Power demand response aggregation system in multi-market environment and implementation method thereof

Abstract

The invention relates to the field of electric power demands, and discloses an electric power demand response aggregation system in a multi-market environment and an implementation method thereof, comprising recruiting and classifying electric power users, grouping according to power utilization characteristics and demand response potentials of different users, providing motivation for the users to participate in a demand response plan, and aggregating the demands of the users. The response willingness and the adjustable load of the user are collected; historical load data, meteorological data and market prices are collected, future load demands are predicted, and supply and demand conditions of the electricity market are analyzed; on the basis of response willingness and demand characteristics of different user groups, diversified incentive schemes are designed; making a personalized demand response strategy according to the rule of the electricity market and the user response intention; and the load response condition of the power consumer is monitored in real time, data are collected and analyzed, the demand response effect is judged, and feedback adjustment is performed according to the monitored data. The method has the advantages of being efficient, flexible and high in adaptability.

Description

Technical Field

[0001] The present invention relates to the field of power demand, and in particular to a power demand response aggregation system in a multi-market environment and an implementation method thereof. Background Art

[0002] In a diversified market environment, the research and application of power demand response aggregation systems have received increasing attention. With the gradual opening and diversified development of the power market, traditional power dispatching methods are facing increasing challenges. Especially in the context of power supply volatility, demand uncertainty and the increasing proportion of renewable energy, how to effectively balance power supply and demand and improve the stability and economy of the power system has become a key issue that needs to be solved urgently. Especially with the increasing proportion of renewable energy such as wind power and solar energy, the dispatch and optimization of the power system has become more complex and challenging due to the volatility and intermittency of these energy sources. As a flexible demand-side management method, power demand response can alleviate the contradiction between power supply and demand to a certain extent by dynamically adjusting user loads.

[0003] The demand response aggregation system came into being in this context. Through intelligent and centralized regulation and management of decentralized electricity demand, it can not only improve the flexibility of the demand side, but also provide regulation resources for the power market and optimize the operation mode of the power system. With the development of technologies such as smart grids, the Internet of Things, and big data, the demand response system can more accurately capture users' real-time electricity demand and, under the guidance of market price signals, mobilize users' enthusiasm for participating in demand response through price incentives or non-price incentives.

[0004] However, in a diversified market environment, the implementation of demand response faces more challenges, including the complexity of market mechanisms, the heterogeneity of electricity users, and how to effectively design incentive mechanisms to mobilize the response willingness of various loads. In addition, the demand response aggregation system also needs to be combined with the existing power market trading platform, power grid dispatching system, and renewable energy generation volatility management strategy to achieve optimal resource allocation. Therefore, it is necessary to design an efficient, flexible, and highly adaptable power demand response aggregation system and its implementation method in a diversified market environment. Summary of the invention

[0005] 1. Technical issues to be resolved

[0006] In view of the deficiencies in the prior art, the present invention provides an electricity demand response aggregation system and an implementation method thereof in a diversified market environment, which has the advantages of high efficiency, flexibility, and strong adaptability, and solves the problems in the above-mentioned background technology.

[0007] (II) Technical solution

[0008] In order to achieve the above-mentioned purpose of being efficient, flexible and highly adaptable, the present invention provides the following technical solutions: an electricity demand response aggregation system in a multi-market environment, comprising a user behavior analysis module, an incentive mechanism module, a load forecasting module, a response scheduling module and a monitoring module;

[0009] The user behavior analysis module is responsible for managing the basic information, electricity usage type, demand response capability and historical response records of power users. It classifies users according to their load characteristics and evaluates their potential to participate in demand response;

[0010] The incentive mechanism module is used to design personalized and diversified incentive measures to encourage different types of users to respond to demand response signals;

[0011] The load forecasting module dispatches demand response resources and optimizes the distribution of power grid load based on the load forecasting results;

[0012] The response scheduling module is responsible for scheduling demand response based on load forecast, market price and other information. The user's load response signal is executed in this module to implement load reduction or transfer and adjust power demand.

[0013] The monitoring module is used to monitor the power consumption of power users in real time and collect various data, including power consumption, response status, and equipment operation status.

[0014] The method for realizing power demand response aggregation in a multi-market environment comprises the following steps:

[0015] S1: Recruit and classify electricity users, group them according to their electricity consumption characteristics and demand response potential, provide incentives for users to participate in demand response programs, and collect users' response willingness and adjustable load;

[0016] Preferably, S1 further includes fine-grained grouping of users according to their electricity consumption characteristics and demand response potential. The basis for user classification includes historical electricity consumption data, electricity consumption pattern, equipment type, load volatility, and response history. By analyzing these data, the user's demand response potential is evaluated. The system will design personalized incentives according to user type and demand response potential, including economic incentives and non-economic incentives. The system will collect users' response willingness and quantify adjustable load capacity based on user feedback data.

[0017] S2: Collect historical load data, meteorological data and market prices, forecast future load demand, analyze supply and demand in the power market, and conduct market positioning in combination with demand response resources;

[0018] Preferably, S2 further includes collecting historical load data, meteorological data and market prices as the basis for load demand forecasting and market analysis. The system establishes the electricity consumption pattern through historical load data, and further analyzes the impact of weather on electricity demand in combination with meteorological data, and evaluates the market supply and demand relationship and price fluctuation trend through market price data.

[0019] S3: Design diversified incentive schemes based on the response willingness and demand characteristics of different user groups;

[0020] Preferably, S3 further includes the system customizing personalized incentive strategies according to the classification of user groups and the response willingness and demand characteristics of each user, providing economic incentives for users with high response potential, and providing non-economic incentives for residential users with more flexible response time but smaller load.

[0021] S4: Develop personalized demand response strategies, including demand reduction and demand shifting, based on the rules of the electricity market and user response willingness;

[0022] Preferably, S4 further includes designing highly adaptable demand response strategies for different types of users based on market price fluctuations, demand-side response rules and the stability requirements of the power system. The demand reduction strategy reduces the grid load by reducing electricity consumption in a certain period of time, and the demand transfer strategy transfers part of the load during peak demand periods to low demand periods.

[0023] S5: Monitor the load response of power users in real time, collect and analyze data, determine the effect of demand response, and make feedback adjustments based on monitoring data.

[0024] Preferably, S5 further includes collecting users' power consumption data in real time, including power consumption, load fluctuation, and response time, by installing smart metering equipment and load monitoring sensors. By analyzing these real-time data, the system can determine the effectiveness of demand response and evaluate the actual performance of users in demand reduction or demand shifting.

[0025] (III) Beneficial effects

[0026] Compared with the prior art, the present invention provides an electricity demand response aggregation system and implementation method under a multi-market environment, which has the following beneficial effects:

[0027] The present invention designs diversified incentive schemes based on the response willingness and demand characteristics of different user groups; formulates personalized demand response strategies, including demand reduction and demand transfer, according to the rules of the power market and the user's response willingness; monitors the load response of power users in real time, collects and analyzes data, judges the effect of demand response, and makes feedback adjustments based on the monitoring data. It has the advantages of high efficiency, flexibility, and strong adaptability. BRIEF DESCRIPTION OF THE DRAWINGS

[0028] Figure 1 Schematic diagram of the method of the present invention. DETAILED DESCRIPTION

[0029] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0030] Example 1

[0031] The present invention provides a technical solution: a method for realizing power demand response aggregation in a multi-market environment, comprising the following steps:

[0032] S1: Recruit and classify electricity users, group them according to their electricity consumption characteristics and demand response potential, provide incentives for users to participate in demand response programs, and collect users' response willingness and adjustable load;

[0033] The process of recruiting and classifying electricity users is crucial. First, users are grouped in a refined manner according to their electricity consumption characteristics and demand response potential. User classification is based on factors such as historical electricity consumption data, electricity consumption patterns, equipment types, load volatility, and response history. By analyzing these data, the user's demand response potential can be evaluated, that is, the amount of load that can be adjusted and the time window of response in a specific situation. In addition, in order to stimulate the user's willingness to participate, the system will design personalized incentives based on the user type and demand response potential, including economic incentives and non-economic incentives. Driven by incentives, the system will collect the user's willingness to respond and quantify the adjustable load capacity based on the data fed back by the user. Specifically, the adjustable load is quantified by monitoring the difference between the user's real-time load curve and the target load, and fed back to the dispatching system to help the system optimize load dispatching and demand response execution. Through this quantification method, the precise dispatching of the system and the reasonable allocation of resources can be ensured, thereby improving the stability and efficiency of power grid operation.

[0034] S2: Collect historical load data, meteorological data and market prices, forecast future load demand, analyze supply and demand in the power market, and conduct market positioning in combination with demand response resources;

[0035] Collecting historical load data, meteorological data, and market prices is the basis for load demand forecasting and market analysis. First, the system uses historical load data to establish electricity consumption patterns, and combines meteorological data to further analyze the impact of weather on electricity demand, especially the potential impact of seasonal fluctuations and extreme weather events on load demand. At the same time, market price data is used to evaluate market supply and demand relationships and price fluctuation trends, which helps predict future electricity price fluctuations and the economic feasibility of user demand response. On this basis, the system uses machine learning and statistical analysis methods to build a load forecasting model based on these data, quantify electricity demand in a certain period of time in the future, and analyze market supply and demand.

[0036] The accuracy of the forecasting model is evaluated by calculating the error range of the load forecast; at the same time, the market value of the demand response resources is determined by the correlation between price fluctuations and load demand. Combined with demand response resources, the system can use optimization algorithms to perform market positioning, evaluate the participation potential and value of various demand response resources under different market conditions, and thus achieve effective matching of market demand and resources. For example, by setting certain load reduction thresholds and market price sensitivity, the market participation degree and response timeliness of demand response resources can be quantified. Ultimately, these quantitative results will help the dispatching system to accurately formulate demand response plans, achieve optimal dispatch of power grid loads, and balance supply and demand in the electricity market.

[0037] S3: Design diversified incentive schemes based on the response willingness and demand characteristics of different user groups;

[0038] Designing a diversified incentive scheme based on the response willingness and demand characteristics of different user groups is the key to improving the effect of power demand response. First, the system customizes personalized incentive strategies based on the classification of user groups and the response willingness and demand characteristics of each user. For users with high response potential, higher economic incentives can be provided, such as discounts, cash subsidies or more favorable electricity prices; for residential users with more flexible response time but smaller load, non-economic incentives can be provided, such as priority electricity rights, green power certification, etc. In addition, multi-level incentive schemes can also be designed, such as progressive rewards based on response frequency, duration, load adjustment and other factors, to encourage users to participate in demand response in a long-term and stable manner.

[0039] First, it is necessary to establish a quantitative model of user response willingness and demand characteristics. For example, the user's historical response data can be used to evaluate its response timeliness and load regulation, and the response potential score of each user can be calculated by combining its economic value and response flexibility. Then, through the cost-benefit analysis of different incentive schemes, the attractiveness and incentive effect of different incentive measures on users can be quantified. By setting parameters such as incentive budget, upper and lower limits of incentive amount, combined with market price fluctuations and grid demand, the incentive scheme can be optimized to achieve a win-win situation for the system and users. For example, for large-scale industrial users, the incentive amount may be weighted according to the proportion and duration of their load reduction; for residential users, the incentive can be priced by the hour or quantified according to the user's response duration. Ultimately, the incentive scheme will be dynamically adjusted according to the user's response behavior to ensure the accuracy and efficiency of the incentive, and further enhance the user's enthusiasm for participating in demand response.

[0040] S4: Develop personalized demand response strategies, including demand reduction and demand shifting, based on the rules of the electricity market and user response willingness;

[0041] According to the rules of the power market and the user's willingness to respond, formulating personalized demand response strategies is the key to achieving optimal dispatch and market balance of the power system. First, according to market price fluctuations, the rules of demand-side response, and the stable needs of the power system, the system can design highly adaptable demand response strategies for different types of users. Common strategies include demand reduction and demand shifting. The demand reduction strategy mainly reduces the load on the power grid by reducing electricity consumption in a certain period of time. It is usually suitable for users with flexible loads and faster responses, such as commercial users or large industrial users. Demand shifting is to transfer part of the load to the trough period during the peak demand period. This strategy is more suitable for users with more concentrated loads and strong adjustability. It can smooth the power demand curve and reduce system fluctuations.

[0042] First, it is necessary to collect information such as the user's historical electricity consumption data, equipment operation status, response history, and willingness to participate, and use this data to evaluate the response capacity of each user. For demand reduction, the quantitative method can determine the amount of electricity to be reduced by calculating the user's maximum adjustable load in a specific time period and its response timeliness. For example, if a user's maximum load during peak hours is 500kW and its response potential is 100kW, then its demand reduction capacity is 100kW. For demand transfer, it can be predicted based on the user's electricity consumption pattern and the time period of demand transfer, and the transfer load can be quantified, such as 300kWh transferred to nighttime and the effect of the transfer, such as the peak-to-valley difference of the power grid is reduced by 30% after the transfer. In addition, the system also needs to combine market prices, incentive mechanisms, and users' willingness to respond, and dynamically adjust strategies through optimization algorithms, such as adjusting the timing and intensity of demand response participation according to electricity price fluctuations to ensure the economy and efficiency of the strategy. Ultimately, these quantitative demand response strategies will help the power market achieve supply and demand balance, optimize power resource allocation, and improve the stability and reliability of the power grid.

[0043] S5: Monitor the load response of power users in real time, collect and analyze data, determine the effect of demand response, and make feedback adjustments based on monitoring data.

[0044] Real-time monitoring of the load response of power users is a key step to ensure the effective implementation of demand response strategies. The system collects users' power consumption data in real time by installing smart metering devices, load monitoring sensors and other tools, including indicators such as power consumption, load fluctuations, and response time. By analyzing these real-time data, the system can judge the effectiveness of demand response and evaluate the actual performance of users in demand reduction or demand shifting. For example, if an industrial user successfully reduces the predetermined load during the demand reduction period, then its response effect is considered successful, otherwise further incentives or adjustments may be required.

[0045] The system is usually evaluated through several key indicators: first, the load reduction amount is a direct indicator of the user's actual response, which can be calculated by comparing the real-time load data with the benchmark load; second, the response timeliness refers to the time interval from the user receiving the demand response notification to the start of load adjustment. The shorter the time, the more timely the response; finally, the response duration measures the length of time the user maintains load reduction or transfer. The longer the duration, the more effective the response.

[0046] The system can continuously analyze the effect of demand response and adjust the strategy in real time. For example, when a user's response does not meet the expected target, the system can adjust the incentive mechanism or optimize the dispatch strategy based on real-time monitoring data to ensure that the grid load is effectively managed. Feedback adjustment not only helps improve the response effect of a single user, but also optimizes the overall demand response plan based on the load situation of the entire network to ensure the stability and operating efficiency of the power system.

[0047] It should be noted that, in this article, relational terms such as first and second, etc. are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device.

[0048] Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the present invention, and that the scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. The power demand response aggregation system in a multi-market environment is characterized by: It includes user behavior analysis module, incentive mechanism module, load forecasting module, response scheduling module and monitoring module; The user behavior analysis module is responsible for managing the basic information, electricity usage type, demand response capability and historical response records of power users. Categorize users according to their load characteristics and assess their potential for participating in demand response; The incentive mechanism module is used to design personalized and diversified incentive measures to encourage different types of users to respond to demand response signals; The load forecasting module dispatches demand response resources and optimizes the distribution of power grid load based on the load forecasting results; The response scheduling module is responsible for scheduling demand response based on load forecast, market price and other information. The user's load response signal is executed in this module to implement load reduction or transfer and adjust power demand; The monitoring module is used to monitor the power consumption of power users in real time and collect various data, including power consumption, response status, and equipment operation status.

2. The method for realizing power demand response aggregation in a multi-market environment according to claim 1, characterized in that: The following steps are involved: S1: Recruit and classify electricity users, group them according to their electricity consumption characteristics and demand response potential, provide incentives for users to participate in demand response programs, and collect users' response willingness and adjustable load; S2: Collect historical load data, meteorological data and market prices, forecast future load demand, analyze supply and demand in the power market, and conduct market positioning in combination with demand response resources; S3: Design diversified incentive schemes based on the response willingness and demand characteristics of different user groups; S4: Develop personalized demand response strategies, including demand reduction and demand shifting, based on the rules of the electricity market and user response willingness; S5: Monitor the load response of power users in real time, collect and analyze data, determine the effect of demand response, and make feedback adjustments based on monitoring data.

3. The power demand response aggregation system and implementation method under a multi-market environment according to claim 1 is characterized in that: The S1 further includes fine-grained grouping of users according to their electricity consumption characteristics and demand response potential. The basis for user classification includes historical electricity consumption data, electricity consumption pattern, equipment type, load volatility, and response history. By analyzing these data, the user's demand response potential is evaluated. The system will design personalized incentives according to user type and demand response potential, including economic incentives and non-economic incentives. The system will collect users' response willingness and quantify adjustable load capacity based on user feedback data.

4. The power demand response aggregation system and implementation method under a multi-market environment according to claim 1, characterized in that: The S2 further includes collecting historical load data, meteorological data and market prices as the basis for load demand forecasting and market analysis. The system establishes the electricity consumption model through historical load data, and further analyzes the impact of weather on electricity demand in combination with meteorological data, and evaluates the market supply and demand relationship and price fluctuation trend through market price data.

5. The power demand response aggregation system and implementation method under a multi-market environment according to claim 1, characterized in that: The S3 further includes a system that customizes personalized incentive strategies based on the classification of user groups and the response willingness and demand characteristics of each user, providing economic incentives for users with high response potential, and providing non-economic incentives for residential users with more flexible response time but smaller load.

6. The power demand response aggregation system and implementation method under a multi-market environment according to claim 1, characterized in that: The S4 further includes designing highly adaptable demand response strategies for different types of users based on market price fluctuations, demand-side response rules and the stable needs of the power system. The demand reduction strategy reduces the grid load by reducing electricity consumption in a certain period of time. The demand transfer strategy transfers part of the load during the peak demand period to the low demand period.

7. The power demand response aggregation system and implementation method under a multi-market environment according to claim 1, characterized in that: The S5 further includes collecting users' power consumption data in real time, including power consumption, load fluctuation, and response time, by installing smart metering equipment and load monitoring sensors. By analyzing these real-time data, the system can determine the effectiveness of demand response and evaluate the actual performance of users in demand reduction or demand shifting.

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