A long-term power outage window intelligent evaluation system in a power grid and a method thereof

Abstract

The present application relates to the field of medium and long-term power outage window intelligent evaluation system, and particularly relates to a power grid medium and long-term power outage window intelligent evaluation system and method thereof, comprising: a user demand filing module, used for obtaining user basic information and power demand data in the power grid coverage range, establishing a user demand file library, and containing user type labels and corresponding power demand characteristic parameters in the user demand file library; the present application establishes a file library containing user type labels and power demand characteristic parameters through the user demand filing module, combines multi-channel data collection and dynamic updating mechanism, can accurately identify the individualized demand of different types of users such as life guarantee type and public safety type, avoids the evaluation deviation caused by the lack of user demand information, ensures that the evaluation process fully covers the user side differences, and solves the defect that the user demand cannot be identified.

Description

Technical Field

[0001] This invention relates to the field of intelligent assessment systems for medium- and long-term power outage windows, and specifically to an intelligent assessment system and method for medium- and long-term power outage windows in power grids. Background Technology

[0002] With the continuous expansion of the power grid and the sustained growth of electricity load, the impact of power outages caused by planned maintenance and emergency repairs of power grid equipment on social production and life is becoming increasingly significant. In order to reduce power outage losses and improve power supply reliability, power grid companies widely use intelligent assessment systems for medium- and long-term power outage windows to assess the time, scope and impact of power outages and provide decision support for the formulation of power outage plans.

[0003] Existing intelligent assessment systems for long-term power outage windows in power grids typically calculate load loss during different outage periods based on the power grid topology and historical electricity load data, using a pre-set load distribution model. They then select the period with the least load loss as the recommended outage window. Some systems further optimize the assessment logic by incorporating factors such as equipment maintenance costs and the risk of overlapping outages. However, the core assessment dimensions still revolve around power grid operating efficiency and maintenance costs, without making refined distinctions regarding the differences in electricity demand on the user side. The core flaw in existing technology lies in treating affected users as a whole and failing to construct a differentiated assessment mechanism for the varying electricity demands of different users. Specifically: 1. The existing system only counts the number of affected users without distinguishing user types (such as life support users who rely on ventilators, public safety users involved in traffic signals, etc.) or recording key electrical equipment parameters (such as equipment battery life and downtime loss coefficient), which makes it impossible to identify users' personalized electricity needs during the assessment process. 2. The existing system only uses the overall load loss as the user impact assessment indicator, which cannot quantify the power outage risk of different types of users (such as the equipment downtime risk of life-saving users and the production loss risk of economic lifeline users). The assessment results cannot reflect the actual impact of power outages on specific user groups. 3. When screening power outage windows, the existing system does not consider the differences in protection priorities among user groups. It may make unreasonable decisions by prioritizing low-load loss windows, which may lead to power outages for life-saving users, and thus fail to meet the needs of people's livelihood protection and public safety. 4. The evaluation process of the existing system is led by power grid operation and maintenance personnel. Users cannot participate in the selection of power outage windows or provide feedback on special power needs. This may result in the recommended power outage windows being out of touch with the actual needs of users, leading to an increase in user complaints. Summary of the Invention

[0004] In order to overcome the above-mentioned technical problems, the purpose of this invention is to provide an intelligent assessment system and method for long-term power outage windows in the power grid, which solves the problem mentioned in the background art that the core defect of the prior art is that it treats the affected users as a whole and does not build a differentiated assessment mechanism for the differences in the electricity demand of different users.

[0005] The objective of this invention can be achieved through the following technical solutions: A smart assessment system for long-term power outage windows in a power grid includes: The user demand filing module is used to obtain basic user information and electricity demand data within the power grid coverage area and establish a user demand archive. The user demand archive contains user type tags and corresponding electricity demand characteristic parameters. The power outage window preliminary screening module is used to obtain power grid equipment maintenance task information and historical power load data, and combined with preset load threshold conditions, to initially screen out multiple candidate power outage windows. The user impact assessment module is used to call the data in the user demand archive and calculate the power consumption impact parameters for different types of users for each candidate power outage window. The power consumption impact parameters include at least the user power outage duration, the risk value of critical equipment shutdown, and the estimated amount of user loss. The multi-dimensional decision-making module is used to comprehensively score each candidate power outage window based on the power consumption impact parameters and in combination with preset user protection priority rules, and select the optimal power outage window. The results output module is used to output the optimal power outage window information and the corresponding user impact assessment report.

[0006] As a further aspect of the present invention: the user requirement filing module includes a multi-channel data collection unit and a tag generation unit; The multi-channel data acquisition unit is used to obtain basic user information, key electrical equipment parameters, and application materials for special electricity needs through the power grid APP, offline business halls, community management platforms, and the database of industry authorities. The tag generation unit is used to generate user type tags based on the user basic information and electricity demand data. The user type tags include at least life-saving users, public safety users, economic lifeline users, and ordinary livelihood users.

[0007] As a further aspect of the present invention: the user demand filing module further includes a dynamic file update unit, which is used to periodically verify the validity of the data in the user demand file database, receive user demand change requests, and update user type tags and electricity demand characteristic parameters in real time.

[0008] As a further aspect of the present invention: the user impact assessment module includes a parameter calculation unit and a risk classification unit; The parameter calculation unit is used to calculate the power outage duration, critical equipment shutdown risk value, and estimated user loss for different types of tagged users based on the expected outage duration of the candidate outage window, the endurance parameters of the user's critical electrical equipment, and historical power load fluctuation data. The risk grading unit is used to classify the power outage risk of different types of users under each candidate power outage window according to the preset risk level classification standard and the outage risk value of the key equipment, and generate risk grading results.

[0009] As a further aspect of the present invention: the multi-dimensional decision-making module includes a weight configuration unit and a comprehensive scoring unit; The weight configuration unit is used to configure the weight coefficients of the electricity impact parameters for users with different types of tags according to the guarantee priority rules corresponding to the user type tags; The comprehensive scoring unit is used to multiply the power consumption impact parameters of different types of labeled users under each candidate power outage window with their corresponding weight coefficients and then sum them to obtain the comprehensive score of each candidate power outage window. The candidate power outage window with the highest comprehensive score is selected as the optimal power outage window.

[0010] As a further aspect of the present invention, the multi-dimensional decision-making module further includes a rule adjustment unit, which is used to receive priority adjustment instructions from the power grid operation and maintenance management department and dynamically adjust the user guarantee priority rules and corresponding weight coefficients.

[0011] As a further aspect of the present invention, it also includes a user feedback module, which is used to push window voting information to users within the coverage area of ​​the candidate power outage window, collect user preference feedback data for different candidate power outage windows, and transmit the preference feedback data to the multi-dimensional decision-making module as a reference for comprehensive scoring.

[0012] As a further aspect of the present invention, it also includes a public service scenario adaptation module, which is used to store preset key public service time period data. When a candidate power outage window overlaps with the key public service time period, a scenario adaptation instruction is sent to the user impact assessment module to adjust the calculation logic of the power consumption impact parameters of the corresponding type of user.

[0013] As a further aspect of the present invention: the user impact assessment report output by the result output module includes the distribution areas of users with different types of tags, recommendations for safeguarding key electrical equipment, and a power outage notification push scheme.

[0014] A smart assessment method for long-term power outage windows in a power grid, the method specifically includes the following steps: S1: Obtain basic user information and electricity demand data within the power grid coverage area, and establish a user demand archive containing user type tags and characteristic parameters of application electricity demand. S2: Obtain information on power grid equipment maintenance tasks and historical power load data, and combine them with preset load threshold conditions to initially screen out multiple candidate power outage windows; S3: For each candidate power outage window, call the data in the user demand archive and calculate the power consumption impact parameters for users with different types of tags. The power consumption impact parameters include at least the user power outage duration, the risk value of critical equipment shutdown and the estimated amount of user loss. S4: Based on the power consumption impact parameters and combined with the preset user protection priority rules, a comprehensive score is given to each candidate power outage window to select the optimal power outage window; S5: Output the optimal power outage window information and the corresponding user impact assessment report.

[0015] The beneficial effects of this invention are: This invention addresses the shortcomings of existing technologies that treat affected users as a whole by constructing a user-differentiated evaluation mechanism, achieving the following beneficial effects: 1. This invention establishes a database containing user type tags and electricity demand characteristic parameters through a user demand filing module. Combined with a multi-channel data collection and dynamic update mechanism, it can accurately identify the personalized needs of different types of users such as life support and public safety, avoid evaluation bias caused by missing user demand information, ensure that the evaluation process fully covers user-side differences, and solve the defect of user demand not being identified. 2. The user impact assessment module of this invention calculates multi-dimensional power consumption impact parameters such as power outage duration and critical equipment shutdown risk value for different types of tagged users. Combined with the risk classification unit, it realizes the refined quantification of power outage risk. Compared with the overall load loss index of the existing technology, it can more accurately reflect the actual impact of power outage on different user groups, provide a more comprehensive basis for decision-making, and solve the deficiency of single user impact assessment. 3. The multi-dimensional decision-making module of the present invention prioritizes the electricity needs of high-priority users (such as life-saving and public safety users) when screening power outage windows by pre-setting user protection priority rules and dynamic weight configuration. This avoids the unreasonable decision-making based solely on load loss in the prior art, ensures that the power outage plan meets the requirements of people's livelihood protection and public safety, enhances the social value of power grid operation and maintenance, and solves the defect of lack of protection priority. 4. This invention collects user preference data on candidate power outage windows through a user feedback module and incorporates it into a comprehensive scoring system. At the same time, it combines a public service scenario adaptation module to achieve automatic peak avoidance during critical periods, making the selection of power outage windows more in line with users' actual needs, effectively reducing user complaint rates, improving power supply service satisfaction, and solving the problem of low user participation. Attached Figure Description

[0016] The invention will now be further described with reference to the accompanying drawings.

[0017] Figure 1 This is a schematic diagram of a smart assessment system for long-term power outage windows in a power grid, as described in this invention. Detailed Implementation

[0018] The technical solutions of the present invention will be clearly and completely described below with reference to the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative effort are within the scope of protection of the present invention. Example 1:

[0019] Please see Figure 1 As shown, this embodiment is an intelligent assessment system for long-term power outage windows in a power grid, including: The user demand filing module is used to obtain basic user information and electricity demand data within the power grid coverage area, and to establish a user demand archive. The user demand archive contains user type tags and corresponding electricity demand characteristic parameters. The power outage window preliminary screening module is used to obtain power grid equipment maintenance task information and historical power load data, and combined with preset load threshold conditions, to initially screen out multiple candidate power outage windows. The user impact assessment module is used to call data from the user demand archive and calculate the power impact parameters for different types of users for each candidate power outage window. The power impact parameters include at least the user power outage duration, the risk value of critical equipment shutdown, and the estimated amount of user loss. The multi-dimensional decision-making module is used to comprehensively score each candidate power outage window based on electricity consumption impact parameters and preset user protection priority rules, and select the optimal power outage window. The results output module is used to output the optimal power outage window information and the corresponding user impact assessment report.

[0020] The user requirement documentation module includes a multi-channel data collection unit and a tag generation unit; The multi-channel data collection unit is used to obtain basic user information, key electrical equipment parameters, and application materials for special electricity needs through the power grid APP, offline business halls, community management platforms, and the database of industry authorities. The tag generation unit is used to generate user type tags based on user basic information and electricity demand data. User type tags include at least life-saving users, public safety users, economic lifeline users, and ordinary livelihood users.

[0021] The user requirement filing module also includes a dynamic file update unit, which is used to periodically verify the validity of the data in the user requirement file database, receive user requirement change requests, and update user type tags and electricity demand characteristic parameters in real time.

[0022] The user impact assessment module includes a parameter calculation unit and a risk classification unit; The parameter calculation unit is used to calculate the power outage duration, critical equipment shutdown risk value, and estimated user loss for different types of tagged users based on the expected outage duration of the candidate outage window, the endurance parameters of the user's critical electrical equipment, and historical power load fluctuation data. The risk grading unit is used to classify the power outage risk of different types of users under each candidate power outage window according to the preset risk level classification standard and the outage risk value of key equipment, and generate risk grading results.

[0023] The multi-dimensional decision-making module includes a weight configuration unit and a comprehensive scoring unit; The weight configuration unit is used to configure the weight coefficients of the electricity impact parameters for users with different types of tags according to the guarantee priority rules corresponding to the user type tags; The comprehensive scoring unit is used to multiply the power consumption impact parameters of different types of users under each candidate power outage window with their corresponding weight coefficients and then sum them to obtain the comprehensive score of each candidate power outage window. The candidate power outage window with the highest comprehensive score is selected as the optimal power outage window.

[0024] The multi-dimensional decision-making module also includes a rule adjustment unit, which receives priority adjustment instructions from the power grid operation and maintenance management department and dynamically adjusts the user guarantee priority rules and corresponding weight coefficients.

[0025] The intelligent assessment system for medium- and long-term power outage windows in the power grid also includes a user feedback module. This module pushes window voting information to users within the coverage area of ​​candidate power outage windows, collects user preference feedback data for different candidate power outage windows, and transmits the preference feedback data to the multi-dimensional decision-making module as a reference for comprehensive scoring.

[0026] The intelligent assessment system for medium- and long-term power outage windows in the power grid also includes a public service scenario adaptation module. This module stores preset data on key public service periods. When a candidate power outage window overlaps with a key public service period, it sends a scenario adaptation instruction to the user impact assessment module to adjust the calculation logic of the power consumption impact parameters for users with the corresponding type of label.

[0027] The user impact assessment report output by the results output module includes the distribution areas of users with different types of tags, recommendations for safeguarding key electrical equipment, and a power outage notification push plan. Example 2:

[0028] A smart assessment method for long-term power outage windows in a power grid, the method specifically includes the following steps: S1: Obtain basic user information and electricity demand data within the power grid coverage area, and establish a user demand archive containing user type tags and characteristic parameters of application electricity demand. S2: Obtain information on power grid equipment maintenance tasks and historical power load data, and combine them with preset load threshold conditions to initially screen out multiple candidate power outage windows; S3: For each candidate power outage window, call the data in the user demand archive and calculate the power impact parameters for users with different types of tags. The power impact parameters include at least the user power outage duration, the risk value of critical equipment shutdown and the estimated amount of user loss. S4: Based on the power consumption impact parameters and the preset user protection priority rules, a comprehensive score is given to each candidate power outage window to select the optimal power outage window; S5: Output the optimal power outage window information and the corresponding user impact assessment report.

[0029] In the description of this specification, references to terms such as "an embodiment," "example," "specific example," etc., indicate that a specific feature, structure, material, or characteristic described in connection with that embodiment or example is included in at least one embodiment or example of the invention. In this specification, illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples. Example 3:

[0030] In this embodiment, the intelligent assessment system for long-term power outage windows in the power grid is deployed on the operation and maintenance management cloud platform of the power grid enterprise, and the hardware architecture includes: Data server: Used to store user demand archives, historical power load data, equipment maintenance task data, and data on key periods of people's livelihood. It adopts a dual-machine hot standby architecture to ensure data reliability, with a storage capacity of no less than 10TB and supports read and write operations of no less than 1,000 data entries per second. Computing server: A high-performance server with 4 CPUs and 128GB of memory is used to run algorithm models for initial screening of power outage windows, user impact assessment and multi-dimensional decision-making. It supports parallel computing and the evaluation time for a single batch of candidate power outage windows does not exceed 5 minutes. Terminal equipment includes management terminals (PC) for power grid operation and maintenance personnel and user interaction terminals (mobile APP, offline business hall touch screen). The management terminal supports the configuration of evaluation parameters and the viewing of results, while the user terminal supports the submission of requests and voting at the window.

[0031] Example 4: This embodiment illustrates the system module operation process using an example. User requirement documentation module running: The multi-channel data collection unit receives user-reported "ventilator user" requests through the power grid APP, obtaining user ID numbers, residential addresses, ventilator models (4-hour battery life), and hospital diagnostic certificates; at the same time, it connects to the Health Commission database to import in batches the list of ICU equipment in hospitals within the jurisdiction (public safety users) and connects to the Market Supervision Bureau database to import information on large cold chain warehouses (economic lifeline users). Based on the collected data, the tag generation unit labels ventilator users as "Life Support Category - Level 1 Priority", hospital ICUs as "Public Safety Category - Level 1 Priority", and cold chain warehouses as "Economic Lifeline Category - Level 2 Priority", and records the key equipment parameters of each user (such as ICU equipment power and cold chain warehouse temperature maintenance threshold). The file dynamic update unit sends file verification SMS messages to users every quarter. After receiving a change request from a ventilator user to "change the device to a model with 6 hours of battery life", it updates the battery life parameters in the user requirement file database in real time.

[0032] The initial screening module for power outage windows is running: Obtain the annual maintenance task of a certain 110kV line (the maintenance time is 8 hours), retrieve the historical power load data of the line in the past 3 years, and calculate that the peak load from 8:00 to 22:00 on weekdays is 80MW and the valley load is 35MW. Set the load threshold to 45MW. Based on meteorological data for the next month (excluding typhoon and cold wave warning periods), three candidate power outage windows have been preliminarily selected: Candidate Window 1 (Saturday 8:00-16:00, historical average load 38MW), Candidate Window 2 (Sunday 14:00-22:00, historical average load 42MW), and Candidate Window 3 (Next Monday 0:00-8:00, historical average load 32MW).

[0033] User impact assessment module running: The parameter calculation unit calls the user demand archive to identify users within the coverage area of ​​three candidate windows: Candidate window 1 covers two life support users (ventilator users) and one community hospital (public safety category). Calculate the power consumption impact parameters: In candidate window 1, the power outage duration for ventilator users is 8 hours (exceeding the equipment's operating time by 4 hours, with a critical equipment downtime risk of 85%), and ICU equipment in community hospitals requires backup power support (estimated loss of 50,000 yuan); In candidate window 3, the power outage duration for ventilator users is 8 hours (equipment usage frequency is low at night, users can prepare backup power in advance, with a critical equipment downtime risk of 30%). The risk grading unit classifies the risk of life-protection users in candidate window 1 as "extremely high risk" and the risk of life-protection users in candidate window 3 as "medium risk".

[0034] Multi-dimensional decision-making module operation: According to preset rules, the weighting configuration unit sets the weight of the electricity consumption impact parameters for life-saving users to 40%, public safety users to 30%, economic lifeline users to 20%, and ordinary livelihood users to 10%. The comprehensive scoring unit calculates the comprehensive score for each candidate window: Candidate Window 1 (risk deduction 35 points, load advantage score 20 points, comprehensive score 45 points), Candidate Window 2 (comprehensive score 58 points), and Candidate Window 3 (risk deduction 12 points, load advantage score 25 points, comprehensive score 73 points). The rule adjustment unit received an instruction from the operation and maintenance department that "there are important events being broadcast at night recently, and it is necessary to ensure the electricity supply for residential televisions." The unit temporarily increased the weight of nighttime electricity consumption for ordinary public service users to 15%, recalculated the comprehensive score of candidate window 3 to 71 points (still the highest), and determined candidate window 3 as the optimal power outage window.

[0035] Results and User Feedback: The output module pushes the optimal power outage window (0:00-8:00 next Monday) and an evaluation report to the operation and maintenance personnel. The report clarifies the backup power reminder plan for life support users and the emergency power supply contact person for community hospitals. The user feedback module pushes the voting results (72% support rate for candidate window 3) to users within the coverage area of ​​the candidate window and sends a power outage notification SMS to inform users to prepare for power outages in advance.

[0036] The above description is merely an example and illustration of the present invention. Those skilled in the art can make various modifications or additions to the specific embodiments described, or use similar methods to replace them, as long as they do not deviate from the invention or exceed the scope defined in the claims, all of which should fall within the protection scope of the present invention.

Claims

1. A smart assessment system for long-term power outage windows in a power grid, characterized in that, include: The user demand filing module is used to obtain basic user information and electricity demand data within the power grid coverage area and establish a user demand archive. The user demand archive contains user type tags and corresponding electricity demand characteristic parameters. The power outage window preliminary screening module is used to obtain power grid equipment maintenance task information and historical power load data, and combined with preset load threshold conditions, to initially screen out multiple candidate power outage windows. The user impact assessment module is used to call the data in the user demand archive and calculate the power consumption impact parameters for different types of users for each candidate power outage window. The power consumption impact parameters include at least the user power outage duration, the risk value of critical equipment shutdown, and the estimated amount of user loss. The multi-dimensional decision-making module is used to comprehensively score each candidate power outage window based on the power consumption impact parameters and in combination with preset user protection priority rules, and select the optimal power outage window. The results output module is used to output the optimal power outage window information and the corresponding user impact assessment report.

2. The intelligent assessment system for long-term power outage windows in a power grid according to claim 1, characterized in that, The user requirement documentation module includes a multi-channel data collection unit and a tag generation unit; The multi-channel data acquisition unit is used to obtain basic user information, key electrical equipment parameters, and application materials for special electricity needs through the power grid APP, offline business halls, community management platforms, and the database of industry authorities. The tag generation unit is used to generate user type tags based on the user basic information and electricity demand data. The user type tags include at least life-saving users, public safety users, economic lifeline users, and ordinary livelihood users.

3. The intelligent assessment system for long-term power outage windows in a power grid according to claim 2, characterized in that, The user demand filing module also includes a dynamic file update unit, which is used to periodically verify the validity of the data in the user demand file database, receive user demand change requests, and update user type tags and electricity demand characteristic parameters in real time.

4. The intelligent assessment system for long-term power outage windows in a power grid according to claim 1, characterized in that, The user impact assessment module includes a parameter calculation unit and a risk classification unit; The parameter calculation unit is used to calculate the power outage duration, critical equipment shutdown risk value, and estimated user loss for different types of tagged users based on the expected outage duration of the candidate outage window, the endurance parameters of the user's critical electrical equipment, and historical power load fluctuation data. The risk grading unit is used to classify the power outage risk of different types of users under each candidate power outage window according to the preset risk level classification standard and the outage risk value of the key equipment, and generate risk grading results.

5. The intelligent assessment system for long-term power outage windows in a power grid according to claim 1, characterized in that, The multi-dimensional decision-making module includes a weight configuration unit and a comprehensive scoring unit; The weight configuration unit is used to configure the weight coefficients of the electricity impact parameters for users with different types of tags according to the guarantee priority rules corresponding to the user type tags; The comprehensive scoring unit is used to multiply the power consumption impact parameters of different types of labeled users under each candidate power outage window with their corresponding weight coefficients and then sum them to obtain the comprehensive score of each candidate power outage window. The candidate power outage window with the highest comprehensive score is selected as the optimal power outage window.

6. The intelligent assessment system for long-term power outage windows in a power grid according to claim 5, characterized in that, The multi-dimensional decision-making module also includes a rule adjustment unit, which is used to receive priority adjustment instructions from the power grid operation and maintenance management department and dynamically adjust the user guarantee priority rules and corresponding weight coefficients.

7. The intelligent assessment system for long-term power outage windows in a power grid according to claim 1, characterized in that, It also includes a user feedback module, which is used to push window voting information to users within the coverage area of ​​the candidate power outage window, collect user preference feedback data on different candidate power outage windows, and transmit the preference feedback data to the multi-dimensional decision module as a reference for comprehensive scoring.

8. The intelligent assessment system for long-term power outage windows in a power grid according to claim 1, characterized in that, It also includes a public service scenario adaptation module, which stores preset data on key public service periods. When a candidate power outage window overlaps with a key public service period, it sends a scenario adaptation instruction to the user impact assessment module to adjust the calculation logic of the power consumption impact parameters for users with the corresponding type of label.

9. The intelligent assessment system for long-term power outage windows in a power grid according to claim 1, characterized in that, The user impact assessment report output by the result output module includes the distribution areas of users with different types of tags, recommendations for protection measures for key electrical equipment, and a power outage notification push plan.

10. A method for intelligent assessment of long-term power outage windows in a power grid, characterized in that, The method specifically includes the following steps: S1: Obtain basic user information and electricity demand data within the power grid coverage area, and establish a user demand archive containing user type tags and characteristic parameters of application electricity demand. S2: Obtain information on power grid equipment maintenance tasks and historical power load data, and combine them with preset load threshold conditions to initially screen out multiple candidate power outage windows; S3: For each candidate power outage window, call the data in the user demand archive and calculate the power consumption impact parameters for users with different types of tags. The power consumption impact parameters include at least the user power outage duration, the risk value of critical equipment shutdown and the estimated amount of user loss. S4: Based on the power consumption impact parameters and combined with the preset user protection priority rules, a comprehensive score is given to each candidate power outage window to select the optimal power outage window; S5: Output the optimal power outage window information and the corresponding user impact assessment report.

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