Electric power smart energy unit and control method based on multi-source data analysis

Abstract

The present invention discloses an electric smart energy unit based on multi-source data analysis and a control method thereof, and relates to the field of electric load management. The technical solution of the present invention collects multi-source data of power equipment of a downstream connected user system, identifies the operating status of adjustable load resources, calculates the demand response capability of adjustable load resources, makes a judgment on the rationality of the adjustable load value, reports the value of the adjustable capacity calculation result that is too small and sets it to zero, realizes user-side load flexible regulation through demand response or direct load regulation, selects a suitable demand response strategy according to the content of the demand response signal, and pushes the demand response signal content and the recommended response strategy through the user system gateway in the demand response interactive interface of the user system, and the user selects a specific response strategy or load regulation means to regulate the adjustable load resources that cannot be directly controlled.

Description

Technical Field

[0001] The present invention relates to the field of power load management, and in particular to a power smart energy unit based on multi-source data analysis and a control method thereof. Background Art

[0002] The power load management system is a basic platform for collecting real-time power consumption information from the client, and monitors the power load by using communication technology, computer technology, and automatic control technology. At present, the power load control methods mainly include indirect, direct, decentralized and centralized control. The indirect control method is to charge different electricity prices according to the customer's maximum power demand, or the peak and valley power consumption, so as to stimulate customers to reduce peaks and fill valleys; the direct control method is to cut off a part of the load that can be interrupted during peak power consumption; the decentralized control method is to control the load of each customer according to the requirements of improving the load curve, through the timer switches, dosing devices and other devices installed at each customer; the centralized control method is that the load control master station controls the customer's interruptible load in a centralized manner according to the requirements of improving the load curve through the control channel connected to the customer and the terminal device installed at the customer.

[0003] The existing load control systems mostly collect and analyze the parameters of one or a few types of power equipment during operation to achieve the adjustment process of adjustable load resources in a specific use environment or time period. However, due to the limitation of the types of collected data, the control strategy based on power demand has a narrow scope of application and cannot achieve comprehensive and intelligent load resource adjustment. To this end, we propose an electric smart energy unit based on multi-source data analysis and its control method. Summary of the invention

[0004] The main purpose of the present invention is to provide an electric smart energy unit and a control method thereof based on multi-source data analysis, which can effectively solve the problems in the background technology.

[0005] In order to achieve the above object, the technical solution adopted by the present invention is:

[0006] The electric power smart energy unit based on multi-source data analysis includes:

[0007] A data acquisition module is used to collect multi-source data of power equipment in the downstream connected user system;

[0008] The data processing module is used to store the most recently collected data of the user system power equipment, identify the operating status of the adjustable load resources, calculate the adjustable load resource demand response capability, judge the rationality of the calculated adjustable load value, report the value of the adjustable capacity calculation result that is too small, and perform zero processing;

[0009] Demand response and load flexibility control module, used to achieve user-side load flexibility adjustment through demand response or load direct control;

[0010] The demand response and load flexible control module includes a demand response strategy submodule, a load direct regulation submodule, and a demand response interaction submodule;

[0011] The demand response strategy submodule supports the formulation, storage and update of demand response strategies. The specific contents are as follows:

[0012] Based on the operating characteristics, operation management methods, and production scheduling mechanism of the user's power equipment or system, a demand response strategy is formulated in advance according to the requirements of the demand response business. The demand response strategy can be associated with the operating strategy of the specific power system or equipment and the user's production scheduling plan;

[0013] Preset built-in demand response strategies to meet the operation mode of the user-side power system or equipment or the actual production scheduling requirements, save no less than 10 demand response strategies, and save and reference relevant parameters set by the user in the strategy;

[0014] Update the built-in demand response strategy through parameter setting or file transfer through the local maintenance interface to adapt to changes in adjustable load resources on the user side and changes in operation management and production scheduling mechanisms. Set and modify user parameters in the built-in strategy of the smart energy unit through the interactive interface of the user interactive device and the user system demand response processing module;

[0015] The load direct control submodule generates device control instructions based on demand response signals or user setting instructions and the strategy preset in the unit to directly control the adjustable load resources. The specific contents are as follows:

[0016] According to the content of the demand response signal sent by the master station, the demand response strategy is selected, and according to the communication protocol supported by the adjustable load resources, a control instruction set is generated, including equipment start and stop instructions, energy storage device switching instructions, charging pile power control instructions, central air-conditioning unit circulating water temperature setting instructions and water pump operating frequency setting instructions;

[0017] According to the air conditioning temperature setting instruction issued by the master station, according to the type of air conditioning unit and the communication protocol it supports, an operating parameter control instruction or a set temperature adjustment instruction is generated;

[0018] The generated equipment control instructions are sent to the user system or adjustable load resources, and the strategy can be dynamically adjusted according to the monitored equipment load changes, operating status changes and other information fed back from the site, and instructions can be sent to achieve a closed control loop;

[0019] Convert the generated control instructions into switch signals to directly control the start and stop status of adjustable load resources;

[0020] The demand response interaction submodule is used to process the adjustable load resources that cannot be directly controlled. The specific contents are as follows:

[0021] Select a demand response strategy based on the content of the demand response signal, and push the demand response signal content and the recommended response strategy on the demand response interactive interface of the user system through the user system gateway;

[0022] Through the user interaction device, the demand response signal content and the recommended response strategy are pushed to the user on its display interface. The user chooses the specific response strategy or load regulation method by himself, and feeds back the willingness to participate in the demand response to the main station on the interface of the user system or interactive device.

[0023] The data acquisition module comprises:

[0024] An electrical quantity data acquisition submodule for acquiring electrical quantity data of adjustable load resources;

[0025] An operating parameter and operating status data collection submodule for collecting operating parameters and operating status of adjustable load resources;

[0026] A demand response capability data collection submodule that reads demand response capability data provided by a user system through a user system gateway device;

[0027] A user's production environment status data collection submodule for collecting the user's production environment status data;

[0028] A DC analog quantity data acquisition submodule for acquiring multiple DC analog quantities;

[0029] A switch state quantity data acquisition submodule used to collect multi-channel switch state quantities and convert the state quantity signals into corresponding state data through built-in parameters.

[0030] The collection method of the electrical quantity data collection submodule includes:

[0031] Read the current electrical quantity data stored in the load monitoring instrument;

[0032] Read the current electrical quantity data stored in other intelligent devices with AC sampling function;

[0033] Among them, the collection time interval is within 1 to 60 minutes;

[0034] The collection method of the operating parameter and operating status data collection submodule includes:

[0035] It is obtained through the communication interface connected to the adjustable load resource or various matching sensors;

[0036] Obtained from the user system and analyzed and judged based on load data;

[0037] Among them, the collection interval is within 1 to 60 minutes;

[0038] The collection method of the production environment status data collection submodule includes:

[0039] Obtained by directly connected environmental status sensors;

[0040] Obtained from the user's system;

[0041] Among them, the collection interval is within 1 to 60 minutes;

[0042] The DC analog quantity includes 4-20mA current signal and 0-5V voltage signal.

[0043] The data processing module comprises:

[0044] A data storage submodule, used for storing collected data;

[0045] The operation status identification submodule is used to automatically identify the operation status of the adjustable load resources through an algorithm model based on the collected current load data, historical load curve data, other types of electrical parameter data, load peak duration, and load fluctuation cycle time data of the adjustable load resources. The specific steps are as follows:

[0046] S11: Setting the threshold range of the i-th type of adjustable load resource [Ad imin ,Ad imax ], among which, Ad imin is the lower limit of the i-th type of adjustable load resources, Ad imax is the upper limit value of the i-th type of adjustable load resources;

[0047] S12: Build a prediction model, and obtain the prediction value in the next sampling period according to the model and the historical data of the adjustable load resource, wherein the expression of the prediction model is:

[0048] Ad i(T+1) =λAd iT +λ(1-λ)Ad i(T-1) +λ(1-λ) 2 Ad i(T-2) +...+λ(1-λ) T-1 Ad i1 +(1-λ) T Ad i1 In the formula, Ad i(T+1)It is expressed as the predicted value of the i-th type of adjustable load resource in the T+1th sampling period; Ad iT It is represented as the sampling value of the i-th type of adjustable load resource in the T-th sampling period, λ is a constant coefficient, where the value range of λ is λ∈(0,1);

[0049] S13: Determine the operating status of the i-th type of adjustable load resource by comparing the predicted value in the T+1th sampling period with the lower limit value and the upper limit value. The determination principle is:

[0050] If Ad imin <Ad i(T+1) <Ad imax , then it is determined that the operating state of the i-th type of adjustable load resources is normal;

[0051] If Ad i(T+1) ≤Ad imin , or Ad i(T+1) ≥Ad imax , then it is determined that the operating state of the i-th type of adjustable load resource is abnormal;

[0052] A demand response capability calculation submodule is used to calculate the demand response capability of adjustable load resources;

[0053] The abnormal data identification and processing submodule is used to judge the rationality and validity of the collected load data, automatically filter out abnormal data caused by communication failures, meter creep problems and ratio errors, avoid reporting too high or too low abnormal load data to the main station and participate in the calculation process of the adjustable load resource demand response capability, judge the rationality of the calculated adjustable load value, and set the reported adjustable capacity data to zero for values ​​that are too small in the adjustable capacity calculation result.

[0054] The specific work content of the data storage submodule includes:

[0055] When data collection fails due to communication failure, the data at the corresponding time is set to null value;

[0056] The stored value of electrical quantity should be primary side data;

[0057] The collected load curve data and ambient temperature and humidity curve data. The curve data storage interval is determined by the interval of the data upload task, with the minimum interval being 1 minute and the maximum interval being 60 minutes.

[0058] The storage of load data and ambient temperature and humidity curve data is to save the data of the latest month or more, and the collection time interval is not less than 15 minutes. Among them, the load curve data refers to the total positive active power, and the ambient temperature and humidity curve data refers to the ambient temperature and relative humidity at the end of the air-conditioning system;

[0059] According to the needs of deepening the application of power grid business, it supports data storage and statistical data query and upload of other electrical and non-electrical quantities.

[0060] The specific work content of the demand response capability calculation submodule includes:

[0061] The demand response capability data of the adjustable load resource at least includes the adjustable load value, the duration of the adjustment process and the sustainable duration of the response;

[0062] Calculate the demand response capacity of each adjustable load resource on the current user side and the total demand response capacity of all adjustable load resources in real time;

[0063] In the air conditioning load adjustment application scenario, the air conditioning load adjustable capacity is divided into two levels. The high-level adjustable capacity is suitable for minute-level demand response business, and the required sustainable duration is no more than 1 hour; the low-level adjustable capacity is suitable for air conditioning energy-saving operation scenarios under orderly power consumption conditions, and the required sustainable duration is more than 2 hours;

[0064] In the air conditioning load adjustment application scenario, based on the air conditioning load data, set temperature and ambient temperature data, and operating parameters, the algorithm model is used to calculate the high-level and low-level load adjustable capabilities of the user-side air conditioning equipment. The specific method is as follows:

[0065] S21: Acquire air conditioning load data;

[0066] S22: Setting the benchmark load P of the user-side air conditioning equipment at high and low levels r1 , P r2 ;

[0067] S23: Calculate the corresponding high-level and low-level cumulative load regulation data in the demand response event, including the single regulation load P t1 , P t2 and the total number of controls N1, N2;

[0068] S24: Calculate and obtain the high-level and low-level load adjustable capacities R1 and R2 of the user-side air-conditioning equipment respectively according to the formula, and the calculation formula is:

[0069]

[0070] Where P t1j It is expressed as the jth regulated load under the high level of demand response event; P t2j It is represented as the j-th regulated load under the low level of demand response event.

[0071] The energy unit also includes a data query and upload module, which is used to query the acquired multi-source data and report the query results, wherein the query method includes querying through the master station via the remote communication interface and querying through the local maintenance tool via the local maintenance interface; the data reporting method includes query result reporting and active reporting;

[0072] The data query and upload module includes:

[0073] The data query submodule is used to support the query of the system master station and local maintenance tools. The queryable data content includes at least:

[0074] Electrical quantity data of adjustable load resources collected by the smart energy unit;

[0075] The data acquisition module collects the operating parameters, operating status, ambient temperature and humidity, and air conditioning set temperature data of the air conditioning unit / system;

[0076] Demand response capability data of adjustable load resources collected from user systems;

[0077] Calculate and analyze the demand response capability data of adjustable load resources;

[0078] The event record query submodule is used to support local maintenance tool query, where the queryable data content includes:

[0079] Demand response event records, including the content of the demand response signal received by the smart energy unit, the user's decision and the decision time;

[0080] Load control event records, including records of air conditioning temperature control commands received by the smart energy unit;

[0081] Smart energy unit abnormal event records, including event occurrence and recovery time, and event content;

[0082] The data active upload submodule is used to support active data upload. The specific contents include:

[0083] The smart energy unit actively sends task data to the master station according to the cycle and data item requirements specified by the sending task parameters;

[0084] When an abnormality occurs, the smart energy unit will actively send abnormal event record data to the main station according to the requirements of the event record parameters;

[0085] The smart energy unit can take flow control measures for wireless public network communications, including 80% flow consumption warning, to avoid flow limit excess and communication interruption caused by abnormal data active upload function;

[0086] The energy unit also includes an event recording module for recording events, which are classified into demand response events, air conditioning load control events, and smart energy unit abnormal events. The event recording module includes:

[0087] The demand response event recording submodule is used to record the received demand response signal during the demand response business process, forward it to the user system or user interaction device, and supplement the record of user decisions. The specific contents are as follows:

[0088] The demand response event record contains information transmitted by the demand response signal and the user's decision;

[0089] Save no less than 100 recent demand response event records;

[0090] The air conditioning load control event recording submodule is used to record the received temperature setting value during the air conditioning load control business process, forward it to the air conditioning control module or user system, and supplement the user's decision. The specific contents are as follows:

[0091] The air conditioning load control event record includes the occurrence time;

[0092] The air conditioning temperature setting value before adjustment;

[0093] The air conditioning temperature setting value sent by the master station;

[0094] Save no less than 100 recent air conditioning load control event records;

[0095] The abnormal operation event recording submodule is used to automatically determine the occurrence or recovery of abnormal operation events according to the set event attributes, and when the event occurs or recovers, decide whether to report it according to the configuration, and record the reporting status. The specific contents are as follows:

[0096] The content of each record includes the event type, occurrence time, and related associated data information;

[0097] Event reporting needs to be reported independently by channel, and the reporting status is recorded separately by channel. The reporting status includes "not reported", "reported", and "reported but not confirmed";

[0098] Detect and record user-defined events of different severity, and actively report user-defined events of importance to the main station;

[0099] Save no less than 100 recent abnormal event records of smart energy units.

[0100] The energy unit also includes a parameter setting and query module, which is used to set and query device configuration parameters, system clock, uplink and downlink communication parameters, event recording parameters, and collection and uploading task parameters according to commands, including:

[0101] The clock call and time setting submodule is used to receive the clock call and time calibration commands from the master station. The time calibration error shall not exceed 5s. Under reference conditions, the clock daily timing error shall be ≤±1s / d.

[0102] Configuration parameter setting submodule, used for setting and querying the logical address and working mode configuration parameters of the smart energy unit by the master station and local maintenance tools, and querying the software and hardware versions and manufacturer information configuration parameters by the master station and local maintenance tools;

[0103] The communication parameter setting submodule is used by the master station and local maintenance tools to set and query remote communication parameters, including wireless remote communication parameters and Ethernet communication parameters; it is also used by local maintenance tools to set and query local IoT communication parameters, including Ethernet communication parameters of the local network, user system gateway communication parameters, communication parameters between load monitoring instruments or collection points, air conditioning control module communication parameters, adjustable load resource communication parameters, environmental monitoring equipment communication parameters, and other local networking equipment communication parameters;

[0104] The event record parameter setting submodule is used to set and query event record parameters by the local maintenance tool, including event recognition and recording switches, event upload switches, and abnormal judgment thresholds for various abnormal events;

[0105] The collection and upload task parameter setting submodule is used by the master station and local maintenance tools to set and query the collection and upload task parameters, specifically:

[0106] Supports the setting and query of the collection data items and collection frequency of adjustable load resource electrical quantity, operating status and operating parameters, and environmental quantity;

[0107] Supports the setting and query of data items and frequency of transmission of adjustable load resource electrical quantity, operating status and operating parameters, and environmental quantity;

[0108] The clear and reset setting submodule is used to realize clearing and resetting through the remote or local maintenance interface. The specific contents are as follows:

[0109] Support data clearing command. When receiving the command, clear all types of data collected by the adjustable load resources stored in the smart energy unit;

[0110] Support event clearing command. When receiving the command, all event records stored in the smart energy unit will be cleared.

[0111] Support parameter reset command. When receiving the command, all parameters except remote communication parameters and clock parameters in the smart energy unit will be initialized to default values.

[0112] Support hardware reset command. When receiving the command, the smart energy unit will be restarted.

[0113] The adjustable load resource parameter setting submodule is used to set and query the adjustable load resource parameters by the local maintenance tool, including the serial number, name, attribute, corresponding load collection point serial number and abnormal load judgment threshold of each adjustable load resource;

[0114] It is also used by local maintenance tools to set and query air-conditioning system parameters, including the rated input power of the air-conditioning unit under different operating conditions, the frequency conversion range, the set temperature of the condenser and evaporator operating conditions, and the system cooling capacity and heating capacity parameters.

[0115] The electric power smart energy unit control method based on multi-source data analysis includes the following steps:

[0116] Step 1: Collect multi-source data of power equipment in the downstream connected user system, including electrical quantity data of adjustable load resources, operating parameters and operating status of adjustable load resources, demand response capability data provided by the user system, production environment status data of the user, DC analog quantity data, and switch status quantity data;

[0117] Step 2: Store the most recently collected data of the user system power equipment, identify the operating status of the adjustable load resources, calculate the adjustable load resource demand response capability, judge the rationality of the calculated adjustable load value, report the value of the adjustable capacity calculation result that is too small, and set it to zero;

[0118] Step 3: For directly controllable adjustable load resources, flexible load adjustment on the user side is achieved through demand response or direct load regulation. The specific steps are as follows:

[0119] Based on the operating characteristics, operation management methods, and production scheduling mechanism of the user's power equipment or system, a demand response strategy is formulated in advance according to the requirements of the demand response business. The demand response strategy can be associated with the operating strategy of the specific power system or equipment and the user's production scheduling plan;

[0120] Preset built-in demand response strategies to meet the operation mode of the user-side power system or equipment or the actual production scheduling requirements, save no less than 10 demand response strategies, and save and reference relevant parameters set by the user in the strategy;

[0121] Update the built-in demand response strategy through parameter setting or file transfer through the local maintenance interface to adapt to changes in adjustable load resources on the user side and changes in operation management and production scheduling mechanisms. Set and modify user parameters in the built-in strategy of the smart energy unit through the interactive interface of the user interactive device and the user system demand response processing module;

[0122] Generate device control instructions based on demand response signals or user-set instructions and preset strategies in the unit to directly control adjustable load resources. The specific contents are as follows:

[0123] According to the content of the demand response signal sent by the master station, the demand response strategy is selected, and according to the communication protocol supported by the adjustable load resources, a control instruction set is generated, including equipment start and stop instructions, energy storage device switching instructions, charging pile power control instructions, central air-conditioning unit circulating water temperature setting instructions and water pump operating frequency setting instructions;

[0124] According to the air conditioning temperature setting instruction issued by the master station, according to the type of air conditioning unit and the communication protocol it supports, an operating parameter control instruction or a set temperature adjustment instruction is generated;

[0125] The generated equipment control instructions are sent to the user system or adjustable load resources, and the strategy can be dynamically adjusted according to the monitored equipment load changes, operating status changes and other information fed back from the site, and instructions can be sent to achieve a closed control loop;

[0126] Convert the generated control instructions into switch signals to directly control the start and stop status of adjustable load resources;

[0127] Step 4: For adjustable load resources that cannot be directly controlled, the specific steps of the control process are:

[0128] Select a demand response strategy based on the content of the demand response signal, and push the demand response signal content and the recommended response strategy on the demand response interactive interface of the user system through the user system gateway;

[0129] Through the user interaction device, the demand response signal content and the recommended response strategy are pushed to the user on its display interface. The user chooses the specific response strategy or load regulation method by himself, and feeds back the willingness to participate in the demand response to the main station on the interface of the user system or interactive device.

[0130] The present invention has the following beneficial effects:

[0131] Compared with the prior art, the technical solution of the present invention collects multi-source data of power equipment in the downstream connected user system, identifies the operating status of adjustable load resources, calculates the demand response capacity of adjustable load resources, makes a judgment on the rationality of the adjustable load value, reports and sets to zero the value of the adjustable capacity calculation result that is too small, realizes flexible load regulation on the user side through demand response or direct load control, selects a demand response strategy according to the content of the demand response signal, and pushes the demand response signal content and the recommended response strategy through the user system gateway in the demand response interactive interface of the user system, and the user selects a specific response strategy or load regulation means to adjust the adjustable load resources that cannot be directly controlled, thereby realizing intelligent management of adjustable load resources. BRIEF DESCRIPTION OF THE DRAWINGS

[0132] Figure 1 It is a structural block diagram of the electric power smart energy unit based on multi-source data analysis of the present invention;

[0133] Figure 2 It is a flow chart of the electric power smart energy unit control method based on multi-source data analysis of the present invention;

[0134] Figure 3 It is the structural block diagram of the data acquisition module;

[0135] Figure 4 It is the structural block diagram of the data processing module;

[0136] Figure 5 It is the structural block diagram of the demand response and load flexible control module;

[0137] Figure 6 It is the structural block diagram of the event recording module;

[0138] Figure 7 This is the structural diagram of the data query and upload module;

[0139] Figure 8 This is the structural block diagram of the parameter setting and query module. DETAILED DESCRIPTION

[0140] The present invention will be further described below in conjunction with specific implementation methods, wherein the accompanying drawings are only used for exemplary descriptions and represent only schematic diagrams rather than actual drawings, and should not be understood as limiting the present invention. In order to better illustrate the specific implementation methods of the present invention, some parts of the accompanying drawings may be omitted, enlarged or reduced, and do not represent the size of the actual product.

[0141] Example 1

[0142] like Figure 1-8As shown, in this embodiment, we take the industrial enterprise demand response interactive scenario and the air conditioning load adjustment application scenario as examples to illustrate the technical solution of this application, and the specific process is as follows:

[0143] Step 1: Electrical quantity data collection, collect electrical quantity data of adjustable load resources, the collection method is to read the current electrical quantity data stored in the load monitoring instrument, read the current electrical quantity data stored in other intelligent devices with AC sampling function (such as intelligent sockets), the collection time interval can be set within 1 to 60 minutes according to the power grid business needs, the default interval time is 1 minute, the collected electrical quantity data items are shown in Table 1 below,

[0144] Table 1 Electrical quantity data data item table

[0145]

[0146] In the table, 1 represents the electrical quantity data that needs to be collected, and 0 represents the electrical quantity data that does not need to be collected.

[0147] Step 2: Operation parameter and status data collection, collect the operation parameters and operation status of the adjustable load resources, the collection method is to connect the communication interface of the adjustable load resources or various supporting sensors, obtain from the user system, and analyze and judge based on the load data. The collection interval can be set within 1 to 60 minutes according to the business needs of the power grid, and the default interval time is 5 minutes. The contents of the operation parameters and operation status data items that can be collected can follow the examples in Table 2 below, but are not limited to those listed in the table below. According to the type of adjustable load resources, collect appropriate operation parameters and operation status data;

[0148] Table 2 Air conditioning / system operating parameters

[0149]

[0150]

[0151] The air conditioning set temperature is defined as the target value of the ambient temperature of the relatively closed space where the air conditioning system terminal (indoor unit) is located. The smart energy unit can collect the set temperature of split air conditioners and multi-split system air conditioners. The collection method includes obtaining directly or through the air conditioning communication control module, air conditioning management and user system. It collects the inlet and outlet water temperature set values ​​of the cold / warm water pipes of the water-cooled unit and the air-cooled heat pump unit, and can assist in analysis based on the indoor ambient temperature to calculate the air conditioning set temperature. Depending on the type of air conditioner, the source of the air conditioning set temperature data complies with the provisions of Table 3.

[0152] Table 3 Comparison table of air conditioning setting temperature

[0153]

[0154] Step 3: Demand response capability collection. When the production equipment management and monitoring system of industrial enterprise users, such as the distributed control system (DCS) of the production line, has developed a power demand response function module through configuration software programming according to the demand response construction requirements. This function module can automatically calculate the demand response capability data of the adjustable load resources based on the operating status data of each equipment in the system, such as the material buffer capacity of each link of the production process, the consumption rate of raw materials and intermediate materials, including the operation time required for equipment shutdown, the load value that can be reduced after shutdown, the shutdown state can be maintained for a certain period of time, and the time required to resume production. The above-mentioned demand response capability data provided by the user system is directly read through the user system gateway device. The collection interval can be set according to the implementation requirements of the demand response business, with a minimum of 1 minute and a maximum of no more than 30 minutes;

[0155] Step 4: Environmental data collection

[0156] Collect the user's production environment status data. The collection methods include directly connecting to the environment status sensor and obtaining from the user system. Collect the user's air conditioning system terminal (indoor) and outdoor ambient temperature and humidity data. The data sources include ambient temperature and humidity sensors and smart sockets. The collection interval can be set within 1 to 60 minutes according to the needs of the power grid business. The default interval is 5 minutes. The contents of the collected environmental status data items follow the following table 4.

[0157] Table 4 Environmental data table

[0158]

[0159] Step 5: DC analog quantity acquisition

[0160] Collect multiple DC analog quantities, which can be 4-20mA current signals and 0-5V voltage signals, and convert the DC analog signals into digital signals for processing. According to the actual needs of user-side data collection, the collected data can be used to characterize environmental status data, as well as the operating status of adjustable load resources and other purposes;

[0161] Step 6: Switch state quantity collection

[0162] Collect the state quantities of multiple switches and convert the state quantity signals into corresponding state data through built-in parameters. According to the actual needs of data collection on the user side, these state data can be used to indicate whether the user has received the demand response signal, or to indicate the open and closed state of the cabinet door, or the switch state of the adjustable load resource power supply circuit;

[0163] Step 7: Data Storage

[0164] The smart energy unit can store the most recently collected data to meet the needs of local computing analysis, active data upload, and master station call for testing. Among them, it should be noted that:

[0165] When data collection fails due to communication failure, the data at the corresponding time is set to NULL, and historical data should not be used as a replacement (except for split air conditioner set temperature using passive collection);

[0166] The stored value of electrical quantity should be primary side data;

[0167] Store the collected load curve data and ambient temperature and humidity curve data. The interval for storing the curve data is determined by the interval for sending data. The minimum interval is 1 minute and the maximum interval is 60 minutes.

[0168] The storage of load data and ambient temperature and humidity curve data is to save the data of the latest month (under the condition of 15-minute interval). The load curve data refers to the total positive active power, and the ambient temperature and humidity curve data refers to the ambient temperature and relative humidity at the end of the air-conditioning system (indoor);

[0169] According to the needs of deepening the application of power grid business, it supports the data storage and statistics of other electrical and non-electrical quantities;

[0170] Step 8: Identify the operating status of adjustable load resources

[0171] According to the collected current load data, historical load curve data, other types of electrical parameter data, load peak duration, and load fluctuation cycle time data of the adjustable load resources, the operating status of the adjustable load resources is automatically identified through the algorithm model. The specific steps are as follows:

[0172] S11: Setting the threshold range of the i-th type of adjustable load resource [Ad imin ,Ad imax ], among which, Ad imin is the lower limit of the i-th type of adjustable load resources, Ad imax is the upper limit value of the i-th type of adjustable load resources;

[0173] S12: Build a prediction model, and obtain the prediction value in the next sampling period according to the model and the historical data of the adjustable load resource, wherein the expression of the prediction model is:

[0174] Ad i(T+1) =λAd iT +λ(1-λ)Ad i(T-1) +λ(1-λ) 2 Ad i(T-2) +...+λ(1-λ) T-1 Ad i1+(1-λ) T Ad i1 In the formula, Ad i(T+1) It is expressed as the predicted value of the i-th type of adjustable load resource in the T+1th sampling period; Ad iT It is represented as the sampling value of the i-th type of adjustable load resource in the T-th sampling period, λ is a constant coefficient, where the value range of λ is λ∈(0,1);

[0175] S13: Determine the operating status of the i-th type of adjustable load resource by comparing the predicted value in the T+1th sampling period with the lower limit value and the upper limit value. The determination principle is:

[0176] If Ad imin <Ad i(T+1) <Ad imax , then it is determined that the operating state of the i-th type of adjustable load resources is normal;

[0177] If Ad i(T+1) ≤Ad imin , or Ad i(T+1) ≥Ad imax , then it is determined that the operating status of the i-th type of adjustable load resources is abnormal.

[0178] Step 9: Calculation and analysis of adjustable load resource demand response capability

[0179] The load resource demand response capacity calculation gap function can be adjusted. The specific functions are as follows:

[0180] The demand response capability data for load regulation resources should include the adjustable load value, the duration of the regulation process, and the duration of the response.

[0181] Calculate the demand response capacity of each adjustable load resource on the current user side and the total demand response capacity of all adjustable load resources in real time;

[0182] In the air conditioning load adjustment application scenario, the air conditioning load adjustable capacity is divided into two levels. The high-level adjustable capacity is suitable for minute-level demand response business, and the generally required sustainable duration is no more than 1 hour. The low-level adjustable capacity is suitable for air conditioning energy-saving operation scenarios under orderly power consumption conditions, and the generally required sustainable duration is more than 2 hours, and there may be multiple periods in a day;

[0183] In the air conditioning load adjustment application scenario, based on the air conditioning load data, set temperature and ambient temperature data, and operating parameters, the algorithm model is used to calculate the load adjustable capacity of the user-side air conditioning equipment corresponding to the above two levels. The specific method is as follows:

[0184] S21: Acquire air conditioning load data;

[0185] S22: Setting the benchmark load P of the user-side air conditioning equipment at high and low levels r1 , P r2 ;

[0186] S23: Calculate the corresponding high-level and low-level cumulative load regulation data in the demand response event, including the single regulation load P t1 , P t2 and the total number of controls N1, N2;

[0187] S24: Calculate and obtain the high-level and low-level load adjustable capacities R1 and R2 of the user-side air-conditioning equipment respectively according to the formula, and the calculation formula is:

[0188]

[0189] Where P t1j It is expressed as the jth regulated load under the high level of demand response event; P t2j It is represented as the j-th regulated load under the low level of demand response event.

[0190] Step 10: Abnormal data identification and processing

[0191] Judge the rationality and validity of the collected load data, automatically filter out abnormal data caused by communication failures, meter creep problems, and ratio errors, and avoid reporting too high or too low abnormal load data to the master station and participating in the calculation process of the adjustable load resource demand response capability. Judge the rationality of the calculated adjustable load value, and set the reported adjustable capacity data to zero for values ​​that are too small for the adjustable capacity calculation result;

[0192] Step 11: Data query and upload

[0193] The query method includes querying through the remote communication interface of the master station and querying through the local maintenance interface of the local maintenance tool. The data reporting method includes query result reporting and active reporting. Specifically:

[0194] Data query, collected data can support system master station and local maintenance tool query, the queryable data content includes at least:

[0195] The collected electrical quantity data of adjustable load resources;

[0196] The collected operating parameters, operating status, ambient temperature and humidity, and air conditioning set temperature data of the air conditioning unit / system;

[0197] Demand response capability data of adjustable load resources collected from user systems;

[0198] The demand response capability data of adjustable load resources is calculated and analyzed.

[0199] Event record query

[0200] The data that can be queried include:

[0201] Demand response event records, including the content of the received demand response signal, the user's decision and the time of the decision;

[0202] Load control event records, including records of received instructions for setting air conditioning temperature control;

[0203] Abnormal event records, including event occurrence and recovery time, and event content.

[0204] Data is sent automatically

[0205] Supports active data upload, as follows:

[0206] Actively send task data to the master station according to the cycle and data item requirements specified by the sending task parameters;

[0207] When an abnormality occurs, it will actively send abnormal event record data to the main station according to the requirements of event record parameters;

[0208] Traffic control measures are taken for wireless public network communications, including 80% traffic consumption warning, to avoid traffic limit excess and communication interruption caused by abnormal data active upload function.

[0209] Event Log

[0210] Events are recorded and divided into three categories: demand response events, air conditioning load control events, and smart energy unit abnormal events. Specifically:

[0211] Demand response event logging

[0212] During the demand response service process, the received demand response signal is recorded, forwarded to the user system or user interaction device, and the user decision is supplemented;

[0213] The demand response event record contains information transmitted by the demand response signal and the user's decision;

[0214] Save no less than 100 recent demand response event records;

[0215] Air conditioning load control event record

[0216] During the air conditioning load control business, the received temperature setting value is recorded, forwarded to the air conditioning control module or user system, and the user decision is supplemented;

[0217] The air conditioning load control event record includes the time of occurrence, the air conditioning temperature setting value before the control, and the air conditioning temperature setting value issued by the master station;

[0218] Save no less than 100 recent air conditioning load control event records.

[0219] Abnormal operation event record

[0220] Automatically determine the occurrence or recovery of abnormal operation events based on custom event attributes. When an event occurs or recovers, determine whether it needs to be reported based on the configuration, and record the reporting status at the same time;

[0221] The content of each record includes the event type, occurrence time, and related associated data information;

[0222] Event reporting needs to be done independently by channel, and the reporting status should be recorded separately by channel, including "not reported", "reported", and "reported but not confirmed";

[0223] Detect and record the following events, as shown in Table 5. Important events can be actively reported to the master station;

[0224] Save no less than 100 recent abnormal event records of smart energy units;

[0225] Table 5 Customized operation abnormal event importance classification table

[0226]

[0227]

[0228] Step 12: Parameter setting and query

[0229] According to the command, set and query the device configuration parameters, system clock, uplink and downlink communication parameters, event recording parameters, and collection and upload task parameters, specifically:

[0230] Clock call and time calibration

[0231] Receive the clock call and time calibration commands from the master station, and the time calibration error shall not exceed 5s. Under reference conditions, the daily timing error of the smart energy unit clock shall be ≤±1s / d;

[0232] Support heartbeat time calibration (automatic synchronization of the master station clock). If the deviation from the master station clock is greater than the set value, the clock can be obtained according to the heartbeat packet for automatic time calibration;

[0233] Configuration parameters

[0234] The master station and local maintenance tools set and query the logical address and working mode (operation mode or maintenance mode) configuration parameters of the smart energy unit;

[0235] The master station and local maintenance tools query the software and hardware versions, manufacturer information and configuration parameters;

[0236] Communication parameters

[0237] The master station and local maintenance tools are used to set and query remote communication parameters, including wireless remote communication parameters and Ethernet communication parameters;

[0238] The local maintenance tool sets and queries the local IoT communication parameters, including the Ethernet communication parameters of the local network, the user system gateway communication parameters, the load monitoring instrument (load collection point) communication parameters, the air conditioning control module communication parameters, the adjustable load resource communication parameters, the environmental monitoring equipment communication parameters, and other local networking equipment communication parameters;

[0239] Event Recording Parameters

[0240] The local maintenance tool sets and queries event recording parameters, including event identification and recording switches, event upload switches, and abnormality judgment thresholds for various abnormal events.

[0241] Collect and upload task parameters

[0242] The master station and local maintenance tools set and query the collection and upload task parameters, specifically:

[0243] Supports the setting and query of the collection data items and collection frequency of adjustable load resource electrical quantity, operating status and operating parameters, and environmental quantity;

[0244] Supports the setting and query of data items and frequency of transmission of adjustable load resource electrical quantity, operating status and operating parameters, and environmental quantity;

[0245] Clear and reset

[0246] Clearing and resetting are achieved through the remote or local maintenance interface. The specific requirements are as follows:

[0247] Supports data clearing instructions. After receiving the instructions, all types of data collected by the adjustable load resources stored in the smart energy unit will be cleared;

[0248] Support event clearing command, clear all event records stored in the smart energy unit after receiving the command;

[0249] Support parameter reset command. After receiving the command, all parameters in the smart energy unit except remote communication parameters and clock are initialized to default values;

[0250] Support hardware reset command, the smart energy unit restarts after receiving the command;

[0251] Adjustable load resource parameters

[0252] The local maintenance tool sets and queries the adjustable load resource parameters, including the serial number, name, attribute, corresponding load collection point serial number and abnormal load judgment threshold of each adjustable load resource;

[0253] The local maintenance tool is used to set and query the air conditioning system parameters, including the rated input power, frequency conversion range, condenser and evaporator setting temperatures under different working conditions of the air conditioning unit, as well as the system cooling capacity and heating capacity parameters;

[0254] Step 13: Demand response and load flexibility control

[0255] Flexible load regulation on the user side is achieved through demand response or direct load control, specifically:

[0256] Demand response strategy

[0257] Supports the development, storage and update of demand response strategies, as follows:

[0258] Based on the operating characteristics, operation management methods, and production scheduling mechanism of the user's power equipment or system, a demand response strategy is formulated in advance according to the requirements of the demand response business. The demand response strategy can be associated with the operating strategy of the specific power system or equipment and the user's production scheduling plan;

[0259] Pre-built-in demand response strategies, the number of strategies must meet the needs of the user-side power system or equipment operation mode or actual production scheduling. The smart energy unit can save no less than 10 demand response strategies, and can save and reference relevant parameters set by the user in the strategy;

[0260] Support updating the built-in demand response strategy through parameter setting or file transfer through the local maintenance interface to adapt to changes in adjustable load resources on the user side and changes in operation management and production scheduling mechanisms. It is advisable to set and modify user parameters in the built-in strategy through the interactive interface of the user interactive device and the user system demand response processing module;

[0261] Direct load control

[0262] For directly controllable adjustable load resources, device control instructions are generated based on demand response signals or air conditioning temperature setting instructions and the strategies preset in the unit. The specific requirements are as follows:

[0263] According to the content of the demand response signal sent by the master station, the demand response strategy is selected, and according to the communication protocol supported by the adjustable load resources, a control instruction set is generated, including equipment start and stop instructions, energy storage device switching instructions, charging pile power control instructions, central air-conditioning unit circulating water temperature setting instructions and water pump operating frequency setting instructions;

[0264] According to the air conditioning temperature setting instruction issued by the master station, according to the type of air conditioning unit and the communication protocol it supports, an operating parameter control instruction or a set temperature adjustment instruction is generated;

[0265] The generated equipment control instructions are sent to the user system or adjustable load resources, and the strategy can be dynamically adjusted according to the monitored equipment load changes, operating status changes and other information fed back from the site, and instructions can be sent to achieve a closed control loop;

[0266] Convert the generated control instructions into switch signals to directly control the start and stop status of adjustable load resources;

[0267] Demand response interaction

[0268] For adjustable load resources that cannot be directly controlled, a demand response strategy is selected based on the content of the demand response signal, and the demand response signal content and the recommended response strategy are pushed through the user system gateway on the demand response interaction interface of the user system; or through the user interaction device, the demand response signal content and the recommended response strategy are pushed to the user on its display interface, and the user selects the specific response strategy or load regulation method by himself, and feeds back the willingness to participate in the demand response to the smart energy unit on the interface of the user system or the interactive device, which is forwarded by the smart energy unit to the main station.

[0269] The above shows and describes the basic principles and main features of the present invention and the advantages of the present invention. It should be understood by those skilled in the art that the present invention is not limited to the above embodiments. The above embodiments and descriptions are only for explaining the principles of the present invention. Without departing from the spirit and scope of the present invention, the present invention may have various changes and improvements, which fall within the scope of the present invention to be protected. The scope of protection of the present invention is defined by the attached claims and their equivalents.

Claims

1. The electric power smart energy unit based on multi-source data analysis is characterized by: include: A data acquisition module is used to collect multi-source data of power equipment in the downstream connected user system; The data processing module is used to store the most recently collected data of the user system power equipment, identify the operating status of the adjustable load resources, calculate the adjustable load resource demand response capability, judge the rationality of the calculated adjustable load value, report the value of the adjustable capacity calculation result that is too small, and perform zero processing; Demand response and load flexibility control module, used to achieve user-side load flexibility adjustment through demand response or load direct control; The demand response and load flexible control module includes a demand response strategy submodule, a load direct regulation submodule, and a demand response interaction submodule; The demand response strategy submodule supports the formulation, storage and update of demand response strategies. The specific contents are as follows: Based on the operating characteristics, operation management methods, and production scheduling mechanism of the user's power equipment or system, a demand response strategy is formulated in advance according to the requirements of the demand response business. The demand response strategy can be associated with the operating strategy of the specific power system or equipment and the user's production scheduling plan; Preset built-in demand response strategies to meet the operation mode of the user-side power system or equipment or the actual production scheduling requirements, save no less than 10 demand response strategies, and save and reference relevant parameters set by the user in the strategy; Update the built-in demand response strategy through parameter setting or file transfer through the local maintenance interface to adapt to changes in adjustable load resources on the user side and changes in operation management and production scheduling mechanisms. Set and modify user parameters in the built-in strategy of the smart energy unit through the interactive interface of the user interactive device and the user system demand response processing module; The load direct control submodule generates device control instructions based on demand response signals or user setting instructions and the strategy preset in the unit to directly control the adjustable load resources. The specific contents are as follows: According to the content of the demand response signal sent by the master station, the demand response strategy is selected, and according to the communication protocol supported by the adjustable load resources, a control instruction set is generated, including equipment start and stop instructions, energy storage device switching instructions, charging pile power control instructions, central air-conditioning unit circulating water temperature setting instructions and water pump operating frequency setting instructions; According to the air conditioning temperature setting instruction issued by the master station, according to the type of air conditioning unit and the communication protocol it supports, an operating parameter control instruction or a set temperature adjustment instruction is generated; The generated equipment control instructions are sent to the user system or adjustable load resources, and the strategy can be dynamically adjusted according to the monitored equipment load changes, operating status changes and other information fed back from the site, and instructions can be sent to achieve a closed control loop; Convert the generated control instructions into switch signals to directly control the start and stop status of adjustable load resources; The demand response interaction submodule is used to process the adjustable load resources that cannot be directly controlled. The specific contents are as follows: Select a demand response strategy based on the content of the demand response signal, and push the demand response signal content and the recommended response strategy on the demand response interactive interface of the user system through the user system gateway; The demand response signal content and the recommended response strategy are pushed to the user on the display interface through the user interaction device. The user selects the specific response strategy or load regulation method by himself and feeds back the demand response participation intention to the main station on the interface of the user system or interactive device. The data acquisition module comprises: An electrical quantity data acquisition submodule for acquiring electrical quantity data of adjustable load resources; An operating parameter and operating status data collection submodule for collecting operating parameters and operating status of adjustable load resources; A demand response capability data collection submodule that reads demand response capability data provided by a user system through a user system gateway device; A user's production environment status data collection submodule for collecting the user's production environment status data; A DC analog quantity data acquisition submodule for acquiring multiple DC analog quantities; The switch state quantity data acquisition submodule is used to collect the state quantity of multiple switches and convert the state quantity signal into the corresponding state data through the built-in parameters. The collection method of the electrical quantity data collection submodule includes: Read the current electrical quantity data stored in the load monitoring instrument; Read the current electrical quantity data stored in other intelligent devices with AC sampling function; Among them, the collection time interval is within 1 to 60 minutes; The collection method of the operating parameter and operating status data collection submodule includes: It is obtained through the communication interface connected to the adjustable load resource or various matching sensors; Obtained from the user system and analyzed and judged based on load data; Among them, the collection interval is within 1 to 60 minutes; The collection method of the production environment status data collection submodule includes: Obtained by directly connected environmental status sensors; Obtained from the user's system; Among them, the collection interval is within 1 to 60 minutes; The DC analog quantity includes 4-20mA current signal and 0-5V voltage signal. The data processing module comprises: A data storage submodule, used for storing collected data; The operation status identification submodule is used to automatically identify the operation status of the adjustable load resources through an algorithm model based on the collected current load data, historical load curve data, other types of electrical parameter data, load peak duration, and load fluctuation cycle time data of the adjustable load resources. The specific steps are as follows: S11: Setting the threshold range of the i-th type of adjustable load resource [Ad imin ,Ad imax ], among which Ad imin is the lower limit of the i-th type of adjustable load resources, Ad imax is the upper limit value of the i-th type of adjustable load resources; S12: Build a prediction model, and obtain the prediction value in the next sampling period according to the model and the historical data of the adjustable load resource, wherein the expression of the prediction model is: Ad i(T+1) =λad iT +λ(1-λ)ad i(T-1) +λ(1-λ) 2 ad i(T-2) +...+λ(1-λ) T-1 ad i1 +(1-λ) T ad i0 In the formula, Ad i(T+1) It is expressed as the predicted value of the i-th type of adjustable load resource in the T+1th sampling period; ad iT It is represented as the sampling value of the i-th type of adjustable load resource in the T-th sampling period, λ is a constant coefficient, where the value range of λ is λ∈(0,1); S13: Determine the operating status of the i-th type of adjustable load resource by comparing the predicted value in the T+1th sampling period with the lower limit value and the upper limit value. The determination principle is: If Ad imin <Ad i(T+1) <Ad imax , then it is determined that the operating state of the i-th type of adjustable load resources is normal; If Ad i(T+1) ≤Ad imin , or Ad i(T+1) ≥Ad imax , then it is determined that the operating state of the i-th type of adjustable load resource is abnormal; A demand response capability calculation submodule is used to calculate the demand response capability of adjustable load resources; The abnormal data identification and processing submodule is used to judge the rationality and validity of the collected load data, automatically filter out abnormal data caused by communication failures, electric energy meter creep problems and ratio errors, avoid reporting too high or too low abnormal load data to the master station and participate in the calculation process of the adjustable load resource demand response capability, judge the rationality of the calculated adjustable load value, and set the reported adjustable capacity data to zero for values ​​whose adjustable capacity calculation results are too small. The specific work content of the data storage submodule includes: When data collection fails due to communication failure, the data at the corresponding time is set to null value; The stored value of electrical quantity should be primary side data; The collected load curve data and ambient temperature and humidity curve data. The curve data storage interval is determined by the interval of the data upload task, with the minimum interval being 1 minute and the maximum interval being 60 minutes. The storage of load data and ambient temperature and humidity curve data is to save the data of the latest month or more, and the collection time interval is not less than 15 minutes. Among them, the load curve data refers to the total positive active power, and the ambient temperature and humidity curve data refers to the ambient temperature and relative humidity at the end of the air-conditioning system; According to the needs of deepening the application of power grid business, it supports data storage and statistical data query and upload of other electrical and non-electrical quantities. The specific work content of the demand response capability calculation submodule includes: The demand response capability data of the adjustable load resource at least includes the adjustable load value, the duration of the adjustment process and the sustainable duration of the response; Calculate the demand response capacity of each adjustable load resource on the current user side and the total demand response capacity of all adjustable load resources in real time; In the air conditioning load adjustment application scenario, the air conditioning load adjustable capacity is divided into two levels. The high-level adjustable capacity is suitable for minute-level demand response business, and the required sustainable duration is no more than 1 hour; the low-level adjustable capacity is suitable for air conditioning energy-saving operation scenarios under orderly power consumption conditions, and the required sustainable duration is more than 2 hours; In the air conditioning load adjustment application scenario, based on the air conditioning load data, set temperature and ambient temperature data, and operating parameters, the algorithm model is used to calculate the high-level and low-level load adjustable capabilities of the user-side air conditioning equipment. The specific method is as follows: S21: Acquire air conditioning load data; S22: Setting the benchmark load P of the user-side air conditioning equipment at high and low levels r1 , P r2 ; S23: Calculate the corresponding high-level and low-level cumulative load regulation data in the demand response event, including the single regulation load P t1 , P t2 and the total number of controls N1, N2; S24: Calculate and obtain the high-level and low-level load adjustable capacities R1 and R2 of the user-side air-conditioning equipment respectively according to the formula, and the calculation formula is: Where P t1j It is expressed as the jth regulated load under the high level of demand response event; P t2j It is represented as the jth regulated load under the low level of demand response event, The control method of the electric power smart energy unit based on multi-source data analysis comprises the following steps: Step 1: Collect multi-source data of power equipment in the downstream connected user system, including electrical quantity data of adjustable load resources, operating parameters and operating status of adjustable load resources, demand response capability data provided by the user system, production environment status data of the user, DC analog quantity data, and switch status quantity data; Step 2: Store the most recently collected data of the user system power equipment, identify the operating status of the adjustable load resources, calculate the adjustable load resource demand response capability, judge the rationality of the calculated adjustable load value, report the value of the adjustable capacity calculation result that is too small, and set it to zero; Step 3: For directly controllable adjustable load resources, flexible load adjustment on the user side is achieved through demand response or direct load regulation. The specific steps are as follows: Based on the operating characteristics, operation management methods, and production scheduling mechanism of the user's power equipment or system, a demand response strategy is formulated in advance according to the requirements of the demand response business. The demand response strategy can be associated with the operating strategy of the specific power system or equipment and the user's production scheduling plan; Preset built-in demand response strategies to meet the operation mode of the user-side power system or equipment or the actual production scheduling requirements, save no less than 10 demand response strategies, and save and reference relevant parameters set by the user in the strategy; Update the built-in demand response strategy through parameter setting or file transfer through the local maintenance interface to adapt to changes in adjustable load resources on the user side and changes in operation management and production scheduling mechanisms. Set and modify user parameters in the built-in strategy of the smart energy unit through the interactive interface of the user interactive device and the user system demand response processing module; Generate device control instructions based on demand response signals or user-set instructions and preset strategies in the unit to directly control adjustable load resources. The specific contents are as follows: According to the content of the demand response signal sent by the master station, the demand response strategy is selected, and according to the communication protocol supported by the adjustable load resources, a control instruction set is generated, including equipment start and stop instructions, energy storage device switching instructions, charging pile power control instructions, central air-conditioning unit circulating water temperature setting instructions and water pump operating frequency setting instructions; According to the air conditioning temperature setting instruction issued by the master station, according to the type of air conditioning unit and the communication protocol it supports, an operating parameter control instruction or a set temperature adjustment instruction is generated; The generated equipment control instructions are sent to the user system or adjustable load resources, and the strategy can be dynamically adjusted according to the monitored equipment load changes, operating status changes and other information fed back from the site, and instructions can be sent to achieve a closed control loop; Convert the generated control instructions into switch signals to directly control the start and stop status of adjustable load resources; Step 4: For adjustable load resources that cannot be directly controlled, the specific steps of the control process are: Select a demand response strategy based on the content of the demand response signal, and push the demand response signal content and the recommended response strategy on the demand response interactive interface of the user system through the user system gateway; Through the user interaction device, the demand response signal content and the recommended response strategy are pushed to the user on its display interface. The user chooses the specific response strategy or load regulation method by himself, and feeds back the willingness to participate in the demand response to the main station on the interface of the user system or interactive device.

2. The electric power smart energy unit based on multi-source data analysis according to claim 1, characterized in that: The energy unit also includes a data query and upload module, which is used to query the acquired multi-source data and report the query results, wherein the query method includes querying through the master station via the remote communication interface and querying through the local maintenance tool via the local maintenance interface; the data reporting method includes query result reporting and active reporting; The data query and upload module includes: The data query submodule is used to support the query of the system master station and local maintenance tools. The queryable data content includes at least: Electrical quantity data of adjustable load resources collected by the smart energy unit; The data acquisition module collects the operating parameters, operating status, ambient temperature and humidity, and air conditioning set temperature data of the air conditioning unit / system; Demand response capability data of adjustable load resources collected from user systems; Calculate and analyze the demand response capability data of adjustable load resources; The event record query submodule is used to support local maintenance tool query, where the queryable data content includes: Demand response event records, including the content of the demand response signal received by the smart energy unit, the user's decision and the decision time; Load control event records, including records of air conditioning temperature control commands received by the smart energy unit; Smart energy unit abnormal event records, including event occurrence and recovery time, and event content; The data active upload submodule is used to support active data upload. The specific contents include: The smart energy unit actively sends task data to the master station according to the cycle and data item requirements specified by the sending task parameters; When an abnormality occurs, the smart energy unit will actively send abnormal event record data to the main station according to the requirements of the event record parameters; The smart energy unit can take flow control measures for wireless public network communications, including 80% flow consumption warning, to avoid flow limit excess and communication interruption caused by abnormal data active upload function.

3. The electric power smart energy unit based on multi-source data analysis according to claim 1, characterized in that: The energy unit also includes an event recording module for recording events, which are classified into demand response events, air conditioning load control events, and smart energy unit abnormal events. The event recording module includes: The demand response event recording submodule is used to record the received demand response signal during the demand response business process, forward it to the user system or user interaction device, and supplement the record of user decisions. The specific contents are as follows: The demand response event record contains information transmitted by the demand response signal and the user's decision; Save no less than 100 recent demand response event records; The air conditioning load control event recording submodule is used to record the received temperature setting value during the air conditioning load control business process, forward it to the air conditioning control module or user system, and supplement the user's decision. The specific contents are as follows: The air conditioning load control event record includes the occurrence time; The air conditioning temperature setting value before adjustment; The air conditioning temperature setting value sent by the master station; Save no less than 100 recent air conditioning load control event records; The abnormal operation event recording submodule is used to automatically determine the occurrence or recovery of abnormal operation events according to the set event attributes, and when the event occurs or recovers, decide whether to report it according to the configuration, and record the reporting status. The specific contents are as follows: The content of each record includes the event type, occurrence time, and related associated data information; Event reporting needs to be reported independently by channel, and the reporting status is recorded separately by channel. The reporting status includes "not reported", "reported", and "reported but not confirmed"; Detect and record user-defined events of different severity, and actively report user-defined events of importance to the main station; Save no less than 100 recent abnormal event records of smart energy units.

4. The electric power smart energy unit based on multi-source data analysis according to claim 1, characterized in that: The energy unit also includes a parameter setting and query module, which is used to set and query device configuration parameters, system clock, uplink and downlink communication parameters, event recording parameters, and collection and uploading task parameters according to commands, including: The clock call and time setting submodule is used to receive the clock call and time calibration commands from the master station. The time calibration error shall not exceed 5s. Under reference conditions, the clock daily timing error shall be ≤±1s / d. Configuration parameter setting submodule, used for setting and querying the logical address and working mode configuration parameters of the smart energy unit by the master station and local maintenance tools, and querying the software and hardware versions and manufacturer information configuration parameters by the master station and local maintenance tools; The communication parameter setting submodule is used by the master station and local maintenance tools to set and query remote communication parameters, including wireless remote communication parameters and Ethernet communication parameters; it is also used by local maintenance tools to set and query local IoT communication parameters, including Ethernet communication parameters of the local network, user system gateway communication parameters, communication parameters between load monitoring instruments or collection points, air conditioning control module communication parameters, adjustable load resource communication parameters, environmental monitoring equipment communication parameters, and other local networking equipment communication parameters; The event record parameter setting submodule is used to set and query event record parameters by the local maintenance tool, including event recognition and recording switches, event upload switches, and abnormal judgment thresholds for various abnormal events; The collection and upload task parameter setting submodule is used by the master station and local maintenance tools to set and query the collection and upload task parameters, specifically: Supports the setting and query of the collection data items and collection frequency of adjustable load resource electrical quantity, operating status and operating parameters, and environmental quantity; Supports the setting and query of data items and frequency of transmission of adjustable load resource electrical quantity, operating status and operating parameters, and environmental quantity; The clear and reset setting submodule is used to realize clearing and resetting through the remote or local maintenance interface. The specific contents are as follows: Support data clearing command. When receiving the command, clear all types of data collected by the adjustable load resources stored in the smart energy unit; Support event clearing command. When receiving the command, all event records stored in the smart energy unit will be cleared. Support parameter reset command. When receiving the command, all parameters except remote communication parameters and clock parameters in the smart energy unit will be initialized to default values. Support hardware reset command. When receiving the command, the smart energy unit will be restarted. The adjustable load resource parameter setting submodule is used to set and query the adjustable load resource parameters by the local maintenance tool, including the serial number, name, attribute, corresponding load collection point serial number and abnormal load judgment threshold of each adjustable load resource; It is also used by local maintenance tools to set and query air-conditioning system parameters, including the rated input power of the air-conditioning unit under different operating conditions, the frequency conversion range, the set temperature of the condenser and evaporator operating conditions, and the system cooling capacity and heating capacity parameters.

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