Power compensation analysis intelligent regulation and control system and method based on demand response and coincidence aggregation

Abstract

The invention discloses a power compensation analysis intelligent regulation and control system and method based on demand response and coincidence aggregation, and the system comprises a demand side management module which carries out the detection and management of the power demands of a user; the load aggregation module is used for aggregating the users of different groups; the intelligent regulation and control module is used for regulating and controlling power supply according to the strategy formulated by the load aggregation module; and the compensation analysis module evaluates the overall benefit of power regulation and control and formulates a compensation standard. The method has the advantage of flexible regulation and control, and solves the problems that in the use process of an existing intelligent regulation and control system for power compensation analysis, dynamic load regulation according to the actual demand of a power grid is inconvenient, unbalanced supply and demand are easily caused, power surplus or shortage occurs, scattered small-scale load resources are difficult to centrally manage, and the power consumption is low. And the power grid pressure in the peak period is increased.

Description

Technical Field

[0001] The present invention relates to the technical field of power systems, and particularly to an intelligent regulation system and method for power compensation analysis based on demand response and load aggregation. Background Technique

[0002] An intelligent regulation system for power compensation analysis based on demand response and load aggregation is an advanced management system designed to optimize the operation of power systems. This system uses advanced information technology to coordinate the balance between power supply and demand. Especially in the case of tight power supply and demand, it helps to stabilize the power grid by motivating users to adjust their power consumption patterns. Demand response means that power users change their normal power consumption patterns according to the signals sent by power suppliers to reduce load and smooth the peak-valley difference. Such a response can be to reduce power use during peak hours or increase power use during off-peak hours. Load aggregation refers to integrating a large number of dispersed small-scale power users or distributed energy resources and participating in power market transactions as a whole. Through load aggregation, originally separate small-scale resources can be transformed into a whole with greater influence, thus better participating in auxiliary services such as peak shaving and valley filling in the power system.

[0003] During the use of existing intelligent regulation systems for power compensation analysis, it is not convenient to dynamically adjust the load according to the actual demand of the power grid, which easily leads to imbalance between supply and demand, resulting in power surplus or shortage. Moreover, it is difficult to centrally manage dispersed small-scale load resources, increasing the pressure on the power grid during peak hours. Summary of the Invention

[0004] The purpose of the present invention is to provide an intelligent regulation system and method for power compensation analysis based on demand response and load aggregation, which has the advantage of flexible regulation, and solves the problems that during the use of existing intelligent regulation systems for power compensation analysis, it is not convenient to dynamically adjust the load according to the actual demand of the power grid, which easily leads to imbalance between supply and demand, resulting in power surplus or shortage, and it is difficult to centrally manage dispersed small-scale load resources, increasing the pressure on the power grid during peak hours.

[0005] To achieve the above purpose, the present invention provides the following technical solution: An intelligent regulation system for power compensation analysis based on demand response and load aggregation, including:

[0006] A demand-side management module, which detects and manages the power consumption demand of users;

[0007] A load aggregation module, which aggregates users of different groups;

[0008] An intelligent regulation module that regulates the power supply according to the strategy formulated by the load aggregation module;

[0009] A compensation analysis module that evaluates the overall benefits of power regulation and formulates compensation standards.

[0010] Preferably, as a power compensation analysis intelligent regulation system based on demand response and load aggregation of the present invention, the demand-side management module includes:

[0011] A data acquisition and monitoring module that monitors the power consumption of users in real time through smart meters and sends power consumption data through a wireless network;

[0012] A data analysis module that receives the power consumption data sent by the smart meters, uses big data to analyze the historical power consumption data of users, identifies the power consumption patterns and preferences of users, and uses machine learning algorithms to predict the future power consumption behavior of users;

[0013] A user classification module that classifies users into different groups according to their power consumption patterns.

[0014] Preferably, as a power compensation analysis intelligent regulation system based on demand response and load aggregation of the present invention, the load aggregation module includes:

[0015] A user grouping module that divides users into different groups according to the power consumption amount and power consumption time period of users in different groups;

[0016] A load characteristic analysis module that analyzes the load characteristics of each group of users and divides the load into high-flexibility loads and low-flexibility loads;

[0017] An aggregation object screening module that screens users who can participate in load aggregation, and preferentially selects users with high-flexibility loads during the screening process;

[0018] An aggregation strategy formulation module that formulates a load scheduling plan in advance according to the actual needs of the power grid and the needs of load users to ensure that the power grid needs can be met, and quickly adjusts the load in case of emergencies to help the power grid quickly restore balance.

[0019] Preferably, as a power compensation analysis intelligent regulation system based on demand response and load aggregation of the present invention, the intelligent regulation module includes:

[0020] An automatic scheduling and execution module, which automatically generates scheduling commands according to the formulated policies and then sends the scheduling commands to power plants, energy storage facilities, and related equipment through a communication network;

[0021] A device response module. When the device response module is working, the power plant adjusts the output power of the generator according to the received scheduling command, the energy storage device performs charge and discharge operations according to the scheduling command, and for interruptible loads or adjustable loads, their operating states are adjusted according to the scheduling command;

[0022] A monitoring and feedback module, which continuously monitors the state of the power grid and the response of the equipment to ensure that the scheduling command is executed and the power grid state meets the expectations. When an abnormal situation is detected, an emergency response mechanism is immediately triggered.

[0023] Preferably, as a power compensation analysis intelligent regulation system based on demand response and compliance aggregation of the present invention, the compensation analysis module includes:

[0024] A cost-benefit analysis module, which analyzes economic benefits and cost cycles;

[0025] A risk assessment module, which assesses system risks and external risks;

[0026] A compensation standard formulation module, which dynamically evaluates and formulates compensation standards according to cost-benefit analysis data and risk assessment data.

[0027] A method for power compensation analysis intelligent regulation based on demand response and compliance aggregation, including the following steps:

[0028] S1. Data collection and monitoring. Install smart meters to monitor users' electricity consumption in real time and send the electricity consumption data to the data center through a wireless network. Deploy sensors to monitor the voltage and frequency of the power grid;

[0029] S2. Big data analysis. Process the received electricity consumption data, use big data technology to analyze users' historical electricity consumption data, identify users' electricity consumption patterns and preferences, and use machine learning algorithms to predict users' future electricity consumption behaviors;

[0030] S3. User grouping. Divide users into different groups according to their electricity consumption patterns, electricity consumption, and electricity consumption time to formulate more targeted demand response strategies;

[0031] S4. Load characteristic analysis. Group users in different groups and analyze the load characteristics of each group of users, distinguish high-flexibility loads and low-flexibility loads, and preferentially select users with high-flexibility loads to participate in load aggregation;

[0032] S5. Strategy formulation: Based on the actual needs of the power grid and the needs of load users, formulate a load scheduling plan to ensure that the power grid's needs can be met, and in case of emergencies, quickly adjust the load to help the power grid quickly restore balance.

[0033] S6. Generation of dispatching commands: Automatically generate dispatching commands according to the formulated strategies, and send the dispatching commands to power plants, energy storage facilities, and other relevant devices through the communication network.

[0034] S7. Real-time monitoring: Continuously monitor the status of the power grid and the response of devices to ensure that the dispatching commands are executed and the power grid status meets expectations. If any abnormal situation is detected, immediately trigger the emergency response mechanism.

[0035] S8. Feedback adjustment: According to the real-time monitoring results, if it is found that the original strategy cannot achieve the expected effect, re-evaluate and adjust the dispatching strategy.

[0036] S9. Cost-benefit analysis: Calculate the cost savings due to demand response, and combine the initial investment cost, operation and maintenance cost, and decommissioning cost to calculate the total cost throughout the life cycle.

[0037] S10. Compensation standard formulation: Evaluate technical failures, operational errors, and cyber security threats, analyze market price fluctuations, policy changes, and natural disaster risks, and dynamically adjust the compensation standard based on the cost-benefit analysis data and risk assessment data.

[0038] Compared with the prior art, the beneficial effects of the present invention are as follows:

[0039] 1. Through the demand response mechanism, the present invention can dynamically adjust the load according to the actual needs of the power grid, achieve the balance between supply and demand, avoid the situation of power surplus or shortage. The load aggregation module can integrate scattered small-scale load resources and participate in the dispatching of the power market as a whole, so as to more flexibly adjust the load curve of the power grid. By optimizing the load distribution, reduce the power demand during peak hours, delay or avoid the investment in building new power plants and expanding the power grid. In case of emergencies, help the power grid quickly restore balance by quickly adjusting the load, and improve the stability and security of the system.

[0040] 2. The present invention reduces the power demand during peak hours through demand response, reduces the demand for building new power plants and expanding the power grid, thereby reducing capital investment. Optimize the use of generator sets and energy storage facilities through the intelligent control module, improve resource utilization efficiency, reduce ineffective power generation. By optimizing the load distribution, reduce the loss during power transmission, and reduce the operation and maintenance cost. Through real-time monitoring and feedback mechanism, timely discover and handle potential faults, and reduce the maintenance cost.

[0041] 3. Through demand response and load aggregation, the power grid can better absorb intermittent renewable energy, improve the utilization rate of renewable energy. When the power generation of renewable energy is unstable, the load is adjusted to balance the power supply and demand of the power system, improving the flexibility of the system. When an abnormal situation is detected, the emergency response mechanism is immediately triggered, and measures are quickly taken to prevent the spread of accidents. Through the risk assessment module, the system risks and external risks are comprehensively evaluated, and countermeasures are formulated in advance to reduce potential system failures.

[0042] 4. Through reasonable compensation standards, the present invention encourages users to participate in demand response, increases the enthusiasm of users to participate, reduces unnecessary power consumption through optimizing power use, reduces carbon emissions, and improves environmental quality. By promoting the integration of renewable energy, the use of fossil energy is reduced, further promoting the development of green energy.

[0043] 5. By introducing advanced technologies such as big data analysis, machine learning, and the Internet of Things, the present invention promotes the digital transformation of the power industry. Through the real-time monitoring and feedback adjustment mechanism, the dispatching strategy is continuously optimized to improve the intelligent level of the system. By optimizing the allocation of power resources, the economic benefits are improved, unnecessary expenses are reduced, and the normal progress of social production and life is ensured by improving the stability and security of the power system. BRIEF DESCRIPTION OF THE DRAWINGS

[0044] Figure 1 It is a schematic diagram of the system of the present invention;

[0045] Figure 2 It is a schematic diagram of the method flow of the present invention; DETAILED DESCRIPTION OF THE INVENTION

[0046] Example 1

[0047] Please refer to Figure 1 , an intelligent regulation system for power compensation analysis based on demand response and compliance aggregation, including:

[0048] The demand-side management module, which detects and manages the power consumption demands of users;

[0049] The load aggregation module, which aggregates users of different groups;

[0050] The intelligent regulation module, which regulates the power supply according to the strategy formulated by the load aggregation module;

[0051] The compensation analysis module, which evaluates the overall benefits of power regulation and formulates compensation standards.

[0052] Furthermore, the demand-side management module includes:

[0053] Data acquisition and monitoring module. The data acquisition and monitoring module uses a smart meter to monitor the user's power consumption in real time and sends the power consumption data via a wireless network.

[0054] Data analysis module. The data analysis module receives the power consumption data sent by the smart meter, uses big data to analyze the user's historical power consumption data, identifies the user's power consumption patterns and preferences, and uses machine learning algorithms to predict the user's future power consumption behavior.

[0055] User classification module. The user classification module classifies users into different groups according to their power consumption patterns.

[0056] Furthermore, the load aggregation module includes:

[0057] User grouping module. The user grouping module divides users into different groups according to the power consumption amount and power consumption time period of users in different groups.

[0058] Load characteristic analysis module. The load characteristic analysis module analyzes the load characteristics of each group of users and classifies the load into high-flexibility load and low-flexibility load.

[0059] Aggregation object screening module. The aggregation object screening module screens users who can participate in load aggregation, and gives priority to users with high-flexibility load during the screening process.

[0060] Aggregation strategy formulation module. The aggregation strategy formulation module formulates the scheduling plan of the load in advance according to the actual needs of the power grid and the needs of load users, ensures that the power grid needs can be met, and quickly adjusts the load in case of emergencies to help the power grid quickly restore balance.

[0061] Furthermore, the intelligent regulation module includes:

[0062] Automatic scheduling execution module. The automatic scheduling execution module automatically generates a scheduling command according to the formulated strategy, and then sends the scheduling command to the power plant, energy storage facility and related equipment via a communication network.

[0063] Device response module. When the device response module works, the power plant adjusts the output power of the generator according to the received scheduling command, the energy storage device performs charge and discharge operations according to the scheduling command, and for interruptible loads or adjustable loads, adjusts their operating states according to the scheduling command.

[0064] Monitoring and feedback module. The monitoring and feedback module continuously monitors the state of the power grid and the response of the equipment, ensures that the scheduling command is executed and the power grid state meets the expectations, and triggers the emergency response mechanism immediately when an abnormal situation is detected.

[0065] Furthermore, the compensation analysis module includes:

[0066] Cost-benefit analysis module, which analyzes economic benefits and cost cycles;

[0067] Risk assessment module, which assesses system risks and external risks;

[0068] Compensation standard formulation module, which dynamically evaluates and formulates compensation standards based on cost-benefit analysis data and risk assessment data.

[0069] When the system is running, smart meters and sensors collect real-time electricity consumption data of users and send it to the data center through a wireless network. The historical electricity consumption data of users is analyzed using big data technology to identify users' electricity consumption patterns and preferences, and machine learning algorithms are used to predict future electricity consumption behavior. Users are divided into different groups according to their electricity consumption patterns, and the load characteristics of each group of users are analyzed to distinguish between high-flexibility loads and low-flexibility loads. Users with high-flexibility loads are screened out, a load aggregation strategy is formulated, a scheduling command is generated according to the load aggregation strategy, and it is sent to relevant devices such as power plants and energy storage facilities through a communication network. Power plants, energy storage devices, and controllable loads adjust their operating states according to the scheduling command, continuously monitor the grid state and device response conditions to ensure that the scheduling command is executed, and adjust the strategy according to the actual situation, evaluate the economic and social benefits of demand response, and formulate reasonable compensation standards.

[0070] Through the demand response mechanism, the load can be dynamically adjusted according to the actual demand of the power grid to achieve supply-demand balance and avoid power overage or shortage. The load aggregation module can integrate scattered small-scale load resources and participate in the dispatching of the power market as a whole, so as to more flexibly adjust the load curve of the power grid. By optimizing the load distribution, the power demand during peak hours is reduced, delaying or avoiding the investment in building new power plants and expanding the power grid. In case of emergency, the power grid can be quickly restored to balance by rapidly adjusting the load, improving the stability and security of the system.

[0071] Reducing the power demand during peak hours through demand response reduces the need for building new power plants and expanding the power grid, thus reducing capital investment. Optimizing the use of power generation units and energy storage facilities through the intelligent control module improves resource utilization efficiency and reduces ineffective power generation. By optimizing the load distribution, the losses during power transmission are reduced, and the operation and maintenance costs are lowered. Through real-time monitoring and feedback mechanisms, potential faults are detected and processed in a timely manner, reducing maintenance costs.

[0072] Through demand response and load aggregation, the power grid can better absorb intermittent renewable energy, improve the utilization rate of renewable energy. When the power generation of renewable energy is unstable, the load is adjusted to balance the power supply and demand of the power system, improving the flexibility of the system. When an abnormal situation is detected, the emergency response mechanism is immediately triggered, and measures are quickly taken to prevent the spread of accidents. Through the risk assessment module, the system risks and external risks are comprehensively evaluated, and countermeasures are formulated in advance to reduce potential system failures.

[0073] Through reasonable compensation standards, users are incentivized to participate in demand response, increasing their enthusiasm for participation. By optimizing power usage, unnecessary power consumption is reduced, carbon emissions are lowered, and environmental quality is improved. By promoting the integration of renewable energy, the use of fossil energy is reduced, further driving the development of green energy.

[0074] By introducing advanced technologies such as big data analysis, machine learning, and the Internet of Things, the digital transformation of the power industry is promoted. Through the real-time monitoring and feedback adjustment mechanism, the dispatching strategy is continuously optimized to improve the intelligent level of the system. By optimizing the allocation of power resources, economic benefits are improved, and unnecessary expenses are reduced. By enhancing the stability and security of the power system, the normal operation of social production and life is guaranteed.

[0075] Embodiment 2

[0076] Please refer to Figure 2 , a method for intelligent regulation of power compensation analysis based on demand response and compliance aggregation, comprising the following steps:

[0077] S1. Data collection and monitoring: Install smart meters to monitor users' electricity consumption in real time, and send the electricity consumption data to the data center through a wireless network. Deploy sensors to monitor the voltage and frequency of the power grid.

[0078] S2. Big data analysis: Process the received electricity consumption data, use big data technology to analyze users' historical electricity consumption data, identify users' electricity consumption patterns and preferences, and use machine learning algorithms to predict users' future electricity consumption behaviors.

[0079] S3. User grouping: Divide users into different groups according to their electricity consumption patterns, electricity consumption amounts, and electricity consumption times, so as to formulate more targeted demand response strategies.

[0080] S4. Load characteristic analysis: Group users of different groups and analyze the load characteristics of each group of users, distinguish high-flexibility loads and low-flexibility loads, and preferentially select users with high-flexibility loads to participate in load aggregation.

[0081] S5. Strategy formulation: According to the actual requirements of the power grid and the demands of load users, formulate a load scheduling plan to ensure that the power grid's requirements can be met, and in case of emergencies, quickly adjust the load to help the power grid rapidly restore balance;

[0082] S6. Generation of dispatching orders: Automatically generate dispatching orders according to the formulated strategies, and send the dispatching orders to power plants, energy storage facilities, and other relevant devices through the communication network;

[0083] S7. Real-time monitoring: Continuously monitor the status of the power grid and the response of devices to ensure that the dispatching orders are executed and the power grid status meets expectations. If any abnormal situation is detected, immediately trigger the emergency response mechanism;

[0084] S8. Feedback and adjustment: According to the real-time monitoring results, if it is found that the original strategy cannot achieve the expected effect, re-evaluate and adjust the dispatching strategy;

[0085] S9. Cost-benefit analysis: Calculate the cost savings resulting from demand response, and combine the initial investment cost, operation and maintenance cost, and decommissioning cost to calculate the total cost over the entire life cycle;

[0086] S10. Compensation standard formulation: Evaluate technical failures, operational errors, and cybersecurity threats, analyze market price fluctuations, policy changes, and natural disaster risks, and dynamically adjust the compensation standard based on the cost-benefit analysis data and risk assessment data.

[0087] The data collected through smart meters and sensors can accurately understand the real-time status of the power grid and users' electricity consumption habits, thereby using historical data analysis to identify users' electricity consumption patterns and then predict future electricity demand. This precise matching helps to formulate a more reasonable scheduling plan to ensure the balance between power supply and demand.

[0088] Load aggregation technology integrates multiple small users into a virtual large user, which not only improves the flexibility of the power market but also allows grid operators to better manage electricity demand. Especially during peak periods, these aggregated loads can be adjusted to avoid overload.

[0089] Through demand response projects, users are encouraged to use electricity during off-peak hours, which helps to cut peaks and fill valleys, reduce the pressure on the power grid during peak periods, and also extend the service life of existing infrastructure.

[0090] In case of emergencies, such as large-scale power outages caused by equipment failures or natural disasters, the system can quickly mobilize backup resources to restore power supply and maintain the stable operation of the power grid.

[0091] By reducing demand during peak hours, the construction of new power plants or transmission lines can be postponed or cancelled, saving huge construction costs.

[0092] Using intelligent control technologies, the operation of power generation equipment and energy storage devices can be made more efficient, reducing unnecessary power production and thus lowering fuel and maintenance costs.

[0093] Optimizing the load distribution of the power grid can reduce energy losses during power transmission, further saving costs.

[0094] By continuously monitoring the power grid status and equipment health conditions, potential problems can be detected and solved in a timely manner, avoiding the occurrence of major faults and reducing the economic losses caused by maintenance and downtime.

[0095] Due to the unpredictability of renewable energy, the power grid needs to be flexibly adjusted to adapt to the fluctuations of these power sources. Demand response and load aggregation technologies can help the power grid better accommodate these clean energies and increase their proportion in the overall energy structure.

[0096] By dynamically adjusting demand-side resources, the power grid's supply-demand balance can be maintained even when the output of renewable energy is unstable.

[0097] After detecting abnormal situations, the system can automatically initiate the emergency response process, timely adjust power dispatching, and prevent the further expansion of accidents.

[0098] By comprehensively evaluating the internal and external risks of the power grid, emergency response plans can be formulated in advance to reduce the risk of accidents.

[0099] By optimizing power usage strategies, unnecessary energy consumption can be reduced, carbon dioxide emissions can be lowered, and a positive impact on environmental protection can be achieved.

[0100] The above are only the preferred embodiments of the present invention and are not intended to limit the present invention. Any modifications, equivalent replacements, and improvements made within the spirit and principles of the present invention shall be included within the protection scope of the present invention.

Claims

1. An intelligent control system for power compensation analysis based on demand response and compliance aggregation, characterized in that, Including: A demand-side management module that detects and manages the electricity consumption demands of users; A load aggregation module that aggregates users of different groups; An intelligent regulation module that regulates the power supply according to the strategies formulated by the load aggregation module; A compensation analysis module that evaluates the overall benefits of power regulation and formulates compensation standards.

2. The intelligent regulation system for power compensation analysis based on demand response and compliance aggregation according to claim 1, wherein: The demand-side management module includes: A data collection and monitoring module that uses smart meters to continuously monitor the electricity consumption of users and transmits electricity consumption data via a wireless network; A data analysis module that receives the electricity consumption data sent by the smart meters, uses big data to analyze the historical electricity consumption data of users, identifies the electricity consumption patterns and preferences of users, and uses machine learning algorithms to predict the future electricity consumption behavior of users; A user classification module that classifies users into different groups according to their electricity consumption patterns.

3. An intelligent control system for power compensation analysis based on demand response and compliance aggregation according to claim 1, characterized in that: The load aggregation module includes: A user grouping module that divides users into different groups according to the electricity consumption volume and electricity consumption time periods of users in different groups; A load characteristic analysis module that analyzes the load characteristics of each group of users and classifies the load into high-flexibility load and low-flexibility load; An aggregation object screening module that screens users who can participate in load aggregation, and preferentially selects users with high-flexibility load during the screening process; An aggregation strategy formulation module that formulates a load scheduling plan in advance according to the actual needs of the power grid and the needs of load users to ensure that the power grid needs can be met, and quickly adjusts the load in case of emergencies to help the power grid quickly restore balance.

4. An intelligent control system for power compensation analysis based on demand response and compliance aggregation according to claim 1, characterized in that: The intelligent regulation module includes: An automatic scheduling execution module that automatically generates a scheduling command according to the formulated strategy and then sends the scheduling command to the power plant, energy storage facility, and related equipment via a communication network; A device response module. When the device response module works, the power plant adjusts the output power of the generator according to the received scheduling command, the energy storage device performs charge and discharge operations according to the scheduling command, and for interruptible loads or adjustable loads, their operating states are adjusted according to the scheduling command; A monitoring and feedback module that continuously monitors the state of the power grid and the response of equipment to ensure that the scheduling command is executed and the power grid state meets expectations. When an abnormal situation is detected, an emergency response mechanism is immediately triggered.

5. An intelligent regulation system for power compensation analysis based on demand response and compliance aggregation according to claim 1, characterized in that: The compensation analysis module includes: A cost-benefit analysis module that analyzes economic benefits and cost cycles; A risk assessment module that assesses system risks and external risks; A compensation standard formulation module that dynamically evaluates and formulates compensation standards according to the cost-benefit analysis data and risk assessment data.

6. A method for intelligent regulation of power compensation analysis based on demand response and compliance aggregation, applicable to the intelligent regulation system for power compensation analysis based on demand response and compliance aggregation according to any one of claims 1-5 above, characterized in that, Including the following steps: S1. Data collection and monitoring: Install smart meters to monitor users' electricity consumption in real time, and send electricity consumption data to the data center via a wireless network. Deploy sensors to monitor the voltage and frequency of the power grid. S2. Big data analysis: Process the received electricity consumption data, analyze users' historical electricity consumption data using big data technology to identify users' electricity consumption patterns and preferences, and use machine learning algorithms to predict users' future electricity consumption behavior. S3. User grouping: Divide users into different groups according to their electricity consumption patterns, electricity consumption amounts, and electricity consumption times to formulate more targeted demand response strategies. S4. Load characteristic analysis: Group users in different groups and analyze the load characteristics of each group of users to distinguish between high-flexibility loads and low-flexibility loads, and preferentially select users with high-flexibility loads to participate in load aggregation. S5. Strategy formulation: According to the actual needs of the power grid and the needs of load users, formulate a load scheduling plan to ensure that the power grid's needs can be met, and in case of emergencies, quickly adjust the load to help the power grid quickly restore balance. S6. Generation of dispatching orders: Automatically generate dispatching orders according to the formulated strategy, and send the dispatching orders to power plants, energy storage facilities, and other related devices through a communication network.

7. A method for intelligent regulation of power compensation analysis based on demand response and compliance aggregation according to claim 6, characterized in that: It also includes S7. Real-time monitoring: Continuously monitor the state of the power grid and the response of equipment to ensure that the dispatching orders are executed and the power grid state meets expectations. If any abnormal situation is detected, immediately trigger the emergency response mechanism.

8. A method for intelligent regulation of power compensation analysis based on demand response and compliance aggregation according to claim 7, characterized in that; It also includes S8. Feedback adjustment: According to the real-time monitoring results, if it is found that the original strategy cannot achieve the expected effect, re-evaluate and adjust the dispatching strategy.

9. The method for intelligent regulation of power compensation analysis based on demand response and compliance aggregation according to claim 8, wherein It also includes S9. Cost-benefit analysis: Calculate the cost savings due to demand response, and calculate the total cost over the entire life cycle by combining the initial investment cost, operation and maintenance cost, and decommissioning cost.

10. A method for intelligent regulation of power compensation analysis based on demand response and compliance aggregation according to claim 9, characterized in that, It also includes S10. Compensation standard formulation: Evaluate technical failures, operation errors, and cybersecurity threats, analyze market price fluctuations, policy changes, and natural disaster risks, and dynamically adjust the compensation standard based on the cost-benefit analysis data and risk assessment data.

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