User demand response evaluation method and system in smart power grid environment

Abstract

The invention discloses a user demand response assessment method and system in an intelligent power grid environment, and relates to the technical field of intelligent power grid demand response assessment, and the method comprises the steps: collecting user power consumption data, and constructing a user power consumption database; according to the evaluation index system and based on the fuzzy optimization set pair, analyzing a potential evaluation model of demand side user response, and performing comprehensive evaluation on the user to obtain a user response potential level; the electricity utilization growth pole prediction model is utilized to predict the future electricity utilization growth pole by analyzing the gravity center correlation, the period correlation and the dynamic interaction influence between the electricity consumption and the economic growth; and formulating a power utilization scheme according to a prediction result. A sound evaluation index system is established, and a demand side user response potential evaluation model is constructed by applying a fuzzy optimization set pair analysis theory, so that the optimization of the energy consumption condition of the user is realized, and the supply and demand optimization balance is promoted.

Description

Technical Field

[0001] The present invention relates to the technical field of smart grid demand response evaluation, and in particular to a user demand response evaluation method and system in a smart grid environment. Background Art

[0002] Smart power consumption is an important part of smart grid. Its function is to connect important equipment from the power supply side to the user side through flexible power network and information network, form an efficient and complete power consumption information service system and service platform, and build a new power supply and consumption relationship with real-time interaction between power grid and user power flow, information flow and business flow; through interactive strategies, mobilize users to participate in demand response or directly conduct remote optimization control, realize the flexibility of power load, guide users or directly optimize power consumption methods, and support reliable and economic operation of the power supply side; the quality of smart power consumption system construction is directly related to the energy efficiency, economic operation and orderly power consumption of the power grid, and will have a far-reaching impact on power grid construction, energy conservation and environmental protection, and power quality management; with the development and improvement of smart grid and power market, the role of demand-side resources in the competitive market is being re-recognized, and demand response is introduced in the competition of power market. The role of demand side in the market is increased through price signals and incentive mechanisms, and the resources on supply side and demand side are comprehensively planned. Demand response for flexible interactive smart power consumption has become a development trend.

[0003] Changes in the pattern of power energy supply and demand and trends in energy development have led to market reforms in the power industry at home and abroad and accelerated the construction of smart grids in various countries. The liberalization of power generation and consumption plans and the centralized grid connection of renewable energy and distributed power sources have gradually transformed the traditional demand-side unilateral random power system into a supply-demand dual-side random system, impacting the smooth operation of the power system and making the work of the dispatching department more difficult. Enhanced power demand-side management and improved terminal power efficiency can make the controllable resources on the demand side, like the supply side, become controllable resources for power system planning and operation. The development of demand-side resources, enhanced demand-side management, and coordination of controllable resources on both the supply and demand sides can help achieve instantaneous balance of power in the power grid. While increasing demand-side power efficiency, renewable energy can also be absorbed, driving the power system to develop in a cleaner, more efficient, and safer direction.

[0004] In the existing technology, the evaluation of user demand response often only considers a single factor, lacks a systematic evaluation index system, and the accuracy of the evaluation results needs to be improved; at the same time, the coupling relationship between smart grids and the macro-economy has not been fully utilized to predict electricity consumption growth poles and conduct macroeconomic evaluations. Summary of the invention

[0005] In view of the above-mentioned problems, the present invention is proposed.

[0006] Therefore, the problem to be solved by the present invention is that in the prior art, the evaluation of user demand response often only considers a single factor, lacks a systematic evaluation index system, and the accuracy of the evaluation results needs to be improved; at the same time, the coupling relationship between the smart grid and the macro-economy has not been fully utilized to predict the growth pole of electricity consumption and conduct macroeconomic evaluation.

[0007] To solve the above technical problems, the present invention provides the following technical solutions: a user demand response evaluation method in a smart grid environment, which includes collecting user electricity consumption data and building a user electricity consumption database; analyzing the potential evaluation model of demand-side user response according to the evaluation index system and based on fuzzy optimization set pairs, comprehensively evaluating users and obtaining user response potential levels; using the electricity consumption growth pole prediction model to predict future electricity consumption growth poles by analyzing the center of gravity correlation, periodic correlation and dynamic interactive influence between electricity consumption and economic growth; and formulating electricity consumption plans based on the prediction results.

[0008] As a preferred scheme of the user demand response evaluation method in a smart grid environment described in the present invention, wherein: the method for constructing the evaluation index system is to establish a sound evaluation index system from four aspects: user situation, user behavior, information value, and electricity consumption plan; the user situation includes electricity consumption, electricity consumption type, and electricity consumption time period; the user behavior includes electricity consumption habits and response history; the information value includes information acquisition capability and information processing capability; the electricity consumption plan is based on the electricity price elasticity theory's electricity consumption behavior prediction and adjustment strategy, and constructs an incentive and subsidy mechanism for electricity energy saving and consumption reduction; for each indicator, a quantitative method is used to assign weights.

[0009] As a preferred solution of the user demand response evaluation method in a smart grid environment described in the present invention, the quantification method includes a fuzzy evaluation method and a hierarchical analysis method.

[0010] As a preferred solution of the user demand response evaluation method in a smart grid environment described in the present invention, the potential evaluation model for analyzing demand-side user response based on fuzzy optimization set pairs includes treating the evaluation sample and the evaluation level as a set pair, performing similarity, difference, and opposite quantitative analysis, and obtaining the potential level through connection degree calculation.

[0011] As a preferred solution of the user demand response evaluation method in a smart grid environment described in the present invention, the formula of the connection degree is expressed as follows:

[0012] u=a+bl+cJ

[0013] Among them, a, b, c represent identity, difference and opposition respectively, and satisfy a+b+c=1, l represents the difference coefficient, and its value range is [-1,1]; when l=-1 or 1, it means that the difference is certain; when l is close to 0, it means that the uncertainty of the difference increases, and J represents the opposition coefficient, and its value is -1.

[0014] As a preferred solution of the user demand response evaluation method in a smart grid environment described in the present invention, wherein: after calculating the connection degree of each indicator, a comprehensive connection degree is obtained by combining the weights of each indicator, and the level of user response potential is determined according to the size of the comprehensive connection degree; the construction content of the electricity consumption growth pole prediction model includes data collection, data processing, center of gravity correlation analysis, periodic correlation analysis, dynamic interactive impact analysis, and prediction of future electricity consumption growth poles.

[0015] As a preferred solution of the user demand response evaluation method in a smart grid environment described in the present invention, wherein: the power consumption plan is formulated based on the prediction results based on information collection and interaction, two-way passage and interactive terminal technology in smart power consumption, so as to automate and intelligentize the entire system.

[0016] Another object of the present invention is to provide a user demand response evaluation system in a smart grid environment, which can formulate an electricity consumption plan based on the prediction results.

[0017] To solve the above technical problems, the present invention provides the following technical solutions: a system of a user demand response evaluation method in a smart grid environment, comprising: a database construction module, a potential level calculation module, a prediction module and a power consumption plan formulation module; the database construction module collects user power consumption data and constructs a user power consumption database; the potential level calculation module analyzes the potential evaluation model of demand-side user response according to the evaluation index system and based on fuzzy optimization set pairs, conducts a comprehensive evaluation of users, and obtains the user response potential level; the prediction module uses the power consumption growth pole prediction model to predict future power consumption growth poles by analyzing the center of gravity correlation, periodic correlation and dynamic interactive influence between power consumption and economic growth; the power consumption plan formulation module formulates a power consumption plan according to the prediction results.

[0018] A computer device includes a memory and a processor, wherein the memory stores a computer program, and when the processor executes the computer program, the steps of the user demand response evaluation method in a smart grid environment are implemented as described above.

[0019] A computer-readable storage medium stores a computer program, which, when executed by a processor, implements the steps of a user demand response evaluation method in a smart grid environment as described above.

[0020] The beneficial effects of the present invention are as follows: by establishing a sound evaluation index system and using fuzzy optimization set pair analysis theory to construct a demand-side user response potential evaluation model, the user's energy consumption is optimized and the supply and demand optimization balance is promoted.

[0021] The use of the electricity-economy bidirectional coupling interaction mechanism to predict electricity consumption growth poles and conduct macroeconomic assessments provides strong support for the planning and operation of smart grids.

[0022] Smart electricity consumption technology is used to automate and intelligentize business processes, thereby improving the operating efficiency and stability of the power system. BRIEF DESCRIPTION OF THE DRAWINGS

[0023] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following briefly introduces the drawings required for describing the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work. Among them:

[0024] Figure 1 This is a flow chart of a method for evaluating user demand response in a smart grid environment in Example 1.

[0025] Figure 2 This is a module structure diagram of a user demand response evaluation system in a smart grid environment in Example 1. DETAILED DESCRIPTION

[0026] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, the specific implementation methods of the present invention are described in detail below in conjunction with the accompanying drawings.

[0027] In the following description, many specific details are set forth to facilitate a full understanding of the present invention, but the present invention may also be implemented in other ways different from those described herein, and those skilled in the art may make similar generalizations without violating the connotation of the present invention. Therefore, the present invention is not limited to the specific embodiments disclosed below.

[0028] Example 1, reference Figure 1 , which is the first embodiment of the present invention, and which provides a method for evaluating user demand response in a smart grid environment, including: Figure 1 As shown:

[0029] S1. Collect user electricity consumption data and build a user electricity consumption database.

[0030] The main source of electricity consumption information for power companies is the reporting system. Every household has an electricity meter that records the corresponding electricity consumption information, and the power company can convert this information into reports. Smart meters are a new type of meters that can realize remote meter reading, based on Internet communication technology, and use cloud computing technology to aggregate and analyze data. Smart meters can upload electricity consumption data to the power company's server in real time. Other data collection technologies: In addition to reporting systems and smart meters, power companies can also obtain users' real-time electricity consumption information through consumer systems, as well as electricity consumption data through household self-installed power monitoring systems.

[0031] Data collection method: 95598 State Grid Online Business Hall or telephone inquiry: Users can query electricity consumption data through 95598 State Grid Online Business Hall or call 95598 to provide electricity bill user number or electricity consumption address.

[0032] Batch acquisition by email: Send the electricity user number, name and address to the designated email address. The power company will usually reply by email within a few working days to provide electricity consumption data.

[0033] Data processing and integration: Data cleaning: clean the collected data to remove duplicate, erroneous or invalid data; Data conversion: convert the collected data into a unified format or standard according to the needs of the power company; Data integration: integrate data from different sources to form a complete user electricity consumption data set.

[0034] Build a user electricity consumption database: Database design: Use tools such as ER diagrams to design the process of electricity fee management of power companies, and convert ER diagrams into data models supported by specific database products, such as relational models; Table structure creation: Create the table structure in the database according to data characteristics and business needs, and define the table's fields, types, indexes and other attributes; Data import: Import the cleaned, converted and integrated user electricity consumption data into the database; Data verification: Verify the imported data to ensure its accuracy and completeness.

[0035] S2. According to the evaluation index system and based on fuzzy optimization set pairing, the potential evaluation model of demand-side user response is analyzed, and users are comprehensively evaluated to obtain the user response potential level.

[0036] The present invention establishes a sound evaluation index system from four aspects: user basic situation, user behavior, information value and electricity consumption plan.

[0037] The basic situation of users includes electricity consumption, type of electricity consumption, time period of electricity consumption, etc.; user behavior includes electricity consumption habits, response history, etc.; information value includes information acquisition ability, information processing ability, etc.; electricity consumption plan is based on the electricity consumption behavior prediction and adjustment strategy of electricity price elasticity theory, and the construction of reward and subsidy mechanism for electricity energy conservation and consumption reduction. The electricity consumption plan can refer to incentive policies, which include electricity price policy, subsidy policy, etc.; for each indicator, appropriate quantitative methods, such as fuzzy evaluation method, hierarchical analysis method, etc., are used to allocate weights.

[0038] Based on fuzzy optimization set pairs, the potential evaluation model for analyzing demand-side user response includes treating the evaluation samples and evaluation levels as a set pair, conducting similarity, difference, and opposite quantitative analysis, and calculating the potential level through the degree of connection.

[0039] The formula of connection degree is expressed as:

[0040] u=a+bl+cJ

[0041] Among them, a, b, c represent identity, difference and opposition respectively, and satisfy a+b+c=1, l represents the difference coefficient, and its value range is [-1,1]; when l=-1 or 1, it means that the difference is certain; when l is close to 0, it means that the uncertainty of the difference increases, and J represents the opposition coefficient, and its value is -1.

[0042] After calculating the connection degree of each indicator, a comprehensive connection degree is obtained by combining the weight of each indicator. According to the size of the comprehensive connection degree, the level of user response potential is determined.

[0043] S3. Use the electricity consumption growth pole prediction model to predict future electricity consumption growth poles by analyzing the center of gravity correlation, cycle correlation and dynamic interaction between electricity consumption and economic growth.

[0044] By utilizing the electricity-economy two-way coupling interaction mechanism and combining it with an evaluation index system, a prediction model for electricity consumption growth poles is constructed. By analyzing the center of gravity correlation, cyclical correlation, and dynamic interactive influence between electricity consumption and economic growth, future electricity consumption growth poles are predicted to provide data support for macroeconomic evaluation.

[0045] It also includes the construction of a prediction model for electricity consumption growth poles, including:

[0046] Data collection:

[0047] Electricity consumption data: Collect electricity consumption within a preset time period. This can be achieved through smart meters, power monitoring systems and other equipment. The data should cover electricity consumption in different time periods (such as days, weeks, months, and years) to facilitate time series analysis.

[0048] Economic growth data: Economic growth data here refers to a wider range of economic indicators, such as gross domestic product (GDP), industrial value added, service industry value added, etc. These data are usually released by government statistical departments or professional research institutions. In order to compare with electricity consumption data, it is necessary to collect economic growth data within the same time period as electricity consumption data;

[0049] Data processing:

[0050] Clean and organize the collected electricity consumption and economic growth data, remove outliers and missing values, and standardize the data to eliminate the impact of different dimensions and data ranges on the analysis results;

[0051] Center of gravity correlation analysis:

[0052] The correlation coefficient between electricity consumption and economic growth is calculated to measure the strength of the linear relationship between the two. Through regression analysis and other methods, a mathematical model between electricity consumption and economic growth is established to reveal the center of gravity relationship between the two.

[0053] Cycle correlation analysis:

[0054] Time series analysis methods, such as the autoregressive moving average model (ARMA) and the seasonal autoregressive integrated moving average model (SARIMA), are used to analyze the cyclical changes in electricity consumption and economic growth. By comparing the cyclicality and phase difference between the two, the cyclical correlation between electricity consumption and economic growth is revealed.

[0055] Dynamic Interaction Impact Analysis:

[0056] The dynamic interaction between electricity consumption and economic growth is analyzed by using methods such as vector autoregression model (VAR) and Granger causality test. The degree and duration of mutual influence between the two are quantified through methods such as impulse response function and variance decomposition.

[0057] Predicting future electricity consumption growth poles:

[0058] Based on the above analysis, combined with factors such as the macroeconomic situation, energy system, and technological progress, we predict future electricity consumption growth trends and potential areas (i.e., electricity consumption growth poles), and use forecasting models to quantitatively predict future electricity demand, providing decision support for grid planning and operations.

[0059] S4. Develop electricity consumption plan based on forecast results.

[0060] The electricity consumption plan can be an economic incentive policy. Based on the prediction results, economic incentive policies can be formulated to guide users to optimize their electricity consumption behavior.

[0061] Through technologies such as information collection and interaction, two-way passage and interactive terminals in smart electricity consumption, the entire business process can be automated and intelligent; users can receive incentive information and price signals through interactive terminals, change and optimize their consumption behavior and electricity usage methods, reduce the power load in the period, and promote the optimal balance of supply and demand.

[0062] In this embodiment, preferably, the economic incentive policies include direct economic subsidies and rewards; tax incentive demand-side management policies; and smart grid construction.

[0063] Direct economic subsidies and rewards: Energy-saving subsidies: For users who purchase and use high-efficiency energy-saving electrical equipment, the government can provide a certain percentage of subsidies, such as subsidies for 30% to 50% of the incremental energy-saving costs.

[0064] Rewards for reducing electricity consumption during peak hours: For users who voluntarily reduce electricity consumption during peak electricity demand periods, the government can provide compensation of 0.2-0.4 yuan per kilowatt-hour.

[0065] Rewards for new energy-saving projects: For new energy-saving projects, a one-time reward will be given based on the project's energy-saving effect and investment scale, such as the rewards for new projects mentioned in the economic incentives issued by the city.

[0066] Tax incentives: Tax incentives for energy-saving equipment purchases: For users or enterprises that purchase energy-saving equipment, a certain percentage of tax reduction or preferential tax rate can be provided, such as a preferential tax rate of 2% to 4%.

[0067] Tax incentives for green energy projects: Enterprises that invest in green energy projects can be given tax exemptions or incentives to encourage them to develop renewable energy.

[0068] Demand-side management (DSM) policy: guide users to adjust their electricity usage behavior through economic incentives, such as implementing time-of-use electricity prices and tiered electricity prices.

[0069] Smart grid construction: Strengthen the construction of smart grids, improve the regulation capacity and operation efficiency of the power system, and provide users with better power services.

[0070] Example 2, reference Figure 2, which is the second embodiment of the present invention, and is different from the first embodiment in that: a system of a user demand response evaluation method in a smart grid environment, comprising a database construction module 100, a potential level calculation module 200, a prediction module 300 and a power consumption plan formulation module 400; the database construction module 100 collects user power consumption data and constructs a user power consumption database; the potential level calculation module 200 analyzes the potential evaluation model of demand-side user response according to the evaluation index system and based on fuzzy optimization set pairing, conducts a comprehensive evaluation of the user, and obtains the user response potential level; the prediction module 300 uses the power consumption growth pole prediction model to predict the future power consumption growth pole by analyzing the center of gravity correlation, periodic correlation and dynamic interactive influence between power consumption and economic growth; the power consumption plan formulation module 400 formulates a power consumption plan according to the prediction results.

[0071] If the functions are implemented in the form of software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art or the part of the technical solution, can be embodied in the form of a software product. The computer software product is stored in a storage medium, including several instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the methods described in each embodiment of the present invention. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), disk or optical disk, etc., which can store program codes.

[0072] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as an ordered list of executable instructions for implementing logical functions, and can be embodied in any computer-readable medium for use by an instruction execution system, device or apparatus (such as a computer-based system, a system including a processor, or other system that can fetch instructions from an instruction execution system, device or apparatus and execute instructions), or in conjunction with such instruction execution systems, devices or apparatuses. For the purposes of this specification, "computer-readable medium" can be any device that can contain, store, communicate, propagate or transmit a program for use by an instruction execution system, device or apparatus, or in conjunction with such instruction execution systems, devices or apparatuses.

[0073] More specific examples of computer-readable media (a non-exhaustive list) include the following: an electrical connection with one or more wires (electronic device), a portable computer disk case (magnetic device), a random access memory (RAM), a read-only memory (ROM), an erasable and programmable read-only memory (EPROM or flash memory), an optical fiber device, and a portable compact disk read-only memory (CDROM). In addition, the computer-readable medium may even be a paper or other suitable medium on which the program is printed, since the program may be obtained electronically, for example, by optically scanning the paper or other medium, followed by editing, deciphering or, if necessary, processing in another suitable manner, and then stored in a computer memory.

[0074] It should be understood that the various parts of the present invention can be implemented by hardware, software, firmware or a combination thereof. In the above-mentioned embodiments, a plurality of steps or methods can be implemented by software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented by hardware, as in another embodiment, it can be implemented by any one of the following technologies known in the art or their combination: a discrete logic circuit having a logic gate circuit for implementing a logic function for a data signal, a dedicated integrated circuit having a suitable combination of logic gate circuits, a programmable gate array (PGA), a field programmable gate array (FPGA), etc.

[0075] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical solutions of the present invention, which should all be included in the scope of the claims of the present invention.

Claims

1. A user demand response evaluation method in a smart grid environment, characterized by: include, Collect user electricity consumption data and build a user electricity consumption database; According to the evaluation index system and based on fuzzy optimization set pairing, the potential evaluation model of demand-side user response is analyzed, and users are comprehensively evaluated to obtain the user response potential level; Using the electricity consumption growth pole prediction model, by analyzing the center of gravity correlation, cycle correlation and dynamic interaction between electricity consumption and economic growth, the future electricity consumption growth pole is predicted; Develop electricity consumption plans based on the forecast results.

2. A user demand response evaluation method in a smart grid environment as claimed in claim 1, characterized in that: The method for constructing the evaluation index system is to establish a sound evaluation index system from four aspects: user situation, user behavior, information value, and electricity consumption plan; The user situation includes power consumption, type of power consumption, and power consumption period; The user behavior includes electricity usage habits and response history; The information value includes information acquisition capability and information processing capability; The electricity consumption plan is based on the electricity price elasticity theory to predict and adjust the electricity consumption behavior, and to build a reward and subsidy mechanism for electricity energy conservation and consumption reduction; For each indicator, a quantitative method is used to assign weights.

3. A user demand response evaluation method in a smart grid environment as claimed in claim 2, characterized in that: The quantification method includes fuzzy evaluation method and hierarchical analysis method.

4. A user demand response evaluation method in a smart grid environment as claimed in claim 3, characterized in that: The potential evaluation model for analyzing the demand-side user response based on fuzzy optimization set pairs includes treating the evaluation sample and the evaluation level as a set pair, performing similarity, difference and opposite quantitative analysis, and obtaining the potential level through connection degree calculation.

5. A user demand response evaluation method in a smart grid environment as claimed in claim 4, characterized in that: The formula of the connection degree is expressed as: u=a+bl+cJ Among them, a, b, c represent identity, difference and opposition respectively, and satisfy a+b+c=1, l represents the difference coefficient, and its value range is [-1,1]; when l=-1 or 1, it means that the difference is certain; when l is close to 0, it means that the uncertainty of the difference increases, and J represents the opposition coefficient, and its value is -1.

6. A user demand response evaluation method in a smart grid environment as claimed in claim 5, characterized in that: After calculating the connection degree of each indicator, a comprehensive connection degree is obtained by combining the weights of each indicator, and the level of user response potential is determined according to the size of the comprehensive connection degree; The construction of the electricity consumption growth pole prediction model includes data collection, data processing, center of gravity correlation analysis, cycle correlation analysis, dynamic interaction impact analysis, and prediction of future electricity consumption growth poles.

7. A user demand response evaluation method in a smart grid environment as claimed in claim 6, characterized in that: According to the prediction results, the power consumption plan is formulated based on the information collection and interaction, two-way passage and interactive terminal technology in smart power consumption, so as to automate and intelligentize the entire system.

8. A system using a user demand response evaluation method in a smart grid environment according to any one of claims 1 to 7, characterized in that: It includes a database construction module (100), a potential level calculation module (200), a prediction module (300) and an electricity consumption plan formulation module (400); The database construction module (100) collects user electricity usage data and constructs a user electricity usage database; The potential level calculation module (200) analyzes the potential evaluation model of the user response on the demand side according to the evaluation index system and based on the fuzzy optimization set pair, performs a comprehensive evaluation on the user, and obtains the user response potential level; The prediction module (300) uses the electricity consumption growth pole prediction model to predict the future electricity consumption growth pole by analyzing the center of gravity correlation, cycle correlation and dynamic interaction between electricity consumption and economic growth; The electricity usage plan formulation module (400) formulates an electricity usage plan according to the prediction results.

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of a method for evaluating user demand response in a smart grid environment according to any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of a method for evaluating user demand response in a smart grid environment according to any one of claims 1 to 7 are implemented.

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