate load reduction quantities and the actual execution quantities under the condition of decreased communication status in Embodiment 1 of the present invention; Figure 13 This is a graph showing the change in the state of charge of the energy storage device under the condition of decreased communication status in Embodiment 1 of the present invention. Figure 14 This is a graph showing the changes in operating costs, information risk costs, and overall costs under the condition of decreased communication status in Embodiment 1 of the present invention. Figure 15 This is a comparison diagram of the voltage of each node under the condition of decreased communication status and maximum load in Embodiment 1 of the present invention; Figure 16 This is a comparison diagram of the lowest node voltage between the traditional scheduling method of Embodiment 1 of the present invention and the method described in the present invention; Figure 17 This is a comparison diagram of the actual power output of distributed power sources between the traditional scheduling method of Embodiment 1 of the present invention and the method described in the present invention; Figure 18This is a comparison diagram of the actual discharge power of the energy storage device between the traditional scheduling method of Embodiment 1 of the present invention and the method of the present invention. Figure 19 This is a comparison chart of the load reduction amounts between the conventional scheduling method of Embodiment 1 of the present invention and the method described in the present invention; Figure 20 This is a comparison diagram of the state of charge changes of the energy storage device between the conventional scheduling method of Embodiment 1 of the present invention and the method described in the present invention; Figure 21 This is a cost comparison chart between the traditional scheduling method of Embodiment 1 of the present invention and the method described in the present invention; Figure 22 This is a comparison diagram of the control execution deviation between the traditional scheduling method of Embodiment 1 of the present invention and the method of the present invention; Figure 23 This is a comparison diagram of the voltage of each node in the traditional scheduling method and the method described in this invention during a typical communication-constrained moment in Embodiment 1 of this invention. Detailed Implementation

[0024] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below in conjunction with the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0025] Example 1 like Figure 1 As shown, the operation scheduling method for a power distribution cyber-physical system includes: S1. Obtain the physical operating status information of the distribution network and the operating status information of the information system. The physical operating status information of the distribution network includes node load power, distributed power output, line connection relationship and line parameters. The operating status information of the information system includes communication link connectivity, data packet arrival rate, control command issuance success rate and communication latency.

[0026] Specifically, this includes: S1.1, during the scheduling cycle. Internally, data acquisition devices from distribution automation systems, smart meters, measurement terminals, feeder terminal units, substation terminal units, or distribution master stations are used to acquire active and reactive power data for each node in the distribution network. For nodes not directly measured, historical operating data, state estimation results, or load forecasting results can be used to supplement the data and form a load vector. (1) (2) In the formula, and This represents the active power vector and reactive power vector of the load at each node in the distribution network. and This represents the active and reactive power of the load at the first node. and This represents the active and reactive power of the load at the second node. and Let T represent the active and reactive power of the load at the Nth node, and T denote the transpose.

[0027] Active and reactive power data of distributed power sources are acquired through data acquisition devices such as local controllers of distributed power sources, inverter monitoring units, smart meters, measurement and control terminals, distribution automation terminals, or distribution master stations. For distributed power source output data that is not directly acquired in real time, it can be supplemented by historical operating data, state estimation results, or short-term power prediction results to form a distributed power source injection vector. (3) (4) In the formula, and This represents the active power vector and reactive power vector of all distributed power sources in the system. and This represents the active and reactive power injected into or absorbed by the first distributed power source into the distribution network. and This represents the active and reactive power injected into or absorbed by the second distributed power source into the distribution network. and This represents the active and reactive power injected or absorbed by the Gth distributed power source into the distribution network, and T represents transpose.

[0028] Based on the distribution network topology, construct a mapping matrix between distributed power sources and nodes. Its elements are defined as: (5) In the formula, , ∈ indicates belonging to.

[0029] Based on equations (1), (2), (3), (4), and (5), and according to the nodal power balance relationship, the active and reactive power injection vectors at the distribution network node level are constructed as follows: (6) (7) In the formula, This represents the net active power injection vector of a node. This represents the net reactive power injection vector of the node.

[0030] The data collection process involves gathering data on the connection relationships and parameters of distribution network lines. The connection relationships can be obtained from distribution automation master stations, distribution management systems, geographic information systems, equipment ledgers, primary wiring diagrams, or as-built drawings. The line parameters can be obtained from line design data, equipment nameplate parameters, line ledgers, completion and acceptance data, or online identification results. Nodes and branches of the distribution network are numbered, the direction of each branch is defined, and a node-branch correlation matrix is ​​constructed after removing reference nodes. The behavior of the nodes other than the reference node is listed as distribution network branches; for any branch, the matrix element corresponding to the starting point of the branch is 1, the matrix element corresponding to the ending point of the branch is -1, and the matrix element corresponding to the other nodes is 0.

[0031] Based on the line parameters, the equivalent resistance and equivalent reactance of each branch are extracted, and the branch resistance diagonal matrix and branch reactance diagonal matrix are constructed respectively: (8) In the formula, This is a diagonal matrix composed of branch resistance parameters. Represents a node With nodes The equivalent resistance of the branch between them, This is a diagonal matrix composed of branch reactance parameters. Represents a node With nodes The equivalent reactance of the branch between them.

[0032] The system collects operational status information related to power distribution network operation and dispatch through communication management modules, network monitoring modules, protocol gateways, dispatch master stations, field terminals, communication node logs, and message statistics modules within the power distribution communication network. This operational status information includes at least communication link connectivity, data packet arrival rate, control command issuance success rate, and communication latency. Specifically, communication link connectivity is obtained based on the online status of communication nodes and link handshake results; data packet arrival rate is calculated as the ratio of successfully received data packets to the total number of sent data packets within a preset time window; control command issuance success rate is calculated as the ratio of successfully issued and received control commands to the total number of issued control commands within a preset time window; and communication latency is calculated as the difference between the data packet transmission and reception times between the control center and the field terminals.

[0033] Construct an information system operation status index vector based on information system operation status information: (9) In the formula, This represents a vector of information system operational status indicators. This represents the first information system operation status indicator. This represents the second information system operational status indicator. Indicates the first Information system operation status indicators.

[0034] Combining equations (6), (7), (8), and (9) forms a unified input set for the operation scheduling optimization model during scheduling period t. : (10) S2. Based on the radial topology of the distribution network, a linearized DistFlow power flow model is used to describe the branch power balance relationship and node voltage constraints. Combined with the information system operation status information, control executability constraints are constructed to form a dynamic state evolution model.

[0035] Specifically, this includes: S2.1, based on the radial topology of the distribution network, a linearized DistFlow power flow model is used to describe the relationship between active power, reactive power, and voltage in the distribution network, and its branch power balance relationship is expressed as: (11) (12) In the formula, and They represent nodes respectively To the node The branch lines have contributing and non-contributing currents. Represents a node The set of downstream nodes, and They represent nodes respectively Pointing to its downstream node The active and passive currents, and Representing nodes respectively The net active and reactive power injection.

[0036] The node voltage drop relationship is expressed as follows: (13) In the formula, Represents a node voltage amplitude, Represents a node voltage amplitude, Represents a node With nodes The equivalent resistance of the branch between them, Represents a node With nodes The equivalent reactance of the branch between them.

[0037] Node voltage vector Represented as: (14) In the formula, This is a node-branch association matrix. This is a diagonal matrix composed of branch resistance parameters. This is a diagonal matrix composed of branch reactance parameters. This represents the net active power injection vector of a node. This represents the net reactive power injection vector at the node. This is the reference node voltage.

[0038] S2.2 To describe the impact of disaster recovery, network reconstruction, and on-grid / off-grid switching on the system state, branch state variables are introduced. ,when When the branch is in operation, it indicates that the branch is in operation. The time indicates that the branch is open; the corresponding voltage constraint is expressed as: (15) In the formula, It is a constant.

[0039] Information system operation status index vector based on equation (9) Based on this, define a set of control instruction executability constraints. , This represents the scheduling and control vector under the constraints of the current information system operating state. This represents the executable control domain, which is jointly determined by communication link connectivity, data packet arrival rate, control command issuance success rate, and communication latency. The dynamic state evolution of the power distribution cyber-physical system within adjacent scheduling cycles is represented as follows: (16) In the formula, This represents the overall operational status of the power distribution cyber-physical system during scheduling period t. Indicates the current scheduling control vector Information system operation status index vector Under this influence, the overall operating status of the system in the next scheduling cycle.

[0040] S3. Based on the dynamic evolution model of the state, construct an operation scheduling optimization model with the goal of minimizing the sum of the economic cost of operation, the penalty term for operation deviation, and the penalty term for information limitation risk. Solve the operation scheduling optimization model to obtain candidate scheduling control vectors. According to the operation status information of the information system, perform gating correction and amplitude contraction on the candidate scheduling control vectors through control gating and amplitude contraction mechanisms to obtain the scheduling control vector.

[0041] Specifically, this includes: S3.1, constructing an operation scheduling objective function with the goal of minimizing the sum of the economic cost of operation, the penalty term for operational deviation, and the penalty term for information limitation risk within the scheduling period t. : (17) in: (18) (19) (20) In the formula, Indicates the economic cost of operation. g Represents a collection of distributed power sources. Indicates the first The weight of the operating cost corresponding to the active power output of each distributed power source unit. Indicates the first Active power output of a distributed power source Represents a collection of energy storage units. Indicates the first The weight of the operating cost per unit charge / discharge power of each energy storage unit. Indicates the first The charging and discharging power of each energy storage unit Represents the set of nodes in the distribution network. Indicates the first d The penalty weight corresponding to the load reduction of each node. Indicates the first d Load reduction per node This indicates the penalty for operational deviation. This represents the voltage deviation weighting coefficient. Represents a node voltage amplitude, Represents a node The reference voltage, This indicates a penalty item for information restriction risks. Indicates the first The weighting coefficients of each information system's operational status indicators in the comprehensive risk assessment. Indicates the first The values ​​of the operational status indicators of an information system. .

[0042] S3.2. While optimizing the objective function, the running scheduling optimization model must satisfy node voltage constraints, distributed power generation output constraints, energy storage state of charge (SOC) constraints, and control executability constraints, as follows: (twenty one) In the formula, and Representing nodes respectively The minimum and maximum values ​​of the voltage amplitude, and They represent the first The minimum and maximum active power output of a distributed power source. Indicates the first The state of charge of each energy storage unit and They represent the first The minimum and maximum values ​​of the state of charge of each energy storage unit.

[0043] To improve the executability of scheduling control strategies under constrained information system operating conditions, a control gating and amplitude contraction mechanism based on the information system's operating state is introduced. Candidate scheduling control vectors are obtained by solving the operational scheduling objective function and its constraints. Based on the current information system operation status indicator vector Constructing a control gating matrix The candidate scheduling control vector is then gating and amplitude-shrinking to obtain the scheduling control vector. Its expression is: (twenty two) In the formula, It is a diagonal matrix, and the values ​​of each diagonal element are in the range of 1. .

[0044] The control gating and amplitude contraction mechanism normalizes the information system's operating status indicators and constructs a control gating matrix that corresponds one-to-one with the scheduling control variables. Each diagonal element is used to characterize the executability of the corresponding control command under the current operating status of the information system.

[0045] When the communication link is stable, data transmission is reliable, the success rate of control command issuance is high, and the communication delay meets the preset real-time requirements, the corresponding element in the control gating matrix takes a larger value, so that the control commands calculated by the operation scheduling optimization model can be executed according to the predetermined range. When the information system's operating status declines, communication conditions are limited, the success rate of control command issuance decreases, or the communication delay increases and exceeds the preset threshold, the corresponding gating element takes a smaller value, adaptively reducing the actual execution range of the scheduling control variable. This improves the executability and robustness of the scheduling control strategy under information-constrained conditions while ensuring the safe operation of the system.

[0046] Through the aforementioned control gating and amplitude contraction mechanisms, the information system's operating status directly constrains the scheduling control behavior, enabling the scheduling decisions to adaptively adjust as the information system's operating status changes.

[0047] Under the premise of satisfying the above objective function and constraints, the scheduling period is obtained by using optimization methods. Candidate scheduling control vector within The scheduling control vector is then subjected to gating correction and amplitude contraction. .

[0048] S4. Based on the real-time physical operation status and information system operation status feedback information, the operation scheduling decision is dynamically corrected and updated on a rolling basis; the candidate scheduling control vector, the scheduling control vector corrected by the control gating and amplitude contraction mechanism, and the corresponding control actions such as distributed power output adjustment, energy storage charging and discharging control and network operation mode adjustment are collectively referred to as operation scheduling decision.

[0049] The scheduling control vector obtained in step S3 The commands are sent to the power distribution information physical system. Under the conditions that the communication link connectivity, data packet arrival rate, control command issuance success rate and communication latency meet the preset execution requirements, the corresponding control operations are executed, including distributed power generation output adjustment, energy storage charging and discharging control and network operation mode adjustment.

[0050] Based on the scheduling execution results and combined with real-time measurement information, the system state vector is updated, and the system operating state for the next scheduling cycle is obtained according to equation (16).

[0051] Entering the next scheduling cycle Beforehand, re-collect or assess the operational status of the information system and update the information system operational status indicator vector. Based on this, the set of control instruction executability constraints in the runtime scheduling optimization model is revised: (twenty three) During rolling scheduling, the system's operating status is continuously monitored. An emergency operation mode is triggered when any of the following conditions are detected: node voltage or branch power flow shows a tendency to exceed limits; critical distributed power sources or energy storage units experience operational anomalies; or information system operating status indicators significantly decline, affecting the executability of scheduling control. In emergency operation mode, priority is given to ensuring system operational safety and power supply continuity. By increasing the penalty weight for operational deviations and the risk weight for information constraints, the operation scheduling optimization model is re-solved, guiding the scheduling strategy to switch to a conservative operation mode.

[0052] Based on the updated integrated operation status of the power distribution cyber-physical system and information system operating status As input, step S3 is repeated to continuously solve the runtime scheduling optimization model for the next scheduling cycle, obtaining new candidate scheduling control vectors. and scheduling control vector This enables adaptive updates of operation scheduling decisions as the physical and information system operating states change.

[0053] The scheduling will continue until the following termination condition is met: (1) The power distribution information physical system has been restored to normal operating status; (2) Simultaneously meet the preset operation requirements; these requirements include at least the node voltage being within the allowable range, the critical load being restored to power supply, and the information system operating status meeting the scheduling execution requirements.

[0054] If none of the above termination conditions are met at present, continue to execute steps S1 to S4 to form a closed loop iteration until all conditions are met.

[0055] Simulation Experiment To further verify the effectiveness of the operation and scheduling method for distribution cyber-physical systems in distribution network operation and scheduling scenarios, this embodiment constructs an improved IEEE 33-node distribution network as a simulation object to verify the operational performance of the method under three scenarios: normal communication state, communication state degradation, and comparison with traditional scheduling methods. The improved IEEE 33-node distribution network includes one reference power node and 32 load nodes. The system is equipped with three distributed generation (DG) access nodes, two battery energy storage (BESS) access nodes, and several controllable load nodes. The DG provides active power support to the distribution network, the BESS provides power regulation capabilities when the load is high or the voltage is low, and the controllable loads participate in load shedding when necessary to ensure that the system node voltage and operating state meet safety constraints.

[0056] In this embodiment, the simulation period is set to 24 scheduling periods, each lasting 1 hour, to simulate the daily operation and scheduling process of the distribution network. The load power of each node changes according to the daily load curve to reflect the load fluctuation characteristics of the distribution network in different time periods. The active power output of distributed generation, the discharge power of energy storage, and the load shedding are used as operation and scheduling optimization variables, and the voltage of each node is calculated based on the linearized DistFlow power flow model. The operating range of node voltage is set to 0.95 per-unit (pu) to 1.05 pu. When the node voltage is below 0.95 pu or above 1.05 pu, the node is considered to have a voltage over-limit risk. The operation and scheduling optimization objective comprehensively considers the operating economic cost (operating cost of distributed generation, charging and discharging cost of energy storage, load shedding penalty), the operation deviation penalty, and the information restriction risk penalty, thereby achieving a coordination between operation economy, safety, and information system executability.

[0057] Specifically, the operating cost of distributed generation reflects the operational cost when distributed generation participates in dispatch; the charging and discharging cost of energy storage reflects the usage cost during the charging and discharging process of energy storage devices; the load shedding penalty constrains unnecessary load shedding during dispatch, ensuring the system prioritizes regulation through distributed generation and energy storage devices; the voltage deviation penalty ensures that the voltage of each node is as close as possible to the reference voltage, improving the voltage quality of the distribution network; and the information risk cost characterizes the impact of communication degradation, unreliable data transmission, or insufficient execution of control commands on dispatch executability and system operational safety. Through the above objective function settings, the method can obtain the dispatch control vector while satisfying node voltage constraints, distributed generation output constraints, energy storage state of charge (SOC) constraints, and control executability constraints.

[0058] In terms of information system modeling, this embodiment selects communication link connectivity, data packet arrival rate, control command issuance success rate, and communication latency as indicators of the information system's operational status. Specifically, communication link connectivity characterizes whether the communication link between the dispatch center and the field control terminal maintains a valid connection; data packet arrival rate characterizes the completeness of measurement data and status information uploaded within the dispatch cycle; control command issuance success rate characterizes whether dispatch control commands can be correctly received and confirmed for execution by the field terminal; and communication latency characterizes the delay impact on control commands and status information during transmission. These indicators collectively constitute the information system operational status indicator vector, which is further used to construct gating coefficients.

[0059] In this embodiment, a gating coefficient is constructed based on communication link connectivity, data packet arrival rate, control command issuance success rate, and communication latency. When the information system is in good condition, the gating coefficient is close to or equal to 1, indicating that the candidate scheduling control vector can be fully executed. When the information system condition deteriorates, the gating coefficient decreases accordingly, indicating that the candidate scheduling control vector needs amplitude contraction during actual execution. Therefore, the actually executed scheduling control vector is obtained from the candidate scheduling control vector after gating correction, ensuring that the scheduling result reflects the degree of control executability under the current information system conditions. The simulation uses the MATLAB platform for modeling and calculation, and employs a quadratic programming method to solve the operation scheduling optimization problem within each scheduling period.

[0060] The first set of simulations is an operation scheduling experiment under normal communication conditions, used to verify the effectiveness of this method for power distribution network voltage safety control and coordination of multiple scheduling resources when the information system is operating well. In this experiment, communication link connectivity, data packet arrival rate, control command issuance success rate, and communication latency are all set to normal conditions, and the gating coefficient remains at 1 throughout the entire scheduling cycle. Therefore, the candidate scheduling control vector obtained from the operation scheduling optimization model can be completely issued and executed, and the actual executed control vector is consistent with the candidate scheduling control vector.

[0061] The first set of simulation results is as follows: Figures 2 to 7 As shown. By Figure 2 It is known that without optimized scheduling, due to the changing load levels over time, the lowest node voltage during some scheduling periods is lower than the lower voltage limit, indicating a risk of voltage exceedance in the distribution network. After adopting the operation and scheduling method described in this invention, the lowest node voltage of the system is significantly improved and maintained within the allowable voltage range, demonstrating that the method can improve the system voltage level through regulation means such as distributed power sources, energy storage, and load shedding. Figure 3 It can be seen that the gating coefficient remains at 1 under normal communication conditions, indicating that the scheduling control command has the complete execution conditions. From Figure 4 It can be seen that distributed power sources, energy storage devices, and load shedding can be coordinated and adjusted according to changes in load levels, increasing the participation of distributed power sources and energy storage during periods of high load, thereby enhancing the system's voltage support capability. Figure 5 It can be seen that the state of charge of the energy storage remained within the allowable range throughout the entire scheduling cycle, and did not fall below the lower limit of the state of charge, indicating that the method can ensure the safe operation of energy storage while calling on it to participate in scheduling. Figure 6 It can be seen that under normal communication conditions, the information risk cost is zero, while the operating cost varies with the load level and the degree of resource allocation. Figure 7It can be seen that at the peak load, the voltage of some nodes is significantly lower in the un-dispatch state, while the voltage of each node is improved and kept within a safe range after optimized dispatching. These results demonstrate that the method described in this invention can achieve coordination between distribution network node voltage constraints, multiple dispatching resource constraints, and operational economy under normal communication conditions.

[0062] The second set of simulations is a gated scheduling experiment under declining communication conditions, used to verify the role of the control gating and amplitude contraction mechanism described in this invention under information-constrained conditions. In this experiment, the 10th to 15th scheduling periods are set as periods of declining communication conditions, specifically manifested as a simultaneous decrease in communication link connectivity, data packet arrival rate, control command issuance success rate, and communication latency. Correspondingly, the gating coefficient decreases during these periods, indicating that the control commands issued by the scheduling center cannot be executed completely according to the candidate scheduling control vector on the field side, and amplitude contraction is required according to the current communication state.

[0063] The second set of simulation results is as follows: Figures 8 to 15 As shown. By Figure 8 It can be seen that during periods of decreased communication, the lowest node voltage in the system is affected by the reduced control execution capability, but it still remains within a safe range, indicating that the proposed method can maintain basic system operational safety under communication-constrained conditions. Figure 9 It can be seen that during the 10th to 15th scheduling periods, communication link connectivity, data packet arrival rate, control command issuance success rate, and communication latency all decrease. The gating coefficient decreases as the information system's operating state decreases, indicating that the information system's operating state can directly affect the scheduling control vector through the gating coefficient. Figures 10 to 12 It can be seen that during periods of communication constraints, the actual executed values ​​of distributed power output, energy storage discharge power, and load reduction are all less than the candidate scheduling values. This indicates that the control gating and amplitude contraction mechanisms can automatically adjust the scheduling control vector according to changes in communication status, avoiding the assumption that control commands can be fully executed even under communication constraints. Figure 13 It can be seen that the energy storage state of charge remains within the allowable range during the gating scheduling process, indicating that the method does not violate the energy storage operation constraints while considering information state constraints. Figure 14 It is evident that the information risk cost increases significantly during periods of communication restriction, leading to a corresponding increase in overall costs. This indicates that the impact of a decline in the operational status of the information system on scheduling execution risk is explicitly incorporated into the operational scheduling evaluation process. Figure 15 It can be seen that at the moment of maximum load, the node voltage distribution after scheduling is still better than that in the unscheduled state, indicating that even when the communication status declines, the method can still maintain the safe operation of the distribution network through the gating-corrected scheduling control vector.

[0064] The third set of simulations compares the traditional scheduling method with the method described in this invention, further illustrating the improvement effect of the method described in this invention compared to the traditional scheduling method. Traditional scheduling methods assume the communication system is reliable during optimization, assuming that scheduling control commands can be executed completely. Therefore, they do not incorporate information system operating conditions such as communication link connectivity, data packet arrival rate, control command issuance success rate, and communication latency into the scheduling optimization process. When the communication status deteriorates, the candidate scheduling control vector obtained by the traditional scheduling method will be affected by communication constraints during actual execution, leading to a deviation between the actual executed control vector and the candidate scheduling control vector. In contrast, the method described in this invention introduces information system operating status indicators, information risk costs, and control gating and amplitude contraction mechanisms during the optimization process, enabling the scheduling control vector to adaptively correct itself according to changes in communication status, thereby improving the actual executability of the scheduling strategy.

[0065] The third set of simulation results is as follows: Figures 16 to 23 As shown. By Figure 16 It is known that during periods of limited communication, traditional scheduling methods, due to insufficient consideration of the reduced control command execution capability, result in inadequate improvement in node voltage after actual execution. The method described in this invention can correct the scheduling control vector based on the current communication status, making the actual operating voltage closer to safe operating requirements. Figures 17 to 19 It can be seen that, in terms of the actual output of distributed power sources, the actual discharge power of energy storage, and the load reduction, the method described in this invention can more rationally allocate and correct control resources based on changes in communication status, avoiding a mismatch between the scheduling control vector and the actual execution capability on site. Figure 20 It is known that the energy storage state of charge meets the constraints under both methods, but the method described in this invention can more rationally arrange the energy storage discharge process in communication-constrained scenarios, making the energy storage regulation behavior compatible with the executable capabilities of the information system. Figure 21 It is evident that the method described in this invention can comprehensively balance operating costs and information risk costs, ensuring that the overall cost reflects the scheduling execution risk caused by a decline in communication status. Figure 22 It is known that traditional scheduling methods suffer from significant control execution deviations during communication-constrained periods. The method described in this invention, however, uses a gating correction mechanism to match the final scheduling control vector with the actual execution capability, thereby reducing the deviation between the candidate scheduling control vector and the actual executed control vector. Figure 23 It can be seen that, under typical conditions of communication constraints, the voltage distribution of each node under the method described in this invention is more in line with the requirements for safe operation, indicating that the method can improve the reliability of the operation and scheduling results of the distribution network under communication constraints.

[0066] In summary, the three sets of simulation results verify the effectiveness of the method described in this invention from three aspects: normal communication operation, operation under declining communication conditions, and comparison with traditional scheduling methods. The first set of simulations shows that when communication is good, the method can achieve coordinated scheduling among distributed power sources, energy storage devices, and controllable loads, keeping node voltages within a safe range. The second set of simulations shows that when communication is declining, the method can correct the scheduling control vector through information state-driven control gating and amplitude contraction mechanisms, making the actual executed control vector more consistent with the control executability under current communication conditions. The third set of simulations shows that, compared with traditional scheduling methods that do not consider the operating state of the information system, the method described in this invention can reduce control execution deviations under communication-constrained conditions, enhance the matching between scheduling strategies and actual operating states, and improve the safety, reliability, and operational scheduling adaptability of the power distribution cyber-physical system in complex operating environments.

[0067] Example 2 Based on Embodiment 1, Embodiment 2 also provides an operation scheduling system for a power distribution cyber-physical system, including: The physical and information system status acquisition module is used to acquire physical operation status information of the distribution network and information system operation status information. The physical operation status information of the distribution network includes node load power, distributed power output, line connection relationship and line parameters. The information system operation status information includes communication link connectivity, data packet arrival rate, control command issuance success rate and communication latency.

[0068] Specifically, this includes: a power distribution network physical operation status information acquisition unit, used to collect active and reactive power data of each node in the power distribution network within the scheduling cycle t, forming a load vector. (1) (2) In the formula, and This represents the active power vector and reactive power vector of the load at each node in the distribution network. and This represents the active and reactive power of the load at the first node. and This represents the active and reactive power of the load at the second node. and Let T represent the active and reactive power of the load at the Nth node, and T denote the transpose.

[0069] Collect active and reactive power data from distributed generation sources to form distributed generation source injection vectors: (3) (4) In the formula, and This represents the active power vector and reactive power vector of all distributed power sources in the system. and This represents the active and reactive power injected into or absorbed by the first distributed power source into the distribution network. and This represents the active and reactive power injected into or absorbed by the second distributed power source into the distribution network. and This represents the active and reactive power injected or absorbed by the Gth distributed power source into the distribution network, and T represents transpose.

[0070] Based on the distribution network topology, construct a mapping matrix between distributed power sources and nodes. Its elements are defined as: (5) In the formula, , ∈ indicates belonging to.

[0071] Based on equations (1), (2), (3), (4), and (5), and according to the nodal power balance relationship, the active and reactive power injection vectors at the distribution network node level are constructed as follows: (6) (7) In the formula, This represents the net active power injection vector of a node. This represents the net reactive power injection vector of the node.

[0072] Collect the connection relationships and parameters of the distribution network lines, number the nodes and branches of the distribution network, define the direction of each branch, and construct a node-branch association matrix. The node-branch association matrix The behavior is the distribution network node, and the column is the distribution network branch; for any branch, the matrix element corresponding to the starting point of the branch is 1, the matrix element corresponding to the ending point of the branch is -1, and the matrix element corresponding to the other nodes is 0.

[0073] Based on the line parameters, the equivalent resistance and equivalent reactance of each branch are extracted, and the branch resistance diagonal matrix and branch reactance diagonal matrix are constructed respectively: (8) In the formula, This is a diagonal matrix composed of branch resistance parameters. Represents a node With nodes The equivalent resistance of the branch between them, This is a diagonal matrix composed of branch reactance parameters. Represents a node With nodes The equivalent reactance of the branch between them.

[0074] The information system operation status information acquisition unit is used to collect information system operation status information related to power distribution network operation and scheduling. The information system operation status information includes at least communication link connectivity, data packet arrival rate, control command issuance success rate, and communication delay. Communication link connectivity is obtained based on the online status of communication nodes and link handshake results. Data packet arrival rate is obtained based on the ratio of the number of successfully received data packets to the total number of sent data packets within a preset time window. Control command issuance success rate is obtained based on the ratio of the number of successfully issued and received execution confirmation control commands to the total number of issued control commands within a preset time window. Communication delay is obtained based on the difference between the data packet sending time and the receiving time between the control center and the field terminal.

[0075] Construct an information system operation status index vector based on information system operation status information: (9) In the formula, This represents a vector of information system operational status indicators. This represents the first information system operation status indicator. This represents the second information system operational status indicator. Indicates the first Information system operation status indicators.

[0076] Combining equations (6), (7), (8), and (9) forms a scheduling optimization model for the scheduling cycle. Unified input set : (10) The state dynamic evolution model construction module is used to describe the branch power balance relationship and node voltage constraints based on the radial topology of the distribution network using a linearized DistFlow power flow model, and to construct control executability constraints by combining the information system operation status information to form a state dynamic evolution model.

[0077] Specifically, it includes: constraint construction units, used to describe the relationship between active power, reactive power, and voltage in a distribution network based on its radial topology and using a linearized DistFlow power flow model. The branch power balance relationship is expressed as: (11) (12) In the formula, and They represent nodes respectively To the node The branch lines have contributing and non-contributing currents. Represents a node The set of downstream nodes, and They represent nodes respectively Pointing to its downstream node The active and passive currents, and Representing nodes respectively The net active and reactive power injection.

[0078] The node voltage drop relationship is expressed as follows: (13) In the formula, Represents a node voltage amplitude, Represents a node voltage amplitude, Represents a node With nodes The equivalent resistance of the branch between them, Represents a node With nodes The equivalent reactance of the branch between them.

[0079] Node voltage vector Represented as: (14) In the formula, This is a node-branch association matrix. This is a diagonal matrix composed of branch resistance parameters. This is a diagonal matrix composed of branch reactance parameters. This represents the net active power injection vector of a node. This represents the net reactive power injection vector at the node. This is the reference node voltage.

[0080] Introducing branch state variables ,when When the branch is in operation, it indicates that the branch is in operation. The time indicates that the branch is open; the corresponding voltage constraint is expressed as: (15) In the formula, It is a constant.

[0081] Information system operation status index vector based on equation (9) Based on this, define a set of control instruction executability constraints. , This represents the actual executable scheduling and control vector under the constraints of the current information system operating state. This represents the executable control domain, which is determined by the connectivity of the communication link, the arrival rate of data packets, the success rate of control command issuance, and the communication latency.

[0082] The state evolution of the power distribution cyber-physical system within adjacent scheduling cycles is represented as follows: (16) In the formula, Indicates the scheduling period The overall operational status of the power distribution information physical system.

[0083] The scheduling control vector generation module is used to construct an operation scheduling optimization model based on the state dynamic evolution model, with the goal of minimizing the sum of the operation economic cost, operation deviation penalty term, and information restriction risk penalty term, and solve the operation scheduling optimization model to obtain candidate scheduling control vectors; according to the information system operation status information, the candidate scheduling control vectors are gating and amplitude shrinking through control gating and amplitude shrinking mechanisms to obtain the scheduling control vector.

[0084] Specifically, the objective function construction unit is used to schedule cycles. t With the goal of minimizing the overall operating cost and operational risk of the internal system, an objective function is constructed for the operation scheduling optimization model. : (17) in, (18) (19) (20) In the formula, Indicates the economic cost of operation. g Represents a collection of distributed power sources. Indicates the first The weight of the operating cost corresponding to the active power output of each distributed power source unit. Indicates the first Active power output of a distributed power source Represents a collection of energy storage units. Indicates the first The weight of the operating cost per unit charge / discharge power of each energy storage unit. Indicates the first The charging and discharging power of each energy storage unit Represents the set of nodes in the distribution network. Indicates the first d The penalty weight corresponding to the load reduction of each node. Indicates the first d Load reduction per node This indicates the penalty for operational deviation. This represents the voltage deviation weighting coefficient. Represents a node voltage amplitude, Represents a node The reference voltage, This indicates a penalty item for information restriction risks. Indicates the first The weighting coefficients of each information system's operational status indicators in the comprehensive risk assessment. Indicates the first The values ​​of the operational status indicators of an information system. .

[0085] The model constraint unit is used to run the scheduling optimization model to satisfy node voltage constraints, distributed power generation output constraints, energy storage state of charge (SOC) constraints, and control executability constraints. The constraints are as follows: (twenty one) In the formula, and Representing nodes respectively The minimum and maximum values ​​of the voltage amplitude, and They represent the first The minimum and maximum active power output of a distributed power source. Indicates the first The state of charge of each energy storage unit and They represent the first The minimum and maximum values ​​of the state of charge of each energy storage unit.

[0086] The gating and amplitude contraction unit is used to introduce control gating and amplitude contraction mechanisms, and obtains candidate scheduling control vectors based on the running scheduling objective function and its constraints. Based on the current information system operation status indicator vector Constructing a control gating matrix The candidate scheduling control vector is then gating and amplitude-shrinking to obtain the scheduling control vector. Its expression is: (twenty two) In the formula, It is a diagonal matrix, and the values ​​of each diagonal element are in the range of 1. .

[0087] The dynamic correction and rolling update module is used to dynamically correct and roll update the scheduling control vector based on the real-time acquired physical operation status information and information system operation status feedback information.

[0088] Specifically, it includes: an update unit, used to update the scheduling control vector obtained by the scheduling control vector generation module. The commands are sent to the power distribution information physical system. Under the conditions that the communication link connectivity, data packet arrival rate, control command issuance success rate and communication latency meet the preset execution requirements, the corresponding control operations are executed, including distributed power generation output adjustment, energy storage charging and discharging control and network operation mode adjustment.

[0089] Based on the scheduling execution results and combined with real-time measurement information, the system state vector is updated, and the system operating state for the next scheduling cycle is obtained according to equation (16).

[0090] Entering the next scheduling cycle Beforehand, re-collect or assess the operational status of the information system and update the information system operational status indicator vector. Based on this, the set of control instruction executability constraints in the runtime scheduling optimization model is revised: (twenty three) Based on the updated integrated operation status of the power distribution cyber-physical system and information system operating status As input, the scheduling control vector generation module is repeatedly executed to continuously solve the running scheduling optimization model for the next scheduling cycle, thereby obtaining new candidate scheduling control vectors. and scheduling control vector This enables adaptive updates of operation scheduling decisions as the physical and information system operating states change.

[0091] The above embodiments are only used to illustrate the technical solutions of the present invention, and are not intended to limit it. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for operation and scheduling of a power distribution cyber-physical system, characterized in that, include: S1. Obtain the physical operating status information of the distribution network and the operating status information of the information system. The physical operating status information of the distribution network includes node load power, distributed power output, line connection relationship and line parameters. The operating status information of the information system includes communication link connectivity, data packet arrival rate, control command issuance success rate and communication delay. S2. Based on the radial topology of the distribution network, a linearized DistFlow power flow model is used to describe the branch power balance relationship and node voltage constraints. Combined with the information system operation status information, control executability constraints are constructed to form a dynamic state evolution model. S3. Based on the aforementioned dynamic state evolution model, construct an operation scheduling optimization model with the objective of minimizing the sum of operational economic costs, operational deviation penalty terms, and information-limited risk penalty terms, and solve the operation scheduling optimization model to obtain candidate scheduling control vectors; according to the information system operation status information, perform gating correction and amplitude contraction on the candidate scheduling control vectors through control gating and amplitude contraction mechanisms to obtain scheduling control vectors; S4. Based on the real-time acquired physical operation status information and information system operation status feedback information, the scheduling control vector is dynamically corrected and continuously updated.

2. The operation scheduling method for a power distribution cyber-physical system according to claim 1, characterized in that, The collection of the physical operating status information of the distribution network includes: During the scheduling period Within the system, active and reactive power data of loads at each node of the distribution network are collected to form a load vector. (1) (2) In the formula, and This represents the active power vector and reactive power vector of the load at each node in the distribution network. and This represents the active and reactive power of the load at the first node. and This represents the active and reactive power of the load at the second node. and Let T represent the active and reactive power of the load at the Nth node, and T denote the transpose. Collect active and reactive power data from distributed generation sources to form distributed generation source injection vectors: (3) (4) In the formula, and This represents the active power vector and reactive power vector of all distributed power sources in the system. and This represents the active and reactive power injected into or absorbed by the first distributed power source into the distribution network. and This represents the active and reactive power injected into or absorbed by the second distributed power source into the distribution network. and This represents the active and reactive power injected or absorbed by the Gth distributed power source into the distribution network, and T represents transpose; Based on the distribution network topology, construct a mapping matrix between distributed power sources and nodes. Its elements are defined as: (5) In the formula, , ∈ indicates belonging to; Based on equations (1), (2), (3), (4), and (5), and according to the nodal power balance relationship, the active and reactive power injection vectors at the distribution network node level are constructed as follows: (6) (7) In the formula, This represents the net active power injection vector of a node. This represents the net reactive power injection vector of the node.

3. The operation scheduling method for a power distribution cyber-physical system according to claim 2, characterized in that, Collecting the physical operating status information of the distribution network also includes: collecting the connection relationships and parameters of the distribution network lines, numbering the nodes and branches of the distribution network, defining the direction of each branch, and constructing a node-branch association matrix. The node-branch association matrix The behavior is the distribution network node, and the column is the distribution network branch; for any branch, the matrix element corresponding to the starting point of the branch is 1, the matrix element corresponding to the ending point of the branch is -1, and the matrix element corresponding to the other nodes is 0. Based on the line parameters, the equivalent resistance and equivalent reactance of each branch are extracted, and the branch resistance diagonal matrix and branch reactance diagonal matrix are constructed respectively: (8) In the formula, This is a diagonal matrix composed of branch resistance parameters. Represents a node With nodes The equivalent resistance of the branch between them, This is a diagonal matrix composed of branch reactance parameters. Represents a node With nodes The equivalent reactance of the branch between them.

4. The operation scheduling method for a power distribution cyber-physical system according to claim 1, characterized in that, The collection of the information system's operational status information includes: The system collects operational status information related to power distribution network operation and scheduling. This operational status information includes at least communication link connectivity, data packet arrival rate, control command issuance success rate, and communication latency. Communication link connectivity is obtained based on the online status of communication nodes and link handshake results. The data packet arrival rate is calculated as the ratio of the number of successfully received data packets to the total number of sent data packets within a preset time window. The control command issuance success rate is calculated as the ratio of the number of successfully issued and received execution confirmation control commands to the total number of issued control commands within a preset time window. Communication latency is obtained as the difference between the sending and receiving times of data packets between the control center and field terminals. Construct an information system operation status index vector based on information system operation status information: (9) In the formula, This represents a vector of information system operational status indicators. This represents the first information system operation status indicator. This represents the second information system operational status indicator. Indicates the first Each information system's operational status indicators; Combining equations (6), (7), (8), and (9) forms a scheduling optimization model for the scheduling cycle. Unified input set : (10)。 5. The operation scheduling method for a power distribution cyber-physical system according to claim 1, characterized in that, S2 includes: Based on the radial topology of the distribution network, a linearized DistFlow power flow model is used to describe the relationship between active power, reactive power, and voltage in the distribution network. The branch power balance relationship is expressed as follows: (11) (12) In the formula, and They represent nodes respectively To the node The branch lines have contributing and non-contributing currents. Represents a node The set of downstream nodes, and They represent nodes respectively Pointing to its downstream node The active and passive currents, and Representing nodes respectively Net active and reactive power injection; The node voltage drop relationship is expressed as follows: (13) In the formula, Represents a node voltage amplitude, Represents a node voltage amplitude, Represents a node With nodes The equivalent resistance of the branch between them, Represents a node With nodes The equivalent reactance of the branches between them; Node voltage vector Represented as: (14) In the formula, This is a node-branch association matrix. This is a diagonal matrix composed of branch resistance parameters. This is a diagonal matrix composed of branch reactance parameters. This represents the net active power injection vector of a node. This represents the net reactive power injection vector at the node. Reference node voltage; Introducing branch state variables ,when When the branch is in operation, it indicates that the branch is in operation. The time indicates that the branch is open; the corresponding voltage constraint is expressed as: (15) In the formula, It is a constant; Information system operation status index vector based on equation (9) Based on this, define a set of control instruction executability constraints. , This represents the actual executable scheduling and control vector under the constraints of the current information system operating state. This represents the executable control domain, which is jointly determined by communication link connectivity, data packet arrival rate, control command issuance success rate, and communication latency. The state evolution of the power distribution cyber-physical system within adjacent scheduling cycles is represented as follows: (16) In the formula, Indicates the scheduling period The overall operational status of the power distribution information physical system.

6. The operation scheduling method for a power distribution cyber-physical system according to claim 1, characterized in that, The operation scheduling optimization model in S3 includes: By scheduling cycle t With the goal of minimizing the overall operating cost and operational risk of the internal system, an objective function is constructed for the operation scheduling optimization model. : (17) in, (18) (19) (20) In the formula, Indicates the economic cost of operation. g Represents a collection of distributed power sources. Indicates the first The weight of the operating cost corresponding to the active power output of each distributed power source unit. Indicates the first Active power output of a distributed power source Represents a collection of energy storage units. Indicates the first The weight of the operating cost per unit charge / discharge power of each energy storage unit. Indicates the first The charging and discharging power of each energy storage unit Represents the set of nodes in the distribution network. Indicates the first d The penalty weight corresponding to the load reduction of each node. Indicates the first d Load reduction per node This indicates the penalty for operational deviation. This represents the voltage deviation weighting coefficient. Represents a node voltage amplitude, Represents a node The reference voltage, This indicates a penalty item for information restriction risks. Indicates the first The weighting coefficients of each information system's operational status indicators in the comprehensive risk assessment. Indicates the first The values ​​of the operational status indicators of an information system. .

7. The operation scheduling method for a power distribution cyber-physical system according to claim 6, characterized in that, The operation scheduling optimization model satisfies node voltage constraints, distributed power generation output constraints, energy storage state of charge (SOC) constraints, and control executability constraints, as follows: (21) In the formula, and Representing nodes respectively The minimum and maximum values ​​of the voltage amplitude, and They represent the first The minimum and maximum active power output of a distributed power source. Indicates the first The state of charge of each energy storage unit and They represent the first The minimum and maximum values ​​of the state of charge of each energy storage unit.

8. The operation scheduling method for a power distribution cyber-physical system according to claim 7, characterized in that, S3 also introduces a control gating and amplitude contraction mechanism, and obtains candidate scheduling control vectors based on the operation scheduling objective function and its constraints. Based on the current information system operation status indicator vector Constructing a control gating matrix The candidate scheduling control vector is then gating and amplitude-shrinking to obtain the scheduling control vector. Its expression is: (22) In the formula, It is a diagonal matrix, and the values ​​of each diagonal element are in the range of 1. .

9. The operation scheduling method for a power distribution cyber-physical system according to claim 1, characterized in that, S4 includes: The scheduling control vector obtained in S3 The command is sent to the power distribution information physical system. Under the condition that the communication link connectivity, data packet arrival rate, control command issuance success rate and communication latency meet the preset execution requirements, the corresponding control operations are executed, including distributed power generation output adjustment, energy storage charging and discharging control and network operation mode adjustment. Based on the scheduling execution results and combined with real-time measurement information, the system state vector is updated, and the system operating state for the next scheduling cycle is obtained according to equation (16). Entering the next scheduling cycle Beforehand, re-collect or assess the operational status of the information system and update the information system operational status indicator vector. Based on this, the set of control instruction executability constraints in the runtime scheduling optimization model is revised: (23) Based on the updated integrated operation status of the power distribution cyber-physical system and information system operating status As input, S3 is executed repeatedly to continuously solve the runtime scheduling optimization model for the next scheduling cycle, thereby obtaining new candidate scheduling control vectors. and scheduling control vector This enables adaptive updates of operation scheduling decisions as the physical and information system operating states change.

10. An operation and scheduling system for a power distribution cyber-physical system, characterized in that, include: The physical and information system status acquisition module is used to acquire physical operation status information of the distribution network and information system operation status information. The physical operation status information of the distribution network includes node load power, distributed power output, line connection relationship and line parameters. The information system operation status information includes communication link connectivity, data packet arrival rate, control command issuance success rate and communication latency. The state dynamic evolution model construction module is used to describe the branch power balance relationship and node voltage constraints based on the radial topology of the distribution network using a linearized DistFlow power flow model, and to construct control executability constraints by combining the information system operation status information to form a state dynamic evolution model. The scheduling control vector generation module is used to construct an operation scheduling optimization model based on the state dynamic evolution model, with the goal of minimizing the sum of the operation economic cost, operation deviation penalty term, and information limitation risk penalty term, and solve the operation scheduling optimization model to obtain candidate scheduling control vectors; according to the information system operation status information, the candidate scheduling control vectors are gating and amplitude shrinking through control gating and amplitude shrinking mechanisms to obtain the scheduling control vector; The dynamic correction and rolling update module is used to dynamically correct and roll update the scheduling control vector based on the real-time acquired physical operation status information and information system operation status feedback information.

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