Power demand response control method and device, terminal and storage medium

Abstract

The invention provides a power demand response control method and device, a terminal and a storage medium. The method comprises the following steps: acquiring power data of a power grid side and a user side in a previous time period; inputting the power data into a trained power load prediction model, and predicting to obtain a power load demand of a next time period; and based on the power load demand, determining scheduling information corresponding to each power device at the user side through a particle swarm optimization algorithm, and controlling each power device according to the scheduling information. By inputting the power data of the previous time period into the trained power load prediction model, the power load demand of the next time period is predicted, the power grid load change can be quickly and accurately reflected, the dispatching information is determined by using the particle swarm optimization algorithm, the optimal dispatching of the power equipment is realized, the power grid regulation delay is avoided, and the power grid regulation efficiency is improved. And the energy utilization efficiency is further improved.

Description

Technical Field

[0001] The present invention relates to the technical field of power systems, and in particular to a power demand response control method, device, terminal and storage medium. Background Art

[0002] With the rapid growth of global energy demand, the balance of supply and demand in the power system has increasingly become a key factor restricting the stability and sustainable development of the power grid. The traditional power system meets the growing demand by adding power stations and power grid facilities. However, this approach not only requires a large amount of investment, but also brings huge pressure on the environment and resource consumption. In addition, with the widespread application of renewable energy (such as wind power and solar energy), the power system faces the volatility and uncertainty of power supply, which further increases the difficulty of load regulation of the power grid.

[0003] In order to solve the above problems, demand response (DR) technology came into being. Demand response refers to adjusting the user's electricity consumption behavior during the peak period of electricity demand to reduce the system load and balance the supply and demand of the power grid. The main strategies of demand response include load reduction, load shifting and load increase. Through these measures, we can effectively reduce the dependence on traditional power generation methods, reduce system operating costs, optimize the load distribution of the power grid, and improve energy efficiency.

[0004] Although demand response technology has made certain progress, the existing electricity demand response systems mostly rely on manual intervention or static scheduling, and the response process is relatively lagging. They cannot quickly and accurately reflect changes in grid load, resulting in grid regulation delays and, in turn, poor energy utilization efficiency.

[0005] Therefore, the prior art has defects and needs to be improved and developed. Summary of the invention

[0006] The technical problem to be solved by the present invention is that, in view of the above-mentioned defects of the prior art, a power demand response control method, device, terminal and storage medium are provided, aiming to solve the problem that the power demand response system in the prior art mostly relies on manual intervention or static scheduling, resulting in poor energy utilization efficiency of power equipment.

[0007] The technical solution adopted by the present invention to solve the technical problem is as follows:

[0008] A power demand response control method, wherein the method comprises:

[0009] Obtain power data from the grid and user sides in the previous period;

[0010] Inputting the power data into a trained power load forecasting model to predict the power load demand for the next period;

[0011] Based on the power load demand, the scheduling information corresponding to each power device on the user side is determined by a particle swarm optimization algorithm, and each power device is controlled according to the scheduling information.

[0012] In one embodiment of the present application, the power data includes grid operation parameters on the grid side and power consumption data on the user side; the grid operation parameters include: voltage, current and frequency on the grid side; the power consumption data includes: power consumption on the user side and the operating status of each power equipment.

[0013] In one embodiment of the present application, the power load forecasting model is a long short-term memory network.

[0014] In one embodiment of the present application, the scheduling information includes: start and stop time, operation mode and operation power of the power equipment.

[0015] In one embodiment of the present application, the power demand response control method further includes:

[0016] Obtaining historical electricity consumption data of each user, classifying each of the historical electricity consumption data based on a cluster analysis algorithm, and obtaining electricity consumption characteristics of each user;

[0017] Generate a corresponding personalized scheduling strategy based on the power consumption characteristics, and send the personalized scheduling strategy to the user side;

[0018] Among them, the personalized scheduling strategy includes the optimal usage time and energy-saving mode of power equipment.

[0019] In one embodiment of the present application, after the power data is input into a trained power load forecasting model and the power load demand for the next period is predicted, the method further includes:

[0020] Obtain the real-time grid load of the target virtual power plant and the power generation capacity and energy storage capacity of the distributed energy in the target virtual power plant;

[0021] Based on the real-time grid load, power generation capacity and energy storage capacity, a linear programming optimization model is used to schedule the distributed energy in the target virtual power plant.

[0022] In one embodiment of the present application, based on the power load demand, the scheduling information corresponding to each power device on the user side is determined by a particle swarm optimization algorithm, and each power device is controlled according to the scheduling information, and further includes:

[0023] Acquire power data and user feedback data from the grid side and the user side in real time, adjust the power load demand according to the real-time power data and user feedback data, and obtain the current power load demand;

[0024] Obtaining a current dispatching strategy based on the current power load demand, executing the current dispatching strategy, and monitoring the execution result;

[0025] The current scheduling strategy is adjusted according to the execution result to complete the loop optimization.

[0026] The present application also provides a power demand response control device, wherein the device comprises:

[0027] An acquisition module is used to acquire power data from the grid side and the user side in the previous period;

[0028] A prediction module, inputting the power data into a trained power load prediction model to predict the power load demand for the next period;

[0029] The control module determines the scheduling information corresponding to each power device on the user side through a particle swarm optimization algorithm based on the power load demand, and controls each power device according to the scheduling information.

[0030] The present application also provides a terminal, which includes: a memory, a processor, and a power demand response control program stored in the memory and executable on the processor, wherein the power demand response control program implements the steps of the power demand response control method described above when executed by the processor.

[0031] The present application also provides a computer-readable storage medium, wherein the computer-readable storage medium stores a computer program, and the computer program can be executed to implement the steps of the power demand response control method as described above.

[0032] The present invention provides a method, device, terminal and storage medium for power demand response control, the method comprising: obtaining power data on the power grid side and the user side in the previous time period; inputting the power data into a trained power load prediction model to predict the power load demand for the next time period; based on the power load demand, determining the scheduling information corresponding to each power device on the user side through a particle swarm optimization algorithm, and controlling each power device according to the scheduling information. The present invention can quickly and accurately reflect the changes in power grid load by inputting the power data of the previous time period into a trained power load prediction model to predict the power load demand for the next time period, and then using the particle swarm optimization algorithm to determine the scheduling information, thereby achieving the optimal scheduling of power equipment, avoiding power grid regulation delays, and thus improving energy efficiency. BRIEF DESCRIPTION OF THE DRAWINGS

[0033] Figure 1 is a flow chart of a preferred embodiment of the power demand response control method of the present invention;

[0034] Figure 2 is a structural block diagram of the power demand response system in the present invention;

[0035] Figure 3 It is a functional principle block diagram of a preferred embodiment of the power demand response control device of the present invention;

[0036] Figure 4 It is a functional principle block diagram of a preferred embodiment of the terminal in the present invention. DETAILED DESCRIPTION

[0037] In order to make the purpose, technical solution and advantages of the present invention clearer and more specific, the present invention is further described in detail below with reference to the accompanying drawings and examples. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not used to limit the present invention.

[0038] The existing systems mostly rely on manual intervention or static scheduling, and the response process is relatively lagging. They cannot quickly and accurately reflect the changes in the grid load, resulting in grid regulation delays and increased system load fluctuation risks. Moreover, the existing systems mostly rely on simple load forecasting models and rule-driven control strategies, which cannot achieve accurate load scheduling and dynamic optimization, resulting in inefficient power scheduling and unsatisfactory response effects. The power demand response control method proposed in the present invention can solve the problems of poor real-time performance and insufficient response accuracy in existing demand response systems. By introducing intelligent control, real-time data analysis and user interaction mechanisms, the present invention can more accurately adjust the grid load and improve the stability of the system and energy efficiency.

[0039] See also Figure 1 , Figure 1 : is a flow chart of the power demand response control method in the present invention. Figure 1 As shown, the power demand response control method described in the embodiment of the present invention includes:

[0040] Step S100: Acquire power data on the grid side and the user side in the previous period.

[0041] like Figure 2As shown, the demand response system of the present application can be provided with a data acquisition module 100, and the data acquisition module 100 is used to collect power data on the grid side and the user side in real time and store it. In one embodiment, the data acquisition module 100 is also integrated with a real-time monitoring unit 101 and a data transmission unit 102; the real-time monitoring unit 101 is used to connect the smart meter, sensor and user-side power equipment installed in the circuit on the user side through the Internet of Things to obtain the power consumption data on the user side, and is also used to connect the grid side through sensors to collect grid operation parameters. The data transmission unit 102 is used to transmit the power data composed of the power consumption data and grid operation parameters used by the real-time monitoring unit 101 to the load forecasting module 200.

[0042] In an embodiment of the present application, the power data includes grid operation parameters on the grid side and power consumption data on the user side; the grid operation parameters include: voltage, current and frequency on the grid side; the power consumption data includes: power consumption on the user side and the operating status of each power equipment.

[0043] like Figure 1 As shown, the power demand response control method described in this embodiment also includes:

[0044] Step S200: input the power data into the trained power load forecasting model to predict the power load demand for the next period of time.

[0045] The demand response system of the embodiment of the present application can be provided with a load forecasting module 200, which is used to perform power load forecasting based on the power load forecasting model and real-time collected power data, obtain load forecasting results, and dynamically adjust the power load forecasting model in combination with power grid demand.

[0046] The load prediction module 200 includes a data preprocessing unit 201, a load prediction model unit 202 and a dynamic adjustment unit 203; the data preprocessing unit 201 is used to clean, denoise and normalize power data; the load prediction model unit 202 is used to perform load prediction based on the collected real-time power data and historical load data, using a power load prediction model to calculate the power load demand within a preset period of time; the dynamic adjustment unit 203 is used to dynamically adjust the load prediction model according to changes in power grid demand, combined with renewable energy generation data and weather forecast information.

[0047] In the embodiment of the present application, the power load forecasting model is a long short-term memory network. Existing systems mostly rely on simple load forecasting models and rule-driven control strategies, which cannot achieve accurate load scheduling and dynamic optimization, resulting in inefficient power scheduling and unsatisfactory response effects.

[0048] The power load forecasting model uses a long short-term memory network for load forecasting, and the load forecasting calculation formula is:

[0049] h t =σ(W hh h t-1 +W hx x t +b h )

[0050]

[0051] Among them, h t is the hidden state at the tth moment; h t-1 is the hidden state at the t-1th moment, used to update the hidden state at the current moment; x t is the input feature at the current moment, representing the historical power load data; W hh is the hidden layer weight matrix, which is used to connect the hidden state of the previous moment to the hidden state of the current moment; W hx is the input weight matrix, which is used to connect the input feature x at the current moment t The hidden state at the current moment; b h is the hidden layer bias term, which is used to adjust the output of the hidden layer; σ is the activation function, w hy is the output layer weight matrix, which is used to transform the hidden state h t Mapped to predicted value; b y is the output layer bias term, which is used to adjust the prediction results; is the load forecast value at time t, that is, the predicted power load demand.

[0052] For example, the power data on the grid side and the user side in the previous period obtained by this application include the user's power consumption in the past week, weather data (temperature, humidity, etc.), and the real-time power load of the power grid. Through the long short-term memory network (LSTM model), the system can predict the power load demand for the next period (such as the next 24 hours). In addition, according to weather changes and fluctuations in renewable energy generation, the parameters of the prediction model are dynamically adjusted to respond to demand fluctuations more accurately, provide accurate load forecasting results, support the dispatching decisions of the power system, and improve the system's ability to respond to sudden load changes and external factors through dynamic adjustment mechanisms.

[0053] The embodiment of the present application can capture the long-term dependence in the power load data by introducing the long short-term memory network, and show higher accuracy when processing nonlinear and complex time series data. Compared with the traditional load forecasting method, it can better cope with the periodic fluctuations and sudden changes of the power load. Through precise load forecasting, the present invention can more accurately predict the power demand in the future period, make grid dispatching arrangements in advance, reduce load fluctuations, avoid overloading or energy waste of the power system, and thus improve the operating efficiency and stability of the system. That is, the present application improves the accuracy of power load forecasting.

[0054] like Figure 1 As shown, the power demand response control method described in this embodiment also includes:

[0055] Step S300: Based on the power load demand, the scheduling information corresponding to each power device on the user side is determined by a particle swarm optimization algorithm, and each power device is controlled according to the scheduling information.

[0056] The demand response system of the embodiment of the present application can be provided with a scheduling management module 300, and the scheduling management module 300 is used to schedule user-side power equipment, adjust the operation mode and time period of the power equipment, and adjust the peak load according to the load forecast results. The scheduling management module 300 includes: a scheduling decision unit 301, a device control unit 302, and a feedback adjustment unit 303. The scheduling decision unit 301 is used to schedule the power equipment on the user side through a particle swarm optimization algorithm according to the power load demand and the power demand curve generated based on the power load demand. The device control unit 302 is used to execute scheduling decisions, control the switching and power regulation of the power equipment, balance the grid load, and reduce the peak load. The feedback adjustment unit 303 is used to adjust the equipment scheduling strategy in real time according to the changes in the power load, optimize the equipment utilization efficiency, and avoid excessive scheduling or equipment loss.

[0057] A user's electrical equipment generally includes multiple items, such as air conditioners, water heaters, washing machines, etc. This application uses a particle swarm optimization algorithm (PSO algorithm) to optimize the start and stop periods and operating power of these devices according to the power load demand. For example, air conditioning equipment can be started during periods of low power demand (such as at night or off-peak hours), and its power can be adjusted to avoid excessive load on the power grid. Washing machines may be operated during off-peak hours to avoid starting during periods of high power load and reduce pressure on the power grid. Through the above optimization, the system can balance the load on the power grid, reduce peak power load, reduce power consumption, and improve the user's power economy.

[0058] The embodiment of the present application can effectively find the optimal scheduling scheme for power equipment by simulating the group behavior of particle swarms, thereby minimizing the cost of system operation or maximizing economic benefits. The different characteristics of the equipment (such as power demand, scheduling period, etc.) are taken into account in the scheduling decision, and the global optimal solution is sought through repeated iterations. The present invention reduces the excessive use or idleness of equipment, reduces energy waste, reduces the peak-to-valley difference of the power grid, and balances the demand and supply of electricity by optimizing the scheduling scheme of power equipment, thereby effectively reducing the cost of electricity and improving the economic benefits of the power grid. Therefore, the present application optimizes the scheduling of power equipment and reduces costs.

[0059] In an embodiment of the present application, the scheduling information includes: start and stop time, operation mode and operation power of the power equipment.

[0060] Specifically, when the scheduling decision unit 301 schedules the user-side power equipment through the particle swarm optimization algorithm, the user-side power equipment is scheduled by minimizing a preset first objective function to calculate the optimal equipment operation mode. The calculation formula of the first objective function is:

[0061]

[0062] Where f(x) is the first objective function, which represents the cost function to be optimized, and is used to minimize the deviation between the peak power demand and the equipment scheduling period; p i is the power demand of the ith power equipment, indicating the power consumption of the power equipment during scheduling; T i is the dispatching time of the ith power equipment, that is, the operating time period of the power equipment at a certain moment; T o is the operating period when the load of the i-th power equipment is lower than the preset load threshold, and n is the total number of power equipment.

[0063] For example, a user has an air conditioner, a refrigerator, and a washing machine at home. The personalized response module 400 first analyzes the user's historical electricity consumption data, combines the weather forecast and the frequency of air conditioner use, and finds that the user frequently uses the air conditioner during the summer when the temperature is high. Based on this analysis, the system will generate a personalized scheduling plan to schedule the use of the air conditioner during periods of low electricity demand, and recommend that users perform high-power operations such as washing when the electricity price is low, thereby optimizing the user's electricity use. Through the above personalized scheduling, users can reduce electricity costs while ensuring a comfortable electricity experience, while supporting the stable operation of the power grid, accurately identifying the electricity usage patterns and behavioral characteristics of different users, realizing differentiated power scheduling, optimizing the user's electricity experience, and reducing the user's electricity costs.

[0064] In an embodiment of the present application, the electricity demand response control method also includes: obtaining historical electricity consumption data of each user, classifying each of the historical electricity consumption data based on a cluster analysis algorithm, and obtaining the electricity consumption characteristics of each user; generating a corresponding personalized scheduling strategy based on the electricity consumption characteristics, and sending the personalized scheduling strategy to the user side; wherein the personalized scheduling strategy includes the optimal usage time and energy-saving mode of the power equipment.

[0065] Specifically, the demand response system of the embodiment of the present application may also be provided with a personalized response module 400, which is used to analyze the user's electricity consumption characteristics based on the user's historical electricity consumption data and behavior data, generate a personalized scheduling strategy, and dynamically optimize in real time according to user feedback. The personalized response module 400 includes: a user behavior analysis unit 401, a personalized scheduling unit 402, and a real-time feedback unit 403. The user behavior analysis unit 401 is used to identify the user's electricity consumption characteristics by using cluster analysis by analyzing the user's historical electricity consumption data, behavior data, and weather change factors. The personalized scheduling unit 402 is used to generate a personalized power scheduling plan based on the user analysis results, wherein the power scheduling plan includes the optimal equipment usage time and energy-saving mode. The real-time feedback unit 403 is used to dynamically optimize the user's personalized scheduling plan by receiving the user's electricity consumption feedback data.

[0066] In the embodiment of the present application, the cluster analysis algorithm is a K-means clustering algorithm. Based on the cluster analysis algorithm, the historical electricity consumption data are classified to obtain the electricity consumption characteristics of each user, which specifically includes:

[0067] Input the user's historical electricity consumption dataset X = {x1, x2, ..., x i}, where x i is the electricity consumption characteristics of the i-th user;

[0068] Randomly initialize k cluster centers C1, C2, ..., C k ;

[0069] For each user x i , calculate the Euclidean distance between it and each cluster center and assign it to the nearest cluster center;

[0070] Update the cluster center position until convergence, and obtain the clustering result, that is, the power consumption characteristics of each user. Finally, generate a personalized scheduling strategy for each user based on the clustering result.

[0071] The embodiments of the present application can classify users into different categories based on their historical electricity consumption data, and identify the electricity consumption characteristics of different user groups. Based on the electricity consumption characteristics, a personalized electricity demand response scheduling plan is formulated for each user, and the user's electricity consumption period or load distribution is flexibly adjusted. The personalized scheduling plan optimizes electricity distribution based on the user's actual needs, load characteristics, and electricity consumption habits, which not only improves the user's comfort and satisfaction, but also avoids the waste of resources caused by a single global scheduling strategy. At the same time, it can also improve the flexibility and accuracy of the demand response system and reduce load fluctuations in the power grid.

[0072] In one embodiment of the present application, step S200 also includes: obtaining the real-time grid load of the target virtual power plant and the power generation capacity and energy storage capacity of the distributed energy in the target virtual power plant; based on the real-time grid load, power generation capacity and energy storage capacity, using a linear programming optimization model to schedule the distributed energy in the target virtual power plant.

[0073] The demand response system of the embodiment of the present application may also be provided with an energy management module 500, which is used to integrate the distributed energy resources on the user side into a target virtual power plant, dispatch the distributed energy resources in the target virtual power plant, and optimize the power dispatch strategy according to real-time data feedback, and adjust the response control system parameters. The energy management module 500 includes: a virtual power plant dispatch unit 501 and an energy flow optimization unit 502. The virtual power plant dispatch unit 501 is used to integrate and dispatch distributed energy resources, build a virtual power plant, and optimize resource allocation; the energy flow optimization unit 502 is used to dispatch the energy resources in the virtual power plant using linear programming according to the real-time power grid load and the power generation and storage capacity of distributed energy to meet the power grid load demand.

[0074] The optimization goal of the linear programming optimization model is to minimize the total energy consumption of the system. The constraints include the grid load and the equipment energy storage capacity. The linear programming optimization model is expressed as:

[0075]

[0076] Where Z is the objective function of the linear programming optimization model, which represents the total energy consumption; c i is the unit energy cost of the ith distributed energy, indicating the cost per unit of energy consumption; x i is the dispatching amount of the i-th energy resource; n is the total number of distributed energy resources; where the constraints of the linear programming are:

[0077] A·x≤b

[0078] Where A is a constraint matrix, which contains the scheduling relationship of each energy resource in different time periods and the constraints of various resources; x is a variable vector, which represents the scheduling amount of all energy resources; b is a constraint vector, which represents the specific values ​​of various constraints, such as maximum energy storage capacity, minimum output limit, load demand, etc.

[0079] For example, a virtual power plant in a certain region is composed of multiple distributed energy resources, including multiple wind turbines, solar panels and energy storage systems. The virtual power plant dispatch unit will decide which energy resources should be dispatched first and how to use the energy storage system to meet the grid demand based on real-time grid load data and available energy resources (such as solar and wind power generation). For example, if the wind turbine generates a high amount of electricity and the grid load is low, the system will choose the energy storage system to store the remaining electricity and release it when demand peaks, reducing dependence on traditional power generation resources.

[0080] The present application ensures the optimal scheduling of energy resources through a linear programming algorithm and an energy flow optimization unit to minimize energy consumption and ensure grid load balance.

[0081] The embodiment of the present application adopts a linear programming optimization model, which can efficiently handle complex energy scheduling problems, consider the different characteristics and constraints of multiple energy resources (such as energy storage capacity, minimum output, etc.), and minimize energy consumption while meeting the load demand of the power grid. Through precise resource allocation, the system can effectively schedule distributed energy resources and improve the overall efficiency of the virtual power plant. The present invention reduces energy consumption and improves the economic benefits of the system through the reasonable scheduling of energy resources. At the same time, the linear programming optimization model can ensure that the system operates under various constraints, ensure the power supply stability of the power grid, and reduce power outages or equipment overloads caused by sudden load fluctuations. Therefore, the present application optimizes the scheduling of energy resources and enhances system stability.

[0082] In an embodiment of the present application, step S300 also includes: acquiring power data and user feedback data from the grid side and the user side in real time, adjusting the power load demand according to the real-time power data and user feedback data, and obtaining the current power load demand; obtaining the current scheduling strategy based on the current power load demand, executing the current scheduling strategy, and monitoring the execution result; adjusting the current scheduling strategy according to the execution result to complete the cycle optimization.

[0083] Specifically, according to the real-time power load data and user feedback data, a closed-loop control method based on a feedback loop is adopted to optimize the scheduling strategy. User feedback data may include: power consumption data, power usage behavior data, power bill feedback, user suggestions, etc. Power usage behavior data refers to the record of users' power consumption patterns in different time periods, including power consumption during peak and off-peak periods, power consumption of specific equipment (such as air conditioners, water heaters, etc.), and whether users have responded to power demand response plans (such as reducing power consumption, adjusting power usage time, etc.). Power bill feedback refers to users' feedback on power bills, including the accuracy of bills, changes in electricity charges, and satisfaction with electricity charge structures. User suggestions refer to suggestions and complaints made by users through customer service channels regarding power supply, prices, services, etc. This information can reveal users' expectations and needs for power services.

[0084] The present invention realizes intelligent dispatching and optimized control of power equipment through the collaborative work of multiple modules, combined with load forecasting, personalized dispatching and energy management. The dispatching management module dispatches power equipment through the particle swarm optimization algorithm to reduce the peak load of the power grid and reduce energy waste; the personalized response module generates a customized dispatching plan based on user historical data and behavior analysis to improve user power efficiency; the energy management module improves the sustainability and economy of the power system through the integration and dispatching of virtual power plants and distributed energy resources.

[0085] The present invention provides highly automated and intelligent decision support for the power demand response system, which can perceive the changes in power demand in real time and respond quickly, thereby optimizing power grid dispatching, equipment operation and energy configuration. The system can realize automated load forecasting, equipment dispatching and resource optimization, significantly improve the intelligence level of the power system, reduce manual intervention and improve work efficiency. At the same time, the system can dynamically adjust decisions based on real-time data, enhance the adaptability of the power system to external disturbances such as load changes and emergencies, and ensure the long-term stable operation of the power system.

[0086] The optimized dispatching of the present invention not only improves the economic benefits of the power system, but also reduces energy waste and unnecessary power consumption. By accurately dispatching distributed energy and reducing peak-to-valley differences, the system can effectively reduce dependence on traditional thermal power generation, reduce carbon emissions, and help promote the application of green energy, save energy, reduce environmental pollution, comply with the concept of sustainable development, and help build a low-carbon, environmentally friendly power system.

[0087] In one embodiment, if Figure 3 As shown, based on the above-mentioned power demand response control method, the present invention also provides a power demand response control device, including:

[0088] The acquisition module 10 is used to acquire the power data of the grid side and the user side in the previous period;

[0089] The prediction module 20 inputs the power data into the trained power load prediction model to predict the power load demand in the next period;

[0090] The control module 30 determines the scheduling information corresponding to each power device on the user side through a particle swarm optimization algorithm based on the power load demand, and controls each power device according to the scheduling information.

[0091] Figure 4 A schematic diagram of the structure of a terminal provided in an embodiment of the present application. The terminal may include:

[0092] A memory 501 , a processor 502 , and a computer program stored in the memory 501 and executable on the processor 502 .

[0093] When the processor 502 executes the program, the power demand response control method provided in the above embodiment is implemented.

[0094] Furthermore, the terminal further includes:

[0095] The communication interface 503 is used for communication between the memory 501 and the processor 502 .

[0096] The memory 501 is used to store computer programs that can be executed on the processor 502 .

[0097] The memory 501 may include a high-speed RAM memory, and may also include a non-volatile memory (non-volatile memory), such as at least one disk memory.

[0098] If the memory 501, the processor 502 and the communication interface 503 are implemented independently, the communication interface 503, the memory 501 and the processor 502 can be connected to each other through a bus and communicate with each other. The bus can be an Industry Standard Architecture (ISA) bus, a Peripheral Component (PCI) bus or an Extended Industry Standard Architecture (EISA) bus. The bus can be divided into an address bus, a data bus, a control bus, etc. For ease of representation, only one line is used in the figure, but it does not mean that there is only one bus or one type of bus.

[0099] Optionally, in a specific implementation, if the memory 501, the processor 502 and the communication interface 503 are integrated on a chip, the memory 501, the processor 502 and the communication interface 503 can communicate with each other through an internal interface.

[0100] The processor 502 may be a central processing unit (CPU), or an application specific integrated circuit (ASIC), or one or more integrated circuits configured to implement the embodiments of the present application.

[0101] This embodiment also provides a computer-readable storage medium, on which a computer program is stored. When the program is executed by a processor, the above-mentioned power demand response control method is implemented.

[0102] In the description of this specification, the description with reference to the terms "one embodiment", "some embodiments", "example", "specific example", or "some examples" etc. means that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present application. In this specification, the schematic representations of the above terms do not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described may be combined in any one or N embodiments or examples in a suitable manner. In addition, those skilled in the art may combine and combine the different embodiments or examples described in this specification and the features of the different embodiments or examples, without contradiction.

[0103] In addition, the terms "first" and "second" are used for descriptive purposes only and should not be understood as indicating or implying relative importance or implicitly indicating the number of technical features indicated. Therefore, a feature defined as "first" or "second" may explicitly or implicitly include at least one of the features. In the description of this application, "N" means at least two, such as two, three, etc., unless otherwise clearly and specifically defined.

[0104] Any process or method description in a flowchart or otherwise described herein may be understood to represent a module, fragment or portion of code comprising one or N executable instructions for implementing the steps of a custom logical function or process, and the scope of the preferred embodiments of the present application includes additional implementations, in which the order shown or discussed may not be followed, including performing functions in a substantially simultaneous manner or in reverse order depending on the functions involved, which should be understood by technicians in the technical field to which the embodiments of the present application belong.

[0105] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as an ordered list of executable instructions for implementing logical functions, and can be embodied in any computer-readable medium for use by an instruction execution system, device or apparatus (such as a computer-based system, a system including a processor, or other system that can read instructions from an instruction execution system, device or apparatus and execute instructions), or in combination with these instruction execution systems, devices or apparatuses. For the purpose of this specification, "computer-readable medium" can be any device that can contain, store, communicate, propagate or transmit a program for use by an instruction execution system, device or apparatus, or in combination with these instruction execution systems, devices or apparatuses. More specific examples of computer-readable media (a non-exhaustive list) include the following: an electrical connection with one or N wirings (electronic devices), a portable computer disk box (magnetic device), a random access memory (RAM), a read-only memory (ROM), an erasable and programmable read-only memory (EPROM or flash memory), a fiber optic device, and a portable compact disk read-only memory (CDROM). In addition, the computer-readable medium may even be paper or other suitable medium on which the program is printed, since the program may be obtained electronically by optically scanning the paper or other medium and then editing, interpreting or otherwise processing in a suitable manner if necessary and then storing it in a computer memory.

[0106] It should be understood that the various parts of the present application can be implemented by hardware, software, firmware or a combination thereof. In the above embodiment, the N steps or methods can be implemented by software or firmware stored in a memory and executed by a suitable instruction execution system. If implemented by hardware, as in another embodiment, it can be implemented by any one of the following technologies known in the art or their combination: a discrete logic circuit having a logic gate circuit for implementing a logic function for a data signal, a dedicated integrated circuit having a suitable combination of logic gate circuits, a programmable gate array (PGA), a field programmable gate array (FPGA), etc.

[0107] A person skilled in the art may understand that all or part of the steps in the method for implementing the above-mentioned embodiment may be completed by instructing related hardware through a program, and the program may be stored in a computer-readable storage medium. When the program is executed, it includes one or a combination of the steps of the method embodiment.

[0108] In addition, each functional unit in each embodiment of the present application can be integrated into a processing module, or each unit can exist physically separately, or two or more units can be integrated into one module. The above-mentioned integrated module can be implemented in the form of hardware or in the form of a software functional module. If the integrated module is implemented in the form of a software functional module and sold or used as an independent product, it can also be stored in a computer-readable storage medium. The above-mentioned storage medium can be a read-only memory, a disk or an optical disk, etc. Although the embodiments of the present application have been shown and described above, it can be understood that the above-mentioned embodiments are exemplary and cannot be understood as limitations on the present application. Ordinary technicians in this field can change, modify, replace and modify the above-mentioned embodiments within the scope of the present application.

[0109] In summary, the present invention discloses a method, device, terminal and storage medium for power demand response control, the method comprising: obtaining power data on the power grid side and the user side in the previous time period; inputting the power data into a trained power load prediction model to predict the power load demand for the next time period; based on the power load demand, determining the scheduling information corresponding to each power device on the user side through a particle swarm optimization algorithm, and controlling each power device according to the scheduling information. The present invention can quickly and accurately reflect the changes in power grid load by inputting the power data of the previous time period into a trained power load prediction model to predict the power load demand for the next time period, and then using a particle swarm optimization algorithm to determine the scheduling information, thereby achieving the optimal scheduling of power equipment, avoiding power grid regulation delays, and thus improving energy efficiency.

[0110] It should be understood that the application of the present invention is not limited to the above examples. For ordinary technicians in this field, improvements or changes can be made based on the above description. All these improvements and changes should fall within the scope of protection of the claims attached to the present invention.

Claims

1. A power demand response control method, characterized in that: The method comprises: Obtain power data from the grid and user sides in the previous period; Inputting the power data into a trained power load forecasting model to predict the power load demand for the next period; Based on the power load demand, the scheduling information corresponding to each power device on the user side is determined by a particle swarm optimization algorithm, and each power device is controlled according to the scheduling information.

2. The power demand response control method according to claim 1, characterized in that: The power data includes power grid operation parameters on the power grid side and power consumption data on the user side; The grid operation parameters include: voltage, current and frequency on the grid side; the power consumption data includes: power consumption on the user side and the operating status of each power device.

3. The power demand response control method according to claim 1, characterized in that: The power load forecasting model is a long short-term memory network.

4. The power demand response control method according to claim 1, characterized in that: The scheduling information includes: start and stop time, operation mode and operation power of the power equipment.

5. The power demand response control method according to claim 1, characterized in that: The power demand response control method further includes: Obtaining historical electricity consumption data of each user, classifying each of the historical electricity consumption data based on a cluster analysis algorithm, and obtaining electricity consumption characteristics of each user; Generate a corresponding personalized scheduling strategy based on the power consumption characteristics, and send the personalized scheduling strategy to the user side; Among them, the personalized scheduling strategy includes the optimal usage time and energy-saving mode of power equipment.

6. The power demand response control method according to claim 1, characterized in that: After inputting the power data into the trained power load forecasting model to predict the power load demand for the next period, the method further includes: Obtain the real-time grid load of the target virtual power plant and the power generation capacity and energy storage capacity of the distributed energy in the target virtual power plant; Based on the real-time grid load, power generation capacity and energy storage capacity, a linear programming optimization model is used to schedule the distributed energy in the target virtual power plant.

7. The power demand response control method according to claim 1, characterized in that: Based on the power load demand, the scheduling information corresponding to each power device on the user side is determined by a particle swarm optimization algorithm, and each power device is controlled according to the scheduling information, and further includes: Acquire power data and user feedback data from the grid side and the user side in real time, adjust the power load demand according to the real-time power data and user feedback data, and obtain the current power load demand; Obtaining a current dispatching strategy based on the current power load demand, executing the current dispatching strategy, and monitoring the execution result; The current scheduling strategy is adjusted according to the execution result to complete the loop optimization.

8. A power demand response control device, characterized in that: The device comprises: An acquisition module is used to acquire power data from the grid side and the user side in the previous period; A prediction module, inputting the power data into a trained power load prediction model to predict the power load demand for the next period; The control module determines the scheduling information corresponding to each power device on the user side through a particle swarm optimization algorithm based on the power load demand, and controls each power device according to the scheduling information.

9. A terminal, characterized in that: include: A memory, a processor, and a power demand response control program stored in the memory and executable on the processor, wherein the power demand response control program, when executed by the processor, implements the steps of the power demand response control method according to any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a computer program, and the computer program can be executed to implement the steps of the power demand response control method according to any one of claims 1 to 7.

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