A multi-feeder power coordinated control system and method

Abstract

This invention discloses a multi-feeder power coordinated control system and method. The method includes the following steps: constructing a multi-dimensional information fusion module to acquire multi-dimensional data including load characteristic parameters, equipment operating status, and environmental conditions; analyzing the voltage-power coupling relationship between feeders based on the multi-dimensional data; obtaining a large-scale optimization problem including all constraints based on the coupling relationship; solving the large-scale optimization problem to obtain a multi-feeder coordinated control strategy; executing the multi-feeder coordinated control strategy, monitoring the execution effect in real time, and updating the parameters of the multi-dimensional information fusion module; monitoring the safety status of the active distribution network multi-feeder system throughout the process; and quantifying the economic value of multi-feeder power coordinated control. This invention can solve the problem of insufficient coupling relationship analysis in traditional methods and significantly reduce the computational complexity of the multi-feeder coordination problem to meet real-time control requirements and improve the robustness and environmental adaptability of the control system.

Description

Technical Field

[0001] This invention relates to electrical engineering and its automation, and in particular to the design of a multi-feeder power coordinated control system and method based on ADN-SLVC. Background Technology

[0002] With the deepening of the global energy transition and the large-scale integration of distributed power sources, distribution networks are undergoing a profound transformation from traditional passive networks to active distribution networks. Active distribution networks are characterized by bidirectional power flow, diversified power source integration, and intelligent control. These new features place unprecedented and higher demands on multi-feeder coordinated control technology.

[0003] Existing multi-feeder coordinated control technologies suffer from a fundamental flaw: inaccurate load modeling. This severely impacts the actual effectiveness of coordinated control. Most existing methods still employ constant power load models for system modeling and control design. This simplified modeling approach completely fails to reflect the passive voltage regulation capabilities of the load. When the output of a distributed power source on a feeder changes, causing voltage fluctuations, the load, with its static voltage characteristics, automatically adjusts its power consumption level, creating a beneficial passive regulation effect. However, existing control methods cannot identify and utilize this valuable regulation resource. More seriously, errors in load modeling are amplified step-by-step through the complex electrical coupling relationships between feeders, ultimately leading to a significant deviation between the control strategy and the actual operating state of the system.

[0004] In analyzing and handling the coupling relationships between feeders, existing methods exhibit a significant lack of analytical depth, directly limiting further improvements in coordinated control effectiveness. Traditional methods primarily consider the direct electrical coupling effects generated by each feeder through the common bus, but completely ignore the significant amplification effect of the load's static voltage characteristics on this coupling relationship. In reality, when the distributed power output of feeder A changes, it not only directly affects the operating status of other feeders by altering the power flow distribution, but also influences the power consumption levels of loads on other feeders by causing changes in the common bus voltage, thus forming a more complex secondary coupling effect. Existing methods cannot accurately describe and handle this complex multi-level coupling relationship.

[0005] Computational efficiency is also a serious challenge facing existing technologies. Traditional centralized multi-feeder coordinated control methods encounter the curse of dimensionality when dealing with large-scale systems. As the number of feeders and the size of nodes increase, the computational complexity of the optimization problem increases exponentially. Existing commercial software often takes far longer than the stringent time requirements of real-time control of distribution networks when dealing with coordinated optimization problems involving multiple feeders. When the load ZIP characteristic is further considered, the already complex problem evolves into an even more difficult mixed-integer nonlinear programming problem, and the convergence performance of existing algorithms deteriorates sharply, often resulting in failure to converge or getting trapped in local optima.

[0006] Existing technologies also exhibit significant shortcomings in system robustness, being overly sensitive to model parameter errors and external disturbances, which severely impacts their reliable application in practical engineering. Real-world power distribution systems commonly contain unavoidable factors such as modeling errors, parameter uncertainties, and measurement noise. Existing control methods lack effective robustness guarantees; even minor changes in load parameters often trigger substantial adjustments to the control strategy, making it difficult for the system to maintain stable and reliable control performance in the face of parameter uncertainties and external disturbances.

[0007] Furthermore, existing methods generally lack real-time feedback and adaptive adjustment capabilities, mostly employing open-loop control modes for system control. This control architecture cannot adjust the control strategy in a timely manner according to real-time changes in the actual operating state of the system. When the system operating conditions deviate from the original design conditions, the control effect will significantly decrease. Since the load characteristics themselves have obvious time-varying features, but existing methods cannot achieve dynamic updates of load parameters, the control model gradually becomes disconnected from the actual system state, leading to a continuous deterioration in control accuracy and effectiveness.

[0008] It is understood that the above statements only provide background information related to the present invention and do not necessarily constitute prior art. Summary of the Invention

[0009] Based on the aforementioned technical problems, the purpose of this invention is to provide a multi-feeder power coordinated control system and method, which can solve the problem of insufficient coupling relationship analysis in traditional methods, significantly reduce the computational complexity of multi-feeder coordination problems, and meet real-time control requirements, thereby improving the robustness and environmental adaptability of the control system.

[0010] To achieve the above objectives, this invention discloses a multi-feeder power coordinated control system, comprising: The multi-dimensional information fusion module is used to output multi-dimensional data including load characteristic parameters, equipment operating status and environmental conditions; A multi-feeder electrical coupling relationship analysis module is used to analyze the voltage-power coupling relationship between feeders based on the multi-dimensional data. The multi-feeder collaborative optimization control module outputs a large-scale optimization problem based on the voltage-power coupling mechanism between the feeders. A distributed cooperative control module is used to solve the large-scale optimization problem and obtain a multi-feeder cooperative control strategy. A real-time measurement feedback and adaptive control module is used to execute and monitor the multi-feeder collaborative control strategy, and at the same time update the parameters of the multi-dimensional information fusion module; A safety protection module is used to monitor the safety status of multi-feeder systems in active distribution networks. The economic benefit assessment module is used to quantify the economic value of multi-feeder power coordination control.

[0011] This invention also discloses a multi-feeder power coordinated control method, comprising the following steps: Construct a multi-dimensional information fusion module to obtain multi-dimensional data including load characteristic parameters, equipment operating status, and environmental conditions; Based on the aforementioned multidimensional data analysis, the voltage-power coupling relationship between feeders is analyzed. Based on the voltage-power coupling relationship between the feeders, a large-scale optimization problem containing all constraints is obtained; Solving the large-scale optimization problem yields a multi-feeder cooperative control strategy; The multi-feeder collaborative control strategy is executed, and the execution effect is monitored in real time and the parameters of the multi-dimensional information fusion module are updated accordingly. Full-process monitoring of the safety status of active distribution network multi-feeder systems; Quantify the economic value of multi-feeder power coordinated control.

[0012] Furthermore, the method for obtaining the multidimensional data includes the following steps: S1.1 Construct a load characteristic module that comprehensively considers multiple influencing factors; The load characteristic module is: In the formula: For a moment node In the feeder CVR coefficient; The baseline CVR coefficient; The number of impact factors; For the first The weighting coefficients of each influencing factor; For the influence factor function; This is a seasonal adjustment factor; It is a seasonal function; This is the daily variation adjustment factor; It is a diurnal variation function; S1.2 The weighted fusion method based on the inverse variance processes multidimensional information sources including load characteristics, environmental conditions and equipment status, and outputs fused multidimensional data. The fused multidimensional data is as follows: In the formula: For the fused multidimensional data, The total number of information sources. For dynamic weighting coefficients, For the first Data from one information source; The dynamic weighting coefficients are dynamically adjusted based on data quality and reliability. In the formula: As a credibility factor for information sources, For the first Variance estimation for each information source Let be the credibility factor of the j-th information source. For the first Variance estimation of each information source.

[0013] Furthermore, the analysis of the voltage-power coupling mechanism between feeders includes the following steps: S2.1 Construct a multi-feeder electrical coupling relationship analysis module to receive multi-dimensional data output by the multi-dimensional information fusion module; S2.2 Establish the state space module of the active distribution network multi-feeder system: In the formula: For the first The nodal voltage vector of the feeder, For the first The power vector of each feeder For the first Number of nodes on a feeder This represents the total number of feeders; S2.3 Establish the power equation considering the load voltage characteristics: In the formula: For nodes In the feeder Net injection power on , These are the power outputs of distributed power sources and energy storage, respectively. For the node at rated voltage The baseline load power is as follows. The node voltage amplitude, Rated voltage; S2.4 Establish a power propagation matrix describing the power propagation characteristics between feeders. : In the formula, For the first The power of the feeder, Let be an element of the power propagation matrix, representing the th The effect of feeder power variation on the first The degree of influence of each feeder To indicate the first Power of each feeder line; The elements of the power propagation matrix are: In the formula: For items with direct impact, This refers to the indirect effects caused by load voltage characteristics. For the first Nodes in a feeder power, For the first Nodes in a feeder The power; S2.5. Establish a voltage sensitivity matrix describing the power propagation characteristics between feeders: In the formula, The elements represent the degree of voltage response caused by changes in node power.

[0014] Furthermore, the method for obtaining large-scale optimization problems includes the following steps: Based on the voltage-power coupling relationship between the feeders, a multi-feeder collaborative optimization control module is constructed that comprehensively considers economic efficiency, safety, and environmental benefits: In the formula: , , The objective functions are respectively economic efficiency, safety, and environmental benefits. , These are the weighting coefficients; The economic objective function is as follows: In the formula: For feeder The set of nodes, This is the operating cost function corresponding to the output of distributed power sources. For the first Nodes in a feeder Distributed power output, The cost function is the operating and charging / discharging losses of the energy storage system. For the first Nodes in a feeder The charging and discharging power of the energy storage system, This is the control cost function corresponding to the control action. For nodes In the Control variables on the feeder line; The security objective function is: In the formula, This refers to the node voltage reference value or rated voltage. For nodes In the Line current on the feeder, This represents the maximum allowable current for the corresponding line. The constraints include: Multi-feeder power balance constraints: In the formula, For the first Nodes in a feeder Net injection power, For the first The boundary power of a feeder at a substation or common busbar; Voltage safety constraints: In the formula, This represents the lower limit of the node voltage allowed by the system. This represents the upper limit of the node voltage allowed by the system.

[0015] Furthermore, the method for solving large-scale optimization problems includes the following steps: S4.1 Construct a distributed collaborative control module to refactor the large-scale optimization problem into a distributed optimization form: In the formula: Let be the local objective function of feeder k. For feeder The decision variable vector, In order to be with the first Coupled variables related to each feeder; The constraints include: (1) Local constraints: In the formula, For the first The local constraint coefficient matrix corresponding to each feeder. For the first The local constraint constant vector corresponding to each feeder; (2) Global coupling constraints: In the formula, For the first The coupling constraint coefficient matrix corresponding to each feeder. This is a global coupling constraint constant vector; (3) Construct the augmented Lagrangian function: In the formula, For penalty parameters, , As dual variables, Auxiliary variables introduced; S4.2. Iterative calculation is performed using a distributed cooperative control algorithm: x-Update Steps: z-Update Steps: λ-Update Steps: In the formula: For penalty parameters, superscript Indicates the first The next iteration.

[0016] Furthermore, the execution of the multi-feeder collaborative control strategy, real-time monitoring of the execution effect, and updating of the multi-dimensional information fusion module parameters include the following steps: A real-time measurement feedback and adaptive control module is constructed, comprising a state estimation module, a parameter identification module, a deviation monitoring module, and an adaptive adjustment module. The state estimation module receives measurement data and estimates the state of the active distribution network multi-feeder system. The parameter identification module updates the module parameters fused from the multi-dimensional information in real time. The deviation detection module monitors the multi-feeder coordinated control deviation. The adaptive adjustment module adjusts the multi-feeder coordinated control strategy based on the multi-feeder coordinated control deviation. The state estimation module uses an extended Kalman filter for real-time state estimation, including: Equations of state: Measurement equation: Status Update: In the formula, , These are the state transition function and the measurement function, respectively. , For process and measurement noise, For Kalman gain, The system at any time The state vector, To control the input vector, Measurement vector, To determine the time based on historical information The predicted value of the state; The parameter identification module updates the CVR parameters in the multidimensional information fusion module using the recursive least squares method. In the formula, For nodes In the The CVR parameters on the feeder at time The estimated value, Update the gain for the parameters. This represents the observed output of the corresponding node. For the regression vector, The deviation detection module uses the CUSUM test method to detect deviations in active distribution network multi-feeder systems. In the formula: For cumulative and statistical measures, For power deviation index, The target threshold for power deviation. These are the drift parameters.

[0017] Furthermore, the method for monitoring the safety status of a multi-feeder system in an active distribution network throughout the entire process includes the following steps: A safety protection module is constructed to monitor the safety status of the active distribution network multi-feeder system throughout the entire process. The safety protection module includes a preventive control layer, a corrective control layer, and an emergency control layer. The preventive control layer is used to issue early warnings based on the results of the state estimation module. The corrective control layer is used to respond quickly when a constraint violation is detected. The emergency control layer is used to activate protective measures in extreme situations. The preventative control layer includes a safety assessment index system, namely: Voltage stability margin: Thermal stability margin: When the safety margin falls below the warning threshold, preventative controls are activated: In the above formula: , These are voltage and thermal stability margin, respectively. This is the critical voltage. For nodes The real-time voltage amplitude, For the line Maximum allowable current, For the line At any moment Operating current, To address the issue of nodes when the safety margin falls below the warning threshold. feeder Preventive control instructions imposed, These are the baseline control commands for the system under normal operating conditions. For safety compensation control quantity; The corrective control layer employs a model predictive control method for rapid correction. In the formula, To predict the time domain, , This is the weight matrix. This represents the system's desired reference state. The emergency control layer implements load shedding and equipment protection: In the formula, This refers to the amount of material removed during the load process. For the first The feeder belongs to the first A set of load nodes at the level, For nodes In the The load power on the feeder.

[0018] Furthermore, the method for quantifying the economic value of multi-feeder power coordinated control includes the following steps: An economic benefit assessment module is constructed to evaluate savings in operating costs, energy costs, network loss costs, investment deferral benefits, and environmental benefits. Among these, environmental benefits include carbon emission reduction benefits and other comprehensive environmental and social benefits. The energy cost savings assessment is as follows: In the formula, , These are the baseline and optimized grid power, respectively. For time step, For a moment The price of electricity; The network loss cost savings assessment is as follows: In the formula, , These are the baseline and optimized line losses, respectively. This represents the total number of lines; The assessment of the investment deferral benefits is as follows: In the formula, For equipment Upgrade investment, The number of power grid devices participating in the assessment. To achieve the first under the coordinated control The actual number of years the investment has been deferred for each piece of equipment. The original planned investment period, The discount rate; The carbon emission reduction benefit assessment is as follows: In the formula, To increase the absorption of new energy sources, As a carbon emission factor, For the first feeder at time Electricity consumption by new energy sources The economic value or carbon trading price per unit of carbon emissions; The economic value of the multi-feeder power coordinated control is: In the formula, To assess the value of operating cost savings, The value of environmental and social benefits beyond carbon emission reduction.

[0019] Compared with the prior art, the present invention has the following advantages: This invention addresses the shortcomings of traditional methods in analyzing coupling relationships by constructing a multi-feeder electrical coupling relationship analysis module based on load voltage characteristics. This module accurately describes the complex mutual influence relationships between feeders generated by voltage-power coupling, resolving the inadequacy of traditional coupling relationship analysis. Furthermore, by constructing a distributed collaborative control module, the large-scale multi-feeder coordination problem is decomposed into parallel solutions to local sub-problems of each feeder, significantly reducing computational complexity to meet real-time control requirements. Finally, by constructing a multi-dimensional information fusion module integrating load characteristic parameters, equipment operating status, and environmental conditions, a real-time measurement feedback and adaptive control module based on real-time measurement data for state estimation, parameter identification, and adaptive adjustment is built, significantly improving the robustness and environmental adaptability of the multi-feeder power collaborative control system. Finally, by constructing a safety protection module encompassing preventative, corrective, and emergency control, and establishing a multi-feeder collaborative optimization control module covering comprehensive economic, safety, and environmental benefits, this invention provides a complete technical solution for the intelligent collaborative control of multi-feeder systems in active distribution networks. Attached Figure Description

[0020] To more clearly illustrate the technical solution of the present invention, the accompanying drawings used in the description will be briefly introduced below. Obviously, the drawings in the following description are one embodiment of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort: Figure 1 This is a schematic diagram of a multi-feeder power coordinated control system proposed in this invention.

[0021] Figure 2 This is a flowchart of a multi-feeder power coordinated control method proposed in this invention. Detailed Implementation

[0022] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0023] It should be noted that, in this document, the terms "comprising," "including," "having," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or terminal device that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or terminal device. Unless otherwise specified, an element defined by the phrase "comprising..." or "including..." does not exclude the presence of additional elements in the process, method, article, or terminal device that includes said element.

[0024] It should be noted that the accompanying drawings are all in a very simplified form and use non-precise ratios, and are only used to facilitate and clearly illustrate the purpose of the embodiments of the present invention.

[0025] like Figure 1 As shown, the present invention proposes a multi-feeder power coordinated control system, comprising: The multi-dimensional information fusion module is used to output multi-dimensional data including load characteristic parameters, equipment operating status, and environmental conditions. A multi-feeder electrical coupling relationship analysis module is used to analyze the voltage-power coupling mechanism between feeders based on the multi-dimensional data. The multi-feeder collaborative optimization control module outputs a large-scale optimization problem based on the voltage-power coupling mechanism between the feeders. A distributed cooperative control module is used to solve the large-scale optimization problem and obtain a multi-feeder cooperative control strategy. The real-time measurement feedback and adaptive control module is used to execute and monitor the multi-feeder collaborative control strategy, and update the module's multi-dimensional information fusion parameters. A safety protection module is used to monitor the safety status of the multi-feeder power coordinated control system; The economic benefit assessment module is used to quantify the economic value of multi-feeder power coordination control.

[0026] like Figure 2 As shown, this invention proposes a method for multi-feeder power coordinated regulation using a multi-feeder power coordinated regulation system, specifically including the following steps: S1. Construct a multi-dimensional information fusion module to obtain the multi-dimensional data including load characteristic parameters, equipment operating status and environmental conditions; Step S1 forms the information foundation for the entire technical solution of the multi-feeder power coordinated control method. It is responsible for collecting, processing, and integrating multi-dimensional information from different sources, providing an accurate and complete system state description for subsequent analysis and module establishment. The multi-dimensional information from different sources refers to environmental condition information, load characteristic information, and equipment status information. Among them, environmental condition information includes electrical quantity information and meteorological information.

[0027] Specifically, step S1 includes: S1.1 Construct a load characteristic module that comprehensively considers multiple influencing factors.

[0028] The load characteristic module is: In the formula: For a moment node In the feeder Load voltage characteristic (CVR) coefficient; The baseline CVR coefficient; The number of impact factors; For the first The weighting coefficients of each influencing factor; For the influence factor function; This is a seasonal adjustment factor; It is a seasonal function; This is the daily variation adjustment factor; This is a diurnal variation function.

[0029] S1.2 The weighted fusion method based on the inverse variance processes multidimensional information sources including load characteristics, environmental conditions and equipment status, and outputs fused multidimensional data.

[0030] The fused multidimensional data is as follows: In the formula: For the fused multidimensional data, The total number of information sources. For dynamic weighting coefficients, For the first Data from one information source.

[0031] The dynamic weighting coefficients are dynamically adjusted based on data quality and reliability. In the formula: As a credibility factor for information sources, For the first Variance estimation for each information source Let be the credibility factor of the j-th information source. For the first Variance estimation of each information source.

[0032] S2. Analyze the voltage-power coupling relationship between feeders based on the multidimensional data; This study employs a multi-feeder electrical coupling analysis module to receive multi-dimensional data output from a multi-dimensional information fusion module. It focuses on analyzing the complex coupling mechanisms between multi-feeders, considering the static voltage characteristics of the load. This accurately describes the complex mutual influence relationships between feeders generated through voltage-power coupling, addressing the shortcomings of traditional coupling analysis methods. The module outputs a power propagation matrix and a voltage sensitivity matrix. The power propagation matrix characterizes the propagation relationship of power changes between feeders, while the voltage sensitivity matrix characterizes the voltage response caused by power changes. Together, they constitute the core parameters for multi-feeder voltage-power coupling analysis.

[0033] Specifically, step S2 includes: S2.1 Construct a multi-feeder electrical coupling relationship analysis module to receive multi-dimensional data output by the multi-dimensional information fusion module; S2.2 Establish the state space module of the active distribution network multi-feeder system: In the formula: It is a state space vector; For the first The nodal voltage vector of the feeder has a dimension of The unit is kV; For the first The power vector of the feeder has a dimension of The unit is kW; For the first Number of nodes in a feeder; This represents the total number of feeders.

[0034] By unifying the node voltage state and power injection state of a multi-feeder system into the state space vector, a unified mathematical foundation can be provided for the subsequent establishment of power equations, voltage sensitivity matrices, and power propagation relationships between feeders that take into account load voltage characteristics.

[0035] S2.3 Establish the power equation considering the load voltage characteristics: In the formula: For nodes In the feeder Net injection power on , These are the power outputs of distributed power sources and energy storage, respectively. For the node at rated voltage The baseline load power is as follows. The node voltage amplitude, This is the rated voltage.

[0036] The power equation describes the influence of the load static voltage characteristics on node power injection and is the physical basis for the formation of voltage-power coupling relationship between feeders.

[0037] S2.4 Establish a power propagation matrix describing the power propagation characteristics between feeders. : In the formula, For the first The power of the feeder, Let be an element of the power propagation matrix, representing the th The effect of feeder power variation on the first The degree of influence of each feeder To indicate the first Power of each feeder line.

[0038] The elements of the power propagation matrix are: In the formula: For items with direct impact, This refers to the indirect effects caused by load voltage characteristics. For the first Nodes in a feeder power, For the first Nodes in a feeder The power.

[0039] S2.5. Establish a voltage sensitivity matrix describing the power propagation characteristics between feeders: in, The elements represent the degree of voltage response caused by changes in node power. The voltage sensitivity matrix comprehensively reflects the combined effect of network parameters and load voltage characteristics on the system voltage response.

[0040] Through the above steps, the power propagation matrix and voltage sensitivity matrix can be obtained, providing a parameter basis for constructing a large-scale collaborative optimization problem with multi-feeder coupling constraints in step S3, and providing a coupling structure basis for the distributed problem decomposition in step S4.

[0041] S3. Based on the voltage-power coupling relationship between the feeders, obtain a large-scale optimization problem that includes all constraints.

[0042] Specifically, based on the coupling relationship output in step S2, a multi-feeder collaborative optimization control module is constructed that comprehensively considers economic efficiency, safety, and environmental benefits to obtain a large-scale optimization problem that includes all constraints.

[0043] The multi-feeder collaborative optimization control module is: In the formula: , , The objective functions are respectively economic efficiency, safety, and environmental benefits. , These are the weighting coefficients.

[0044] The economic objective function is as follows: In the formula: For feeder The set of nodes, This is the operating cost function corresponding to the output of distributed power sources. For the first Nodes in a feeder The output power of distributed power sources, measured in kW. The cost function is the operating and charging / discharging losses of the energy storage system. For the first Nodes in a feeder The charging and discharging power of the energy storage system, measured in kW. This is the control cost function corresponding to the control action. For nodes In the Control variables on the feeder include active power regulation, reactive power regulation, or voltage regulation commands.

[0045] The security objective function is: In the formula, This refers to the node voltage reference value or rated voltage. For nodes In the The line current on the feeder, in amperes (A). This represents the maximum allowable current for the corresponding line.

[0046] The constraints include: Multi-feeder power balance constraints: In the formula, For the first Nodes in a feeder The net injected power is equal to the sum of the output of distributed power sources, energy storage, and load power. For the first The boundary power of a feeder at a substation or common bus.

[0047] Voltage safety constraints: In the formula, This represents the lower limit of the node voltage allowed by the system. This represents the upper limit of the node voltage allowed by the system.

[0048] S4. Solve the large-scale optimization problem to obtain the multi-feeder cooperative control strategy.

[0049] A distributed cooperative control module is used to solve large-scale optimization problems. Specifically, an ADMM-based distributed cooperative control algorithm is used to solve large-scale optimization problems, which can decompose large-scale optimization problems into local subproblems that can be solved in parallel, thereby significantly reducing computational complexity.

[0050] Step S4 includes: S4.1 Construct a distributed collaborative control module to refactor the large-scale optimization problem into a distributed optimization form: In the formula: Let be the local objective function of feeder k. For feeder The decision variable vector, In order to be with the first The coupling variables associated with each feeder are used to describe the power or voltage coupling relationship between the feeder and other feeders; The constraints include: (1) Local constraints: In the formula, For the first The local constraint coefficient matrix corresponding to each feeder. For the first The local constraint constant vector corresponding to each feeder.

[0051] (2) Global coupling constraints: In the formula, For the first The coupling constraint coefficient matrix corresponding to each feeder. This is a global coupling constraint constant vector.

[0052] (3) Construct the augmented Lagrangian function: In the formula, For penalty parameters, , As dual variables, The auxiliary variable is introduced to achieve variable separation and distributed solution of multi-feeder coupling constraints.

[0053] S4.2. Iterative calculation is performed using a distributed cooperative control algorithm: x-Update Steps (Parallel Solution for Each Feeder): z-Update Steps: λ-Update Steps: In the formula: For penalty parameters, superscript Indicates the first The next iteration.

[0054] S5. Execute the multi-feeder collaborative control strategy, monitor the execution effect in real time, and update the parameters of the multi-dimensional information fusion module.

[0055] Specifically, a real-time measurement feedback and adaptive control module is constructed to achieve closed-loop control through state estimation, parameter identification, and deviation detection. This module comprises four sub-modules: a state estimation module, a parameter identification module, a deviation monitoring module, and an adaptive adjustment module. The state estimation module receives measurement data and estimates the state of the active distribution network multi-feeder system. The parameter identification module updates module parameters and feeds them back to step S1. The deviation detection module monitors the multi-feeder coordinated control deviation. The adaptive adjustment module adjusts the multi-feeder coordinated control strategy based on the multi-feeder coordinated control deviation.

[0056] The state estimation module uses an extended Kalman filter for real-time state estimation, including: Equations of state: Measurement equation: Status Update: In the formula, , These are the state transition function and the measurement function, respectively. , For process and measurement noise, For Kalman gain, The system at any time The state vector is used to describe the voltage, power, and related operating states of a multi-feeder system. The control input vector represents the control command or control action. Measurement vectors represent system operation data acquired by sensors or monitoring devices. To determine the time based on historical information The predicted value of the state.

[0057] The parameter identification module updates the CVR parameters in step S1 using the recursive least squares method: In the formula, For nodes In the The CVR parameters on the feeder at time The estimated value, Update the gain for the parameters. This represents the observed output of the corresponding node. This is a regression vector used to describe the relationship between voltage changes and power response.

[0058] The deviation detection module uses the CUSUM test method to detect deviations in active distribution network multi-feeder systems. In the formula: For cumulative and statistical measures, As a power deviation indicator, The target threshold for power deviation. This is the drift parameter, used to adjust the sensitivity of deviation detection.

[0059] S6. Monitor the safety status of the active distribution network multi-feeder system throughout the entire process.

[0060] The aforementioned safety protection module monitors the safety status of the active distribution network multi-feeder system throughout the entire process and intervenes promptly when an anomaly is detected. The safety protection module comprises a three-layer safety protection system consisting of preventive control, corrective control, and emergency control. The preventive control layer is used to issue early warnings based on the state estimation results from step S5; the corrective control layer is used to respond quickly when constraint violations are detected; and the emergency control layer is used to activate protective measures in extreme situations.

[0061] Specifically, the preventive control layer includes a safety assessment index system, namely: Voltage stability margin: Thermal stability margin: When the safety margin falls below the warning threshold, preventative controls are activated: In the above formula: , These are voltage and thermal stability margin, respectively. This is the critical voltage. For nodes The real-time voltage amplitude, For the line Maximum allowable current, For the line At any moment Operating current, To address the issue of nodes when the safety margin falls below the warning threshold. feeder Preventive control instructions imposed, These are the baseline control commands for the system under normal operating conditions. This is a safety compensation control quantity used to adjust the system's operating status before a safety risk occurs.

[0062] The corrective control layer employs a model predictive control method for rapid correction. In the formula, To predict the time domain, , This is the weight matrix. This represents the system's desired reference state.

[0063] The emergency control layer implements load shedding and equipment protection: In the formula, This refers to the amount of material removed during the load process. For the first The feeder belongs to the first A set of load nodes at the level, For nodes In the The load power on the feeder.

[0064] S7, quantifying the economic value of multi-feeder power collaborative control.

[0065] The economic benefit assessment module receives the operational results of all the aforementioned modules and quantifies the economic benefits of multi-feeder power line coordinated control. The economic benefit assessment includes operational cost savings assessment, energy cost savings assessment, network loss cost savings assessment, investment deferral benefit assessment, and environmental benefit assessment. Specifically, the operational cost savings assessment is calculated based on the optimization results of step S4, the investment deferral benefit assessment is based on the capacity margin improvement assessment of the active distribution network multi-feeder system, and the environmental benefit assessment is based on the incremental calculation of new energy consumption.

[0066] Specifically, the operating cost savings assessment is based on the multi-feeder collaborative optimization control strategy obtained in step S4, and is calculated by comparing the changes in scheduling costs of various control resources in the operating state of the multi-feeder system before and after collaborative optimization control. The operating cost savings include distributed power output costs, energy storage charging and discharging costs, and control action costs, etc.

[0067] The energy cost savings assessment is as follows: In the formula, , These are the baseline and optimized grid power, respectively. For time step, For a moment The price of electricity.

[0068] The network loss cost savings assessment is as follows: In the formula, , These are the baseline and optimized line losses, respectively. This represents the total number of lines.

[0069] The assessment of the investment deferral benefits is as follows: In the formula, For equipment Upgrade investment, The number of power grid devices participating in the assessment. To achieve the first under the coordinated control The actual number of years the investment has been deferred for each piece of equipment. The original planned investment period, This is the discount rate, used to reflect the time value of money.

[0070] The environmental benefit assessment includes carbon emission reduction benefits and other comprehensive environmental and social benefits, among which carbon emission reduction benefits are assessed through… Quantify the environmental benefits, and record the remaining environmental benefits as follows: .

[0071] The carbon emission reduction benefits are as follows: In the formula, To increase the absorption of new energy sources, As a carbon emission factor, For the first feeder at time Electricity consumption by new energy sources The economic value or carbon trading price per unit of carbon emissions.

[0072] The economic value of the multi-feeder power coordinated control is: .

[0073] In the formula, To assess the value of operating cost savings, The value of carbon emission reduction and environmental benefits brought about by multi-feeder power coordinated control. Other comprehensive environmental and social benefits besides carbon emission reduction may include pollutant emission reduction benefits, social benefits from improved power supply reliability, and benefits from improved user satisfaction.

[0074] This invention forms a complete technical system from multi-dimensional information fusion to economic benefit assessment. Each module is connected through clearly defined data and control flows, achieving intelligent collaborative control of multi-feeder power: the multi-dimensional information fusion module provides the data foundation for system modeling; the multi-feeder electrical coupling relationship analysis module reveals the mutual influence mechanism between feeders; the multi-feeder collaborative optimization control module achieves globally optimal decision-making; the distributed collaborative control module ensures computational efficiency; the real-time measurement feedback and adaptive control module maintains control accuracy; the safety protection module ensures reliable operation; and the economic benefit assessment module reflects application value. Together, they constitute a complete solution for collaborative control of multi-feeders in active distribution networks.

[0075] Although the present invention has been described in detail through the preferred embodiments above, it should be understood that the above description should not be considered as a limitation of the present invention. Various modifications and substitutions to the present invention will be apparent to those skilled in the art after reading the above description. Therefore, the scope of protection of the present invention should be defined by the appended claims.

Claims

1. A multi-feed power coordinated regulation system, characterized in that, Comprise: A multi-dimensional information fusion module for outputting multi-dimensional data containing load characteristic parameters, equipment operating states and environmental conditions; A multi-feeder electrical coupling relationship analysis module for analyzing feeder-to-feeder voltage-power coupling relationships according to the multi-dimensional data; a multi-feeder collaborative optimization control module for outputting a large-scale optimization problem based on the feeder-to-feeder voltage-power coupling relationships; A distributed collaborative control module for solving the large-scale optimization problem to obtain a multi-feeder collaborative control strategy; a real-time measurement feedback and adaptive control module for executing and monitoring the multi-feeder collaborative control strategy while updating parameters of the multi-dimensional information fusion module.

2. The multi-feed power coordination system of claim 1, wherein, Also comprise: A safety protection module for monitoring safety states of the multi-feeder power collaborative regulation system; An economic benefit evaluation module for quantifying economic values of multi-feeder power collaborative control.

3. A method for multi-feed line coordinated control using the multi-feed line power coordinated system according to any one of claims 1-2, characterized in that, Comprise the following steps: Constructing a multi-dimensional information fusion module to obtain multi-dimensional data containing load characteristic parameters, equipment operating states and environmental conditions; Analyzing feeder-to-feeder voltage-power coupling relationships based on the multi-dimensional data; Obtaining a large-scale optimization problem containing all constraint conditions based on the feeder-to-feeder voltage-power coupling relationships; Solving the large-scale optimization problem to obtain a multi-feeder collaborative control strategy; Executing the multi-feeder collaborative control strategy, monitoring execution effects in real time and updating parameters of the multi-dimensional information fusion module; Monitoring safety states of the multi-feeder system of the active distribution network throughout the process; Quantifying economic values of multi-feeder power collaborative control.

4. The method of claim 3, wherein, The method for constructing the multi-dimensional information fusion module to obtain multi-dimensional data comprises the following steps: S1.

1. Constructing a load characteristic module that comprehensively considers multiple influencing factors; The load characteristic module is: wherein: is the time of day is the node is the CVR factor on the feeder ; is the reference CVR factor is the number of impact factors is the weight coefficient of the th impact factor is the impact factor function is the seasonal adjustment factor is the seasonal function is the diurnal adjustment factor is the diurnal function S1.

2. Processing multi-dimensional information sources including load characteristics, environmental conditions and equipment states based on a weighted fusion method of reciprocal variance to output fused multi-dimensional data; The fused multi-dimensional data are: In the formula: is the fused multi-dimensional data, is the total number of information sources, is the dynamic weight coefficient, is the data of the i-th information source; and is the data of the i-th information source. Wherein, dynamic weight coefficients are dynamically adjusted according to data quality and reliability: where: is the credibility factor for the information source, is the variance estimate for the jth information source, is the variance estimate for the jth information source, is the credibility factor for the jth information source, is the variance estimate for the jth information source. is the variance estimate for the jth information source.

5. The method of claim 3, wherein, The method for analyzing feeder-to-feeder voltage-power coupling mechanisms comprises the following steps: S2.

1. Constructing a multi-feeder electrical coupling relationship analysis module to receive multi-dimensional data output by the multi-dimensional information fusion module; S2.

2. Establishing a state space module of the multi-feeder system of the active distribution network: wherein: is the node voltage vector of the th feeder, is the power vector of the th feeder, is the number of nodes of the th feeder, is the total number of feeders; S2.

3. Establishing a power equation considering load voltage characteristics: wherein: P is the net injection power at the node P is the net injection power at the node P is the net injection power at the node P is the net injection power at the node P is the net injection power at the node P is the net injection power at the node P is the net injection power at the node P is the net injection power at the node P is the net injection power at the node S2.4, establishing a power propagation matrix describing the power propagation characteristics between the feeders : In the formula, For the first The power of the feeder, Let be an element of the power propagation matrix, representing the th The effect of feeder power variation on the first The degree of influence of each feeder To indicate the first Power of each feeder line; Wherein, power propagation matrix elements are: wherein: is a direct influence term, is an indirect influence term generated by the load voltage characteristic, is the power of the node in the m-th feeder, is the power of the node in the m-th feeder, is the power of the node in the m-th feeder; S2.

5. Establishing a voltage sensitivity matrix describing feeder-to-feeder power propagation characteristics: wherein The element of the equation represents the degree of voltage response caused by the change in node power.

6. The method of claim 3, wherein, The method for obtaining a large-scale optimization problem comprises the following steps: Based on the feeder-to-feeder voltage-power coupling relationships, constructing a multi-feeder collaborative optimization control module that comprehensively considers economy, safety and environmental benefits: wherein: , , are the economic, safety, and environmental benefit objective functions, respectively, , are the weight coefficients. Wherein, an economy objective function is: In the formula: For feeder The set of nodes, The corresponding operating cost function for distributed power generation output. For the first Nodes in a feeder Distributed power output, The cost function is the operating and charging / discharging losses of the energy storage system. For the first Nodes in a feeder The charging and discharging power of the energy storage system, This is the control cost function corresponding to the control action. For nodes In the Control variables on the feeder line; A safety objective function is: wherein is a node voltage reference value or rated voltage, is a node In a first line current on the feeder, is the maximum allowed current for the corresponding line; Constraint conditions include: Multi-feeder power balance constraints: In the formula, For the first Nodes in a feeder Net injection power, For the first The boundary power of a feeder at a substation or common busbar; Voltage safety constraints: wherein Vmin is the lower limit of the node voltage allowed by the system, Vmax is the upper limit of the node voltage allowed by the system.

7. The method of claim 3, wherein, The method for solving a large-scale optimization problem comprises the following steps: S4.

1. Constructing a distributed collaborative control module to reconstruct the large-scale optimization problem into a distributed optimization form: wherein: is a local objective function for feeder k, is a local objective function for feeder k, is a decision variable vector for feeder k, is a coupling variable associated with the jth feeder; and is a coupling variable associated with the jth feeder. Constraint conditions include: (1) Local constraints: In the formula, For the first The local constraint coefficient matrix corresponding to each feeder. For the first The local constraint constant vector corresponding to each feeder; (2) Global coupling constraints: In the formula, is the first The coupling constraint coefficient matrix corresponding to the feeder pair, is the global coupling constraint constant vector; (3) Constructing an augmented Lagrangian function: wherein is a penalty parameter, , is a dual variable, is an introduced auxiliary variable; S4.

2. Iterative calculation is performed using a distributed cooperative control algorithm: x-Update Steps: z-Update Steps: λ-Update Steps: In the formulae: is a penalty parameter, and denotes the th iteration.

8. The method of claim 4, wherein, The execution of the multi-feeder collaborative control strategy, real-time monitoring of the execution effect, and updating of the parameters of the multi-dimensional information fusion module include the following steps: A real-time measurement feedback and adaptive control module is constructed, comprising a state estimation module, a parameter identification module, a deviation monitoring module, and an adaptive adjustment module. The state estimation module receives measurement data and estimates the state of the active distribution network multi-feeder system. The parameter identification module updates the module parameters fused from the multi-dimensional information in real time. The deviation detection module monitors the multi-feeder coordinated control deviation. The adaptive adjustment module adjusts the multi-feeder coordinated control strategy based on the multi-feeder coordinated control deviation. The state estimation module uses an extended Kalman filter for real-time state estimation, including: Equations of state: Measurement equation: Status Update: wherein , are state transition and measurement functions, respectively, , are process and measurement noise, is the Kalman gain, is the state vector of the system at time , is the control input vector, is the measurement vector, is the predicted value of the state at time based on historical information; The parameter identification module updates the CVR parameters in the multidimensional information fusion module using the recursive least squares method. In the formula, For nodes In the The CVR parameters on the feeder at time The estimated value, Update the gain for the parameters. This represents the observed output of the corresponding node. For the regression vector, The deviation detection module uses the CUSUM test method to detect deviations in active distribution network multi-feeder systems. In the formula: is a cumulative and statistical quantity, is a power deviation index, is a power deviation target threshold, is a drift parameter.

9. The method of claim 8, wherein, The method for monitoring the safety status of a multi-feeder system in an active distribution network throughout the entire process includes the following steps: A safety protection module is constructed to monitor the safety status of the active distribution network multi-feeder system throughout the entire process. The safety protection module includes a preventive control layer, a corrective control layer, and an emergency control layer. The preventive control layer is used to issue early warnings based on the results of the state estimation module. The corrective control layer is used to respond quickly when a constraint violation is detected. The emergency control layer is used to activate protective measures in extreme situations. The preventative control layer includes a safety assessment index system, namely: Voltage stability margin: Thermal stability margin: When the safety margin falls below the warning threshold, preventative controls are activated: in the above formulae: , are voltage and thermal stability margin, respectively, is the critical voltage, is the real-time voltage amplitude of the node , is the maximum allowable current of the line , is the operating current of the line at time , is the preventive control instruction applied to the node , feeder when the safety margin is lower than the early warning threshold, is the reference control instruction of the system under normal operating state, is the safety compensation control amount; The corrective control layer employs a model predictive control method for rapid correction. wherein is the prediction horizon, , is the weight matrix, is the system desired reference state; The emergency control layer implements load shedding and equipment protection: wherein is the load shedding amount, is the first is the set of load nodes belonging to the first is the set of load nodes belonging to the first is the node is the load power on the first is the load power on the first 10. The method of claim 3, wherein, The method for quantifying the economic value of multi-feeder power coordinated control includes the following steps: An economic benefit assessment module is constructed to evaluate savings in operating costs, energy costs, network loss costs, investment deferral benefits, and environmental benefits. Among these, environmental benefits include carbon emission reduction benefits and other comprehensive environmental and social benefits. The energy cost savings assessment is as follows: wherein , are the reference and optimized grid power, respectively, is the time step, is the energy price at time . The network loss cost savings assessment is as follows: In the formula, , are the reference and optimized line losses, respectively, is the total number of lines. The assessment of the investment deferral benefits is as follows: In the formula, For equipment Upgrade investment, The number of power grid devices participating in the assessment. To achieve the first under the coordinated control The actual number of years the investment has been deferred for each piece of equipment. The original planned investment period, The discount rate; The carbon emission reduction benefit assessment is as follows: In the formula, is the new energy consumption increment, is the carbon emission factor, is the first The feeder in time The new energy consumption electric quantity, is the economic value of unit carbon emission or the carbon trading price; The economic value of the multi-feeder power coordinated control is: In the formula, The evaluation value of operation cost saving, The value of other environmental and social comprehensive benefits in addition to carbon emission reduction.

Images