Transformer area electric heating flexible control technical method and system

Abstract

The invention relates to the technical field, and discloses a platform area electric heating flexible control technical method and system, a complete layered architecture is constructed, the platform area electric heating flexible control technical method and system comprises a master station layer, a communication layer, a terminal layer, a user meter box and an equipment layer, and all the layers are clear in function and work cooperatively. The master station layer is responsible for data summarization, analysis, load prediction and regulation and control strategy generation; the communication layer ensures the reliability of information interaction among the master station, the terminal and the equipment through an efficient data transmission channel; the terminal layer serves as a bridge for connecting the master station and the household meter box and is responsible for data acquisition and instruction transmission; a flexible load regulation and control terminal is deployed in the household meter box and is used for receiving and executing a regulation and control strategy issued by the master station; the equipment layer is composed of electric heating equipment and specifically achieves regulation and control actions. All the layers are in close cooperation, and dynamic monitoring and flexible regulation and control of the electric heating load of the transformer area are jointly completed.

Description

Technical Field

[0001] The present invention relates to the technical field, and in particular to a flexible control technology method and system for electric heating in a substation area. Background Art

[0002] In recent years, with the promotion of the clean heating policy, distributed electric heating equipment has been rapidly popularized in the northern regions. Especially during the peak winter heating period, the electric heating load has become an important part of the power supply in the substation area. However, due to its concentrated and volatile load, the substation area often faces problems of heavy overload and power fluctuation, seriously threatening the safety and stability of the power grid operation. To cope with the peak load, the distribution network needs to invest a large amount of cost for capacity expansion. Although traditional rigid regulation methods (such as directly cutting off user equipment) can relieve the load pressure, they have a greater impact on the comfort of user heating. Economic regulation methods such as time-of-use electricity price are difficult to significantly cut peaks and fill valleys due to lack of real-time performance. Although some regions have optimized regulation strategies through load forecasting models and resource pool management, due to insufficient real-time monitoring and precise regulation capabilities, especially when facing equipment such as distributed electric boilers, air source heat pumps, and heating films, there are no flexible control means, making it difficult to balance operation efficiency and user experience. In addition, with the continuous increase in the proportion of new energy power generation, the uncertainty of power grid operation increases, and the existing load management system still has obvious shortcomings in real-time monitoring, regulation flexibility, prediction accuracy, and execution ability. Currently, the operation data collection of electric heating equipment in the substation area is incomplete, the rigid regulation method is single, and it is difficult to implement flexible regulation means such as voltage regulation, phase loss, and temperature control. The load forecasting model is insufficiently combined with meteorological conditions and user habits, resulting in obvious prediction lag. At the same time, there is a lack of flexible load regulation terminals on the user side, and hierarchical and precise control cannot be achieved, making it difficult to refine and implement regulation measures. Therefore, there is an urgent need for a technology based on flexible load regulation to effectively relieve the overload problem in the substation area, optimize the power grid operation efficiency, and support the needs of smart grid and high-proportion renewable energy access through real-time monitoring, high-precision prediction, and distributed precise control. Summary of the Invention

[0003] In view of the above existing problems, the present invention is proposed.

[0004] Therefore, the present invention provides a flexible control technology method for electric heating in a substation area, which can solve the problems of insufficient real-time monitoring ability, single regulation method, low prediction model accuracy, and weak distributed regulation ability in the existing load management technology. By constructing a hierarchical and flexible load regulation system, the present invention realizes real-time monitoring, dynamic prediction, flexible regulation, and closed-loop optimization of the electric heating load in the substation area, effectively improving the power grid operation efficiency and user electricity consumption experience.

[0005] To solve the above technical problems, the present invention provides the following technical solutions. A flexible control technology method for electric heating in a substation area includes: obtaining real-time data and basic data through data collection and analysis, and performing load forecasting; dynamically adjusting the supply of electric heating load according to the load forecasting results and actual load demands; adjusting the power supply status of each electric heating unit according to the real-time demands of users and load dispatching strategies; and autonomously adjusting the electric heating load based on an artificial intelligence algorithm to reduce energy consumption.

[0006] As a preferred solution of the flexible control technology method for electric heating in a substation area described in the present invention, wherein: the real-time data includes the real-time operating voltage, current, power, temperature, heating duration, and equipment operating status of the equipment;

[0007] The basic data includes historical load data, meteorological conditions, and user electricity consumption habits.

[0008] As a preferred solution of the flexible control technology method for electric heating in a substation area described in the present invention, wherein: the load forecasting includes, based on the real-time data, the master station layer uses load forecasting to analyze the load change trend in the substation area; based on the basic data, by using deep learning, predicting the future peak load periods and potential overload risks, and generating load forecasting results.

[0009] As a preferred solution of the flexible control technology method for electric heating in a substation area described in the present invention, wherein: the adjustment of the supply of electric heating load includes, through the trained load forecasting model, combining the load forecasting results, the master station layer generates a multi-dimensional flexible regulation strategy, and preferentially selects flexible regulation methods with a low user influence rate;

[0010] When the load forecasting results show that the load demand in any period exceeds the maximum carrying capacity, the master station reduces the load; when the load forecasting results show that the load exceeds the maximum carrying capacity in any period, and the load peak is mainly caused by high-power electric heating equipment, the master station reduces the voltage output to reduce the power consumption of the equipment.

[0011] As a preferred solution of the flexible control technology method for electric heating in a substation area described in the present invention, wherein: the adjustment of the power supply status of each electric heating unit includes, according to the load forecasting results, real-time load demands, and grid operation conditions, automatically adjusting the distribution and dispatching of electric heating load through optimized dispatching; performing load balancing control by setting constraint conditions to adjust the load distribution of equipment in the substation area and optimize the operation efficiency of the electric heating system.

[0012] As a preferred solution of the flexible control technology method for electric heating in a substation area described in the present invention, wherein: the artificial intelligence algorithm includes genetic algorithm optimization, and the steps are as follows:

[0013] Perform individual coding, where each individual represents a set of control parameters p = (p1, p2,..., p k ), and each parameter p i is a control parameter in the load scheduling system;

[0014] Construct a fitness function, and the fitness function F(p) is defined according to the objective function:

[0015]

[0016] The fitness function is consistent with the optimization objective and is used to measure the quality of each set of control parameters;

[0017] Perform selection operations, select individuals with higher fitness as parents according to the fitness function; perform crossover operations, and the crossover operations generate new individuals and new combinations of control parameters; perform mutation operations, and randomly change the control parameters in the individuals through mutation operations.

[0018] As a preferred solution of a flexible control technology system for district electric heating in the present invention, it includes: a district electric heating load prediction module, a load scheduling module, a user demand response module, an intelligent control algorithm module, and a communication and feedback module;

[0019] The district electric heating load prediction module is used to obtain real-time data of the district electric heating load through data collection and analysis, and perform load prediction based on historical data and meteorological conditions to provide a basis for subsequent regulation and control decisions;

[0020] The load scheduling module is used to dynamically adjust the supply of electric heating load according to the load prediction results and the actual load demand of the district;

[0021] The user demand response module flexibly adjusts the power supply status of each electric heating unit according to the real-time needs of users and the load scheduling strategy to achieve refined control of electric heating load;

[0022] The intelligent control algorithm module, based on artificial intelligence algorithms, autonomously adjusts the electric heating load through deep learning and optimization models to optimize the overall heating efficiency and reduce energy consumption;

[0023] The communication and feedback module realizes real-time communication and data feedback between the node devices in the district and the central control system;

[0024] The system also includes a local control unit for each electric heating device in the district, which is used to adjust the device status according to remote control commands and feedback local data to the central control system; a data security and privacy protection module to ensure the security and privacy of user data and device data.

[0025] As a preferred solution of a flexible control technology system for substation area electric heating in the present invention, wherein: the load forecasting module includes a meteorological data collection unit, a historical load analysis unit, and a load forecasting model;

[0026] The load scheduling module includes an optimal scheduling algorithm unit and a load balancing control unit;

[0027] The intelligent control algorithm module includes a deep learning control unit and a genetic algorithm optimization unit;

[0028] The communication and feedback module uses Internet of Things technology for data transmission.

[0029] A computer device includes a memory and a processor, the memory stores a computer program, and is characterized in that when the processor executes the computer program, the steps of a flexible control technology method for substation area electric heating are realized.

[0030] A computer-readable storage medium stores a computer program thereon, and is characterized in that when the computer program is executed by a processor, the steps of a flexible control technology method for substation area electric heating are realized.

[0031] The beneficial effects of the present invention: Through the flexible load regulation terminal, precise control of time-sharing, sub-region, and sub-user is realized, effectively alleviating the problem of heavy overload in the substation area. Based on the load forecasting model and the regulation strategy optimization algorithm, the electric heating load is dynamically adjusted, significantly improving the grid operation efficiency and energy utilization rate. Adopting flexible control methods (such as phase loss, voltage regulation, and temperature control, etc.), the load reduction target is completed while minimizing the impact on user comfort. The present invention is applicable to the regulation scenarios of various electric heating devices (such as distributed electric boilers, air source heat pumps, heating films, etc.) and can meet the needs of different users. BRIEF DESCRIPTION OF THE DRAWINGS

[0032] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings required for description in the embodiments will be briefly introduced below. Obviously, the drawings in the following description are only some embodiments of the present invention, and those of ordinary skill in the art can obtain other drawings according to these drawings without creative efforts. Among them:

[0033] Figure 1 It is the overall architecture of the flexible control system of a flexible control technology method for substation area electric heating provided by an embodiment of the present invention.

[0034] Figure 2 It is the flexible control scenario of a distributed electric boiler electric heating device of a flexible control technology method for substation area electric heating provided by an embodiment of the present invention.

[0035] Figure 3The flexible control scenario of an air source heat pump electric heating device for a flexible control technology method of district electric heating provided by an embodiment of the present invention.

[0036] Figure 4 The flexible control scenario of carbon crystal panel, heating film, and heating cable electric heating devices for a flexible control technology method of district electric heating provided by an embodiment of the present invention.

[0037] Figure 5 The logic block diagram of the flexible control strategy for a flexible control technology method of district electric heating provided by an embodiment of the present invention.

[0038] Figure 6 The schematic diagram of the single-phase loss control principle for a flexible control technology method of district electric heating provided by an embodiment of the present invention.

[0039] Figure 7 The schematic diagram of the voltage regulation control principle for a flexible control technology method of district electric heating provided by an embodiment of the present invention.

[0040] Figure 8 The schematic diagram of the temperature control principle for a flexible control technology method of district electric heating provided by an embodiment of the present invention.

[0041] Figure 9 The schematic diagram of the rigid control principle for a flexible control technology method of district electric heating provided by an embodiment of the present invention. Specific embodiments

[0042] To make the above objects, features, and advantages of the present invention more obvious and understandable, the following detailed description of the specific embodiments of the present invention will be made in conjunction with the accompanying drawings of the specification. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all of them. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the scope of protection of the present invention.

[0043] In the following description, many specific details are set forth to facilitate a thorough understanding of the present invention. However, the present invention can also be implemented in other ways different from those described herein. Those skilled in the art can make similar extensions without departing from the spirit of the present invention. Therefore, the present invention is not limited by the specific embodiments disclosed below.

[0044] Secondly, the so-called "one embodiment" or "embodiment" herein refers to a specific feature, structure, or characteristic that can be included in at least one implementation of the present invention. The phrase "in one embodiment" appearing in different places in this specification does not necessarily refer to the same embodiment, nor is it a separate or alternative embodiment that excludes other embodiments.

[0045] The present invention will be described in detail with reference to the schematic diagrams. When describing the embodiments of the present invention, for the convenience of explanation, the cross-sectional views showing the device structure will be enlarged locally in a non-general proportion, and the schematic diagrams are only examples and should not limit the scope of protection of the present invention herein. In addition, in actual production, three-dimensional spatial dimensions including length, width, and depth should be included.

[0046] At the same time, in the description of the present invention, it should be noted that the orientation or positional relationship indicated by terms such as "upper, lower, inner, and outer" is based on the orientation or positional relationship shown in the drawings. It is only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore should not be construed as a limitation to the present invention. In addition, the terms "first, second, or third" are only used for descriptive purposes and cannot be understood as indicating or implying relative importance.

[0047] Unless otherwise clearly defined and limited in the present invention, the terms "installed, connected, and coupled" shall be understood in a broad sense. For example, it can be a fixed connection, a detachable connection, or an integral connection; it can also be a mechanical connection, an electrical connection, or a direct connection, and can also be indirectly connected through an intermediate medium, or can be the communication inside two elements. For those of ordinary skill in the art, the specific meanings of the above terms in the present invention can be understood according to specific situations.

[0048] Embodiment 1, referring to Figure 5 , which is the first embodiment of the present invention. This embodiment provides a flexible control technology method for district electric heating, including:

[0049] S1: Through data acquisition and analysis, obtain real-time data and basic data, and conduct load forecasting.

[0050] Furthermore, the real-time data includes the real-time operating voltage, current, power, temperature, heating duration, and device operating status of the device;

[0051] The basic data includes historical load data, meteorological conditions, and user electricity consumption habits.

[0052] Through the deployment of concentrators / SCUs, protocol converters (PCs), and flexible load regulation terminals, real-time monitoring of the operating status of decentralized electric heating equipment in the substation area is achieved. The concentrator is the core device for data collection, connecting to the electric heating equipment and obtaining the operating data of the equipment in real time. Through sensors connected to the equipment (such as current, voltage sensors, and temperature sensors), the concentrator can collect the real-time operating parameters of the electric heating equipment, including voltage, current, power, temperature, heating duration, and equipment operating status, etc. The protocol converter is used to convert the original data collected by the concentrator into a standardized communication protocol, facilitating the transmission of data to the master station system through the communication network. The flexible load regulation terminal is installed at the user end (inside the household meter box), collecting the operating status data of user equipment and transmitting this data to the master station system to ensure that the master station layer can comprehensively grasp the load distribution and dynamic changes in the substation area. The collected data is transmitted to the master station system through a wireless communication network (4G / 5G), providing data support for subsequent load forecasting and the generation of regulation strategies.

[0053] S2: Dynamically adjust the supply of electric heating load according to the load forecasting results and actual load demands.

[0054] Furthermore, based on the real-time data, the master station layer uses a load forecasting model to analyze the load change trend in the substation area. Specifically, the load forecasting model used by the master station realizes load forecasting through machine learning algorithms, and the formula is:

[0055] P t =β0 + β1X1(t) + β2X2(t) + … β n X n (t) + ∈ t

[0056] Where P t is the load demand at time t, X1(t), X2(t), …, X n (t) are the characteristics affecting the load (such as temperature, humidity, historical load data, time period, etc.), β0, β1, β2, …, β n are the regression coefficients, and ∈ t is the random error term.

[0057] In an alternative embodiment, load forecasting further includes that, based on real-time data, the master station captures spatio-temporal features in the data through a Convolutional Neural Network (CNN). The convolutional layer slides over the time series data through multiple convolutional kernels, capturing load change features at different time scales and improving the accuracy of load forecasting. The input features still include temperature, humidity, historical load data, time periods, etc. At the end of the CNN, the features extracted by the convolutional layer are aggregated, and feature fusion is performed through one or more Dense Layers. These dense layers convert the learned high-level features into the final load prediction value.

[0058] Based on the basic data, the master station uses a Long Short-Term Memory (LSTM) deep learning model to predict future peak load periods and potential overload risks. The formula is as follows:

[0059]

[0060] where L is the loss function, T is the total number of time steps in the training period, is the load predicted by the model, P t is the actual load;

[0061] In an alternative embodiment, load forecasting further includes that, based on the basic data, by refining the parameter selection and model structure of the Autoregressive Integrated Moving Average (ARIMA) model, predicting future peak load periods and potential overload risks. Specifically, the master station first performs differencing on the historical load data to achieve stationarity, and then determines the orders of the autoregressive term (p) and the moving average term (q) in the ARIMA model through the autocorrelation function and the partial autocorrelation function. The master station trains the historical data of the model and evaluates the prediction performance of the model through cross-validation, enabling the model to capture seasonal and periodic changes caused by meteorological conditions and user electricity consumption habits, and more accurately predicting the peak load at future specific time steps, thereby helping the master station to identify and prevent potential overload risks in advance.

[0062] Combined with the prediction results, the main station layer generates multi-dimensional flexible control strategies, including specific methods such as open-phase control, voltage regulation control, temperature control, and rigid control. The flexible control methods with less impact on users are preferentially selected. When the prediction results show that the load demand in a certain period is about to approach or exceed the maximum carrying capacity of the substation transformer, the main station can reduce the load through open-phase control. Under this strategy, the system will disconnect the phases of some electric heating equipment to reduce the overall load, but will not affect the basic functions of the equipment and reduce the inconvenience to users. When the prediction results show that the load is high in a certain period and the load peak is mainly caused by high-power electric heating equipment, the main station can reduce the voltage output through voltage regulation control to reduce the power consumption of the equipment. By adjusting the voltage to 80% of the rated value, the load can be effectively reduced while ensuring the normal operation of the electric heating equipment. When the load prediction shows that the temperature is too high (such as the temperature rises in winter), it may cause unnecessary operation of the electric heating equipment. At this time, the temperature control strategy can reduce the load by adjusting the set room temperature or outlet water temperature. When the load demand is extremely high and the flexible control method cannot meet the load reduction demand, the main station can select the rigid control strategy to quickly reduce the load by directly powering off some equipment. This is usually the last resort when the system load is overloaded and peak shaving cannot be achieved through other methods. For example, the main station can choose to disconnect the electric heating equipment of low-priority users to ensure the stable operation of the power grid.

[0063] It should be noted that according to the load prediction results, real-time load demand, and the operation status of the power grid, the distribution and scheduling of the electric heating load are automatically adjusted to ensure the balance between the power grid stability and the heating demand. The load scheduling is carried out through the following optimization model:

[0064] Objective function (F):

[0065]

[0066] Among them, C i is the cost coefficient of the i-th device, P i is the load of the i-th device, P i,target is the target load of the i-th device, λ is the weight coefficient for adjusting the deviation between the load and the target load, and N is the total number of electric heating devices in the substation area. This objective function optimizes the load distribution to minimize the load adjustment cost and power grid fluctuations while ensuring that the load of each device is close to its target value. The optimization process can be realized through common solution methods such as the gradient descent method.

[0067] Furthermore, adjust the load distribution of each electric heating device in the substation area to avoid local overload or power grid fluctuations and optimize the operation efficiency of the electric heating system:

[0068] Constraint conditions:

[0069]

[0070] where P min,i and P max,i are the minimum and maximum load limits of the i-th device respectively, and P i is the actual load of the i-th device. Through this constraint control, the load balancing control unit ensures that each device operates within its adjustable range, while avoiding overloading of individual devices, thus maintaining the stability and efficiency of the entire system.

[0071] S3: Adjust the power supply status of each electric heating unit according to the real-time demand of the user and the load scheduling strategy.

[0072] Furthermore, the regulation strategy is executed by the flexible load regulation terminal deployed in the household meter box. The regulation terminal adjusts the operating status of the electric heating equipment on the user side in real time according to the master station instruction, including single-phase operation, adjusting the output power, reducing the outlet water temperature or room temperature, time-sharing power-off, etc., to achieve load peak shaving and valley filling and dynamic balance. The flexible load regulation terminal can flexibly select the optimal control strategy according to the device type and the electricity consumption scenario, minimizing the impact on the heating comfort of users. The distributed electric boiler usually consists of an electromagnetic heater, a circulating pump, etc. Its load characteristic is that the power demand is large and the operation is relatively stable. When the load is high, in order to avoid grid overload, the flexible load regulation terminal can adopt single-phase loss control, adjust the output power and time-sharing power-off strategies; the air source heat pump is widely used in winter heating. Its characteristic is to provide heat through the heat exchange method, and the load fluctuates greatly. For this kind of equipment, the regulation strategy mainly focuses on temperature control and power regulation; the electric heating equipment such as heating film and carbon crystal plate is usually a resistive load, and the power consumption is stable during operation and is easy to regulate. For this kind of equipment, the selection of the regulation strategy is mainly based on voltage regulation control and temperature control.

[0073] S4: Based on the artificial intelligence algorithm, autonomously adjust the electric heating load to reduce energy consumption.

[0074] Furthermore, perform genetic algorithm optimization, and the steps are as follows:

[0075] Perform individual coding, and each individual represents a set of control parameters p = (p1, p2,..., p k ), where each parameter p i is a control parameter in the load scheduling system;

[0076] Construct a fitness function, and the fitness function F(p) is defined according to the objective function:

[0077]

[0078] The fitness function is consistent with the optimization objective and is used to measure the quality of each set of control parameters;

[0079] Perform a selection operation, and select individuals with higher fitness as parents according to the fitness function; perform a crossover operation, which generates a new generation of individuals and a new combination of control parameters; perform a mutation operation, which randomly changes the control parameters in the individuals through the mutation operation.

[0080] Furthermore, after the control strategy is executed, the master station system compares and analyzes the control results with the expected goals through real-time monitoring. If the target load reduction amount is not achieved, the system will dynamically adjust the control strategy based on the feedback data and issue it for execution again, forming a closed-loop optimization process, thereby improving the control effect.

[0081] It should be noted that the present invention is applicable to a variety of electric heating equipment and application scenarios, including distributed electric boilers, air source heat pumps, heating films, carbon crystal plates and other equipment, and corresponding flexible control schemes are proposed for different equipment types. For distributed electric boilers, phase loss control can be adopted, and the load can be reduced by cutting off power to some phases while ensuring the normal operation of the equipment; for carbon crystal plate and heating film equipment, the power is reduced by adjusting the voltage output through a flexible load control terminal; for air source heat pumps and direct heating electric heating equipment, the load is dynamically reduced by adjusting the outlet water temperature or the room temperature setting value; in the case of a large load reduction demand, some equipment power can also be directly disconnected for rigid control to achieve rapid load peak shaving. The above schemes are flexibly applied according to the equipment characteristics, taking into account both the load control effect and the user's heating comfort.

[0082] Example 2, refer to Figure 1 - Figure 9 This is an embodiment of the present invention, which provides a flexible control technology method for electric heating in a substation area. In order to verify the beneficial effects of the present invention, scientific demonstration is carried out through experiments.

[0083] The present invention realizes real-time monitoring, dynamic prediction, flexible control and closed-loop optimization of the electric heating load in the substation area by constructing a hierarchical and graded flexible load control system. The overall architecture of the flexible control system is as Figure 1 shown. The system architecture includes a master station layer, a communication layer, a terminal layer, a household meter box and an equipment layer. Each layer cooperates closely to complete the refined management and dynamic adjustment of the electric heating load. There are two ways to execute the substation area control strategy. One is that the master station is responsible for receiving all on-site collected data, carrying out real-time monitoring and calculation on the real-time status of all substations, and carrying out substation area load regulation according to the operation conditions of the substations. The other is to introduce edge computing technology. An edge computing terminal is installed locally in the substation area, and the edge computing terminal is responsible for real-time monitoring, calculation and command execution, reducing the calculation pressure on the master station. The master station is responsible for generating the regulation strategy and issuing commands for the whole network load regulation, and the edge computing terminal is responsible for executing the commands.

[0084] During operation, the master station system is the core of the entire regulation system, responsible for data aggregation, analysis, load forecasting, and strategy generation. In the power distribution area, the concentrator / SCU, protocol converter (PC), and flexible load regulation terminal work together to achieve real-time monitoring of the operating status of dispersed electric heating equipment. According to the relevant technical specifications of the master station of the flexible load regulation terminal and the data of the flexible load regulation terminal, combined with the actual business requirements, the acquisition plan is configured. The acquired data items and acquisition frequencies are shown in Tables 1 and 2:

[0085] Table 1 Data Acquisition Items and Acquisition Frequencies of Concentrator / SCU

[0086]

[0087]

[0088] Table 2 Data Acquisition Items and Acquisition Frequencies of Flexible Load Regulation Terminal

[0089]

[0090] In daily monitoring, during the usage period of the user's electric heating equipment, the usage data of the user's electric heating equipment is collected regularly at a frequency of 15 minutes. According to business needs, key users and key periods are monitored for high-frequency data such as voltage, current, and overload of the power distribution area load, with the ability to collect data at the 1-minute level.

[0091] According to the calculation of the flexible load adjustable resource pool in the power distribution area, theoretically, all users who can perform flexible load regulation should be connected. However, considering cost reduction, the number of connected users can also be reduced. The minimum connection requirements are shown in Table 3.

[0092] Table 3 Calculation of the Connection of Flexible Load Regulation Terminal

[0093]

[0094] The above table is the preliminary calculation for participating in flexible load regulation, which is only used as a rough estimate reference for the installation scale before the detailed investigation of the power distribution area. The actual installation quantity of the flexible load regulation terminal needs to be comprehensively calculated by combining the value of the average load exceeding the power in the power distribution area and the actual power of the electric heating equipment.

[0095] Based on the load prediction results, the master station system dynamically generates control strategies. The strategies cover methods such as single-phase loss control, voltage regulation control, temperature control, and rigid control. The flexible control means with the least impact on the user's heating comfort is preferentially selected. The flexible load control terminal is installed in the user's meter box and establishes a communication connection with the protocol converter to collect the real-time operation parameters of the electric heating equipment, including voltage, current, power, operation duration, ambient temperature, etc. The collected data is uploaded to the master station system through the communication network. The master station system analyzes the future load change trend based on real-time data, historical load records, and meteorological data, combined with the user's electricity consumption habits, through the load prediction model, predicts the peak load period and potential overload risks, and provides support for the formulation of control strategies. According to the analysis of the existing electric heating load characteristics, the control strategy is formulated based on the principle of minimizing the impact on users. The master station sets the load limit value of the transformer area. When the load in the transformer area exceeds the set load, the control strategy is executed. The logic block diagram of the flexible control strategy is as Figure 5 shown. The total load Pt of the transformer area is collected in real time, and it is judged whether the total load of the transformer area exceeds the limit. If it exceeds the limit, it is judged whether it is in the power protection state. In the power protection state, subsequent user load collection and control are not performed, and only the corresponding over-capacity event is reported; in the non-power protection state, the adjustable single-phase load Pb on the user side of the electric heating users in the transformer area is collected at the minute level; the over-capacity ΔP = Ps - Pt of the transformer area is calculated according to the total load and the set capacity limit value Ps of the transformer area. It is judged whether to perform load management according to whether it is in the power protection state. In the non-power protection state, the over-capacity value of the transformer area is reported in time for the power supply system to make subsequent decisions. When ΔP < 0, it indicates that the capacity of the transformer area exceeds the limit, and the load control strategy is calculated. Calculate the sum of all adjustable single-phase loads and use it as the adjustment resource pool. The load capacity of the resource pool is ΔPb. If ΔPb + ΔP > 0, the adjustment is carried out in turn according to the adjustment priority of the adjustable loads in the resource pool. The adjustment priority is comprehensively evaluated based on several dimensions such as the user's room temperature judged by the terminal, the user's heating duration on the current day, special users, and the execution situation of the previous round of adjustment. When the capacity of the adjustable resource pool is sufficient, the adjustment is carried out in the order of priority; if the capacity of the resource pool is insufficient, all the loads in the adjustable resource pool are adjusted after excluding special users. After determining the load control strategy, the control command is sent down, and the current load control round, the participating users, and the user participation capacity are recorded. At the same time, the subsequent load management user objects in this round are calculated and load management control is carried out. After the control strategy is sent to the flexible load control terminal, the terminal makes real-time adjustments to the electric heating equipment according to the equipment type and the control command.

[0096] In practical applications, the present invention is applicable to a variety of electric heating equipment and typical scenarios, including distributed electric boiler electric heating equipment ( Figure 2 ), air source heat pump electric heating equipment ( Figure 3 ), and carbon crystal board, heating film, and heating cable electric heating equipment ( Figure 4 ).

[0097] The distributed electric boiler electric heating equipment mainly consists of an electromagnetic heater, a circulation pump, a temperature controller, etc. By installing a protocol converter at the downstream position of the concentrator / SCU, it is used for data aggregation and conversion. A flexible load control terminal is installed on the user side of the electric heating, which receives and executes instructions. In the application scenario of the distributed electric boiler, the control methods include open-phase control, temperature control, and rigid control.

[0098] The air source heat pump electric heating equipment mainly consists of a compressor, a heat exchanger, an axial flow fan, a heat preservation water tank, a water pump, a liquid storage tank, a filter, a throttling device, and a temperature controller, etc. By installing a protocol converter at the downstream position of the concentrator / SCU, it is used for centralized data interaction. A flexible load control terminal is installed on the user side of the electric heating, which receives and implements instructions. In the application scenario of the air source heat pump, the control methods include temperature control and rigid control.

[0099] Equipment such as carbon crystal panels, heating films, and heating cables are composed of "heat preservation board + temperature controller + heating equipment", and they are all single-phase electric operation equipment. By installing a protocol converter at the downstream position of the concentrator / SCU, it is used for centralized data interaction. A flexible load control terminal is installed on the user side of the electric heating, which receives and implements instructions. In the application scenario of carbon crystal panels and heating films, the control methods include voltage regulation control, temperature control, and rigid control.

[0100] Open-phase control means installing an intelligent circuit breaker between the electricity meter and the circuit breaker behind the meter, and statistically analyzing the load conditions of the three phases A, B, and C of the user. The power of each phase is about 33% of the total power of the electric heating equipment. The load of the phase where the water pump is located is in a locked state, and the other two phases are in a controllable state. Disconnection does not affect the normal use of the electric heating equipment and reduces the power of the electric heating equipment. The maximum adjustable value of the power is 66% of the total power of the electric heating equipment. The main station sorts and groups all the controllable phases in the transformer area to establish a regulation resource pool. The schematic diagram of the open-phase control principle is as Figure 6 shown.

[0101] Voltage regulation control means installing a voltage regulation controller between the electricity meter and the circuit breaker behind the meter. The electrical part of the radiation panel mainly consists of electric heating elements and a control circuit. The electric heating elements usually use stainless steel sheathed electric heating tubes or heating wires and heating cloth as heat sources, belonging to resistive loads. Thyristors can meet the control requirements of such loads. The control circuit is usually powered by a switching power supply method, and the voltage range is generally 86V - 240V AC. Therefore, the voltage regulator uses a voltage regulation method to output a voltage to maintain the power supply requirements of the control circuit, which can not only ensure the normal operation of the equipment but also reduce the power of the electric heating elements, and the power reduction can reach 60%. The schematic diagram of the voltage regulation control principle is as Figure 7 shown.

[0102] Temperature control refers to installing a temperature controller between the electricity meter and the circuit breaker behind the meter. According to the requirements of temperature control, the electric heating equipment itself supports temperature setting. For direct heating electric heating equipment, by remotely sending control strategy commands, when the set temperature is lower than the room temperature, the load can be reduced. For indirect electric heating equipment, by setting the outlet water temperature / set temperature of the unit controller, the purpose of reducing the output of the unit and lowering the load can be achieved. The schematic diagram of the temperature control principle is as shown in Figure 8 shown.

[0103] Rigid control refers to the concentrator / SCU remotely sending remote control commands to control the on / off state of the relay of the on-site user electricity meter, so as to achieve the goal of load regulation in the substation area. The schematic diagram of the rigid control principle is as shown in Figure 9 shown.

[0104] After the regulation strategy is executed, the master station system analyzes the data fed back by the flexible load regulation terminal through the protocol converter and the concentrator, and compares the actual reduced load with the target value. If the expected reduction effect is not achieved, the master station system dynamically adjusts the regulation strategy based on the feedback data, such as increasing the number of regulated users, extending the regulation time or adjusting the regulation range, and sending the strategy command again. Through this closed-loop optimization mechanism, the regulation process can be continuously iterated to ensure the balance and optimization of the substation area load finally.

[0105] The system architecture design of the present invention is based on hierarchical classification. The master station layer is responsible for global load forecasting and strategy generation; the communication layer ensures the stability of data transmission through an efficient communication network; the terminal layer connects the master station and the household meter box to realize data collection and command transmission; the flexible load regulation terminal in the household meter box is responsible for executing the regulation strategy; the equipment layer is composed of electric heating equipment and responds to regulation actions. The efficient cooperation between layers enables the present invention to meet the requirements of the smart grid with a high proportion of renewable energy access, effectively relieve the problem of heavy overload in the substation area, optimize the grid operation efficiency, while ensuring the user heating experience and improving the energy utilization efficiency.

[0106] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not restrictive. Although the present invention has been described in detail with reference to the preferred embodiments, those of ordinary skill in the art should understand that the technical solutions of the present invention can be modified or equivalently replaced without departing from the spirit and scope of the technical solutions of the present invention, and they should all be covered within the scope of the claims of the present invention.

[0107] Embodiment 3 is the third embodiment of the present invention. What is different from the first three embodiments is that

[0108] If the above-mentioned 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, in essence, or the part that contributes to the prior art, or a part of this technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the methods described in various embodiments of the present invention. The aforementioned storage medium includes: various media that can store program codes, such as USB flash drives, mobile hard disks, read-only memories (ROM, Read-Only Memory), random access memories (RAM, Random Access Memory), magnetic disks, or optical discs.

[0109] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as a predefined sequence list of executable instructions for implementing logical functions, and can be specifically implemented in any computer-readable medium for use by an instruction execution system, apparatus, or device (such as a computer-based system, a system including a processor, or other systems that can fetch and execute instructions from the instruction execution system, apparatus, or device), or in combination with these instruction execution systems, apparatus, or devices. For the purposes of this specification, a "computer-readable medium" can be any device that can contain, store, communicate, propagate, or transport a program for use by or in combination with an instruction execution system, apparatus, or device.

[0110] More specific examples (non-exhaustive list) of computer-readable media include the following: electrical connection parts with one or more wirings (electronic devices), portable computer disk cartridges (magnetic devices), random access memories (RAM), read-only memories (ROM), erasable programmable read-only memories (EPROM or flash memories), fiber optic devices, and portable compact disc read-only memories (CDROM). Additionally, the computer-readable medium can even be paper or other suitable media on which the program can be printed, because the program can be obtained electronically, for example, by optically scanning the paper or other media, then editing, interpreting, or otherwise processing it as necessary, and then storing it in a computer memory.

[0111] 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 embodiments, multiple 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, any one or a combination of the following techniques well known in the art can be used: discrete logic circuits with logic gate circuits for implementing logical functions on data signals, application specific integrated circuits with suitable combinational logic gate circuits, programmable gate arrays (PGAs), field programmable gate arrays (FPGAs), etc.

[0112] Embodiment 4, referring to Figure 1 , which is an embodiment of the present invention, provides a flexible control technology system for district heating with electric heating, constructs a complete hierarchical architecture, including a master station layer, a communication layer, a terminal layer, a household meter box, and a device layer, and the functions of each layer are clear and work together. The master station layer is responsible for data aggregation, analysis, load forecasting, and generation of control strategies; the communication layer ensures the reliability of information interaction between the master station, terminals, and devices through an efficient data transmission channel; the terminal layer, as a bridge connecting the master station and the household meter box, is responsible for data collection and instruction transmission; a flexible load control terminal is deployed in the household meter box to receive and execute the control strategies issued by the master station; the device layer consists of electric heating devices, which specifically implement the control actions. Each layer closely cooperates to jointly complete the dynamic monitoring and flexible control of the district heating load with electric heating.

[0113] Furthermore, it includes a district heating load forecasting module, a load scheduling module, a user demand response module, an intelligent control algorithm module, and a communication and feedback module;

[0114] The district heating load forecasting module is used to obtain the real-time data of the district heating load through data collection and analysis, and perform load forecasting based on historical data and meteorological conditions to provide a basis for subsequent control decisions;

[0115] The load scheduling module is used to dynamically adjust the supply of the district heating load according to the load forecasting results and the actual load demand of the district;

[0116] The user demand response module flexibly adjusts the power supply status of each electric heating unit according to the real-time needs of users and the load scheduling strategy to achieve refined control of the district heating load;

[0117] The intelligent control algorithm module, based on artificial intelligence algorithms, autonomously adjusts the district heating load through deep learning and optimization models, optimizes the overall heating efficiency, and reduces energy consumption;

[0118] The communication and feedback module realizes real-time communication and data feedback between the device at each node in the district and the central control system;

[0119] The system further includes local control units for each electric heating device within the substation area, which are used to adjust the device status according to remote control commands and feedback local data to the central control system; a data security and privacy protection module to ensure the security and privacy of user data and device data.

[0120] The load prediction module includes a meteorological data collection unit, a historical load analysis unit, and a load prediction model;

[0121] The load scheduling module includes an optimal scheduling algorithm unit and a load balancing control unit;

[0122] The intelligent control algorithm module includes a deep learning control unit and a genetic algorithm optimization unit;

[0123] The communication and feedback module uses Internet of Things technology for data transmission. The data includes, but is not limited to:

[0124] (1) Real-time operation status data of the electric heating device;

[0125] (2) Electric power parameters such as current, voltage, and power of each load node within the substation area;

[0126] (3) Demand information feedback by users, including temperature adjustment requirements, etc.

[0127] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not restrictive. Although the present invention has been described in detail with reference to the preferred embodiments, those of ordinary skill in the art should understand that the technical solutions of the present invention can be modified or equivalently replaced without departing from the spirit and scope of the technical solutions of the present invention, and they should all be covered within the scope of the claims of the present invention.

Claims

1. A flexible control technology method for district electric heating, characterized in that: including Obtain real-time data and basic data through data collection and analysis for load forecasting; Dynamically adjust the supply of electric heating load according to the load forecasting results and actual load demand; Adjust the power supply status of each electric heating unit according to the user's real-time demand and load scheduling strategy; Based on artificial intelligence algorithms, autonomously adjust the electric heating load to reduce energy consumption.

2. The flexible control technology method for district electric heating according to claim 1, characterized in that: The real-time data includes the real-time operating voltage, current, power, temperature, heating duration, and equipment operating status of the equipment; The basic data includes historical load data, meteorological conditions, and user electricity consumption habits.

3. The flexible control technology method for district electro - heating according to claim 2, wherein: The load forecasting includes, based on the real-time data, the master station layer uses a load forecasting model to analyze the load change trend of the substation area; based on the basic data, through the use of deep learning, predict future peak load periods and potential overload risks, and generate load forecasting results.

4. The method for flexible control technology of electric heating in a transformer substation area according to claim 3, characterized in that: The adjustment of the supply of electric heating load includes, through the trained load forecasting model, combined with the load forecasting results, the master station layer generates a multi-dimensional flexible control strategy, and preferentially selects flexible control methods with a low user impact rate; When the load forecasting results show that the load demand in any period exceeds the maximum carrying capacity, the master station reduces the load; When the load forecasting results show that the load exceeds the maximum carrying capacity in any period, and the load peak is mainly caused by high-power electric heating equipment, the master station reduces the voltage output to reduce the power consumption of the equipment.

5. The flexible control technology method for district electro - heating according to claim 4, characterized in that: The adjustment of the power supply status of each electric heating unit includes, according to the load forecasting results, real-time load demand, and grid operation conditions, automatically adjust the distribution and scheduling of electric heating load through optimal scheduling; Conduct load balancing control by setting constraint conditions to adjust the load distribution of equipment in the substation area.

6. The flexible control technology method for substation electric heating according to claim 5, characterized in that: The artificial intelligence algorithm includes genetic algorithm optimization, and the steps are as follows: Individual coding is performed, and each individual represents a set of control parameters p = (p1, p2,..., p k ), where each parameter p i is a control parameter in the load dispatching system; Construct a fitness function, and the fitness function F(p) is defined according to the objective function: The fitness function is consistent with the optimization goal and is used to measure the quality of each set of control parameters; Perform a selection operation, select individuals with higher fitness as parents according to the fitness function; perform a crossover operation, the crossover operation generates a new generation of individuals, generating a new combination of control parameters; perform a mutation operation, and randomly change the control parameters in the individual through the mutation operation.

7. A system adopting a flexible control technology method for district electric heating as described in any one of claims 1 to 6, characterized in that: Including a substation area electric heating load forecasting module, a load scheduling module, a user demand response module, an intelligent control algorithm module, a communication and feedback module; The substation area electric heating load forecasting module is used to obtain the real-time data of the substation area electric heating load through data collection and analysis, and conduct load forecasting based on historical data and meteorological conditions to provide a basis for subsequent control decisions; The load scheduling module is used to dynamically adjust the supply of electric heating load according to the load forecasting results and the actual load demand of the substation area; The user demand response module flexibly adjusts the power supply status of each electric heating unit according to the user's real-time demand and load scheduling strategy to achieve refined control of the electric heating load; The intelligent control algorithm module, based on artificial intelligence algorithms, autonomously adjusts the electric heating load through deep learning and optimization models, optimizes the overall heating efficiency, and reduces energy consumption; The communication and feedback module realizes real-time communication and data feedback between each node device in the substation area and the central control system; The system further includes local control units for each electric heating device in the substation area, which are used to adjust the device status according to remote control commands and feedback local data to the central control system; a data security and privacy protection module to ensure the security and privacy of user data and device data.

8. A flexible control technology system for electric heating in a substation area according to claim 7, characterized in that: The load prediction module includes a meteorological data collection unit, a historical load analysis unit, and a load prediction model; The load scheduling module includes an optimal scheduling algorithm unit and a load balancing control unit; The intelligent control algorithm module includes a deep learning control unit and a genetic algorithm optimization unit; The communication and feedback module uses Internet of Things technology for data transmission.

9. A computer device, comprising a memory and a processor, the memory storing a computer program, characterized in that, When the processor executes the computer program, it implements the steps of the method described in any one of claims 1 to 7.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements the steps of the method described in any one of claims 1 to 7.

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