A main distribution network collaborative multi-time scale collaborative capability evaluation and analysis device based on a cloud edge collaborative architecture

Abstract

The application discloses a kind of based on cloud edge collaborative architecture's main distribution network collaborative multi-time scale collaborative capability evaluation analysis device, including multi-time scale evaluation module, for the regulation and control capability of the demand of distribution network is evaluated, output evaluation result;Interactive power monitoring module is used to real-time monitoring the interactive power between main distribution network;Data processing module is used to the data of interactive power monitoring module acquisition is analyzed calculation, obtains power calculation result;Self-diagnosis module is used to based on the evaluation result and power calculation result, the operating state monitoring and fault diagnosis of each module are carried out.The application can realize the accurate evaluation of the multi-time scale regulation capability of distribution network, provide reliable decision basis for main network dispatching control, through multidimensional information perception, analysis and intelligent decision, optimize the dispatching efficiency of main distribution network, improve the overall reliability and economy of power system.

Description

Technical Field

[0001] This invention relates to the field of power technology, and in particular to a device for evaluating and analyzing the collaborative capabilities of main and distribution networks across multiple time scales based on a cloud-edge collaborative architecture. Background Technology

[0002] In today's power systems, the coordinated operation of the distribution network and the main grid plays a crucial role in ensuring the stability, reliability, and efficiency of power supply. With the continuous growth of electricity demand, the widespread integration of new energy sources, and the rapid development of distributed energy, traditional power system operation modes face many severe challenges.

[0003] On the one hand, as a power network directly facing users, the stability and reliability of the distribution network's operation directly affect users' electricity experience. However, existing distribution networks have significant shortcomings in terms of regulation capacity, response speed, and capacity adaptability. For example, the regulation capacity of distribution networks is usually limited, making it difficult to respond quickly to drastic changes in power load. In terms of second-level regulation capacity, when the power system experiences frequency fluctuations, the distribution network often cannot effectively adjust the frequency, leading to frequency instability and affecting the safe operation of power equipment. In terms of minute-level regulation capacity, facing rapid increases in load demand, the power sources and energy storage devices in the distribution network often cannot quickly adjust their output power, making it difficult to meet the rapid growth of load. In terms of hourly regulation capacity, facing peak-shaving demands, traditional distribution networks struggle to effectively adjust the load and adapt to changes in power demand at different times, affecting the overall dispatch efficiency of the system.

[0004] On the other hand, the information exchange and coordination mechanism between the main grid and the distribution network is still imperfect. Main grid dispatch and control personnel have difficulty grasping the adjustment capabilities of the distribution network at different time scales in real time, which makes it impossible to fully realize the potential of the distribution network. Traditional distribution network dispatching models cannot make full use of the advantages of big data, cloud computing and edge computing for dynamic adjustment and predictive dispatching, resulting in insufficient coordination between the main grid and the distribution network. They are unable to cope with sudden changes and demand fluctuations in the system. In the actual power system dispatching process, due to the lack of accurate monitoring and analysis of the interactive power between the main grid and the distribution network, problems such as unreasonable power allocation and energy waste are hard to avoid.

[0005] With the rapid development of distributed photovoltaic (PV) and other new energy sources within the distribution network, the power system faces new challenges. The intermittency and uncertainty of distributed PV output power require support from the main grid's regulation resources. However, the existing coordination and management of distributed PV between the main grid and the distribution network still has many shortcomings. Main grid dispatchers find it difficult to grasp the real-time operating status of distributed PV, leading to inaccurate regulation timing, which in turn affects voltage stability and power balance, threatening the overall security of the power grid. Summary of the Invention

[0006] The purpose of this section is to outline some aspects of embodiments of the present invention and to briefly describe some preferred embodiments. Simplifications or omissions may be made in this section, as well as in the abstract and title of this application, to avoid obscuring the purpose of these documents; however, such simplifications or omissions should not be construed as limiting the scope of the invention.

[0007] In view of the aforementioned existing problems, this invention is proposed. Therefore, this invention provides a multi-timescale collaborative capability assessment and analysis device for main and distribution network collaboration based on a cloud-edge collaborative architecture to solve the collaborative operation problem between the distribution network and the main grid in traditional power systems, thereby improving the stability, reliability, and dispatch efficiency of the power system.

[0008] To solve the above-mentioned technical problems, the present invention provides the following technical solution:

[0009] In a first aspect, the present invention provides a main distribution network collaborative multi-timescale collaborative capability assessment and analysis device based on cloud-edge collaborative architecture, comprising: a multi-timescale assessment module for assessing the control capability of the distribution network for various demands and outputting assessment results;

[0010] The interactive power monitoring module is used to monitor the interactive power between the main and distribution networks in real time.

[0011] The communication module is used to send the evaluation results of the multi-timescale evaluation module and the analysis results of the interactive power monitoring module to the main network scheduling and control center.

[0012] The data processing module is used to analyze and calculate the data collected by the interactive power monitoring module to obtain the power calculation results;

[0013] The self-diagnostic module is used to monitor the operating status and diagnose faults of each module based on the evaluation results and power calculation results.

[0014] As a preferred embodiment of the main-distribution network collaborative multi-timescale collaborative capability assessment and analysis device based on cloud-edge collaborative architecture described in this invention, the workflow of the multi-timescale assessment module includes:

[0015] Assess the regulatory capacity at different levels for different regulatory needs;

[0016] The constraints of the power flow equations of the distribution network are also considered during the evaluation process;

[0017] By real-time monitoring and analysis of various power sources, energy storage devices, and loads in the distribution network, the adjustability at different time scales can be determined.

[0018] As a preferred embodiment of the main-distribution network collaborative multi-timescale collaborative capability assessment and analysis device based on cloud-edge collaborative architecture described in this invention, the interaction probability monitoring workflow includes:

[0019] The interactive power monitoring module is installed at the access point between the distribution network and the main network. By installing multiple sensors of the same specifications, redundant data is obtained, and the interactive power between the main and distribution networks is monitored in real time.

[0020] As a preferred embodiment of the main-distribution network collaborative multi-timescale collaborative capability evaluation and analysis device based on cloud-edge collaborative architecture described in this invention, the communication module's workflow includes:

[0021] The communication module transmits multi-timescale evaluation results, interactive power monitoring and analysis results, distribution network parameters, and real-time operating power of each node in the distribution network to the main grid dispatch and control center through a communication encryption protocol for data verification.

[0022] As a preferred embodiment of the main-distribution network collaborative multi-timescale collaborative capability assessment and analysis device based on cloud-edge collaborative architecture described in this invention, the workflow of the multi-timescale assessment module further includes:

[0023] The multi-timescale assessment module obtains the operating information of all nodes in the entire distribution network by combining the power flow equations with data, and discovers the location of faults in the sensor data.

[0024] When sensor data is interrupted, the average value is calculated based on historical data over a given time window to replenish the data.

[0025] As a preferred embodiment of the main distribution network collaborative multi-timescale collaborative capability assessment and analysis device based on cloud-edge collaborative architecture described in this invention, the distribution network power flow equations include:

[0026]

[0027] Where Pi represents the active power of node i, and V i θ represents the voltage magnitude at node i. ij G represents the voltage phase difference between node i and node j. ij B represents the real part of the admittance matrix between node i and node j. ij Let n represent the imaginary part of the admittance matrix between node i and node j, and n represent the total number of nodes.

[0028] As a preferred embodiment of the main and distribution network collaborative multi-timescale collaborative capability assessment and analysis device based on cloud-edge collaborative architecture described in this invention, the self-diagnosis module's workflow includes:

[0029] The power and voltage distribution of each node are obtained by calculating the results of the power flow equations.

[0030] By comparing the power flow equation calculation results with sensor data, the data difference is obtained, and the operating status of each module is monitored and faults are diagnosed.

[0031] If the data difference exceeds a preset threshold, the data is determined to be abnormal, triggering a data verification or alarm mechanism.

[0032] Secondly, this invention provides a method for evaluating and analyzing the collaborative capabilities of a main and distribution network across multiple time scales based on a cloud-edge collaborative architecture, including:

[0033] The system assesses the ability to regulate various demands of the power distribution network and outputs the assessment results.

[0034] Real-time monitoring of the power exchange between the main and distribution networks;

[0035] The evaluation results of the multi-timescale evaluation module and the analysis results of the interactive power monitoring module are sent to the main network scheduling and control center.

[0036] The data on the interaction power are analyzed and calculated to obtain the power calculation results;

[0037] Based on the evaluation results and power calculation results, the operating status of each module is monitored and faults are diagnosed.

[0038] Thirdly, the present invention provides an electronic device, comprising:

[0039] Memory and processor;

[0040] The memory is used to store computer-executable instructions, and the processor is used to execute the computer-executable instructions. When the computer-executable instructions are executed by the processor, they implement the steps of the main distribution network collaborative multi-timescale collaborative capability assessment and analysis device based on cloud-edge collaborative architecture.

[0041] Fourthly, the present invention provides a computer-readable storage medium storing computer-executable instructions, which, when executed by a processor, implement the steps of the main distribution network collaborative multi-timescale collaborative capability assessment and analysis device based on cloud-edge collaborative architecture.

[0042] Compared with the prior art, the beneficial effects of the present invention are as follows: The present invention, through multiple components including a multi-timescale evaluation module, a communication module, an interactive power monitoring module, a data processing module, and a self-diagnosis module, can accurately evaluate the multi-timescale regulation capability of the distribution network, monitor the interactive power between the distribution network and the main grid in real time, provide reliable decision-making basis for the main grid dispatch control, optimize the dispatch efficiency of the main and distribution networks through multi-dimensional information perception, analysis, and intelligent decision-making, and improve the overall reliability and economy of the power system. Attached Figure Description

[0043] To more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings used in the following description of the embodiments will be briefly introduced. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0044] Figure 1 This is a schematic diagram of the structure of the main distribution network collaborative multi-timescale collaborative capability assessment and analysis device based on cloud-edge collaborative architecture according to an embodiment of the present invention;

[0045] Figure 2 This is a schematic diagram of the overall workflow of the main distribution network collaborative multi-timescale collaborative capability assessment and analysis device based on cloud-edge collaborative architecture according to an embodiment of the present invention. Detailed Implementation

[0046] To make the above-mentioned objects, features, and advantages of the present invention more apparent and understandable, specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of the present invention, and not all of them. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the protection scope of the present invention.

[0047] Many specific details are set forth in the following description in order to provide a full understanding of the invention. However, the invention may also be practiced in other ways different from those described herein, and those skilled in the art can make similar extensions without departing from the spirit of the invention. Therefore, the invention is not limited to the specific embodiments disclosed below.

[0048] Secondly, the term "one embodiment" or "embodiment" as used herein refers to a specific feature, structure, or characteristic that may be included in at least one implementation of the present invention. The phrase "in one embodiment" appearing in different places in this specification does not necessarily refer to the same embodiment, nor is it a single or selective embodiment that is mutually exclusive with other embodiments.

[0049] This invention is described in detail with reference to the schematic diagrams. When detailing the embodiments of this invention, for ease of explanation, the cross-sectional views illustrating the device structure may be partially enlarged, not adhering to the usual scale. Furthermore, the schematic diagrams are merely examples and should not be construed as limiting the scope of protection of this invention. In actual fabrication, the three-dimensional spatial dimensions of length, width, and depth should be included.

[0050] Furthermore, in the description of this invention, it should be noted that the terms "upper," "lower," "inner," and "outer," etc., indicate the orientation or positional relationship based on the orientation or positional relationship shown in the accompanying drawings. These terms are used solely for the convenience of describing the invention and for simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation. Therefore, they should not be construed as limitations on the invention. In addition, the terms "first," "second," or "third" are used for descriptive purposes only and should not be construed as indicating or implying relative importance.

[0051] Unless otherwise explicitly specified and limited, the terms "installation," "connection," and "joining" in this invention should be interpreted broadly. For example, they can refer to fixed connections, detachable connections, or integral connections; similarly, they can refer to mechanical connections, electrical connections, or direct connections, or indirect connections through an intermediate medium, or internal connections between two components. Those skilled in the art can understand the specific meaning of the above terms in this invention based on the specific circumstances.

[0052] Example 1

[0053] Reference Figures 1-2 As an embodiment of the present invention, a device for evaluating and analyzing the collaborative capabilities of a main distribution network based on a cloud-edge collaborative architecture across multiple time scales is provided, comprising:

[0054] The multi-timescale assessment module is used to assess the control capabilities of the distribution network for various demands and output the assessment results.

[0055] The interactive power monitoring module is used to monitor the interactive power between the main and distribution networks in real time.

[0056] The communication module is used to send the evaluation results of the multi-timescale evaluation module and the analysis results of the interactive power monitoring module to the main network scheduling and control center.

[0057] The data processing module is used to analyze and calculate the data collected by the interactive power monitoring module to obtain the power calculation results;

[0058] The self-diagnostic module is used to monitor the operating status and diagnose faults of each module based on the evaluation results and power calculation results.

[0059] It should be noted that the main grid and distribution network coordinated scheduling under the cloud-edge framework has become an important way to solve the above problems. Through the synergy of cloud computing and edge computing, the main grid can use the big data analysis and high-performance computing capabilities of the cloud, combined with the real-time data perception of the distribution network edge devices, to achieve precise scheduling and rapid response of the distribution network. The cloud is responsible for large-scale data processing and decision support, while the edge devices realize local data collection and preliminary processing, ensuring that the regulation capacity of the distribution network is fully utilized under the demand changes of seconds, minutes and hours.

[0060] In a preferred embodiment, the multi-timescale evaluation module workflow includes:

[0061] Assess the regulatory capacity at different levels for different regulatory needs;

[0062] The constraints of the power flow equations of the distribution network are also considered during the evaluation process;

[0063] By real-time monitoring and analysis of various power sources, energy storage devices, and loads in the distribution network, the adjustability at different time scales can be determined.

[0064] It should be noted that, in terms of multi-timescale assessment, the device dynamically assesses the regulation capabilities of the distribution network at the second (frequency regulation demand), minute (ramp-up demand), and hour (peak-shaving demand) levels, and, in conjunction with the power flow equation constraints of the distribution network, comprehensively analyzes the real-time status of various power sources, energy storage devices, and loads in the distribution network to determine their regulation capabilities.

[0065] In one alternative implementation, evaluating the different levels of regulation capability for different regulation needs includes:

[0066] The second-level, minute-level, and hour-level regulation capabilities are evaluated, corresponding to the frequency regulation, ramp-up, and peak-shaving regulation needs in the power system, respectively. The evaluation of the second-level, minute-level, and hour-level regulation capabilities can be calculated by multiplying the regulation rate by the regulation duration.

[0067] For example, this embodiment evaluates and calculates the regulation capacity at the second, minute, and hour levels (taking a 2MW distribution network as an example). With fluctuations in distributed generation and electricity demand, the distribution network needs to possess multi-timescale regulation capabilities to ensure the stable operation of the power system. To better evaluate the regulation capacity of the distribution network, this embodiment considers the regulation rate and duration at the second, minute, and hour levels, combined with voltage constraints, to accurately assess the regulation capacity of the distribution network. Taking a 2MW distribution network capacity as an example, this embodiment specifically explains how to calculate the regulation capacity of the distribution network, evaluate the regulation capacity of a 2MW capacity distribution network under second, minute, and hourly regulation demands, and corrects the calculation results by considering the impact of voltage constraints.

[0068] For the assessment of second-level regulation capability (frequency regulation demand), the second-level regulation capability of the distribution network is mainly used to cope with frequency fluctuations in the power system. When frequency deviation occurs in the power grid, the energy storage devices, electric vehicle charging stations and other loads of the distribution network can respond quickly in a short time to regulate the power supply to maintain system stability.

[0069] Specifically, the regulation rate: Assuming the second-level regulation rate of the distribution network is 0.001MW / s, that is, the distribution network can regulate 0.001MW of power per second;

[0070] Adjustment duration: Second-level adjustments generally have a short duration; we assume the adjustment duration is 5 seconds.

[0071] Regulation capacity calculation: Second-level regulation capacity = Regulation rate × Regulation duration = 0.001MW / s × 5s = 0.005MW;

[0072] Since voltage fluctuations may occur during regulation, assuming the voltage range of the distribution network is ±10%, if the regulation amplitude is too large, it may cause the voltage to exceed the allowable range. Therefore, in actual regulation, the regulation capacity of the distribution network is affected by voltage limits, and the actual regulation capacity is corrected to 0.0045MW (considering voltage constraints).

[0073] For minute-level regulation capacity assessment (ramp-up demand), the minute-level regulation capacity of the distribution network is mainly to cope with the rapid rise in load. During peak load periods, the distribution network needs to adjust power output according to the expected load growth.

[0074] Specifically, the regulation rate: considering that the minute-level regulation rate cannot exceed 0.06MW / min, we assume that the regulation rate of the distribution network is 0.06MW / min;

[0075] Adjustment duration: Minute-level adjustments generally last for a longer period of time, assuming the adjustment duration is 10 minutes;

[0076] Calculation of regulation capacity: Minute-level regulation capacity = Regulation rate × Regulation duration = 0.06 MW / min × 10 min = 0.6 MW;

[0077] Since rapid load growth may cause voltage fluctuations, assuming that voltage offset constraints reduce the regulation capacity by 3% when the distribution network is performing minute-level regulation, the corrected minute-level regulation capacity is 0.58MW.

[0078] The hourly regulation capacity assessment (peak shaving demand) of the distribution network is used to cope with the gradual increase or decrease of load, especially during the daily peak load period.

[0079] Specifically, the regulation rate: Assume the regulation rate of the distribution network is 0.1 MW / h;

[0080] Adjustment duration: Hourly adjustments can last for a relatively long time, assuming an adjustment duration of 2 hours;

[0081] Calculation of regulation capacity: Hourly regulation capacity = Regulation rate × Regulation duration = 0.1MW / h × 2h = 0.2MW;

[0082] Considering the impact of voltage constraints on long-term regulation, it is assumed that the voltage deviation constraint of the distribution network reduces the regulation capacity by 5% in hourly regulation. Therefore, the corrected hourly regulation capacity is 0.19MW.

[0083] Finally, a comprehensive evaluation of multi-timescale regulation capability is conducted. This embodiment combines the evaluation results of second-level, minute-level, and hourly-level regulation capabilities to comprehensively calculate the overall regulation capability of the distribution network under different regulation demands. Specifically, firstly, second-level, minute-level, and hourly-level regulation capability evaluations are performed separately. Then, based on actual load changes and system regulation demands during operation, the total regulation capability of the distribution network is comprehensively evaluated. The actual comprehensive regulation capability will be dynamically adjusted according to factors such as the specific load of the distribution network, distributed power source output, and voltage constraints. The device will monitor these parameters in real time to ensure that the distribution network can provide the required regulation capability within voltage limits under regulation demands at different timescales.

[0084] Through the multi-timescale assessment module, the distribution network can accurately assess its regulation capacity under regulation requirements at different time scales, and take into account the impact of voltage constraints. For a 2MW distribution network, the regulation capacity at the second, minute, and hour levels is 0.0045MW, 0.58MW, and 0.19MW, respectively. The main grid dispatchers can optimize the power resource dispatch strategy based on these assessment results to ensure the stable operation of the power grid.

[0085] In a preferred embodiment, the multi-timescale evaluation module workflow further includes:

[0086] The multi-timescale assessment module uses advanced algorithms and models to obtain the operating information of all nodes in the entire distribution network by combining the power flow equations of the distribution network with data, and to discover the location of faults in sensor data.

[0087] When sensor data is interrupted, the average value is calculated based on historical data over a given time window to replenish the data.

[0088] In a preferred embodiment, the interaction probability monitoring workflow includes:

[0089] The interactive power monitoring module is installed at the access point between the distribution network and the main network. By installing multiple sensors of the same specifications, redundant data is acquired, and the interactive power between the main and distribution networks is monitored in real time, thereby improving the accuracy and reliability of the collected data.

[0090] In a preferred embodiment, the communication module's workflow includes:

[0091] The communication module transmits multi-timescale assessment results, interactive power monitoring and analysis results, distribution network parameters, and real-time operating power of each node in the distribution network to the main grid dispatch and control center for data verification through a communication encryption protocol.

[0092] It should be noted that the communication module can send the evaluation results and monitoring data to the main grid dispatch and control center to ensure the accurate transmission of information. The main grid dispatch and control personnel can verify all the data based on the power flow calculation of the distribution network. If it is found that the sent data cannot be verified, the missing data nodes can be located by combining the power flow calculation results and following the principle of eliminating the least bad data.

[0093] In a preferred embodiment, the data processing module analyzes and calculates the data collected by the interactive power monitoring module, and combines the results of the multi-timescale evaluation module to provide support for scheduling decisions, ensuring that the coordinated operation of the main and distribution networks is more efficient and stable.

[0094] In a preferred embodiment, the self-diagnostic module workflow includes:

[0095] The power and voltage distribution of each node are obtained by calculating the results of the power flow equations.

[0096] By comparing the power flow equation calculation results with sensor data, the data difference is obtained, and the operating status of each module is monitored and faults are diagnosed.

[0097] If the data difference exceeds the preset threshold, the data is judged to be abnormal, triggering a data verification or alarm mechanism.

[0098] It should be noted that the self-diagnostic module continuously monitors the device's own operating status. By comparing the results with the power flow equation calculations, it can promptly detect and diagnose equipment faults, ensuring the long-term stable operation of the device.

[0099] Through the cloud-edge collaborative architecture, the coordination mechanism between the main grid and the distribution network can be effectively strengthened. During peak load periods, the main grid can coordinate distributed power sources, energy storage devices and other resources in the distribution network to share the load and reduce the power supply pressure on the main grid. When the output of distributed photovoltaic power fluctuates greatly, the main grid can use the scheduling algorithm in the cloud, combined with the real-time operation data fed back from the distribution network, to quickly provide adjustment resources, smooth out power fluctuations and ensure the stable operation of the grid.

[0100] The device of this invention provides new solutions to the challenges faced by traditional power systems, especially in the context of new energy and distributed power generation integration. Through precise regulation capability assessment and real-time power interaction monitoring, it enhances the flexibility and adaptability of the power grid. By achieving coordinated dispatching of the main grid and distribution network, the device provides reliable data support for power system dispatchers. Through multi-dimensional information perception, analysis, and intelligent decision-making, it optimizes the dispatching efficiency of the main and distribution networks, thereby optimizing the allocation of power resources, reducing regulation costs, and improving the reliability and security of the power system.

[0101] The above is a schematic scheme of a main distribution network collaborative multi-timescale collaborative capability assessment and analysis device based on a cloud-edge collaborative architecture according to this embodiment. It should be noted that the technical solution of this main distribution network collaborative multi-timescale collaborative capability assessment and analysis method based on a cloud-edge collaborative architecture belongs to the same concept as the technical solution of the aforementioned main distribution network collaborative multi-timescale collaborative capability assessment and analysis device based on a cloud-edge collaborative architecture. Details not described in detail in the technical solution of the main distribution network collaborative multi-timescale collaborative capability assessment and analysis method based on a cloud-edge collaborative architecture in this embodiment can be found in the description of the technical solution of the aforementioned main distribution network collaborative multi-timescale collaborative capability assessment and analysis device based on a cloud-edge collaborative architecture.

[0102] This embodiment presents a method for evaluating and analyzing the multi-timescale collaborative capabilities of the main and distribution networks based on a cloud-edge collaborative architecture, including:

[0103] The system assesses the ability to regulate various demands of the power distribution network and outputs the assessment results.

[0104] Real-time monitoring of the power exchange between the main and distribution networks;

[0105] The evaluation results from the multi-timescale evaluation module and the analysis results from the interactive power monitoring module are sent to the main network scheduling and control center.

[0106] The data on interactive power are analyzed and calculated to obtain the power calculation results;

[0107] Based on the evaluation results and power calculation results, the operating status of each module is monitored and faults are diagnosed.

[0108] In a preferred embodiment, the ability to regulate various demands of the distribution network is evaluated, and the evaluation results are output, including:

[0109] The second-level, minute-level, and hour-level regulation capabilities are evaluated, corresponding to the frequency regulation, ramp-up, and peak-shaving regulation needs in the power system, respectively.

[0110] The constraints of the power flow equations of the distribution network are also considered during the evaluation process;

[0111] By real-time monitoring and analysis of various power sources, energy storage devices, and loads in the distribution network, the adjustability at different time scales can be determined.

[0112] In a preferred embodiment, real-time monitoring of the interaction power between the primary and distribution networks includes:

[0113] By installing multiple sensors of the same specifications at the access points of the distribution network and the main network, redundant data is acquired, and the interaction power between the main and distribution networks is monitored in real time.

[0114] In a preferred embodiment, the ability to regulate various demands of the distribution network is evaluated, and the evaluation results are further included in the output:

[0115] By combining the power flow equations of the distribution network with data, the operating information of all nodes in the entire distribution network can be obtained, and the location of faults in sensor data can be found.

[0116] When sensor data is interrupted, the average value is calculated based on historical data over a given time window to replenish the data.

[0117] Distribution network power flow equations include:

[0118]

[0119] Where Pi represents the active power of node i, and V i θ represents the voltage magnitude at node i. ij G represents the voltage phase difference between node i and node j. ij B represents the real part of the admittance matrix between node i and node j. ij Let n represent the imaginary part of the admittance matrix between node i and node j, and n represent the total number of nodes.

[0120] In a preferred embodiment, based on the evaluation results and power calculation results, the operation status monitoring and fault diagnosis of each module includes:

[0121] The power and voltage distribution of each node are obtained by calculating the results of the power flow equations.

[0122] By comparing the power flow equation calculation results with sensor data, the data difference is obtained, and the operating status of each module is monitored and faults are diagnosed.

[0123] If the data difference exceeds the preset threshold, the data is judged to be abnormal, triggering a data verification or alarm mechanism.

[0124] This embodiment also provides an electronic device suitable for evaluating and analyzing the multi-timescale collaborative capabilities of primary and secondary networks based on a cloud-edge collaborative architecture, including:

[0125] The system includes a memory and a processor. The memory stores computer-executable instructions, and the processor executes these instructions to implement the main and distribution network collaborative capability assessment and analysis device based on a cloud-edge collaborative architecture, as proposed in the above embodiments.

[0126] This embodiment also provides a storage medium storing a computer program, which, when executed by a processor, implements the main distribution network collaborative multi-timescale collaborative capability assessment and analysis device based on the cloud-edge collaborative architecture proposed in the above embodiment.

[0127] The storage medium proposed in this embodiment and the main distribution network collaborative multi-timescale collaborative capability assessment and analysis device based on cloud-edge collaborative architecture proposed in the above embodiments belong to the same inventive concept. Technical details not described in detail in this embodiment can be found in the above embodiments, and this embodiment has the same beneficial effects as the above embodiments.

[0128] Based on the above description of the implementation methods, those skilled in the art can clearly understand that the present invention can be implemented using software and necessary general-purpose hardware, and of course, it can also be implemented using hardware, but in many cases the former is a better implementation method. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product can be stored in a computer-readable storage medium, such as a computer floppy disk, read-only memory (ROM), random access memory (RAM), flash memory, hard disk, or optical disk, etc., including several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute the methods of the various embodiments of the present invention.

[0129] Example 2

[0130] This embodiment provides a data supplementation method for the multi-timescale evaluation module in a main-distribution network collaborative multi-timescale collaborative capability evaluation and analysis device based on a cloud-edge collaborative architecture.

[0131] When sensor data is interrupted, the system can automatically fill in missing operational data by averaging historical data over a given time window.

[0132] It should be noted that in power systems, the accuracy and completeness of real-time operating data are crucial for power dispatching decisions. However, due to equipment failure, communication interruption, or other unexpected situations, some operating data may be missing. In this case, the device of the present invention provides an automatic data filling capability to ensure that accurate dispatching basis can still be provided when data is missing. This embodiment describes how to automatically fill missing data by using the arithmetic average method within a given time window when the equipment sensor data is interrupted.

[0133] In one alternative implementation, this embodiment utilizes historical data and fills the data using an arithmetic average method to maintain the accuracy of the distribution network regulation capacity assessment.

[0134] Step 1, Analysis of Missing Data, includes:

[0135] Suppose a sensor in the power distribution network fails, resulting in the loss of data for the most recent minute. Under normal circumstances, the device records data at a sampling rate of once per second. However, due to the equipment failure, the data for the last minute (60 seconds) is missing. To fill in this missing data, the arithmetic mean method is used.

[0136] Suppose that 60 data points should have been collected in the past 60 seconds of the past minute, but due to a malfunction, these data points were not collected. In order to fill the missing data during this period, valid data from the past minute will be used to fill the gaps.

[0137] Step two, obtain historical data;

[0138] Suppose that 60 random data points between 0.9MW and 1.2MW were collected in the past minute (i.e., 60 seconds). Here is an example of simulated data:

[0139] Data points (unit: MW): 0.9, 1.1, 1.0, 1.2, 1.0, 1.1, 0.95, 1.05, 1.0, 1.1, ..., 1.0 (60 data points in total).

[0140] Step 3: Fill in the data using the arithmetic mean calculation method;

[0141] To fill in the data gaps within this 1 minute, the missing data was generated by calculating the arithmetic mean of 60 known data points, specifically including:

[0142] Summing up all data points from the past minute, the total is 0.9 + 1.1 + 1.0 + 1.2 + 1.0 + ... + 1.0 = 60MW

[0143] Calculate the arithmetic mean, expressed as: Average = Sum / 60 = 1.0 MW;

[0144] Due to data loss, the average value of the data over the past 60 seconds (1.0MW) was used as the filler value to replace the missing data points;

[0145] During operation, if a missing data point is detected in a sensor, a data filling operation is automatically performed. The missing data point will be filled with 1.0MW.

[0146] It should be noted that the filled data can be used for subsequent analysis such as regulation capacity assessment and load forecasting. After the data is filled, the operation status of the distribution network will continue to be monitored to ensure the integrity and continuity of the data. For other situations where data may be missing, adjustments will be made flexibly according to different time windows (such as 2 minutes, 5 minutes, etc.).

[0147] By using the arithmetic mean filling method, this invention can maintain data integrity and ensure normal system operation even when sensor data is missing. In this embodiment, the missing data is automatically filled with 1.0MW based on the data from the past minute. The filled data can continue to participate in power dispatch analysis, ensuring that the main grid dispatch center can continue to make reasonable dispatch decisions even when data is missing.

[0148] Example 3

[0149] This embodiment provides a data verification method for the multi-timescale evaluation module and the self-diagnosis module in a main-distribution network collaborative multi-timescale collaborative capability evaluation and analysis device based on a cloud-edge collaborative architecture.

[0150] The multi-timescale assessment module combines power flow equations with data to obtain operational information of all nodes in the entire power distribution network and to identify the locations where sensor data shows faults.

[0151] The self-diagnosis module workflow includes:

[0152] The power and voltage distribution of each node are obtained by calculating the results of the power flow equations.

[0153] By comparing the power flow equation calculation results with sensor data, the data difference is obtained, and the operating status of each module is monitored and faults are diagnosed.

[0154] If the data difference exceeds the preset threshold, the data is judged to be abnormal, triggering a data verification or alarm mechanism.

[0155] It should be noted that in power systems, real-time data of the distribution network is collected by sensors, and ensuring the accuracy of sensor data is crucial for power dispatch and system stability. However, due to various factors (such as equipment failure, signal interference, etc.), sensor data may contain errors or be missing. To ensure data reliability, the device of this invention uses power flow equations to verify the sensor data. By comparing the calculation results of the power flow equations with the data provided by the sensors, possible erroneous data can be identified and corrected. In this embodiment, the accuracy of the sensor data is verified by comparing the calculation results of the power flow equations with the actual data collected by the sensors. A generalized power flow equation is used for calculation, and data errors are identified based on the calculation results.

[0156] In a preferred embodiment, the sensor data is verified by comparing the calculated results of the power flow equations with the actual sensor data. The power flow equations for the distribution network include:

[0157]

[0158] Where Pi represents the active power of node i, and V i θ represents the voltage magnitude (V) at node i. ij G represents the voltage phase difference (in degrees) between node i and node j. ij B represents the real part of the admittance matrix between node i and node j (representing the conductive part of the admittance). ij Let n represent the imaginary part of the admittance matrix between node i and node j (representing the reactance part of the admittance), and n represent the total number of nodes.

[0159] It should be noted that this equation represents the power balance of each node in a power system, including the voltage, phase, conductance, and reactance characteristics of the nodes and other nodes. In a power system, the power flow equation (also known as the electric current equation or the power grid flow equation) describes the relationship between the voltage magnitude and phase of each node in the power network. Power flow calculation is based on Kirchhoff's Current Law (KCL) and Kirchhoff's Voltage Law (KVL), and is solved under the power balance conditions of each node.

[0160] In an alternative implementation, in this embodiment, we assume that the given power grid network contains multiple nodes (such as several subgrids of a distribution network), and the voltage and power demand of each node are known. Through the power flow equations, we can calculate the power distribution and voltage variation of each node in the system.

[0161] The sensor data verification method involves real-time data collected by the sensors, including the voltage and power (active and reactive power) of each node. This data is transmitted to the main network dispatch center through the communication module. To ensure the accuracy of the data, the real-time data provided by the sensors is compared with the theoretical data calculated based on the power flow equation. If there is a large deviation, the system will issue an alarm, indicating possible sensor failure or data anomaly.

[0162] The specific steps include:

[0163] Power flow calculation, using the power flow equations mentioned above, can calculate the power and voltage distribution of each node based on the known distribution network voltage, power, admittance matrix and network topology.

[0164] Data comparison involves comparing the calculated results with the actual data measured by the sensors. For example, if the node power measured by the sensor is Pmeasured, it is compared with the power calculated by the power flow Pcalculated, generating a difference, which is expressed as:

[0165] ΔPi=Pmeasured,i-Pcalculated,i

[0166] If the difference ΔPi exceeds a preset threshold (e.g., 5%), it is considered an abnormal data and triggers a data verification or alarm mechanism.

[0167] Specifically, assume there are 3 nodes in the distribution network, and the voltage and power of the nodes are as follows:

[0168] Node 1: Voltage V1 = 1.02 pu, measured power Pmeasured,1 = 1.1 MW;

[0169] Node 2: Voltage V2 = 1.00 pu, measured power Pmeasured,2 = 0.95 MW;

[0170] Node 3: Voltage V3 = 1.01 pu, measured power Pmeasured,3 = 1.2 MW;

[0171] Given that the admittance matrix and the voltage phase differences between nodes (such as G12 and B12) are known in the power flow equations, the device calculates the following node powers using the power flow equations:

[0172] Node 1: Pcalculated, 1 = 1.05 MW;

[0173] Node 2: Pcalculated,2 = 0.96MW;

[0174] Node 3: Pcalculated,3 = 1.18MW;

[0175] Data comparison:

[0176] Node 1: ΔP1 = 1.1 - 1.05 = 0.05 MW;

[0177] Node 2: ΔP2 = 0.95 - 0.96 = -0.01 MW;

[0178] Node 3: ΔP3 = 1.2 - 1.18 = 0.02 MW;

[0179] Since the power deviation of node 1 is 0.05MW, which exceeds the 5% threshold, the system identifies it as a data anomaly, triggers an alarm, and begins to check whether the sensor is faulty.

[0180] It should be noted that the device's self-diagnostic module can not only compare the calculation results of the power flow equation with the sensor data, but also perform dynamic self-correction based on the accuracy comparison of the power flow calculation and the comparison with historical data. If the system identifies a long-term deviation or a large error in the system, the device will activate the correction algorithm to adjust the measurement data or perform external calibration.

[0181] By comparing power flow equations and sensor data, the device can effectively identify data anomalies. When the deviation exceeds the preset tolerance threshold, the system will issue an alarm and activate the data verification or correction mechanism to ensure the accuracy and reliability of the power system's dispatch data.

[0182] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention, and all such modifications or substitutions should be covered within the scope of the claims of the present invention.

Claims

1. A device for evaluating and analyzing the collaborative capabilities of a main and distribution network based on a cloud-edge collaborative architecture across multiple time scales, characterized in that, include: The multi-timescale assessment module is used to assess the control capabilities of the distribution network for various demands and output the assessment results. The interactive power monitoring module is used to monitor the interactive power between the main and distribution networks in real time. The communication module is used to send the evaluation results of the multi-timescale evaluation module and the analysis results of the interactive power monitoring module to the main network scheduling and control center. The data processing module is used to analyze and calculate the data collected by the interactive power monitoring module to obtain the power calculation results; The self-diagnosis module is used to monitor the operating status and diagnose faults of each module based on the evaluation results and power calculation results. The workflow of the multi-timescale evaluation module includes: The regulation capabilities at different levels are evaluated for different regulation needs, namely, the regulation capabilities at the second, minute, and hour levels, which correspond to the frequency regulation, ramp-up, and peak-shaving regulation needs in the power system, respectively. The evaluation of the regulation capabilities at the second, minute, and hour levels is calculated by multiplying the regulation rate by the regulation duration. By combining the evaluation results of the regulation capabilities at the second, minute, and hour levels, the comprehensive regulation capability of the distribution network under different regulation needs is calculated. The constraints of the power flow equations of the distribution network are also considered during the evaluation process; By real-time monitoring and analysis of various power sources, energy storage devices and loads in the distribution network, the adjustability at different time scales can be determined. The workflow of the multi-timescale evaluation module also includes: The multi-timescale assessment module obtains the operating information of all nodes in the entire distribution network by combining the power flow equations of the distribution network with data, and discovers the location of faults in the sensor data. When sensor data is interrupted, the average value is calculated based on historical data over a given time window to replenish the data. The communication module's workflow includes: The communication module transmits multi-timescale assessment results, interactive power monitoring and analysis results, distribution network parameters, and real-time operating power of each node in the distribution network to the main grid dispatch and control center via a communication encryption protocol for data verification; the self-diagnosis module's workflow includes: The power and voltage distribution of each node are obtained by calculating the results of the power flow equations. By comparing the power flow equation calculation results with sensor data, the data difference is obtained, and the operating status of each module is monitored and faults are diagnosed. If the data difference exceeds a preset threshold, the data is determined to be abnormal, triggering a data verification or alarm mechanism.

2. The main and distribution network collaborative multi-timescale collaborative capability assessment and analysis device based on cloud-edge collaborative architecture as described in claim 1, wherein the interactive power monitoring module's workflow includes: The interactive power monitoring module is installed at the access point between the distribution network and the main network. By installing multiple sensors of the same specifications, redundant data is obtained to monitor the interactive power between the main and distribution networks in real time.

3. The main distribution network collaborative multi-timescale collaborative capability assessment and analysis device based on cloud-edge collaborative architecture as described in claim 2, wherein the distribution network power flow equations include: ； Among them, P i V represents the active power at node i. i This represents the voltage magnitude at node i. G represents the voltage phase difference between node i and node j. ij B represents the real part of the admittance matrix between node i and node j. ij Let n represent the imaginary part of the admittance matrix between node i and node j, and n represent the total number of nodes.

4. A method for evaluating and analyzing the multi-timescale collaborative capabilities of a main distribution network based on a cloud-edge collaborative architecture, used in the main distribution network multi-timescale collaborative capability evaluation and analysis device based on a cloud-edge collaborative architecture as described in any one of claims 1 to 3, characterized in that, include, The system assesses the ability to regulate various demands of the power distribution network and outputs the assessment results. Real-time monitoring of the power exchange between the main and distribution networks; The evaluation results of the multi-timescale evaluation module and the analysis results of the interactive power monitoring module are sent to the main network scheduling and control center. The data on the interaction power are analyzed and calculated to obtain the power calculation results; Based on the evaluation results and power calculation results, the operating status of each module is monitored and faults are diagnosed.

5. An electronic device, characterized in that, include: Memory and processor; The memory is used to store computer-executable instructions, and the processor is used to execute the computer-executable instructions. When the computer-executable instructions are executed by the processor, the workflow of the main distribution network collaborative multi-timescale collaborative capability assessment and analysis device based on cloud-edge collaborative architecture as described in any one of claims 1 to 3 is implemented.

6. A computer-readable storage medium, characterized in that, It stores computer-executable instructions, which, when executed by a processor, implement the workflow of the main distribution network collaborative multi-timescale collaborative capability assessment and analysis device based on cloud-edge collaborative architecture as described in any one of claims 1 to 3.

Images