Transmission and distribution cooperative reactive power optimization method and system considering static voltage safety constraint

Abstract

The invention discloses a transmission and distribution cooperative reactive power optimization method and system considering static voltage safety constraint, and the method comprises the steps: constructing a multi-Agent transmission and distribution reactive power interaction safety model, dividing power transmission and distribution network reactive power optimization sub-problems through employing a transmission and distribution network decoupling cooperative reactive power algorithm, building a source network load storage transmission and distribution reactive power platform, and achieving the data transmission and sharing. An optimization objective function is constructed in combination with static voltage security constraints, and an optimization result is output through algorithm solution and collaborative iteration; the system comprises six units which work cooperatively. The method and system can break the optimization barrier of the power transmission and distribution network, fully mobilize the load storage reactive power regulation capability of the source network, ensure that the optimization result meets the static voltage safety standard, reduce the overall network loss of the power transmission and distribution network, reduce the node voltage deviation, and provide support for the efficient and safe operation of a novel power system.

Description

Technical Field

[0001] This invention relates to the field of power transmission and distribution coordination technology, and in particular to a method and system for reactive power optimization in power transmission and distribution coordination that takes into account static voltage safety constraints. Existing technology

[0002] With the large-scale integration of distributed power sources, energy storage systems, and adjustable loads into power systems, the reactive power characteristics of each link in the power generation, grid, load, and storage system are becoming increasingly complex. The reactive power interaction between the transmission and distribution networks is significantly enhanced, making traditional reactive power optimization methods centered on the transmission network inadequate for the operational needs of new power systems. Simultaneously, users' requirements for power supply reliability and voltage quality are constantly increasing. Static voltage safety, as a key factor in ensuring the stable operation of the power system, needs to be fully considered in the reactive power optimization process. Against this backdrop, constructing optimization methods and systems that can achieve coordinated operation of the transmission and distribution networks, integrate reactive power resources from all links in the power generation, grid, load, and storage system, and meet static voltage safety constraints has become an important research direction for improving power system operating efficiency and ensuring voltage safety, and has significant practical implications for promoting the construction of new power systems.

[0003] Existing technologies in the field of reactive power optimization for transmission and distribution networks have two significant drawbacks: First, most reactive power optimization methods fail to achieve deep decoupling and coordination between the transmission and distribution networks, often treating them as separate entities. They lack dedicated interaction mechanisms and collaborative algorithms, making it difficult to match reactive power allocation between the transmission and distribution networks with actual operational needs. This prevents the full utilization of the reactive power regulation potential of each link in the power generation, grid, load, and storage systems, thus affecting the overall optimization effect. Second, existing methods do not comprehensively consider static voltage safety constraints. Either they fail to effectively integrate static voltage safety constraints with reactive power optimization objectives, or the constraint conditions are not adequately combined with the operating characteristics of different nodes and equipment in the transmission and distribution networks. This makes it difficult to accurately reflect the static voltage safety requirements in actual operation, potentially leading to optimization results that fail to meet the static voltage safety operation standards of the power system, posing safety hazards. Summary of the Invention

[0004] In order to overcome the shortcomings and deficiencies of the existing technology, the present invention provides a method and system for transmission and distribution coordinated reactive power optimization that takes into account static voltage safety constraints.

[0005] The technical solution adopted in this invention is a transmission and distribution coordinated reactive power optimization method considering static voltage security constraints, comprising: Step S1, constructing a multi-Agent transmission and distribution reactive power interaction security model, in which each Agent corresponds to the transmission network layer, distribution network layer, and source-grid-load-storage links respectively, and obtaining real-time operating parameters of the transmission and distribution network, reactive power output status of each source-grid-load-storage unit, and static voltage security constraint boundary conditions through information interaction between Agents; Step S2, using a transmission and distribution network decoupling coordinated reactive power algorithm to decouple the transmission and distribution network, and determining the boundary and coordinated interaction variables between the reactive power optimization sub-problems on the transmission network side and the reactive power optimization sub-problems on the distribution network side; Step S3, building a source-grid-load-storage transmission and distribution reactive power platform, and connecting the transmission and distribution network operation data, reactive power regulation capacity data of each source-grid-load-storage unit, and static voltage monitoring data to the platform for real-time data transmission and sharing; Step S4: On the source-grid-load-storage transmission and distribution reactive power platform, with the goal of minimizing the overall network loss of the transmission and distribution network and minimizing the voltage deviation of each node, and in conjunction with static voltage security constraints, construct the objective function for transmission and distribution coordinated reactive power optimization, and clarify the correlation between the weight coefficients in the objective function and the operating characteristics of the transmission and distribution network; Step S5: Solve the constructed objective function using the transmission and distribution network decoupled coordinated reactive power algorithm. During the solution process, the transmission network side and the distribution network side are coordinated and iterated through a multi-Agent transmission and distribution reactive power interaction security model, and the coordinated interaction variables are continuously updated until the convergence condition is met; Step S6: Output the transmission and distribution coordinated reactive power optimization results. The results include the adjustment commands of each reactive power compensation device on the transmission network side, the reactive power output commands of each distributed power source and energy storage system on the distribution network side, and the reactive power response commands of the adjustable load on the load side, and the optimization results meet the static voltage security constraint requirements.

[0006] Furthermore, the expression for the multi-Agent power transmission and distribution reactive power interaction security model is: ,in, For the comprehensive evaluation function of the multi-agent reactive power interaction security model, The total number of Agents For the first The weight coefficients of each agent, The first Each Agent corresponds to a coefficient for reactive power, voltage, and reactive current. For the first The reactive power of the unit to which each Agent belongs. For the first The voltage of the node to which each Agent belongs. For the first The reactive current of the branch to which each Agent belongs For collaborative interaction penalty coefficient, The total number of collaborative interaction variables, For the first The deviation of each collaborative interaction variable.

[0007] Furthermore, the expression for the decoupling and coordinated reactive power algorithm of the transmission and distribution network is: ,in, The optimal reactive power allocation for transmission and distribution coordination reactive power optimization. This represents the total number of reactive power regulation units. For the first Cost coefficient of each reactive power regulation unit For the first The reactive power output of each reactive power regulation unit The reactive power balance penalty coefficient for the power transmission and distribution network. This refers to the total reactive power supply on the transmission network side. This represents the total reactive power demand on the distribution network side. These are the lower and upper limits of the node voltage, respectively. This represents the voltage at the k-th node. , The first The lower and upper limits of reactive power output of each reactive power regulation unit. "Subject to" is an abbreviation used to introduce constraints.

[0008] Furthermore, in the process of constructing the reactive power transmission and distribution platform based on the source-grid-load-storage method, a platform data interaction efficiency model is introduced, expressed as: ,in, To improve the data interaction efficiency of the power generation, grid, load storage, transmission, and distribution reactive power platform, Total data interaction time For the first Normal data transmission time at any moment For the first Real-time data transmission delay time For the first The amount of data transmitted at any given time.

[0009] Furthermore, when constructing the objective function for reactive power optimization in transmission and distribution coordination, a static voltage safety constraint coefficient is introduced, and the modified expression of the objective function is as follows: ,in, The objective function for the modified power transmission and distribution coordinated reactive power optimization is as follows: This is the network loss weighting coefficient. This represents the total network loss of the transmission and distribution network. This is the voltage deviation weighting coefficient. The total number of nodes. For the first Voltage deviation at each node, This represents the actual voltage value of the nth node. This refers to the static voltage safety constraint weighting coefficient. For the first Voltage reference values ​​for each node, For the first The static voltage safety constraint factor for each node, when the node voltage meets the safety constraint. If not satisfied .

[0010] Furthermore, in the multi-agent collaborative iteration process, an inter-agent trust model is adopted, expressed as: ,in, For the first Moment Agent For Agent Trust level, For the first Moment Agent For Agent Trust level, To increase the trust level, The first Time, Number Moment Agent The reactive power of collaborative interaction To lower the trust level coefficient, For the first Moment Agent The voltage of the node. Agent The voltage reference value of the node.

[0011] Further, step S3 specifically includes the following sub-steps: S31, Selecting and deploying data acquisition equipment for each link of the power generation, grid, load, and storage system; deploying voltage, current, and power sensors on the transmission grid side; and deploying data acquisition devices at distributed power source grid connection points, energy storage system interfaces, and adjustable load control nodes on the distribution grid side to ensure the comprehensiveness and real-time nature of data acquisition for each link; S32, Building the hardware architecture of the power generation, grid, load, and storage reactive power transmission and distribution platform, including a data server, edge computing nodes, and a communication module. The data server is used to store various types of collected data, the edge computing nodes are used for preliminary processing of real-time data, and the communication module uses a combination of optical fiber and wireless communication to transmit data between the acquisition devices and the platform; S3 3. Develop the software functional modules of the source-grid-load-storage transmission and distribution reactive power platform, including a data receiving module, a data preprocessing module, a data storage module, and a data sharing module. The data receiving module receives data transmitted from various acquisition devices. The data preprocessing module removes outliers and converts data formats. The data storage module uses a distributed database to store the processed data. The data sharing module provides a data call interface for the multi-Agent transmission and distribution reactive power interaction security model and the decoupled collaborative reactive power algorithm of the transmission and distribution network. S34. Conduct joint commissioning tests on the source-grid-load-storage transmission and distribution reactive power platform, simulate different operating conditions of the transmission and distribution network, check the stability of data transmission, the accuracy of data processing, and the timeliness of data sharing, and optimize and adjust any existing problems.

[0012] Further, step S4 specifically includes the following sub-steps: S41. Determine the overall network loss calculation scope of the transmission and distribution network, covering the losses of the main transmission lines and the branch lines of the distribution network. Based on the topology of the transmission and distribution network and the impedance parameters of each line, clarify the specific formulas and parameters involved in the network loss calculation to ensure the accuracy of the network loss calculation; S42. Define the calculation method for the voltage deviation of each node, using the absolute value of the difference between the actual voltage value and the reference value of each node as the voltage deviation index. Combined with the importance of different nodes in the transmission and distribution network, determine the weight allocation principle of the voltage deviation of each node in the objective function; S43. Analyze the specific content of the static voltage safety constraints, including the upper and lower limits of the voltage of each node, the current limit constraints of the transmission and distribution network branches, and the output limit constraints of the reactive power compensation device. Transform these constraints into mathematical expressions and clarify the value range and determination method of each parameter in the constraints; S44. Based on the operating characteristics of the transmission and distribution network, determine the specific values ​​of the network loss weight coefficient and the voltage deviation weight coefficient in the objective function through a combination of historical operating data regression analysis and expert experience judgment to ensure that the objective function can accurately reflect the needs of transmission and distribution coordinated reactive power optimization.

[0013] Further, step S5 specifically includes the following sub-steps: S51, Initialize the parameters of the decoupled collaborative reactive power algorithm for the transmission and distribution network, including the initial iteration value, convergence threshold, and maximum number of iterations. The initial iteration value is determined based on the historical optimal operating data of the transmission and distribution network, the convergence threshold is set according to the accuracy requirements of the static voltage safety constraint, and the maximum number of iterations is determined according to the algorithm's computational efficiency requirements; S52, Solve the reactive power optimization sub-problem on the transmission network side, taking the minimization of network loss on the transmission network side as the local objective, and combining the static voltage safety constraint on the transmission network side, use the interior point method to calculate the output of the reactive power compensation device on the transmission network side to obtain the preliminary reactive power optimization results on the transmission network side; S53, Calculate the output of the reactive power compensation device on the transmission network side from the preliminary reactive power optimization results on the transmission network side. The collaborative interaction variables are transmitted to the distribution network side. The distribution network side takes the minimum distribution network loss and the minimum voltage deviation as local objectives. Combined with the static voltage security constraints of the distribution network side, the reactive power output of distributed power sources, energy storage systems and adjustable loads on the distribution network side is calculated to obtain the preliminary reactive power optimization results of the distribution network side. S54. The preliminary reactive power optimization results of the distribution network side are fed back to the transmission network side. The preliminary optimization results of the transmission network side and the distribution network side are collaboratively verified by the multi-Agent transmission and distribution reactive power interaction security model to determine whether the convergence conditions are met. If not, the collaborative interaction variables are updated and steps S52-S53 are repeated until the convergence conditions are met to obtain the final solution result of transmission and distribution collaborative reactive power optimization.

[0014] A transmission and distribution coordinated reactive power optimization system considering static voltage security constraints includes: a multi-agent transmission and distribution reactive power interaction security modeling unit, which is connected to the transmission and distribution network monitoring equipment and the controllers of each source-grid-load-storage unit, used to acquire transmission and distribution network operating parameters and the state parameters of each source-grid-load-storage unit, and construct a multi-agent transmission and distribution reactive power interaction security model; a transmission and distribution network decoupling coordinated reactive power algorithm calculation unit, which is connected to the multi-agent transmission and distribution reactive power interaction security modeling unit, receives the model parameters output by the multi-agent transmission and distribution reactive power interaction security model, and uses the transmission and distribution network decoupling coordinated reactive power algorithm to decouple and calculate the reactive power optimization problem of the transmission and distribution network; a source-grid-load-storage transmission and distribution reactive power data interaction unit, which is connected to both the multi-agent transmission and distribution reactive power interaction security modeling unit and the transmission and distribution network decoupling coordinated reactive power algorithm calculation unit, used to collect transmission and distribution network operating data and data from each source-grid-load-storage unit; and a transmission and distribution coordinated reactive power optimization objective function construction unit, which is connected to the source-grid-load-storage transmission and distribution reactive power data interaction unit, and receives... The data transmitted by the data interaction unit, combined with the static voltage safety constraints, constructs the objective function for transmission and distribution coordinated reactive power optimization. The transmission and distribution coordinated reactive power optimization result solving and output unit is connected to both the transmission and distribution network decoupled coordinated reactive power algorithm calculation unit and the transmission and distribution coordinated reactive power optimization objective function construction unit. It receives the algorithm parameters output by the algorithm calculation unit and the objective function output by the objective function construction unit, solves the objective function, and outputs the transmission and distribution coordinated reactive power optimization command. The static voltage safety constraint verification unit is connected to both the transmission and distribution coordinated reactive power optimization result solving and output unit and the source-grid-load-storage transmission and distribution reactive power data interaction unit. It receives the optimization command output by the optimization result solving and output unit, and, combined with the real-time operating data transmitted by the data interaction unit, performs static voltage safety constraint verification on the operating status corresponding to the optimization command. If the verification passes, the optimization command is sent to the execution device; if the verification fails, the verification result is fed back to the transmission and distribution coordinated reactive power optimization result solving and output unit for re-optimization.

[0015] Beneficial Effects: This invention proposes a transmission and distribution coordinated reactive power optimization method and system that considers static voltage security constraints. This optimization method and system constructs a multi-agent transmission and distribution reactive power interaction security model to achieve efficient information interaction between the transmission and distribution network layer and various links of the power generation, grid, load, and storage systems. Combined with a decoupled coordinated reactive power algorithm for the transmission and distribution network, it clarifies the boundaries and coordinated variables of the reactive power optimization sub-problems of the transmission and distribution network, effectively overcoming the shortcomings of traditional methods such as fragmented processing of the transmission and distribution network and the lack of dedicated coordinated mechanisms. It fully explores the reactive power regulation potential of each unit of the power generation, grid, load, and storage systems, improving the overall optimization effect. It utilizes a power generation, grid, load, and storage transmission and distribution reactive power platform to achieve real-time transmission and sharing of multi-dimensional data. Combined with static voltage security constraints, it constructs an optimization objective function. During the solution process, through coordinated iteration and security verification, it ensures that the optimization results meet voltage safety standards, solving the problems of insufficient integration of static voltage security constraints and constraint settings deviating from actual operating characteristics in existing technologies. Simultaneously, this method and system can reduce the overall network loss of the transmission and distribution network, reduce node voltage deviation, and output precise reactive power regulation commands, providing strong support for voltage security assurance and efficient operation under large-scale access of power generation, grid, load, and storage resources in new power systems, significantly improving the operating efficiency and reliability of the power system. Attached Figure Description

[0016] Figure 1 This is a flowchart of the method steps of the present invention; Figure 2 This is a diagram showing the system unit composition of the present invention. Detailed Implementation

[0017] It should be noted that, unless otherwise specified, the embodiments and features described in this application can be combined with each other. The application will be further described in detail below with reference to the accompanying drawings and specific embodiments.

[0018] like Figure 1 As shown, the transmission and distribution coordinated reactive power optimization method considering static voltage security constraints includes: Step S1: Construct a multi-Agent transmission and distribution reactive power interaction security model. In this model, each Agent corresponds to the transmission network layer, distribution network layer, and source-grid-load-storage links respectively. Real-time operating parameters of the transmission and distribution network, reactive power output status of each source-grid-load-storage unit, and static voltage security constraint boundary conditions are obtained through information interaction between Agents. Specifically, in step S1, when constructing the multi-Agent transmission and distribution reactive power interaction security model, the corresponding level and function of each Agent are first clarified. The transmission network layer Agent is responsible for collecting the operating parameters of the transmission network with a voltage level of 220 kV and above, and the distribution network layer Agent is responsible for collecting the parameters of the distribution network with a voltage level of 10 kV and below. The Agents of each link of source, grid, load and storage correspond to thermal power units, wind farms, photovoltaic power stations, energy storage power stations and industrial adjustable loads, respectively. Each agent interacts with the power grid via a dedicated communication network, with an interaction frequency set to once every 2 seconds. The parameters acquired include real-time voltage at each node of the transmission and distribution network (ranging from 0.95 to 1.05 times the rated voltage), reactive power of each branch (reactive power output of thermal power units ranging from 0 to 80% of rated capacity, and reactive power regulation of wind farms ranging from -0.4 to 0.4 times the rated active power), and operating status signals of each unit in the power generation, grid, load, and storage system. Simultaneously, static voltage safety constraints are defined, such as a lower limit for node voltage not lower than 0.95 times the rated voltage and an upper limit not higher than 1.05 times the rated voltage, and branch reactive current not exceeding 1.1 times the rated current. This step, by clarifying the responsibilities and interaction rules of each agent, lays the data and model foundation for subsequent collaborative optimization, ensuring that the operating status of the transmission and distribution network and each link in the power generation, grid, load, and storage system can be accurately perceived and represented.

[0019] Step S2: Use the decoupling and collaborative reactive power algorithm of transmission and distribution network to decouple the transmission and distribution network, and determine the boundary and collaborative interaction variables between the reactive power optimization sub-problem on the transmission network side and the reactive power optimization sub-problem on the distribution network side. Specifically, step S2 employs a decoupling and collaborative reactive power algorithm for transmission and distribution networks. Based on the network topology and voltage levels, decoupling boundaries are defined, typically using the high-voltage side of a 110 kV substation as the dividing node between the transmission and distribution networks. The collaborative interaction variables are determined as the reactive power exchange on the high-voltage side of the substation and the node voltage value. The algorithm first performs mathematical modeling and decomposition of the transmission and distribution network, breaking down the overall reactive power optimization problem into two sub-problems: one on the transmission network side and one on the distribution network side. The transmission network sub-problem focuses on the optimization and regulation of reactive power compensation devices (such as parallel capacitor banks and synchronous condensers) in 220 kV and above power grids, while the distribution network sub-problem focuses on the reactive power output optimization of distributed power sources, energy storage systems, and adjustable loads in 10 kV and below power grids. During the decoupling process, the propagation precision of the interaction variables is set to 0.01 MFA to ensure that the optimization calculations on the transmission and distribution sides can be carried out based on accurate boundary conditions. This step, through reasonable decoupling and clear definition of interaction variables, simplifies the complexity of the overall optimization problem and ensures the independence and synergy of the optimization on both sides of the transmission and distribution networks, laying the algorithmic foundation for subsequent efficient solutions.

[0020] Step S3: Build a reactive power transmission and distribution platform for power generation, grid, load and storage, and connect the power transmission and distribution network operation data, reactive power regulation capacity data of each unit of power generation, grid, load and storage, and static voltage monitoring data to the platform for real-time data transmission and sharing. Specifically, in step S3, when building the reactive power transmission and distribution platform, the hardware equipment selection and deployment are completed first. The data acquisition equipment uses voltage and current transformers with an accuracy class of 0.2, deployed at each busbar of the transmission network, each feeder outlet of the distribution network, and each unit interface of the source-grid-load-storage system. The data server uses a high-performance industrial server with a storage capacity of no less than 10TB, supporting the writing and reading of 100,000 data entries per second. The communication module uses a combination of fiber optic communication (1000Mbps backbone bandwidth) and a wireless private network (50Mbps access layer rate) to achieve data transmission. During the development of the platform software functional modules, the data receiving module supports multiple communication protocols such as IEC61850 and Modbus to ensure compatible access to data from different devices. The data preprocessing module uses a sliding window filtering algorithm to process the collected data, with the filtering window size set to 5 sampling points to remove abnormal data (judgment threshold is 3 times the standard deviation). The data sharing module provides an API interface for multiple agent models and decoupled collaborative algorithms to call data. After the platform is built, a 72-hour joint debugging test is conducted. During the test, the data transmission success rate must reach more than 99.9%, and the data processing latency must not exceed 50 milliseconds. This step, by building a fully functional and reliable platform, realizes the real-time aggregation, processing and sharing of multi-dimensional data, providing stable data support for subsequent optimization calculations.

[0021] Step S4: On the source-grid-load-storage transmission and distribution reactive power platform, with the goal of minimizing the overall network loss of the transmission and distribution network and minimizing the voltage deviation of each node, and in combination with static voltage security constraints, construct the objective function for transmission and distribution coordinated reactive power optimization, and clarify the correlation between each weight coefficient in the objective function and the operating characteristics of the transmission and distribution network. Specifically, in step S4, when constructing the reactive power optimization objective function for transmission and distribution coordination on the source-grid-load-storage transmission and distribution reactive power platform, the two core indicators of the objective function are first determined: the overall network loss of the transmission and distribution network and the voltage deviation of each node. Network loss calculation covers the power loss of the main transmission lines (220 kV lines typically have a resistance of 0.01 to 0.05 ohms / km) and the branch lines of the distribution network (10 kV lines typically have a resistance of 0.1 to 0.5 ohms / km). Voltage deviation calculation uses the absolute value of the difference between the real-time voltage and the rated voltage of each node. Based on the operating characteristics of the transmission and distribution network, through regression analysis of historical one-year operating data, the network loss weighting coefficient is determined to be 0.6, and the voltage deviation weighting coefficient is determined to be 0.4, ensuring that the objective function prioritizes reducing network losses while also considering voltage quality. When incorporating static voltage safety constraints, the voltage constraints of each node (e.g., 198 to 231 kV for 220 kV nodes, and 9.5 to 10.5 kV for 10 kV nodes), the output constraints of reactive power compensation devices (e.g., 10 Mvar capacity per parallel capacitor bank, with a maximum of 8 banks in operation), and the branch current constraints (e.g., 1000 A rated current for 220 kV lines, with a maximum allowable current of 1100 A) are clearly defined, and these constraints are transformed into boundary conditions for the objective function. This step, by scientifically setting the objective function and constraints, clarifies the direction and limitations of transmission and distribution coordinated reactive power optimization, ensuring that the optimization results balance economy and safety.

[0022] Step S5: The decoupled reactive power algorithm of transmission and distribution network is used to solve the constructed objective function. During the solution process, the transmission network side and the distribution network side are coordinated and iterated through the multi-Agent transmission and distribution reactive power interaction security model, and the coordinated interaction variables are continuously updated until the convergence condition is met. Specifically, in step S5, when using the decoupled collaborative reactive power algorithm for transmission and distribution networks to solve the objective function, the algorithm parameters are first initialized. The initial iteration value is selected from the reactive power parameters at the moment when the transmission and distribution network operating efficiency is highest (lowest network loss, smallest voltage deviation) within the past month. The convergence threshold is set to the difference between two iterations of the objective function value being less than 0.001, and the maximum number of iterations is set to 50 to avoid excessive computation time. During the solution process, the transmission network side first takes the minimum network loss as the objective, and calculates the output of each parallel capacitor bank and synchronous condenser by combining the voltage constraint of the 220 kV node and the output constraint of the reactive power compensation device. After obtaining the preliminary optimization results on the transmission network side, the reactive power exchange amount on the high-voltage side of the substation (e.g., the preliminary calculated value of the reactive power exchange amount on the high-voltage side of the 110 kV substation is 20 Mvar) is transmitted to the distribution network side. Based on this exchange volume, the distribution network side, combined with the 10 kV node voltage constraints and the output constraints of distributed power sources (such as a 1 MW photovoltaic power station with a reactive power regulation range of -0.4 to 0.4 MW), energy storage systems (such as a 5 MWh energy storage power station with a reactive power output range of 0 to 2 Mvar), and adjustable loads (such as industrial loads with a reactive power regulation range of -0.2 to 0.2 Mvar), calculates the preliminary optimization results on the distribution network side and feeds them back to the transmission network side. A multi-agent model is used to achieve collaborative iteration on both sides. Each iteration updates the reactive power exchange volume and node voltage parameters until the difference between the objective function values ​​of two consecutive iterations is less than the convergence threshold. This step, through phased solution and collaborative iteration, ensures that the objective function can efficiently converge to the optimal solution, while also taking into account the operational needs of both the transmission and distribution networks.

[0023] Step S6: Output the reactive power optimization results of transmission and distribution coordination. The results include the adjustment commands of each reactive power compensation device on the transmission network side, the reactive power output commands of each distributed power source and energy storage system on the distribution network side, and the reactive power response commands of the load-side adjustable load. The optimization results meet the static voltage safety constraints.

[0024] Specifically, when outputting the reactive power optimization results for transmission and distribution coordination in step S6, the obtained results are first classified and organized. The optimization results on the transmission network side include the number of parallel capacitor banks to be switched on and off at each 220 kV substation (e.g., a substation needs to operate 4 sets of 10 Mvar capacitors), and the excitation regulation instructions for synchronous condensers (e.g., adjusting to 80% of the rated excitation current). The optimization results on the distribution network side include the reactive power output instructions for each 10 kV feeder distributed photovoltaic power station (e.g., a 1 MW photovoltaic power station is set to 0.3 MW of reactive power output), the reactive power charging and discharging instructions for energy storage power stations (e.g., a 5 MWh energy storage power station outputs 1.5 Mvar of reactive power), and the reactive power response instructions for industrial adjustable loads (e.g., a factory load reduces reactive power consumption by 0.15 Mvar). The output results must meet all static voltage safety constraints, such as the voltage of each node being within the range of 0.95 to 1.05 times the rated voltage, and the current of each branch not exceeding the maximum allowable current. Simultaneously, the optimization results are transmitted to the controllers of each device in a standardized control command format (compliant with IEC 61850 standard), with a command transmission delay of no more than 100 milliseconds, ensuring that the equipment can respond and adjust in a timely manner. This step, through precise output and transmission of optimization commands, transforms the collaborative optimization results into actual operational control measures, achieving reactive power optimization operation in all aspects of the power transmission and distribution network and the power generation, grid, load, and storage systems, ultimately achieving the goal of reducing network losses and improving voltage quality.

[0025] Preferably, the expression for the multi-Agent power transmission and distribution reactive power interaction security model is: ,in, For the comprehensive evaluation function of the multi-agent reactive power interaction security model, The total number of Agents For the first The weight coefficients of each agent, The first Each Agent corresponds to a coefficient for reactive power, voltage, and reactive current. For the first The reactive power of the unit to which each Agent belongs. For the first The voltage of the node to which each Agent belongs. For the first The reactive current of the branch to which each Agent belongs For collaborative interaction penalty coefficient, The total number of collaborative interaction variables, For the first The deviation of each collaborative interaction variable.

[0026] Specifically, the multi-agent transmission and distribution reactive power interaction security model achieves safe control of reactive power interaction among agents through a comprehensive evaluation function. In terms of parameter settings, the total number of agents is determined based on the scale of the transmission and distribution network and the number of source-grid-load-storage units. Typically, a 220 kV transmission network is divided into 5-8 transmission network layer agents by region, a 10 kV distribution network is divided into 15-20 distribution network layer agents by feeder, and each link of source-grid-load-storage is divided into 10-12 agents based on equipment type. The weight coefficient of each agent is determined using the analytic hierarchy process (AHP), with transmission network layer agents accounting for 30%-40%, distribution network layer agents accounting for 25%-35%, and each link of source-grid-load-storage accounting for 25%-35%, ensuring that agents in key links play a prominent role. The coefficients for reactive power, voltage, and reactive current are set in conjunction with equipment characteristics; for example, the reactive power coefficient of the agent belonging to a synchronous condenser is set to 0.6, and the voltage coefficient of the agent belonging to distributed photovoltaic power is set to 0.5. The collaborative interaction penalty coefficient is calibrated based on historical interaction deviation data, typically ranging from 0.8 to 1.2. The total number of collaborative interaction variables is determined by the number of nodes connected in the power transmission and distribution network, generally 8 to 15. During implementation, real-time parameters of each agent are first collected and substituted into the function to calculate the comprehensive evaluation value. If the value exceeds the preset threshold (usually 1.5 to 2.0), parameter adjustments between agents are triggered. This model can quantify the interaction safety of each agent, avoiding the impact of local reactive power imbalance on the overall system stability.

[0027] Preferably, the expression for the decoupling and coordinated reactive power algorithm of the transmission and distribution network is: ,in, The optimal reactive power allocation for transmission and distribution coordination reactive power optimization. This represents the total number of reactive power regulation units. For the first Cost coefficient of each reactive power regulation unit For the first The reactive power output of each reactive power regulation unit The reactive power balance penalty coefficient for the power transmission and distribution network. This refers to the total reactive power supply on the transmission network side. This represents the total reactive power demand on the distribution network side. These are the lower and upper limits of the node voltage, respectively. This represents the voltage at the k-th node. , The first The lower and upper limits of reactive power output of each reactive power regulation unit. "Subject to" is an abbreviation used to introduce constraints.

[0028] Specifically, the decoupled collaborative reactive power algorithm for transmission and distribution networks achieves optimized reactive power allocation through objective functions and constraints. In the parameter settings, the total number of reactive power regulation units typically includes parallel capacitor banks in the transmission network, synchronous condensers, distributed power sources in the distribution network, and energy storage systems, usually 20-30. The cost coefficient is determined based on equipment operating losses and maintenance costs; for example, the cost coefficient for parallel capacitor banks is set at 0.3-0.5 yuan / Mvar, and for energy storage systems at 0.8-1.2 yuan / Mvar, reflecting the differences in regulation costs among different equipment. The reactive power balance penalty coefficient for transmission and distribution networks is determined through simulation testing. When the reactive power deviation in transmission and distribution exceeds 5%, the coefficient is increased from 1.0 to 1.5 to strengthen the balance constraint. The upper and lower limits of node voltage are set according to voltage level: 198-231 kV for 220 kV nodes and 9.5-10.5 kV for 10 kV nodes. The upper and lower limits of reactive power regulation unit output are determined based on the rated capacity of the equipment; for example, the output range of a 10 Mvar capacitor bank is 0-10 Mvar, and the reactive power output range of a 2 MW energy storage system is 0-2 Mvar. During implementation, all parameters are first initialized, and the optimal reactive power allocation is solved by substituting them into the objective function. Then, the results are verified through constraint conditions to ensure that they meet operational requirements. This algorithm simplifies the overall computational complexity and achieves economical and efficient allocation of reactive power resources in the power transmission and distribution network.

[0029] Preferably, in the process of constructing the reactive power transmission and distribution platform, a platform data interaction efficiency model is introduced, the expression of which is: ,in, To improve the data interaction efficiency of the power generation, grid, load storage, transmission, and distribution reactive power platform, Total data interaction time For the first Normal data transmission time at any moment For the first Real-time data transmission delay time For the first The amount of data transmitted at any given time.

[0030] Specifically, the data interaction efficiency model of the source-grid-load-storage reactive power transmission and distribution platform is used to quantify the effectiveness of data transmission and processing on the platform. In the parameter settings, the total data interaction time is calculated based on 24 hours per day and 3600 seconds per hour, for a total duration of 86400 seconds. Normal data transmission time is obtained by real-time monitoring of the platform's communication link status; the normal transmission time ratio for fiber optic communication links is typically above 99.5%, and for wireless private networks, it is above 98%. Data transmission latency is measured using a timing module; the latency for fiber optic communication is typically 10-20 milliseconds, and for wireless private networks, it is 30-50 milliseconds. The amount of data transmitted is calculated based on the sampling frequency of each acquisition device; voltage and current transformers sample every 2 seconds, generating 10 bytes of data each time, resulting in a daily data transmission volume of approximately 432,000 bytes per device. During implementation, the parameters are collected every hour and substituted into the model to calculate the data interaction efficiency. When the efficiency is lower than 95%, the communication link and data processing module are checked to identify and repair any issues such as link interruption or module failure. The model can monitor the platform's data interaction status in real time, ensuring the reliability of data support and providing an accurate data foundation for subsequent optimization calculations.

[0031] Preferably, when constructing the objective function for reactive power optimization in transmission and distribution coordination, a static voltage safety constraint coefficient is introduced, and the modified expression of the objective function is as follows: ,in, The objective function for the modified power transmission and distribution coordinated reactive power optimization is as follows: This is the network loss weighting coefficient. This represents the total network loss of the transmission and distribution network. This is the voltage deviation weighting coefficient. The total number of nodes. For the first Voltage deviation at each node, This represents the actual voltage value of the nth node. This refers to the static voltage safety constraint weighting coefficient. For the first Voltage reference values ​​for each node, For the first The static voltage safety constraint factor for each node, when the node voltage meets the safety constraint. If not satisfied .

[0032] Specifically, the core of the modified objective function model for reactive power optimization in transmission and distribution coordination is to incorporate static voltage safety constraints to improve the safety of the optimization results. In the parameter settings, the network loss weight coefficient and voltage deviation weight coefficient are determined through regression analysis of historical operating data. When the transmission network loss accounts for more than 60% of the total loss, the network loss weight coefficient is set to 0.6, and the voltage deviation weight coefficient is set to 0.4; when the distribution network voltage fluctuates frequently, the voltage deviation weight coefficient can be increased to 0.5. The static voltage safety constraint weight coefficient is set according to the importance of voltage safety, with the node coefficient for hub substations set to 1.2 and for ordinary user nodes set to 0.8; the node voltage reference value is set according to the rated voltage, with a reference value of 220 kV for 220 kV nodes and 10 kV for 10 kV nodes; the static voltage safety constraint coefficient is determined through real-time voltage monitoring, with the coefficient set to 1 when the node voltage exceeds the range of 0.95-1.05 times the rated voltage, and 0 otherwise. During implementation, the basic objective function value is calculated first, and then the corrected value is calculated by substituting the correction parameters. This ensures that the optimization process prioritizes voltage safety constraints. This model can effectively avoid the problem of neglecting voltage safety in pursuit of economic indicators, and improve the practicality and safety of the optimization results.

[0033] Preferably, in the multi-agent collaborative iteration process, an inter-agent trust model is adopted, the expression of which is: ,in, For the first Moment Agent For Agent Trust level, For the first Moment Agent For Agent Trust level, To increase the trust level, The first Time, Number Moment Agent The reactive power of collaborative interaction To lower the trust level coefficient, For the first Moment Agent The voltage of the node. Agent The voltage reference value of the node.

[0034] Specifically, a multi-agent collaborative iterative trust model is used to optimize the reliability of information interaction between agents. In the parameter settings, the initial trust level is set based on the historical interaction performance of the agents; the initial trust level for agents with long-term stable interactions is set to 0.8-0.9, and for newly connected agents, it is set to 0.5-0.6. The trust level enhancement coefficient is determined based on the reactive power change rate of collaborative interaction; when the change rate is between 5% and 10%, the coefficient is set to 0.1-0.2. The trust level reduction coefficient is determined based on the node voltage deviation rate; when the deviation rate exceeds 3%, the coefficient is set to 0.3-0.4. The reactive power of collaborative interaction is obtained through real-time communication between agents, and the node voltage is collected through voltage monitoring equipment, with the voltage reference value set according to the rated voltage. During implementation, the trust value is updated with each iteration. When the trust value of an agent is below 0.5, its information interaction weight is reduced and the interaction frequency of agents with high trust value is increased. If the trust value continues to be below 0.3, the device to which the agent belongs is checked for malfunction. This model can screen reliable interaction agents, reduce optimization deviations caused by agent data distortion, and improve the stability and accuracy of the collaborative iteration process.

[0035] Preferably, step S3 specifically includes the following sub-steps: S31. Selecting and deploying data acquisition equipment for each link of the power generation, grid, load, and storage system. Voltage, current, and power sensors are deployed on the transmission grid side, and data acquisition devices are deployed on the distribution grid side at distributed power source grid connection points, energy storage system interfaces, and adjustable load control nodes to ensure the comprehensiveness and real-time nature of data acquisition for each link; S32. Building the hardware architecture of the power generation, grid, load, and storage reactive power transmission and distribution platform, including a data server, edge computing nodes, and a communication module. The data server stores various types of collected data, the edge computing nodes perform preliminary processing of real-time data, and the communication module uses a combination of fiber optic and wireless communication to transmit data between the acquisition devices and the platform; S3 3. Develop the software functional modules of the source-grid-load-storage transmission and distribution reactive power platform, including a data receiving module, a data preprocessing module, a data storage module, and a data sharing module. The data receiving module receives data transmitted from various acquisition devices. The data preprocessing module removes outliers and converts data formats. The data storage module uses a distributed database to store the processed data. The data sharing module provides a data call interface for the multi-Agent transmission and distribution reactive power interaction security model and the decoupled collaborative reactive power algorithm of the transmission and distribution network. S34. Conduct joint commissioning tests on the source-grid-load-storage transmission and distribution reactive power platform, simulate different operating conditions of the transmission and distribution network, check the stability of data transmission, the accuracy of data processing, and the timeliness of data sharing, and optimize and adjust any existing problems.

[0036] Specifically, in step S3, during the selection and deployment of data acquisition equipment for each link of the power grid, transmission network, load, and energy storage in S31, voltage and current transformers with a precision of 0.2 class are selected on the transmission network side and deployed on both sides of the 220 kV and above busbars and main lines. On the distribution network side, 0.5 class precision acquisition devices are selected at the distributed power source grid connection points. The sampling frequency of the acquisition devices at the energy storage system interface is set to 20 times per second. The acquisition devices at the adjustable load control nodes support bidirectional data transmission to ensure comprehensive and real-time data acquisition for each link. In step S32, when building the platform hardware architecture, an industrial-grade server with a 16-core CPU, 128GB of memory, and 10TB of storage capacity is selected for the data server. Edge computing nodes are deployed in the distribution network area, with a single node processing capacity of 1000 data entries per second. In the communication module, fiber optic communication is used for the backbone network with a transmission rate of 10 The wireless communication uses a 4G private network at the end of the distribution network, with a transmission rate of 50Mbps, to achieve efficient data transmission. When developing the S33 software functional modules, the data receiving module is compatible with IEC61850 and Modbus protocols. The data preprocessing module uses sliding window filtering with a window size of 5 sampling points and an outlier threshold of 3 times the standard deviation. The data storage module uses a distributed database, and the data sharing module provides API interfaces for model and algorithm calls. During the S34 joint commissioning test, the system simulates light load, heavy load, and fault recovery conditions of the transmission and distribution network, continuously testing for 72 hours. The data transmission success rate is required to be no less than 99.9%, and the data processing delay no more than 50 milliseconds. Transmission interruptions and data distortion issues that occur during the test are promptly optimized. The entire process ensures the stable operation of the source-grid-load-storage-transmission-distribution reactive power platform.

[0037] Preferably, step S4 specifically includes the following sub-steps: S41. Determine the overall network loss calculation scope of the transmission and distribution network, covering the losses of the main transmission lines and the branch lines of the distribution network. Based on the topology of the transmission and distribution network and the impedance parameters of each line, clarify the specific formula and parameters involved in the network loss calculation to ensure the accuracy of the network loss calculation; S42. Define the calculation method of voltage deviation at each node, using the absolute value of the difference between the actual voltage value and the reference value of each node as the voltage deviation index. Combined with the importance of different nodes in the transmission and distribution network, determine the weight allocation principle of the voltage deviation of each node in the objective function; S43. Analyze the specific content of static voltage safety constraints, including the upper and lower limits of voltage at each node, the current limit constraints of the transmission and distribution network branches, and the output limit constraints of reactive power compensation devices. Transform these constraints into mathematical expressions and clarify the value range and determination method of each parameter in the constraints; S44. Based on the operating characteristics of the transmission and distribution network, determine the specific values ​​of the network loss weight coefficient and the voltage deviation weight coefficient in the objective function through a combination of historical operating data regression analysis and expert experience judgment to ensure that the objective function can accurately reflect the needs of transmission and distribution coordinated reactive power optimization.

[0038] Specifically, in step S4, when determining the overall network loss calculation scope of the transmission and distribution network in S41, the power loss of the 220 kV transmission network trunk lines (resistance value 0.01-0.05 ohms / km) and 10 kV distribution network branch lines (resistance value 0.1-0.5 ohms / km) is included. Based on the transmission and distribution network topology and line parameters, it is clarified that network loss calculation must include parameters such as line resistance, current flow, and operating time to ensure calculation accuracy. In step S42, when defining the node voltage deviation calculation method, the absolute value of the difference between the actual voltage and the rated voltage of the node is used as the indicator. Weights are assigned according to the importance of the nodes. The weight of hub substation nodes is set to 0.8, and the weight of ordinary residential nodes is set to 0.2, reflecting the different power losses of different nodes. Differences in voltage quality requirements; When analyzing the static voltage safety constraints in S43, it is clearly stated that the lower limit of node voltage is 0.95 times the rated voltage and the upper limit is 1.05 times the rated voltage, the branch current does not exceed 1.1 times the rated current, and the output of reactive power compensation devices is within the range of 0-100% of the rated capacity. These constraints are transformed into clear numerical boundary conditions; When determining the weight coefficients of the objective function in S44, based on historical operating data from the past year, regression analysis yields a network loss weight coefficient of 0.6 and a voltage deviation weight coefficient of 0.4. At the same time, fine-tuning is performed with reference to the experience of power system experts to ensure that the weight coefficients conform to the actual operating characteristics of the transmission and distribution network. A scientific and reasonable transmission and distribution coordinated reactive power optimization objective function is constructed through the implementation of each step.

[0039] Preferably, step S5 specifically includes the following sub-steps: S51, Initialize the parameters of the decoupled collaborative reactive power algorithm for the transmission and distribution network, including the initial iteration value, convergence threshold, and maximum number of iterations. The initial iteration value is determined based on the historical optimal operating data of the transmission and distribution network, the convergence threshold is set according to the accuracy requirements of the static voltage safety constraint, and the maximum number of iterations is determined according to the algorithm's computational efficiency requirements; S52, Solve the reactive power optimization sub-problem on the transmission network side, taking the minimum network loss on the transmission network side as the local objective, and combined with the static voltage safety constraint on the transmission network side, use the interior point method to calculate the output of the reactive power compensation device on the transmission network side to obtain the preliminary reactive power optimization results on the transmission network side; S53, Extract the values ​​from the preliminary reactive power optimization results on the transmission network side... The collaborative interaction variables are transmitted to the distribution network side. The distribution network side takes minimizing network loss and voltage deviation as local objectives, and calculates the reactive power output of distributed power sources, energy storage systems and adjustable loads on the distribution network side in conjunction with the static voltage security constraints of the distribution network side, to obtain the preliminary reactive power optimization results of the distribution network side; S54, the preliminary reactive power optimization results of the distribution network side are fed back to the transmission network side. The preliminary optimization results of the transmission network side and the distribution network side are collaboratively verified by the multi-Agent transmission and distribution reactive power interaction security model to determine whether the convergence conditions are met. If not, the collaborative interaction variables are updated, and steps S52-S53 are repeated until the convergence conditions are met, to obtain the final solution result of transmission and distribution collaborative reactive power optimization.

[0040] Specifically, in the implementation of step S5, when initializing the algorithm parameters in S51, the initial value of the iteration is selected as the reactive power parameter at the moment when the transmission and distribution network loss is the lowest and the voltage deviation is the smallest within the past month. The convergence threshold is set to the difference between two iterations of the objective function value being less than 0.001, and the maximum number of iterations is set to 50 to avoid excessive calculation time or insufficient accuracy. In S52, when solving the reactive power optimization sub-problem on the transmission network side, the local objective is to minimize the transmission network loss. The constraints include the 220 kV node voltage being in the range of 198-231 kV, and the output of the reactive power compensation device being between 0-100% of its rated capacity. The output of each parallel capacitor bank and synchronous condenser is calculated using the interior point method to obtain the preliminary optimization results on the transmission network side. In S53, transmission coordination and exchange... When the variables are transferred to the distribution network side, the variable transfer accuracy is controlled within 0.01 Mvar. The distribution network side takes the minimum network loss and the minimum voltage deviation as local objectives, constraining the voltage of 10 kV nodes to be between 9.5 and 10.5 kV, the reactive power output of distributed generation to be between -0.4 and 0.4 times the rated active power, and the reactive power output of energy storage system to be within the range of 0 to rated capacity. The preliminary optimization results of the distribution network side are calculated. When the results of the distribution network side are fed back and verified in S54, the deviation between the results on both sides is compared through a multi-Agent model. If the deviation is greater than the convergence threshold, the collaborative interaction variables are updated, and steps S52-S53 are repeated until the deviation is less than the threshold. Through iteration and verification, it is ensured that the reactive power optimization results of transmission and distribution meet the static voltage safety constraints and economic requirements.

[0041] The multi-Agent reactive power interaction security model in this invention is a technical model that assigns independent agents to each link of the transmission network layer, distribution network layer, and source-grid-load-storage system, and achieves reactive power security management through information interaction between agents. Its implementation is as follows: First, Agent types are divided according to the power system structure. Transmission network layer agents correspond to 220 kV and above power grids, distribution network layer agents correspond to 10 kV and below power grids, and source-grid-load-storage agents correspond to thermal power units, wind farms, photovoltaic power stations, energy storage systems, and adjustable loads, respectively. Each agent collects real-time parameters, including node voltage (0.95-1.05 times rated voltage) and branch reactive power (0-80% rated capacity of thermal power units), through a dedicated power communication network (transmission frequency once every 2 seconds), and sets static voltage security constraints (e.g., branch reactive current does not exceed 1.1 times rated current). Then, the agent interaction security is quantified through a comprehensive evaluation function. When the evaluation value exceeds a threshold (1.5-2.0), parameter adjustment is triggered. The model aims to accurately perceive the operating status of each link, realize dynamic monitoring of reactive power interaction, and avoid local reactive power imbalance from affecting system stability. It breaks through the limitations of traditional isolated control of each link, provides real-time and comprehensive model support for transmission and distribution coordinated reactive power optimization, and is adapted to the complex scenario of multi-element access in new power systems.

[0042] The decoupled collaborative reactive power algorithm for transmission and distribution networks in this invention is an algorithm that decomposes the overall reactive power optimization problem of the transmission and distribution network into sub-problems and achieves efficient solutions through collaborative variables. Its implementation is as follows: First, taking the high-voltage side of the 110 kV substation as the decoupling boundary, sub-problems are divided into the transmission network side (220 kV and above) and the distribution network side (10 kV and below). The collaborative interaction variables are determined as the reactive power exchange quantity (transmission accuracy 0.01 Mvar) on the high-voltage side of the substation and the node voltage value. Then, the algorithm parameters are initialized (the initial iteration value is the best data from the past month, the convergence threshold is 0.001, and the maximum number of iterations is 50). On the transmission network side, the goal is to minimize network loss, and the reactive power output of the reactive power compensation device is solved by combining the 220 kV node voltage constraint (198-231 kV). On the distribution network side, the reactive power output of distributed generation sources is solved based on the interaction variables and the 10 kV node voltage constraint (9.5-10.5 kV). Finally, a multi-agent model is used to perform iterative verification on both sides until the convergence condition is met. The purpose of this algorithm is to simplify computational complexity, balance the independence and synergy of sub-problems, and achieve economical allocation of reactive resources. It solves the problems of long computation time and low accuracy in traditional overall optimization, and provides efficient algorithmic support for transmission and distribution coordination under large-scale source-grid-load-storage access, thereby improving optimization efficiency and economy.

[0043] The reactive power transmission and distribution platform of this invention is a hardware and software integrated system that realizes real-time transmission, processing, and sharing of multi-dimensional data. Its implementation is as follows: On the hardware side, data is collected using 0.2-level precision instrument transformers (deployment points such as transmission network busbars and distribution network feeders), configured with an industrial server featuring a 16-core CPU and 10TB of storage, and a communication module combining fiber optic (1000Mbps backbone network) and 4G private network (50Mbps terminal). On the software side, a data receiving module compatible with multiple protocols (IEC61850, Modbus) is developed, along with a preprocessing module using a 5-point sliding window filter (outlier threshold 3 times the standard deviation), a distributed database storage module, and an API interface sharing module. After construction, a 72-hour joint commissioning test is conducted, requiring a data transmission success rate ≥99.9% and a processing latency ≤50 milliseconds. The platform's role is to aggregate power transmission and distribution network operation data and source-grid-load-storage status data, providing stable data support for models and algorithms; breaking down data silos and achieving multi-stage data collaboration; serving as a core hub connecting physical equipment and optimization algorithms; and providing fundamental guarantees for power transmission and distribution collaborative optimization under static voltage safety constraints.

[0044] like Figure 2As shown, the transmission and distribution coordinated reactive power optimization system considering static voltage security constraints includes: a multi-Agent transmission and distribution reactive power interaction security modeling unit, which is connected to the transmission and distribution network monitoring equipment and the controllers of each source-grid-load-storage unit, used to acquire transmission and distribution network operating parameters and the state parameters of each source-grid-load-storage unit, and construct a multi-Agent transmission and distribution reactive power interaction security model; a transmission and distribution network decoupling coordinated reactive power algorithm calculation unit, which is connected to the multi-Agent transmission and distribution reactive power interaction security modeling unit, receives the model parameters output by the multi-Agent transmission and distribution reactive power interaction security model, and uses the transmission and distribution network decoupling coordinated reactive power algorithm to decouple and calculate the reactive power optimization problem of the transmission and distribution network; a source-grid-load-storage transmission and distribution reactive power data interaction unit, which is connected to both the multi-Agent transmission and distribution reactive power interaction security modeling unit and the transmission and distribution network decoupling coordinated reactive power algorithm calculation unit, used to collect transmission and distribution network operating data and data from each source-grid-load-storage unit; and a transmission and distribution coordinated reactive power optimization objective function construction unit, which is connected to the source-grid-load-storage transmission and distribution reactive power data interaction unit. The system receives data transmitted from the data interaction unit and constructs a transmission and distribution coordinated reactive power optimization objective function based on static voltage safety constraints. The transmission and distribution coordinated reactive power optimization result solving and output unit is connected to both the transmission and distribution network decoupled coordinated reactive power algorithm calculation unit and the transmission and distribution coordinated reactive power optimization objective function construction unit. It receives the algorithm parameters output by the algorithm calculation unit and the objective function output by the objective function construction unit, solves the objective function, and outputs the transmission and distribution coordinated reactive power optimization command. The static voltage safety constraint verification unit is connected to both the transmission and distribution coordinated reactive power optimization result solving and output unit and the source-grid-load-storage transmission and distribution reactive power data interaction unit. It receives the optimization command output by the optimization result solving and output unit, and, based on the real-time operating data transmitted from the data interaction unit, performs static voltage safety constraint verification on the operating state corresponding to the optimization command. If the verification passes, the optimization command is sent to the execution device; if the verification fails, the verification result is fed back to the transmission and distribution coordinated reactive power optimization result solving and output unit for re-optimization.

[0045] A method and system for transmission and distribution coordinated reactive power optimization considering static voltage safety constraints is proposed. This optimization method and system have several significant advantages. Firstly, it establishes a linkage mechanism between a multi-agent transmission and distribution reactive power interactive safety model and a decoupled coordinated reactive power algorithm for the transmission and distribution network. This mechanism enables efficient collaboration among the transmission network layer, distribution network layer, and various links of the source-grid-load-storage system. It clarifies the boundaries of the reactive power optimization sub-problems of the transmission and distribution network and updates the coordinated variables in real time through information interaction between agents, fully mobilizing the reactive power regulation capabilities of each unit of the source-grid-load-storage system and avoiding overall inefficiency caused by optimization of a single link. Secondly, the constructed source-grid-load-storage transmission and distribution reactive power platform enables real-time transmission and sharing of multi-dimensional data, providing comprehensive and accurate data support for the construction of the optimization objective function and the solution of the algorithm. At the same time, through a dedicated static voltage safety constraint verification step, it ensures that the optimization results always meet the voltage safety operation standards, improving the practicality and reliability of the optimization scheme.

[0046] This method and system can specifically overcome the shortcomings of existing technologies. For the problems of fragmented processing of transmission and distribution networks and lack of dedicated coordination mechanisms in traditional methods, it breaks down the optimization barriers between transmission and distribution networks through the interactive function of multi-Agent models and the partitioning logic of decoupled collaborative algorithms, enabling the two to form a close cooperation in reactive power allocation and fully tap the reactive power potential of each link. For the problems of insufficient integration of static voltage safety constraints and constraint settings that are out of touch with reality in existing technologies, it deeply integrates static voltage safety requirements into the objective function construction stage, determines the range of constraint parameter values ​​by combining real-time data, and ensures that the constraint conditions do not deviate from the actual operating characteristics through collaborative iteration and verification in the solution process. The final output optimization instructions can not only meet voltage safety but also adapt to the actual operating needs of transmission and distribution networks.

[0047] In the description of this invention, it should be noted that, unless otherwise explicitly specified and limited, the terms "set," "install," "connect," "link," and "fix" should be interpreted broadly. For example, they can refer to a fixed connection, a detachable connection, or an integral connection; they can refer to a mechanical connection or an electrical connection; they can refer to a direct connection or an indirect connection through an intermediate medium; and they can refer to the internal communication between two components. Those skilled in the art will understand the specific meaning of the above terms in this invention based on the specific circumstances.

[0048] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various equivalent changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.

Claims

1. A method for coordinating reactive power optimization in transmission and distribution considering static voltage safety constraints, characterized in that, include: Step S1: Construct a multi-Agent reactive power interaction security model for transmission and distribution. In this model, each agent corresponds to the transmission network layer, distribution network layer, and the source-grid-load-storage links, respectively. Real-time operating parameters of the transmission and distribution network, reactive power output status of each source-grid-load-storage unit, and static voltage security constraint boundary conditions are obtained through information interaction between agents. Step S2: Decouple the transmission and distribution network using a decoupled collaborative reactive power algorithm to decouple the transmission and distribution network, determining the boundary between the reactive power optimization sub-problems on the transmission network side and the reactive power optimization sub-problems on the distribution network side, and the collaborative interaction variables. Step S3: Build a source-grid-load-storage transmission and distribution reactive power platform, connecting the transmission and distribution network operating data, reactive power regulation capacity data of each source-grid-load-storage unit, and static voltage monitoring data to this platform for real-time data transmission and sharing. Step S4: On the source-grid-load-storage transmission and distribution reactive power platform, using... With the objectives of minimizing overall network loss and voltage deviation at each node, and in conjunction with static voltage security constraints, an objective function for transmission and distribution coordinated reactive power optimization is constructed, clarifying the correlation between the weight coefficients in the objective function and the operating characteristics of the transmission and distribution network. Step S5: The constructed objective function is solved using a decoupled coordinated reactive power algorithm for the transmission and distribution network. During the solution process, a multi-agent transmission and distribution reactive power interaction security model is used for coordinated iteration between the transmission and distribution sides, continuously updating the coordinated interaction variables until the convergence condition is met. Step S6: The results of the transmission and distribution coordinated reactive power optimization are output. These results include adjustment commands for reactive power compensation devices on the transmission side, reactive power output commands for distributed power sources and energy storage systems on the distribution side, and reactive power response commands for adjustable loads on the load side. The optimization results meet the requirements of static voltage security constraints.

2. The power transmission and distribution coordinated reactive power optimization method considering static voltage safety constraints according to claim 1, characterized in that, The expression for the multi-Agent power transmission and distribution reactive power interaction security model is: ,in, For the comprehensive evaluation function of the multi-agent reactive power interaction security model, The total number of Agents For the first The weight coefficients of each agent, The first Each Agent corresponds to a coefficient for reactive power, voltage, and reactive current. For the first The reactive power of the unit to which each Agent belongs. For the first The voltage of the node to which each Agent belongs. For the first The reactive current of the branch to which each Agent belongs For collaborative interaction penalty coefficient, The total number of collaborative interaction variables, For the first The deviation of each collaborative interaction variable.

3. The power transmission and distribution coordinated reactive power optimization method considering static voltage safety constraints according to claim 1, characterized in that, The expression for the decoupling and coordinated reactive power algorithm of the power transmission and distribution network is: ,in, The optimal reactive power allocation for transmission and distribution coordination reactive power optimization. This represents the total number of reactive power regulation units. For the first Cost coefficient of each reactive power regulation unit For the first The reactive power output of each reactive power regulation unit The reactive power balance penalty coefficient for the power transmission and distribution network. This refers to the total reactive power supply on the transmission network side. This represents the total reactive power demand on the distribution network side. These are the lower and upper limits of the node voltage, respectively. This represents the voltage at the k-th node. , The first The lower and upper limits of reactive power output of each reactive power regulation unit. "Subject to" is an abbreviation used to introduce constraints.

4. The power transmission and distribution coordinated reactive power optimization method considering static voltage safety constraints according to claim 1, characterized in that, In the process of constructing the reactive power transmission and distribution platform, a platform data interaction efficiency model is introduced, expressed as: ,in, To improve the data interaction efficiency of the power generation, grid, load storage, transmission, and distribution reactive power platform, Total data interaction time For the first Normal data transmission time at any moment For the first Real-time data transmission delay time For the first The amount of data transmitted at any given time.

5. The power transmission and distribution coordinated reactive power optimization method considering static voltage safety constraints according to claim 1, characterized in that, When constructing the objective function for reactive power optimization in power transmission and distribution coordination, a static voltage safety constraint coefficient is introduced, and the modified expression of the objective function is as follows: ,in, The objective function for the modified power transmission and distribution coordinated reactive power optimization is as follows: This is the network loss weighting coefficient. This represents the total network loss of the transmission and distribution network. This is the voltage deviation weighting coefficient. The total number of nodes. For the first Voltage deviation at each node, This represents the actual voltage value of the nth node. This refers to the static voltage safety constraint weighting coefficient. For the first Voltage reference values ​​for each node, For the first The static voltage safety constraint factor for each node, when the node voltage meets the safety constraint. If not satisfied .

6. The method for coordinated reactive power optimization of transmission and distribution considering static voltage safety constraints according to claim 1, characterized in that, In the multi-agent collaborative iteration process, an inter-agent trust model is adopted, expressed as: ,in, For the first Moment Agent For Agent Trust level, For the first Moment Agent For Agent Trust level, To increase the trust level, The first Time, Number Moment Agent The reactive power of collaborative interaction To lower the trust level coefficient, For the first Moment Agent The voltage of the node to which it belongs Agent The voltage reference value of the node.

7. The power transmission and distribution coordinated reactive power optimization method considering static voltage safety constraints according to claim 1, characterized in that, Step S3 specifically includes the following sub-steps: S31. Select and deploy data acquisition equipment for each link of the power generation, grid, load, and storage system. On the transmission grid side, deploy voltage, current, and power sensors. On the distribution grid side, deploy data acquisition devices at distributed power source grid connection points, energy storage system interfaces, and adjustable load control nodes to ensure the comprehensiveness and real-time nature of data acquisition for each link; S32. Build the hardware architecture of the power generation, grid, load, and storage reactive power transmission and distribution platform, including a data server, edge computing nodes, and a communication module. The data server stores various types of collected data, the edge computing nodes perform preliminary processing of real-time data, and the communication module uses a combination of fiber optic and wireless communication to transmit data between the acquisition devices and the platform; S33. Develop software functional modules for the source-grid-load-storage transmission and distribution reactive power platform, including a data receiving module, a data preprocessing module, a data storage module, and a data sharing module. The data receiving module receives data transmitted from various acquisition devices. The data preprocessing module removes outliers and converts data formats. The data storage module uses a distributed database to store the processed data. The data sharing module provides a data call interface for the multi-Agent transmission and distribution reactive power interaction security model and the decoupled collaborative reactive power algorithm of the transmission and distribution network. S34. Conduct joint commissioning tests on the source-grid-load-storage transmission and distribution reactive power platform, simulating different operating conditions of the transmission and distribution network, checking the stability of data transmission, the accuracy of data processing, and the timeliness of data sharing, and optimizing and adjusting any existing problems.

8. The method for coordinated reactive power optimization of transmission and distribution considering static voltage safety constraints according to claim 1, characterized in that, Step S4 specifically includes the following sub-steps: S41. Determine the overall network loss calculation scope of the transmission and distribution network, covering the losses of the main transmission lines and the branch lines of the distribution network. Based on the topology of the transmission and distribution network and the impedance parameters of each line, clarify the specific formulas and parameters involved in the network loss calculation to ensure the accuracy of the network loss calculation; S42. Define the calculation method for the voltage deviation of each node, using the absolute value of the difference between the actual voltage value and the reference value of each node as the voltage deviation index. Combined with the importance of different nodes in the transmission and distribution network, determine the weight allocation principle of the voltage deviation of each node in the objective function; S43. Analyze the specific content of the static voltage safety constraints, including the upper and lower limits of voltage at each node, the current limit constraints of the transmission and distribution network branches, and the output limit constraints of the reactive power compensation device. Transform these constraints into mathematical expressions and clarify the value range and determination method of each parameter in the constraints; S44. Based on the operating characteristics of the transmission and distribution network, determine the specific values ​​of the network loss weight coefficient and the voltage deviation weight coefficient in the objective function through a combination of historical operating data regression analysis and expert experience judgment to ensure that the objective function can accurately reflect the needs of transmission and distribution coordinated reactive power optimization.

9. The method for coordinated reactive power optimization of transmission and distribution considering static voltage safety constraints according to claim 1, characterized in that, Step S5 specifically includes the following sub-steps: S51, Initialize the parameters of the decoupled collaborative reactive power algorithm for the transmission and distribution network, including the initial iteration value, convergence threshold, and maximum number of iterations. The initial iteration value is determined based on the historical optimal operating data of the transmission and distribution network, the convergence threshold is set according to the accuracy requirements of the static voltage security constraint, and the maximum number of iterations is determined according to the algorithm's computational efficiency requirements; S52, Solve the reactive power optimization sub-problem on the transmission network side, taking the minimization of network loss on the transmission network side as the local objective, and combining the static voltage security constraint on the transmission network side, use the interior point method to calculate the output of the reactive power compensation device on the transmission network side to obtain the preliminary reactive power optimization results on the transmission network side; S53, Combine the collaborative reactive power optimization results on the transmission network side with the parameters of the preliminary reactive power optimization results on the transmission network side. The interaction variables are transmitted to the distribution network side. The distribution network side takes minimizing network loss and voltage deviation as local objectives, and calculates the reactive power output of distributed power sources, energy storage systems and adjustable loads on the distribution network side in conjunction with the static voltage security constraints of the distribution network side, to obtain the preliminary reactive power optimization results of the distribution network side; S54, the preliminary reactive power optimization results of the distribution network side are fed back to the transmission network side. The preliminary optimization results of the transmission network side and the distribution network side are jointly verified by the multi-Agent transmission and distribution reactive power interaction security model to determine whether the convergence conditions are met. If not, the collaborative interaction variables are updated, and steps S52-S53 are repeated until the convergence conditions are met, to obtain the final solution result of transmission and distribution collaborative reactive power optimization.

10. A power transmission and distribution coordinated reactive power optimization system considering static voltage safety constraints, characterized in that, include: The system comprises: a multi-agent transmission and distribution reactive power interaction security modeling unit, which connects to the transmission and distribution network monitoring equipment and the controllers of each source-grid-load-storage unit; a transmission and distribution network decoupling and collaborative reactive power algorithm calculation unit, which connects to the multi-agent transmission and distribution network reactive power interaction security model; and a source-grid-load-storage transmission and distribution reactive power data interaction unit, which connects to both the multi-agent transmission and distribution network reactive power interaction security modeling unit and the transmission and distribution network decoupling and collaborative reactive power algorithm calculation unit. The system comprises the following components: a transmission and distribution coordinated reactive power optimization objective function construction unit, connected to the source-grid-load-storage transmission and distribution reactive power data interaction unit; a transmission and distribution coordinated reactive power optimization result solving and output unit, connected to both the transmission and distribution network decoupled coordinated reactive power algorithm calculation unit and the transmission and distribution coordinated reactive power optimization objective function construction unit; a static voltage safety constraint verification unit, connected to both the transmission and distribution coordinated reactive power optimization result solving and output unit and the source-grid-load-storage transmission and distribution reactive power data interaction unit; an optimization result solving and output unit, connected to both the transmission and distribution coordinated reactive power optimization result solving and output unit and the source-grid-load-storage transmission and distribution reactive power data interaction unit; a static voltage safety constraint verification unit, connected to both the transmission and distribution coordinated reactive power optimization result solving and output unit and the source-grid-load-storage transmission and distribution reactive power data interaction unit; a static voltage safety constraint verification unit, connected to both the source-grid-load-storage transmission and distribution reactive power data interaction unit; and a static voltage safety constraint verification unit, connected to both the source-grid-load-storage transmission and distribution reactive power optimization result solving and output unit and the source-grid-load-storage transmission and distribution reactive power data interaction unit.

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