Power demand response system

Abstract

The invention discloses a power demand response system which comprises the following steps: S1, data acquisition and monitoring: the system acquires detailed power consumption data such as power consumption, power consumption time, power consumption power and the like of a user in real time through widely distributed intelligent electric meters, and uses sensors installed at key nodes of a power grid to determine the power consumption of the user; operation parameters such as voltage, frequency and load of a power grid are monitored in an omnibearing manner; and S2, data analysis and load prediction: fusing and sorting various types of data from a user side and a power grid side, and eliminating data format differences and repeated data. According to the power demand response system, a power consumption mode is remodeled through two-way interaction, the operation efficiency and economical efficiency of a power grid are improved, key support is provided for energy transformation and carbon neutralization targets, and the power demand response system is an important tool for achieving an intelligent power grid and a sustainable energy system and has wide application prospects. The successful implementation of the method depends on policy support, technical innovation and collaborative optimization of a user participation mechanism.

Description

Technical Field

[0001] The present invention relates to the technical field of smart grids, and particularly to a power demand response system. Background Technique

[0002] The power demand response system is a key component in the power consumption link of the smart grid and plays a crucial role in maintaining the balance between power supply and demand, improving the operation efficiency and stability of the power grid. With the continuous development and transformation of the power industry, this system is gradually becoming one of the core elements to ensure the reliable operation of the power system. When the price in the power wholesale market climbs, or the reliability of the system is threatened, power users will receive a direct compensation notice from the power supply side to induce load reduction, or a power price increase signal. At this time, based on this information, users change their original habitual power consumption patterns, reduce or shift the power load during a certain period. This behavior is the power demand response. The power demand response system is constructed around this series of behaviors, aiming to promote the effective interaction between power users and the power supply side and realize the optimal allocation of power resources. It achieves the precise guidance and regulation of users' power consumption behaviors by integrating various technologies and management means, ultimately ensuring the stable operation of the power grid and suppressing the excessive rise of electricity prices.

[0003] In the operation process of the existing power demand response system, the enthusiasm of users to respond is low. On the one hand, some users have insufficient understanding of the concept of power demand response, are not clear about the benefits that participation can bring, and lack the willingness to actively participate. On the other hand, the existing incentive measures are sometimes not attractive enough. For example, some reward amounts are relatively low and are difficult to offset the inconvenience or costs caused by users' adjustment of power consumption behaviors, resulting in low enthusiasm of users to participate. Moreover, the power consumption characteristics and adjustable capabilities of different user groups are different. The adjustable loads of residential users are relatively small and scattered, and the coordination difficulty is large. Although industrial users have great potential for load adjustment, their production processes are complex, and adjusting power consumption may affect normal production, resulting in limited response flexibility and making it difficult to uniformly incorporate them into the demand response system.

[0004] Therefore, we propose a power demand response system to facilitate the solution of the problems raised above. Summary of the Invention

[0005] The purpose of the present invention is to provide a power demand response system to solve the problems raised in the above background technique, that is, some users have insufficient understanding of the concept of power demand response, are not clear about the benefits that participation can bring, and lack the willingness to actively participate. On the other hand, the existing incentive measures are sometimes not attractive enough. For example, some reward amounts are relatively low and are difficult to offset the inconvenience or costs caused by users' adjustment of power consumption behaviors, resulting in low enthusiasm of users to participate.

[0006] To achieve the above object, the present invention provides the following technical solutions: A power demand response system, comprising the following steps: S1, data collection and monitoring, the system collects detailed power consumption data of users in real time through widely distributed smart meters, such as power consumption, power consumption time, power consumption power, etc., and uses sensors installed at various key nodes of the power grid to comprehensively monitor operation parameters of the power grid, such as voltage, frequency, load, etc.; S2, data analysis and load forecasting, various data from the user side and the power grid side are fused and sorted to eliminate data format differences and duplicate data; S3, demand response strategy formulation, according to the load forecasting results and the real-time operation status of the power grid, clarify the specific objectives of demand response; S4, information release and user interaction, through a variety of information release platforms, release demand response information to users; S5, response execution and monitoring, for users participating in demand response, through terminal devices such as smart meters and smart power consumption device controllers, accurately adjust the power consumption behavior of users; S6, effect evaluation and reward settlement, establish a scientific and perfect effect evaluation index system.

[0007] Preferably, in S1, the power consumption degree is accurate to every hour or even every minute; the power consumption time, that is, the on and off time of electrical equipment; the power consumption power can display in real time the power consumption of different electrical appliances when they are working. These data will be automatically uploaded at a certain time interval (such as once every 15 minutes) through the communication module. Some large commercial users or industrial users may also be equipped with an energy management system, which can collect more detailed information, such as the power consumption of different production processes, etc.

[0008] Preferably, in S1, the collected data is continuously transmitted to the data management center through a communication network, such as power line carrier communication, wireless communication (4G / 5G), etc. At the same time, the sensors monitor operation parameters of the power grid, such as voltage, frequency, power flow distribution, etc. These parameters reflect the real-time operation status of the power grid and are also transmitted to the data management center to provide comprehensive data support for subsequent analysis.

[0009] Preferably, in S2, professional data analysis software is used to import historical power load data. Combining weather data (such as temperature and humidity have a great impact on air conditioner power consumption), seasonal factors (there are significant differences in power consumption between summer cooling and winter heating), date types (the power consumption patterns are different on weekdays, weekends, and holidays), etc., and using time series analysis models such as the ARIMA model to analyze the variation law of historical load data, predict the power load in different future time periods (such as the next 1 hour, 1 day, 1 week), for example, predict the load during the peak power consumption period from 18:00 to 20:00 tomorrow, providing a basis for formulating demand response strategies, and mining users' electricity consumption habits from users' electricity consumption data. For example, some household users have a large electricity consumption for watching TV and turning on lights from 7 to 9 pm; commercial users have a large electricity consumption during business hours on weekends and holidays; industrial users have regular electricity consumption patterns for different production shifts. Through methods such as clustering analysis, users are classified, and more targeted demand response strategies are formulated for different types of users.

[0010] Preferably, in S2, a data cleaning algorithm is adopted to remove abnormal data and noise interference, ensuring the accuracy and reliability of the data. Based on the cleaned data, combined with multi-dimensional information such as historical electricity consumption data, meteorological data, seasonal factors, and economic development trends, advanced prediction algorithms such as time series analysis models (such as ARIMA) and neural network models (such as LSTM) are used to construct a load prediction model to accurately predict the power load in different future time periods.

[0011] Preferably, in S3, according to the load prediction results, if it is predicted that the load in a certain future time period will exceed the grid's carrying capacity, a peak shaving strategy is formulated. For example, for residential users, during the peak power consumption period (such as from 7 to 10 pm in summer), by increasing the electricity price, users are encouraged to reduce non-essential electricity consumption, such as reducing the air conditioner usage time and turning off unnecessary lighting. For industrial users, negotiate with enterprises to suspend some interruptible production links during peak hours, such as some non-continuous processing processes. If it is predicted that there is an excess of electricity during the valley period, a valley filling strategy can be formulated, such as introducing preferential valley electricity prices to encourage users to use adjustable electricity-consuming devices such as electric water heaters and electric vehicle charging during this period.

[0012] Preferably, in S3, if it is predicted that there will be a peak power consumption in a certain future time period and the grid power supply capacity is close to the limit, then reducing the load peak value during this period is taken as the demand response target. Around this target, corresponding demand response strategies are formulated. If the goal is to encourage users to shave peaks and fill valleys, a time-of-use electricity price strategy can be adopted, and the peak and valley periods and the electricity price difference are reasonably set in combination with the local actual situation; if it is aimed at coping with an emergency power supply shortage, direct load control or emergency demand response strategies may be activated, and specific contents such as the scope of users to be controlled, the load reduction amount, and the compensation standard are determined.

[0013] Preferably, in S4, the information release platform, such as mobile applications, SMS notifications, smart meter displays, and the official websites of power companies, etc., releases demand response information to users. These information cover the current power supply and demand situation, the specific content of demand response strategies, incentive measures, and participation methods, etc.; users decide whether to participate in demand response according to their actual situations, such as electricity costs, production arrangements, living habits, etc. If they choose to participate, users can feedback their response willingness and response ability through the above-mentioned release platform, and the system receives and aggregates the response information of users in real time to prepare for subsequent response execution.

[0014] Preferably, in S5, during peak hours, the cooling power of the smart air conditioner in the user's home is automatically reduced, or the power supply of some non-essential smart sockets is turned off; for industrial users, the operating time or power of production equipment is adjusted according to the agreement. During the execution of demand response, the system continuously monitors the electricity consumption of users and the operation status of the power grid in real time, compares the actual electricity consumption data with the expected response target, and timely discovers and solves possible problems, such as some users not reducing the load as required, voltage fluctuations occurring locally in the power grid, etc., and feeds back the monitoring results to users and relevant management departments in a timely manner.

[0015] Preferably, in S6, calculate the change in the peak-valley difference of the load before and after the implementation of demand response, evaluate the improvement of the stability indicators of the power grid voltage and frequency, and count the reduction in energy consumption, etc. According to the actual response effect of users and the pre-established reward rules, conduct reward settlement for users participating in demand response. The forms of rewards are rich and diverse, including electricity bill reduction, cash subsidy, points exchange, etc. At the same time, publicly commend users who perform outstandingly during the implementation of demand response to encourage more users to actively participate in future demand response activities and continuously optimize the operation effect of the power demand response system.

[0016] Compared with the prior art, the beneficial effects of the present invention are as follows: This power demand response system reshapes the power consumption mode through "two-way interaction", not only improving the operation efficiency and economy of the power grid, but also providing key support for the energy transformation and carbon neutrality goals. It is an important tool for realizing a smart grid and a sustainable energy system. Its successful implementation depends on the coordinated optimization of policy support, technological innovation, and user participation mechanisms. The specific content is as follows: (1) When the price in the electricity wholesale market fluctuates or the system reliability is threatened, this system can prompt power users to change their electricity consumption patterns; for example, during peak hours, users reduce or shift their electricity loads, effectively reducing power demand and alleviating the power supply pressure; during off-peak hours, users are encouraged to increase electricity consumption, improving the utilization efficiency of power resources and avoiding waste of electric energy, so that power supply and demand can tend to be balanced at different times.

[0017] (2) By shaving the peak and filling the valley, the peak-valley difference of the load is reduced. For example, under the time-of-use electricity price strategy, users are guided to use less electricity during peak hours and more electricity during off-peak hours, making the grid load curve smoother, reducing the impact on grid equipment caused by sudden load changes, extending the service life of the equipment, reducing equipment maintenance costs, and thus improving the overall operating efficiency of the grid. At the same time, when the grid faces emergencies such as emergency power shortages, strategies such as direct load control and emergency demand response can quickly reduce the load, ensure the safe and stable operation of the grid, and reduce the power outage risk and outage scope.

[0018] (3) For users, participating in demand response can obtain economic benefits. In incentive-based strategies, users sign interruptible load contracts with power companies and actively interrupt the use of some non-critical loads during power supply shortages, and can obtain economic compensation or enjoy preferential electricity prices. Industrial users adjust their production plans, suspend non-emergency production processes when real-time electricity prices rise, and resume them when electricity prices fall, reducing electricity costs. Residential users can also save on electricity bills by reducing electricity consumption during peak electricity price periods.

[0019] (4) Strategies such as demand-side bidding allow users to participate in the electricity market bidding and report the price for adjusting the electricity load according to their own electricity costs and market expectations. This enriches the trading entities and trading methods in the electricity market, enhances market vitality and competitiveness, promotes the development of the electricity market towards a more fair, efficient and flexible direction, and promotes the more optimal allocation of electricity resources under the market mechanism.

[0020] (5) Users adjust their electricity consumption behaviors according to demand response strategies, which to a certain extent reduces unnecessary electricity consumption. For example, industrial users reduce electricity consumption during non-essential periods, reducing energy consumption and pollutant emissions during the power generation process, playing a positive role in energy conservation, emission reduction, alleviating energy pressure, and improving environmental quality, which is in line with the concept of sustainable development. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] Figure 1 is a schematic flow diagram of the power demand response method of the present invention; Figure 2 is a schematic diagram of the system operation of the present invention. DETAILED DESCRIPTION OF THE INVENTION

[0022] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present invention.

[0023] Please refer to Figure 1 and Figure 2, the present invention provides a technical solution: a power demand response system, including the following steps: S1, data collection and monitoring. The system uses widely distributed smart meters to collect detailed power consumption data of users in real time, such as power consumption, power consumption time, and power consumption power, and uses sensors installed at various key nodes of the power grid to comprehensively monitor operating parameters of the power grid, such as voltage, frequency, and load. In S1, the power consumption degree is accurate to every hour or even every minute; the power consumption time, that is, the on and off times of electrical equipment; the power consumption power can display in real time the power consumption of different electrical appliances when they are working. These data will be automatically uploaded at regular time intervals (such as once every 15 minutes) through the communication module. Some large commercial users or industrial users may also be equipped with an energy management system, which can collect more detailed information, such as the power consumption of different production processes, etc. In S1, the collected data is continuously transmitted to the data management center through communication networks, such as power line carrier communication, wireless communication (4G / 5G), etc. At the same time, the operating parameters of the power grid monitored by the sensors, such as grid voltage, frequency, and power flow distribution, which reflect the real-time operating state of the power grid, are also transmitted to the data management center to provide comprehensive data support for subsequent analysis; S2, data analysis and load forecasting. Various data from the user side and the power grid side are fused and sorted out to eliminate data format differences and duplicate data. In S2, professional data analysis software is used to import historical power load data, combined with weather data (such as temperature and humidity have a great impact on air conditioner power consumption), seasonal factors (there are large differences in power consumption for cooling in summer and heating in winter), date types (different power consumption patterns on weekdays, weekends, and holidays), etc. Using time series analysis models, such as the ARIMA model, to analyze the change laws of historical load data and predict the power load in different future periods (such as the next 1 hour, 1 day, 1 week). For example, predict the load volume during the peak power consumption period from 18:00 to 20:00 tomorrow, providing a basis for formulating demand response strategies. And mining the electricity consumption habits of users from the user electricity consumption data, such as some household users have a large electricity consumption for watching TV and turning on lights from 7 to 9 pm; commercial users have a large electricity consumption during business hours on weekends and holidays; industrial users have regular electricity consumption in different production shifts, etc. Through methods such as clustering analysis, users are classified, and more targeted demand response strategies are formulated for different types of users. In S2, data cleaning algorithms are used to remove abnormal data and noise interference to ensure the accuracy and reliability of the data. Based on the cleaned data, combined with multi-dimensional information such as historical electricity consumption data, meteorological data, seasonal factors, and economic development trends, advanced prediction algorithms such as time series analysis models (such as ARIMA) and neural network models (such as LSTM) are used to construct a load forecasting model to accurately predict the power load in different future periods;S3. Demand response strategy formulation: Based on the load forecasting results and the real-time operation status of the power grid, clarify the specific goals of demand response. In S3, according to the load forecasting results, if it is predicted that the load in a certain future period will exceed the power grid's carrying capacity, a peak shaving strategy is formulated. For example, for residential users, during peak electricity consumption hours (such as 7 - 10 pm in summer), by raising the electricity price, users are encouraged to reduce non-essential electricity consumption, such as reducing the air conditioner usage time and turning off unnecessary lighting. For industrial users, negotiations are carried out with enterprises to suspend some interruptible production links during peak hours, such as some non-continuous production processes. If it is predicted that there is an oversupply of electricity during off-peak hours, a valley filling strategy can be formulated, such as introducing preferential off-peak electricity prices to encourage users to use adjustable electrical equipment such as electric water heaters and electric vehicle charging during this period. In S3, if it is predicted that there will be a peak in electricity consumption in a certain future period and the power supply capacity of the power grid is close to the limit, then reducing the peak load during this period is taken as the demand response goal. Around this goal, corresponding demand response strategies are formulated. If the goal is to encourage users to shave peaks and fill valleys, a time-of-use electricity price strategy can be adopted, and the peak-valley periods and electricity price differences are reasonably set in combination with the local actual situation; if it is aimed at coping with emergency power shortages, direct load control or emergency demand response strategies may be activated, and specific contents such as the scope of users to be controlled, the amount of load reduction, and the compensation standard are determined; S4. Information release and user interaction: Through diverse information release platforms, demand response information is released to users. In S4, information release platforms such as mobile applications, SMS notifications, smart meter displays, and the official website of the power company are used to release demand response information to users. This information covers the current power supply and demand situation, the specific content of demand response strategies, incentive measures, and participation methods, etc.; users, based on their actual situations such as electricity costs, production arrangements, and living habits, decide whether to participate in demand response. If they choose to participate, users can feedback their response willingness and response ability through the above-mentioned release platforms, and the system receives and aggregates the users' response information in real time to prepare for subsequent response execution; S5. Response execution and monitoring: For users participating in demand response, through terminal devices such as smart meters and smart electrical equipment controllers, the electricity consumption behaviors of users are precisely adjusted. In S5, during peak hours, the cooling power of smart air conditioners in users' homes is automatically reduced, or the power supply of some non-essential smart sockets is turned off; for industrial users, the operation time or power of production equipment is adjusted according to the agreement. During the execution of demand response, the system continuously monitors the electricity consumption of users and the operation status of the power grid in real time, compares the actual electricity consumption data with the expected response goal, and promptly discovers and solves possible problems, such as some users not reducing the load as required and local voltage fluctuations in the power grid, and feeds back the monitoring results to users and relevant management departments in a timely manner;S6. Effect evaluation and reward settlement: Establish a scientific and perfect effect evaluation index system. In S6, calculate the change in the peak-valley difference of the load before and after the implementation of demand response, evaluate the improvement of the stability indexes of the grid voltage and frequency, and count the reduction in energy consumption, etc. According to the actual response effect of users and the pre-established reward rules, conduct reward settlement for users participating in demand response. The forms of rewards are rich and diverse, including electricity bill reduction, cash subsidy, point exchange, etc. At the same time, publicly commend users who perform outstandingly during the implementation of demand response to encourage more users to actively participate in future demand response activities and continuously optimize the operation effect of the power demand response system.;

[0024] Although the present invention has been described in detail with reference to the foregoing embodiments, for those skilled in the art, they can still modify the technical solutions described in the foregoing embodiments, or perform equivalent replacements for some of the technical features. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principle of the present invention shall be included within the protection scope of the present invention.

Claims

1. A power demand response system, characterized in that, Including the following steps: S1. Data collection and monitoring: The system collects detailed electricity consumption data of users in real time, such as electricity consumption, electricity usage time, and electricity power, through a wide range of intelligent electricity meters, and comprehensively monitors the operating parameters of the power grid, such as voltage, frequency, and load, using sensors installed at various key nodes of the power grid; S2. Data analysis and load forecasting: Integrate and organize various data from the user side and the power grid side to eliminate data format differences and duplicate data; S3. Demand response strategy formulation: Define the specific goals of demand response according to the load forecasting results and the real-time operating conditions of the power grid; S4. Information release and user interaction: Release demand response information to users through diverse information release platforms; S5. Response execution and monitoring: For users participating in demand response, precisely adjust the electricity consumption behavior of users through terminal devices such as intelligent electricity meters and intelligent electricity equipment controllers; S6. Effect evaluation and reward settlement: Establish a scientific and complete effect evaluation index system.

2. The power demand response system according to claim 1, characterized in that: In S1, the electricity consumption is accurate to the degree of electricity consumption per hour or even per minute; the electricity usage time refers to the on and off times of electrical equipment; the electricity power can display in real time the power consumption of different electrical appliances when they are working. These data will be automatically uploaded at regular time intervals (such as once every 15 minutes) through the communication module. Some large commercial users or industrial users may also be equipped with an energy management system, which can collect more detailed information, such as the electricity consumption of different production processes, etc.

3. The power demand response system according to claim 1, wherein: In S1, the collected data is continuously transmitted to the data management center through communication networks, such as power line carrier communication, wireless communication (4G / 5G), etc. At the same time, the sensors monitor the operating parameters of the power grid, such as voltage, frequency, and power flow distribution. These parameters reflect the real-time operating state of the power grid and are also transmitted to the data management center to provide comprehensive data support for subsequent analysis.

4. A power demand response system according to claim 1, characterized in that: In S2, professional data analysis software is used to import historical power load data, combined with weather data (such as temperature and humidity have a great impact on air conditioner electricity consumption), seasonal factors (there are significant differences in electricity consumption for cooling in summer and heating in winter), date types (the electricity consumption patterns are different on weekdays, weekends, and holidays), etc. Using time series analysis models, such as the ARIMA model, analyze the change laws of historical load data, predict the power load in different future periods (such as the next 1 hour, 1 day, 1 week), for example, predict the load during the peak electricity consumption period from 18:00 to 20:00 tomorrow, providing a basis for formulating demand response strategies, and mine the electricity consumption habits of users from the user electricity consumption data. For example, some household users have high electricity consumption for watching TV and turning on lights from 7 to 9 pm; commercial users have high electricity consumption during business hours on weekends and holidays; industrial users have regular electricity consumption patterns for different production shifts, etc. Through methods such as clustering analysis, classify users and formulate more targeted demand response strategies for different types of users.

5. A power demand response system according to claim 1, characterized in that: In S2, a data cleaning algorithm is adopted to remove abnormal data and noise interference, ensuring the accuracy and reliability of the data. Based on the cleaned data, combined with multi-dimensional information such as historical electricity consumption data, meteorological data, seasonal factors, and economic development trends, advanced prediction algorithms such as time series analysis models (such as ARIMA) and neural network models (such as LSTM) are used to construct a load prediction model to accurately predict the power load at different future times.

6. A power demand response system according to claim 1, characterized in that: In S3, according to the load prediction results, if it is predicted that the load in a certain future time period will exceed the grid's carrying capacity, a peak shaving strategy is formulated. For example, for residential users, during peak electricity consumption hours (such as 7-10 pm in summer), by increasing the electricity price, users are encouraged to reduce non-essential electricity consumption, such as reducing the air conditioner usage time and turning off unnecessary lighting. For industrial users, negotiate with enterprises to suspend some interruptible production links during peak hours, such as some non-continuous production processes. If it is predicted that there is an excess of electricity during off-peak hours, a valley filling strategy can be formulated, such as introducing preferential off-peak electricity prices to encourage users to use adjustable electrical equipment such as electric water heaters and electric vehicle charging during this period.

7. A power demand response system according to claim 1, characterized in that: In S3, if it is predicted that there will be a peak electricity consumption in a certain future time period and the grid power supply capacity is close to the limit, then reducing the peak load value in this time period is taken as the demand response target. Around this target, corresponding demand response strategies are formulated. If the goal is to encourage users to shave peaks and fill valleys, a time-of-use electricity price strategy can be adopted, and the peak-valley time periods and electricity price differences are reasonably set in combination with the local actual situation; if it is aimed at coping with an emergency power supply shortage, direct load control or emergency demand response strategies may be activated, and specific contents such as the scope of users to be controlled, the load reduction amount, and the compensation standard are determined.

8. A power demand response system according to claim 1, characterized in that: In S4, an information release platform, such as a mobile application, SMS notification, smart meter display screen, and the official website of the power company, etc., releases demand response information to users. This information covers the current power supply and demand situation, the specific content of the demand response strategy, incentive measures, and participation methods, etc.; users decide whether to participate in the demand response according to their own actual situations, such as electricity consumption costs, production arrangements, and living habits, etc. If they choose to participate, users can feedback their response willingness and response ability through the above-mentioned release platform, and the system receives and summarizes the response information of users in real time to prepare for subsequent response execution.

9. A power demand response system according to claim 1, characterized in that: In S5, during peak hours, automatically reduce the cooling power of smart air conditioners in users' homes, or turn off the power supply of some non-essential smart sockets; for industrial users, adjust the operating time or power of production equipment according to the agreement. During the implementation of the demand response, the system continuously monitors the electricity consumption situation of users and the grid operation status in real time, compares the actual electricity consumption data with the expected response target, and timely discovers and solves possible problems, such as some users not reducing the load as required and local voltage fluctuations in the grid, etc., and feeds back the monitoring results to users and relevant management departments in a timely manner.

10. A power demand response system according to claim 1, characterized in that: In S6, calculate the change in the peak-valley difference of the load before and after the implementation of demand response, evaluate the improvement of the stability indicators of the grid voltage and frequency, and count the reduction in energy consumption. According to the actual response effect of the users and the pre-established reward rules, conduct reward settlement for the users participating in the demand response. The forms of rewards are rich and diverse, including electricity bill reduction, cash subsidy, integral exchange, etc. At the same time, publicly commend the users who have performed outstandingly during the implementation of demand response to encourage more users to actively participate in future demand response activities and continuously optimize the operation effect of the power demand response system.

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