Power consumption supply and demand cooperative intelligent control method, device, equipment and medium

Abstract

The invention provides an electricity supply and demand cooperative intelligent control method, device and equipment and a medium, and relates to the technical field of power systems, the method comprises the following steps: obtaining historical electricity data of a target user, the historical electricity data comprising historical electricity power data of each circuit in different historical time periods; calculating electricity utilization power data of the target user in different time periods in the future according to the historical electricity utilization data; according to the duration of each time period, the power utilization power data corresponding to each time period and the power utilization cost corresponding to each time period, determining the total power utilization cost of the target user; and judging whether the prepayment cost of the target user is greater than or equal to the total electricity utilization cost, and if the prepayment cost is greater than or equal to the total electricity utilization cost, determining the total supply electric quantity according to the duration of each time period and the electricity utilization power data corresponding to each time period. According to the invention, the electricity consumption of the demander can be predicted, so that the generating capacity is determined.

Description

Technical Field

[0001] The present application relates to the technical field of power systems, and in particular, to a method, device, equipment, and medium for collaborative intelligent control of electricity supply and demand. Background Art

[0002] The coordinated control of power supply and demand in a power system is a method for managing and regulating the operation of a power system. Its main goal is to ensure the balance between power supply and power demand to maintain the stable operation of the power system. This process involves real-time monitoring, dispatching, and control of the power system to respond to changing power demands and continuously changing power production conditions.

[0003] Through a modern monitoring system, the power system can collect the operation data of various parts of the power grid in real time, including information such as generator output, current, voltage, and frequency. At the same time, by collecting the different powers of different electricity consumption categories of the demand side, such as lighting, industrial processing, other air-conditioning appliances, and heating electricity, the electricity demand information is determined, so as to adjust the power generation. However, when performing coordinated control of power supply and demand, it is usually necessary to predict the electricity consumption of the demand side in a future period of time to provide corresponding electricity to achieve the balance between power supply and demand. Therefore, a method is needed to predict the electricity consumption of the demand side to determine the power generation. Summary of the Invention

[0004] The present application provides a method, device, equipment, and medium for collaborative intelligent control of electricity supply and demand, which can predict the electricity consumption of the demand side to determine the power generation.

[0005] In the first aspect of the present application, a method for collaborative intelligent control of electricity supply and demand is provided. The method includes: Obtain the historical electricity consumption data of the target user, where the historical electricity consumption data includes the historical electricity consumption power data of each circuit in different historical time periods; Calculate the electricity consumption power data of the target user in different future time periods according to the historical electricity consumption data; Determine the total electricity cost of the target user according to the duration of each time period, the electricity consumption power data corresponding to each time period, and the electricity cost corresponding to each time period; Judge whether the prepaid cost of the target user is greater than or equal to the total electricity cost. If the prepaid cost is greater than or equal to the total electricity cost, determine the total power supply according to the duration of each time period and the electricity consumption power data corresponding to each time period.

[0006] By adopting the above technical solution, the historical power consumption data of the target user is obtained, including the historical power consumption data of each circuit in different historical time periods, which can deeply analyze information such as the periodic change and trend of the target user's power consumption. Then, based on the historical power consumption data, the power consumption data of the target user in different future time periods is calculated. Furthermore, through the duration, power consumption data, and corresponding electricity costs of each time period, the total electricity cost of the target user can be calculated. When the prepaid cost is greater than or equal to the total electricity cost, according to the duration and power consumption data of each time period, the total supply power is determined, which means that the user's prepaid cost is sufficient to cover the future electricity costs. Therefore, the total supply power can be reasonably determined to meet the user's needs. The power consumption of the target user is predicted to determine the power generation amount.

[0007] Optionally, before predicting the power consumption data of the target user in different future time periods based on the historical power consumption data, the method further includes: Obtain multiple first power consumption data of the first circuit in the first historical time period, where the first circuit is any one of the multiple circuits, the first historical time period is any one of the multiple historical time periods, and the first power consumption data is the historical power consumption data corresponding to the first historical time period in the historical power consumption data; Obtain multiple second power consumption data of the first circuit in the second historical time period, where the second historical time period is any one of the multiple historical time periods except the first historical time period, the start time of the first historical time period is the same as the start time of the second historical time period, and the end time of the first historical time period is the same as the end time of the second historical time period, and the second power consumption data is the historical power consumption data corresponding to the second historical time period in the historical power consumption data; Calculate the fitting degree of the power consumption data between the first historical time period and the second historical time period, specifically calculated by the following formula: ; where R is the fitting degree of the power consumption data, X i is the i-th first power consumption data, Y i is the i-th second power consumption data, P(X i ) is the fitting error value of the multiple first power consumption data, and Δ Y is the average value of the multiple second power consumption data; Determine whether the fitting degree of the electricity consumption power data is less than or equal to a preset fitting degree threshold. If the fitting degree of the electricity consumption power data is less than or equal to the preset fitting degree threshold, it is determined that the multiple first electricity consumption power data and the multiple second electricity consumption power data can be used to calculate the electricity consumption power data.

[0008] By adopting the above technical solution, by calculating the fitting degree of the electricity consumption power data in different time periods, a quantitative index of the fitting degree is introduced, so as to more accurately evaluate the reliability and consistency of historical electricity consumption data. The fitting degree reflects the similarity between the electricity consumption power data in different time periods, and improves the accuracy of data screening. By judging whether the fitting degree of the electricity consumption power data is less than or equal to the preset fitting degree threshold, the credibility of the data in different time periods can be dynamically evaluated. This helps to exclude abnormal or inconsistent historical data and improve the quality of historical data. Determine the historical electricity consumption data that passes the fitting degree evaluation and then use it for predicting future electricity consumption power. This helps to improve the availability of the electricity consumption power data and makes the prediction results more accurate and reliable.

[0009] Optionally, calculating the electricity consumption power data of the target user in different future time periods according to the historical electricity consumption data specifically includes: ; where P is the electricity consumption power data, α is the weight parameter, and Y t is the t-th historical electricity consumption data, and Z t is the adjustment data corresponding to the t-th historical electricity consumption data.

[0010] By adopting the above technical solution, by performing weighted calculation on the historical electricity consumption data, a weight parameter is introduced, which can more flexibly adjust the influence degree of historical data in future prediction. The adjustment of the weight parameter can be optimized according to the actual situation to better reflect the dynamic changes of the target user's electricity consumption. The adjustment data corresponding to the historical electricity consumption data is considered in the solution, which may reflect some special situations or trends of the historical data. By introducing this adjustment term, the characteristics of the historical data can be more accurately reflected and the fitting ability of the model is improved.

[0011] Optionally, before determining the total electricity consumption cost of the target user according to the duration of each time period, the electricity consumption power data corresponding to each time period, and the electricity consumption cost corresponding to each time period, the method further includes: Obtain the target duration and target electricity consumption power of the target time period. The target time period is any one of the multiple time periods, and the target electricity consumption power is the electricity consumption power data corresponding to the target time period among the multiple electricity consumption power data; Calculate the target power consumption for the target time period according to the target power consumption and the target duration. Obtain the target electricity cost for the target time period, where the target electricity cost is the electricity cost corresponding to the target time period among multiple time periods. Calculate the target electricity cost for the target time period according to the target power consumption and the target electricity cost.

[0012] By adopting the above technical solution, the target electricity cost for the target time period is obtained, which is obtained through comprehensive calculation considering the target power consumption and electricity cost. This calculation takes into account multiple factors such as electricity price, power consumption, and power consumption duration, improving the accuracy of the electricity cost for the target time period. By considering the target power consumption and duration of the target time period, as well as the corresponding electricity cost, a more accurate prediction of the electricity cost is provided. This helps users better understand and manage their electricity costs, providing more targeted data support for the intelligent regulation of the power system.

[0013] Optionally, determining the total electricity cost of the target user according to the duration of each time period, the power consumption data corresponding to each time period, and the electricity cost corresponding to each time period specifically includes: ; where D is the total electricity cost, T j is the duration of the j-th time period, P j is the power consumption data corresponding to the j-th time period, and Q j is the electricity cost corresponding to the j-th time period.

[0014] Optionally, after determining whether the prepaid cost of the target user is greater than or equal to the total electricity cost, the method further includes: If it is determined that the prepaid cost is less than the total electricity cost, then determine the total power supply according to the prepaid cost, the duration of each time period, the power consumption data corresponding to each time period, and the electricity cost corresponding to each time period.

[0015] By adopting the above technical solution, when it is determined that the user's prepaid cost is not sufficient to cover the total electricity cost, by considering the power consumption, duration, and cost of each time period, the total power supply can be reasonably determined according to the actual needs of the user to ensure sufficient power to meet the user's needs.

[0016] Optionally, after determining whether the prepayment of the target user is greater than or equal to the total electricity cost, if the prepayment is greater than or equal to the total electricity cost, and after determining the total electricity supply according to the duration of each time period and the electricity consumption power data corresponding to each time period, the method further includes: Obtain the total electricity costs of multiple users in the target area, where the multiple users include the target user; Determine the total electricity supply of each user according to the magnitude relationship between the total electricity cost of each user and the prepayment of the user; Determine the total power generation according to the total electricity supply of the multiple users.

[0017] By adopting the above technical solution, according to the magnitude relationship between the total electricity cost and the prepayment of each user, personalized power allocation for different users is realized. This allocation method enables the power system to more flexibly meet the electricity consumption needs of different users and improves the user experience. By comprehensively considering the total electricity supply of multiple users, the total power generation is finally determined. This helps the power system to more accurately plan and dispatch power generation resources to meet the electricity consumption needs of the entire area and improves the overall efficiency of the power system. By comparing the electricity cost of users with the prepayment, the balance between cost and supply-demand is achieved. This helps to ensure that while providing sufficient electricity, the user cost is effectively controlled, and the power system operates more economically and sustainably.

[0018] In the second aspect of the present application, an intelligent control device for electricity supply-demand coordination is provided, including an acquisition module, a calculation module, and a judgment module, where: The acquisition module is used to acquire the historical electricity consumption data of the target user, and the historical electricity consumption data includes the historical electricity consumption power data of each circuit in different historical time periods; The calculation module is used to calculate the electricity consumption power data of the target user in different future time periods according to the historical electricity consumption data; The calculation module is used to determine the total electricity cost of the target user according to the duration of each time period, the electricity consumption power data corresponding to each time period, and the electricity cost corresponding to each time period; The judgment module is used to judge whether the prepayment of the target user is greater than or equal to the total electricity cost. If the prepayment is greater than or equal to the total electricity cost, the total electricity supply is determined according to the duration of each time period and the electricity consumption power data corresponding to each time period.

[0019] Optionally, the obtaining module is configured to obtain a plurality of first power consumption data of a first circuit in a first historical time period, where the first circuit is any one of the plurality of circuits, the first historical time period is any one of the plurality of historical time periods, and the first power consumption data is the historical power consumption data corresponding to the first historical time period in the historical power consumption data; The obtaining module is configured to obtain a plurality of second power consumption data of the first circuit in a second historical time period, where the second historical time period is any one of the plurality of historical time periods except the first historical time period, the start time of the first historical time period is the same as the start time of the second historical time period, and the end time of the first historical time period is the same as the end time of the second historical time period, and the second power consumption data is the historical power consumption data corresponding to the second historical time period in the historical power consumption data; The calculation module is configured to calculate the fitting degree of the power consumption data between the first historical time period and the second historical time period, specifically calculated by the following formula: ; where R is the fitting degree of the power consumption data, X i is the i-th first power consumption data, Y i is the i-th second power consumption data, P(X i ) is the fitting error value of the plurality of first power consumption data, and Δ Y is the average value of the plurality of second power consumption data; The judgment module is configured to judge whether the fitting degree of the power consumption data is less than or equal to a preset fitting degree threshold. If the fitting degree of the power consumption data is less than or equal to the preset fitting degree threshold, it is determined that the plurality of first power consumption data and the plurality of second power consumption data can be used to calculate the power consumption data.

[0020] Optionally, the calculation module is configured to calculate the power consumption data of the target user in different future time periods according to the historical power consumption data, specifically including: ; where P is the power consumption data, α is a weight parameter, Y t is the t-th historical power consumption data, and Z t is the adjustment data corresponding to the t-th historical power consumption data.

[0021] Optionally, the obtaining module is configured to obtain a target duration and a target power consumption within a target time period, where the target time period is any one of the multiple time periods, and the target power consumption is the power consumption data corresponding to the target time period among the multiple power consumption data; The calculating module is configured to calculate a target power consumption within the target time period according to the target power consumption and the target duration; The obtaining module is configured to obtain a target electricity cost within the target time period, where the target electricity cost is the electricity cost corresponding to the target time period among the multiple time periods; The calculating module is configured to calculate a target electricity cost within the target time period according to the target power consumption and the target electricity cost;

[0022] Optionally, the calculating module is configured to determine a total electricity cost of the target user according to the duration of each time period, the power consumption data corresponding to each time period, and the electricity cost corresponding to each time period, which specifically includes: ; where D is the total electricity cost, T j is the duration of the j-th time period, P j is the power consumption data corresponding to the j-th time period, and Q j is the electricity cost corresponding to the j-th time period.

[0023] Optionally, the determining module is configured to, if it is determined that the prepaid cost is less than the total electricity cost, determine a total power supply amount according to the prepaid cost, the duration of each time period, the power consumption data corresponding to each time period, and the electricity cost corresponding to each time period.

[0024] Optionally, the obtaining module is configured to obtain the total electricity costs of multiple users in a target area, where the multiple users include the target user; The determining module is configured to determine a total power supply amount for each user according to the magnitude relationship between the total electricity cost of each user and the prepaid cost of the user; The calculating module is configured to determine a total power generation amount according to the total power supply amounts of the multiple users.

[0025] In a third aspect of the present application, an electronic device is provided, including a processor, a memory, a user interface, and a network interface. The memory is configured to store instructions, the user interface and the network interface are both configured to communicate with other devices, and the processor is configured to execute the instructions stored in the memory so that the electronic device performs the above method steps.

[0026] In a fourth aspect of the present application, a computer-readable storage medium is provided. The computer-readable storage medium stores instructions that, when executed, perform the above method steps.

[0027] In summary, one or more technical solutions provided in the embodiments of the present application have at least the following technical effects or advantages: 1. Obtain the historical power consumption data of the target user, including the historical power consumption data of each circuit in different historical time periods, which can deeply analyze information such as the periodic changes and trends of the target user's power consumption. Then, based on the historical power consumption data, calculate the power consumption data of the target user in different future time periods. Furthermore, through the duration, power consumption data, and corresponding electricity costs of each time period, the total electricity cost of the target user can be calculated. When the prepaid cost is greater than or equal to the total electricity cost, determine the total power supply based on the duration and power consumption data of each time period. This indicates that the user's prepaid cost is sufficient to cover future electricity costs. Therefore, the total power supply can be reasonably determined to meet the user's needs. Achieve the prediction of the power consumption of the target user, thereby determining the power generation.

[0028] 2. By calculating the fitting degree of the power consumption data in different time periods, a quantitative index of the fitting degree is introduced, thereby more accurately evaluating the reliability and consistency of the historical power consumption data. The fitting degree reflects the similarity between the power consumption data in different time periods, improving the accuracy of data screening. By determining whether the fitting degree of the power consumption data is less than or equal to a preset fitting degree threshold, the credibility of the data in different time periods can be dynamically evaluated. This helps to exclude abnormal or inconsistent historical data, improving the quality of historical data. Determine the historical power consumption data that passes the fitting degree evaluation and then use it for the prediction of future power consumption. This helps to improve the usability of the power consumption data, making the prediction results more accurate and reliable. Description of the Drawings

[0029] Figure 1 is a schematic flowchart of a method for intelligent control of power supply and demand coordination according to an embodiment of the present application; Figure 2 is a schematic block diagram of a device for intelligent control of power supply and demand coordination according to an embodiment of the present application; Figure 3 is a schematic structural diagram of an electronic device according to an embodiment of the present application.

[0030] Description of the reference numerals: 201, acquisition module; 202, calculation module; 203, judgment module; 301, processor; 302, communication bus; 303, user interface; 304, network interface; 305, memory. Detailed Embodiments

[0031] To enable those skilled in the art to better understand the technical solutions in this specification, the technical solutions in the embodiments of this specification will be clearly and completely described below in conjunction with the accompanying drawings in the embodiments of this specification. Obviously, the described embodiments are only a part of the embodiments of this application, rather than all of the embodiments.

[0032] In the description of the embodiments of this application, words such as "for example" or "for illustration" are used to represent examples, illustrations or explanations. Any embodiment or design solution described as "for example" or "for illustration" in the embodiments of this application should not be construed as being more preferred or having more advantages than other embodiments or design solutions. Rather, the use of words such as "for example" or "for illustration" is intended to present relevant concepts in a specific manner.

[0033] In the description of the embodiments of this application, the meaning of the term "plurality" refers to two or more. For example, a plurality of systems refers to two or more systems, and a plurality of screen terminals refers to two or more screen terminals. In addition, the terms "first" and "second" are only used for descriptive purposes and cannot be understood as indicating or implying relative importance or implicitly indicating the technical features indicated. Thus, the features defined with "first" and "second" may explicitly or implicitly include one or more of such features. The terms "include", "comprise", "have" and their variants all mean "including but not limited to", unless otherwise specifically emphasized in other ways.

[0034] The coordinated control of power supply and demand in a power system is a method of managing and regulating the operation of a power system, and its main goal is to ensure the balance between power supply and power demand to maintain the stable operation of the power system. This process involves real-time monitoring, dispatching and control of the power system to cope with changing power demands and constantly changing power production conditions.

[0035] Through a modern monitoring system, the power system can collect the operation data of various parts of the power grid in real time, including information such as generator output, current, voltage, frequency, etc. At the same time, by collecting the different powers of different electricity consumption categories such as lighting, industrial processing, other air-conditioning appliances, and heating electricity on the demand side, the electricity demand information is determined, so as to adjust the power generation. However, when performing coordinated control of power supply and demand, it is usually necessary to predict the electricity consumption of the demand side in a future period of time to provide the corresponding electricity quantity to achieve the balance between power supply and demand. Therefore, a method is needed to realize the prediction of the electricity consumption of the demand side, so as to determine the power generation.

[0036] This embodiment discloses a method for intelligent coordinated control of power supply and demand, referring to Figure 1 , including the following steps S110-S140: S110, obtain the historical electricity consumption data of the target user.

[0037] A method for collaborative intelligent control of power supply and demand disclosed in an embodiment of the present application is applied to a server, which is a server of a power supply party. The server includes, but is not limited to, electronic devices such as mobile phones, tablet computers, wearable devices, PCs (Personal Computers), etc., and can also be a background server running a method for collaborative intelligent control of power supply and demand. The server can be implemented by an independent server or a server cluster composed of multiple servers. The server is connected to the power consumption devices of multiple users. Multiple power consumption devices of each user form multiple power consumption circuits. By setting appropriate sensors and instrument devices in different circuits, the power consumption data of each power consumption circuit is collected. These devices can be smart meters, current sensors, voltage sensors, etc.

[0038] Hereinafter, any one user among multiple users, the target user, is taken as an example for illustration. During the power consumption process of the power consumption devices on multiple circuits of the target user, the server acquires the power consumption data collected by relevant devices and stores it as historical power consumption data. The historical power consumption data at least includes the historical power consumption power data of each circuit in different historical time periods. For example, the power consumption power of a certain heating circuit of the target user from December 1st to December 31st is collected in real time and then filled in a form for storage.

[0039] S120. According to the historical power consumption data, calculate the power consumption power data of the target user in different future time periods.

[0040] In order to make the inferred power consumption power data more valuable for reference, the historical power consumption data is recorded in time segments, and then the power consumption power data of a subsequent day is inferred according to the power consumption power data of different time segments. For example, by recording the power consumption power data during the period from 12:00 to 13:00 on the 1st of each month, the power consumption power data during the period from 12:00 to 13:00 on the 1st of the next month at the current time is inferred.

[0041] Before inferring the future power consumption data through the historical power consumption data, it is first necessary to calculate the fitting degree of different historical power consumption data to determine whether multiple historical power consumption data have similarity, so as to determine whether regular inference can be performed for subsequent power consumption data calculation. It should be noted that during the actual power consumption process, the power consumption power data within a period of time will fluctuate up and down. For the sake of simplifying the calculation, the power consumption power data of each time period is taken as the average value of that time period.

[0042] Specifically, the server obtains multiple first power consumption data of the first circuit in the first historical time period, and multiple second power consumption data of the first circuit in the second historical time period. The first circuit is any one of the multiple circuits, the first historical time period is any one of the multiple historical time periods, and the first power consumption data is the historical power consumption data corresponding to the first historical time period in the historical power consumption data. Obtain multiple second power consumption data of the first circuit in the second historical time period. The second historical time period is any one of the multiple historical time periods except the first historical time period. The start time of the first historical time period is the same as the start time of the second historical time period, and the end time of the first historical time period is the same as the end time of the second historical time period. The second power consumption data is the historical power consumption data corresponding to the second historical time period in the historical power consumption data.

[0043] Then, calculate the fitting degree of the power consumption data between the first historical time period and the second historical time period through the following formula: ; where R is the fitting degree of the power consumption data, X i is the i-th first power consumption data, Y i is the i-th second power consumption data, P(X i ) is the fitting error value of the multiple first power consumption data, and Δ Y is the average value of the multiple second power consumption data.

[0044] The fitting error value can be calculated through the polynomial function obtained by fitting. Generally speaking, polynomial fitting can use the least squares method to find a most suitable polynomial function to minimize the fitting error at the given data points.

[0045] For the multiple first power consumption data and second power consumption data, assuming a set of data points is (X1, Y1), (X2, Y2), ……, (X n , Y n ), find an m-degree polynomial as follows: ; where a0, a1, …, a m are undetermined coefficients, and the goal of the least squares method is to minimize the gap between the first power consumption data and the second power consumption data.

[0046] The process of polynomial fitting can be achieved by solving a system of linear equations, where the coefficients of the equations are determined by the data points X i and Y i . Specifically, the matrix representation method can be used. Let: ; ; ; The solution of the least squares method is obtained by solving the following linear equations: X T XA = X T Y.

[0047] where X T represents the transpose of X. After solving for A, the coefficients for polynomial fitting can be obtained. Finally, the fitting error value can be calculated by substituting X i into the polynomial P(x): ; By substituting the fitting error value into the calculation formula for the fitting degree of electricity consumption power data, the fitting degree of electricity consumption power data is obtained. The smaller the value of the fitting degree of electricity consumption power data, the higher the fitting degree between the historical electricity consumption power data in the first historical time period and the historical electricity consumption power data in the second historical time period. Therefore, by presetting a preset fitting degree threshold, it can be determined whether the fitting degree of electricity consumption power data is less than or equal to the preset fitting degree threshold. If the fitting degree of electricity consumption power data is less than or equal to the preset fitting degree threshold, it is determined that multiple first electricity consumption power data and multiple second electricity consumption power data can be used to calculate the electricity consumption power data.

[0048] By calculating the fitting degree of electricity consumption power data in different time periods, a quantitative index of the fitting degree is introduced, thereby more accurately evaluating the reliability and consistency of historical electricity consumption data. The fitting degree reflects the similarity between the electricity consumption power data in different time periods, improving the accuracy of data screening. By determining whether the fitting degree of electricity consumption power data is less than or equal to the preset fitting degree threshold, the credibility of data in different time periods can be dynamically evaluated. This helps to exclude abnormal or inconsistent historical data, improving the quality of historical data. Determine the historical electricity consumption data that passes the fitting degree evaluation and then use it for predicting future electricity consumption power. This helps to improve the usability of electricity consumption power data, making the prediction results more accurate and reliable.

[0049] Then, based on the historical electricity consumption data, calculate the electricity consumption power data of the target user in different future time periods, specifically calculated by the following formula: ; where P is the electricity consumption power data, α is the weight parameter, Y t is the t-th historical electricity consumption data, and Z t is the adjustment data corresponding to the t-th historical electricity consumption data.

[0050] The adjustment data can be obtained by dividing the actual observed value by the corresponding periodic index. The specific formula is as follows: ; Among them, Z t is the adjustment data corresponding to the t-th historical power consumption data, X t is the t-th first power consumption data, and Y t is the t-th second power consumption data. Apply the periodic adjustment term to the future power consumption prediction to consider the change in power consumption data caused by periodic changes.

[0051] By performing weighted calculations on historical power consumption data, a weight parameter is introduced, which can more flexibly adjust the influence degree of historical data in future predictions. The adjustment of the weight parameter can be optimized according to the actual situation to better reflect the dynamic changes in the power consumption of the target user. The adjustment data corresponding to the historical power consumption data is considered in the solution, which may reflect some special situations or trends in the historical data. By introducing this adjustment term, the characteristics of the historical data can be more accurately reflected, and the fitting ability of the model is improved.

[0052] S130. Determine the total power consumption cost of the target user according to the duration of each time period, the power consumption data corresponding to each time period, and the power consumption cost corresponding to each time period.

[0053] Since the rate standards in different time periods are different and the power consumption situations during peak and off-peak hours are different, when calculating the total power consumption cost of the target user, it is necessary to calculate it by time period. The server first obtains the target duration and target power consumption of the target time period. The target time period is any one of multiple time periods, and the target power consumption is the power consumption data corresponding to the target time period among multiple power consumption data. Use the power consumption data and duration within the target time period to calculate the target power consumption of the target time period, which can be simply obtained by multiplying the power by the time. Then, according to the queried real-time electricity price information and considering the electricity price changes in different time periods, determine the target power consumption cost of the target time period. The target power consumption cost is the power consumption cost corresponding to the target time period among multiple time periods. Finally, use the target power consumption and real-time power consumption cost to calculate the power consumption cost of the target time period, which can be simply obtained by multiplying the power consumption by the electricity price, to obtain the target power consumption cost of the target time period.

[0054] Obtain the target power consumption cost of the target time period, which is obtained through comprehensive calculations considering the target power consumption and power consumption cost. This calculation considers multiple factors such as electricity price, power consumption, and power consumption duration, improving the accuracy of the power consumption cost of the target time period. By considering the target power consumption and duration of the target time period, as well as the corresponding power consumption cost, a more accurate prediction of the power consumption cost is provided. This helps users better understand and manage their power consumption costs and provides more targeted data support for the intelligent regulation of the power system.

[0055] The total electricity consumption costs for multiple time periods are calculated as follows. The specific calculation formula is as below: ; where D is the total electricity consumption cost, T j is the duration of the j-th time period, P j is the electricity consumption power data corresponding to the j-th time period, and Q j is the electricity consumption cost corresponding to the j-th time period.

[0056] S140: Determine whether the prepayment of the target user is greater than or equal to the total electricity consumption cost. If the prepayment is greater than or equal to the total electricity consumption cost, then determine the total electricity supply according to the durations of each time period and the electricity consumption power data corresponding to each time period.

[0057] For the target user, usually, the electricity bill is prepaid in advance to the power supply side, that is, the prepayment. After calculating the total electricity consumption cost that the target user needs to consume through the above steps, if it is determined that the prepayment is greater than or equal to the total electricity consumption cost, it indicates that the electricity bill prepaid by the target user in advance can provide power supply within the preset time period. Then, directly multiply the duration of each time period by the predicted electricity consumption power data of the corresponding time period to obtain the electricity consumption of each time period. Finally, sum up the electricity consumption of each time period to obtain the total electricity consumption, that is, the total electricity supply.

[0058] Similarly, for multiple circuits of each user, intelligent control can be achieved through a power supply model or an expert model. After fully understanding the load distribution and the distributed load electrical energy demand of the user, according to the calculated parameters, the regulation information is sent to each sub-control point to achieve intelligent regulation of multiple circuits of each user. The server completes the parameter setting of each point through the parameter setting of the master control point for the sub-control point, and completes the intelligent load regulation of each point, so as to achieve negative control and multi-circuit control of multiple circuits. Negative control refers to the management of the entire power system by adjusting or controlling the load (electricity use) in the circuit. This includes adjusting the size, distribution, time period, etc. of the load to maintain the balance and stability of the power system. Multi-circuit control is a means of centralized management and control of multiple circuits or channels in the power system. It is achieved through technologies such as intelligent systems, remote monitoring and control to ensure that the operating state of each circuit meets the overall requirements of the system. In addition, through intelligent control, the server can more accurately match the actual electricity consumption demand of the user, supply the corresponding amount of electricity according to the demand, and improve the energy efficiency of the electricity consumption system.

[0059] Obtain the historical electricity consumption data of the target user, including the historical electricity consumption power data of each circuit in different historical time periods, which can deeply analyze information such as the periodic changes and trends of the target user's electricity consumption. Then, based on the historical electricity consumption data, calculate the electricity consumption power data of the target user in different future time periods. Furthermore, through the duration, electricity consumption power data, and corresponding electricity costs of each time period, the total electricity cost of the target user can be calculated. When the prepaid cost is greater than or equal to the total electricity cost, determine the total electricity supply based on the duration and electricity consumption power data of each time period. This indicates that the user's prepaid cost is sufficient to cover future electricity costs. Therefore, the total electricity supply can be reasonably determined to meet the user's needs. Achieve the prediction of the electricity consumption of the target user, and thus determine the power generation.

[0060] Conversely, if it is determined that the prepaid cost is less than the total electricity cost, it indicates that the electricity fee prepaid by the target user is not sufficient to provide power supply within the preset time period. Then, based on the prepaid cost, the duration of each time period, the electricity consumption power data corresponding to each time period, and the electricity cost corresponding to each time period, determine the total electricity supply. Specifically, calculate the electricity cost of each time period in the order from front to back, and finally sum them up until the total cost reaches the prepaid cost. Then, sum up the electricity consumption of each time period to obtain the total electricity supply of the target user. When it is judged that the user's prepaid cost is not sufficient to cover the total electricity cost, by considering the electricity consumption power, duration, and cost of each time period, the total electricity supply can be reasonably determined according to the actual needs of the user to ensure that sufficient electricity is available to meet the user's needs.

[0061] Since the power supply party supplies electricity to multiple users in the target area simultaneously, therefore, referring to the calculation method of the total electricity cost of the target user, calculate the total electricity cost of each user in the target area. Similarly, by comparing the total electricity cost of each user with the prepaid cost, then determine the total electricity supply of each user. Finally, the server sums up the total electricity supply of each user to obtain the total power generation that needs to be supplied to the target area.

[0062] Based on the relationship between the total electricity consumption cost and the prepaid cost of each user, personalized electricity allocation for different users is achieved. This allocation method enables the power system to more flexibly meet the electricity demands of different users and improves the user experience. By integrating the total electricity supply of multiple users, the total power generation is finally determined. This helps the power system to more accurately plan and dispatch power generation resources to meet the electricity demands of the entire region and improves the overall efficiency of the power system. By comparing the electricity consumption cost and the prepaid cost of users, the balance between cost and supply-demand is achieved. This helps to ensure that while providing sufficient electricity, the power system effectively controls the costs of users and promotes the more economical and sustainable operation of the power system. It greatly saves social resources, is cleaner and more low-carbon, reduces duplicate investments, and thus reduces social investment costs and operating costs.

[0063] Finally, during the process of supplying electricity to multiple users in the target area of the power supply direction, an overload detection algorithm can be implemented on the server to monitor the load conditions of electricity consumption nodes. When an overload situation is detected, power off or other protection controls are performed on the electricity consumption nodes, and reminder messages can be sent to relevant users, such as through text messages, emails, App notifications, etc. The server can implement an area electricity allocation and refined regulation algorithm. It can reasonably allocate electricity resources according to factors such as real-time load conditions, power grid status, electricity prices, etc., to reduce the system load. Users can monitor their electricity consumption in real time, receive reminder messages, and can interact with the system. Users can set electricity consumption targets, view electricity consumption history, etc. through the interface.

[0064] This embodiment also discloses an intelligent control device for electricity supply-demand coordination, referring to Figure 2 , including an acquisition module 201, a calculation module 202, and a judgment module 203, where: The acquisition module 201 is used to acquire the historical electricity consumption data of the target user, and the historical electricity consumption data includes the historical electricity consumption power data of each circuit in different historical time periods.

[0065] The calculation module 202 is used to calculate the electricity consumption power data of the target user in different future time periods according to the historical electricity consumption data.

[0066] The calculation module 202 is used to determine the total electricity consumption cost of the target user according to the duration of each time period, the electricity consumption power data corresponding to each time period, and the electricity consumption cost corresponding to each time period.

[0067] The judgment module 203 is used to judge whether the prepaid cost of the target user is greater than or equal to the total electricity consumption cost. If the prepaid cost is greater than or equal to the total electricity consumption cost, the total electricity supply is determined according to the duration of each time period and the electricity consumption power data corresponding to each time period.

[0068] In a possible implementation, an acquisition module 201 is configured to acquire a plurality of first power consumption data of a first circuit in a first historical time period. The first circuit is any one of a plurality of circuits, the first historical time period is any one of a plurality of historical time periods, and the first power consumption data is the historical power consumption data corresponding to the first historical time period in the historical power consumption data.

[0069] The acquisition module 201 is further configured to acquire a plurality of second power consumption data of the first circuit in a second historical time period. The second historical time period is any one of the plurality of historical time periods except the first historical time period. The start time of the first historical time period is the same as the start time of the second historical time period, and the end time of the first historical time period is the same as the end time of the second historical time period. The second power consumption data is the historical power consumption data corresponding to the second historical time period in the historical power consumption data.

[0070] A calculation module 202 is configured to calculate the fitting degree of the power consumption data between the first historical time period and the second historical time period, specifically calculated by the following formula: ; where R is the fitting degree of the power consumption data, X i is the i-th first power consumption data, Y i is the i-th second power consumption data, P(X i ) is the fitting error value of the plurality of first power consumption data, and Δ Y is the average value of the plurality of second power consumption data.

[0071] A judgment module 203 is configured to judge whether the fitting degree of the power consumption data is less than or equal to a preset fitting degree threshold. If the fitting degree of the power consumption data is less than or equal to the preset fitting degree threshold, it is determined that the plurality of first power consumption data and the plurality of second power consumption data can be used to calculate the power consumption data.

[0072] In a possible implementation, the calculation module 202 is configured to calculate the power consumption data of the target user in different future time periods according to the historical power consumption data, specifically including: ; where P is the power consumption data, α is a weight parameter, Y t is the t-th historical power consumption data, and Z t is the adjustment data corresponding to the t-th historical power consumption data.

[0073] In a possible implementation, an acquisition module 201 is configured to acquire a target duration and a target power consumption within a target time period, where the target time period is any one of a plurality of time periods, and the target power consumption is the power consumption data corresponding to the target time period among a plurality of power consumption data.

[0074] A calculation module 202 is configured to calculate a target power consumption within the target time period according to the target power consumption and the target duration.

[0075] The acquisition module 201 is configured to acquire a target power consumption cost within the target time period, where the target power consumption cost is the power consumption cost corresponding to the target time period among a plurality of time periods.

[0076] The calculation module 202 is configured to calculate the target power consumption cost within the target time period according to the target power consumption and the target power consumption cost.

[0077] In a possible implementation, the calculation module 202 is configured to determine the total power consumption cost of a target user according to the duration of each time period, the power consumption data corresponding to each time period, and the power consumption cost corresponding to each time period, specifically including: ; where D is the total power consumption cost, T j is the duration of the j-th time period, P j is the power consumption data corresponding to the j-th time period, and Q j is the power consumption cost corresponding to the j-th time period.

[0078] In a possible implementation, a judgment module 203 is configured to, if it is determined that the prepaid cost is less than the total power consumption cost, determine the total power supply according to the prepaid cost, the duration of each time period, the power consumption data corresponding to each time period, and the power consumption cost corresponding to each time period.

[0079] In a possible implementation, the acquisition module 201 is configured to acquire the total power consumption costs of multiple users in a target area, where the multiple users include the target user.

[0080] The judgment module 203 is configured to determine the total power supply of each user according to the magnitude relationship between the total power consumption cost of each user and the prepaid cost of the user.

[0081] The calculation module 202 is configured to determine the total power generation according to the total power supply of multiple users.

[0082] It should be noted that when the device provided in the above embodiment realizes its functions, only the division of the above function modules is used for illustration. In practical applications, the above functions can be allocated to different function modules according to needs, that is, the internal structure of the device is divided into different function modules to complete all or part of the functions described above. In addition, the device and method embodiments provided in the above embodiment belong to the same concept. For the specific implementation process, please refer to the method embodiment and will not be elaborated here.

[0083] This embodiment also discloses an electronic device. Referring to Figure 3 , the electronic device may include: at least one processor 301, at least one communication bus 302, a user interface 303, a network interface 304, and at least one memory 305.

[0084] Among them, the communication bus 302 is used to realize the connection and communication between these components.

[0085] Among them, the user interface 303 may include a display screen (Display) and a camera (Camera). Optionally, the user interface 303 may further include a standard wired interface and a wireless interface.

[0086] Among them, the network interface 304 may optionally include a standard wired interface and a wireless interface (such as a WI-FI interface).

[0087] Among them, the processor 301 may include one or more processing cores. The processor 301 connects various parts within the entire server through various interfaces and lines. By running or executing instructions, programs, code sets, or instruction sets stored in the memory 305, and by calling the data stored in the memory 305, the processor 301 executes various functions of the server and processes data. Optionally, the processor 301 may be implemented in at least one hardware form of digital signal processing (DSP), field-programmable gate array (FPGA), or programmable logic array (PLA). The processor 301 may integrate one or a combination of a central processing unit 301 (CPU), a graphics processing unit 301 (GPU), and a modem, etc. Among them, the CPU mainly processes the operating system, user interface, and application programs, etc.; the GPU is responsible for rendering and drawing the content to be displayed on the display screen; the modem is used to process wireless communication. It can be understood that the above modem may not be integrated into the processor 301 and may be implemented separately by a single chip.

[0088] Among them, the memory 305 may include a Random Access Memory (RAM), or may also include a Read-Only Memory. Optionally, the memory 305 includes a non-transitory computer-readable storage medium. The memory 305 can be used to store instructions, programs, codes, code sets or instruction sets. The memory 305 may include a program storage area and a data storage area. Among them, the program storage area can store instructions for implementing the operating system, instructions for at least one function (such as touch function, sound playback function, image playback function, etc.), instructions for implementing the above method embodiments, etc.; the data storage area can store the data involved in the above method embodiments. Optionally, the memory 305 may also be at least one storage device located far from the aforementioned processor 301. As shown in the figure, the memory 305, as a computer storage medium, may include an operating system, a network communication module, a user interface 303 module, and an application program for an intelligent control method for electricity supply and demand coordination.

[0089] In Figure 3 In the electronic device shown, the user interface 303 is mainly used to provide an input interface for the user to obtain the data input by the user; and the processor 301 can be used to call the application program for an intelligent control method for electricity supply and demand coordination stored in the memory 305. When executed by one or more processors 301, the electronic device executes the method of one or more of the above embodiments.

[0090] It should be noted that for the foregoing method embodiments, for the sake of simple description, they are all expressed as a series of action combinations. However, those skilled in the art should know that this application is not limited by the described action sequence, because according to this application, certain steps can be performed in other sequences or simultaneously. Secondly, those skilled in the art should also know that the embodiments described in the specification are all preferred embodiments, and the actions and modules involved are not necessarily essential to this application.

[0091] In the above embodiments, the descriptions of each embodiment have their own emphases. For the parts not detailed in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0092] In several embodiments provided in the present application, it should be understood that the disclosed device can be implemented in other ways. For example, the device embodiments described above are merely illustrative. For example, the division of units is only a logical function division. In actual implementation, there may be other division methods. For example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the displayed or discussed coupling or direct coupling or communication connection between each other can be through some service interfaces. The indirect coupling or communication connection of the device or unit can be in electrical or other forms.

[0093] The units described as separate components may or may not be physically separated. The components shown as units may or may not be physical units, that is, they may be located in one place or distributed to multiple network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0094] In addition, in each embodiment of the present application, the functional units can be integrated in a processing unit, or each unit can exist physically alone, or two or more units can be integrated in one unit. The above-mentioned integrated units can be implemented in the form of hardware or in the form of software functional units.

[0095] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable memory 305. Based on this understanding, the technical solution of the present application, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a memory 305 and includes several instructions for causing a computer device (which can be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the methods in the various embodiments of the present application. And the aforementioned memory 305 includes: various media such as USB flash drives, mobile hard disks, magnetic disks, or optical discs that can store program codes.

[0096] The above are only exemplary embodiments of the present disclosure and cannot be used to limit the scope of the present disclosure. That is, any equivalent changes and modifications made in accordance with the teachings of the present disclosure still fall within the scope covered by the present disclosure. Those skilled in the art will easily think of other implementation schemes of the present disclosure after considering the specification and the disclosure of the practical truth. The present application aims to cover any variations, uses, or adaptive changes of the present disclosure. These variations, uses, or adaptive changes follow the general principles of the present disclosure and include common general knowledge or conventional technical means in the technical field not recorded in the present disclosure. The specification and the embodiments are only regarded as exemplary, and the scope and spirit of the present disclosure are defined by the claims.

Claims

1. An intelligent control method for electricity supply and demand coordination, characterized in that, The method includes: Obtaining historical power consumption data of a target user, where the historical power consumption data includes historical power consumption power data of each circuit in different historical time periods; Calculating power consumption power data of the target user in different future time periods according to the historical power consumption data; Determining the total power consumption cost of the target user according to the duration of each of the time periods, the power consumption power data corresponding to each of the time periods, and the power consumption cost corresponding to each of the time periods; Judging whether the prepaid cost of the target user is greater than or equal to the total power consumption cost. If the prepaid cost is greater than or equal to the total power consumption cost, then determining the total power supply according to the duration of each of the time periods and the power consumption power data corresponding to each of the time periods; 2. The intelligent control method for power consumption and supply coordination according to claim 1, wherein Before speculating the power consumption power data of the target user in different future time periods according to the historical power consumption data, the method further includes: Obtaining a plurality of first power consumption power data of a first circuit in a first historical time period, where the first circuit is any one of the plurality of circuits, the first historical time period is any one of the plurality of historical time periods, and the first power consumption power data is the historical power consumption power data corresponding to the first historical time period in the historical power consumption data; Obtaining a plurality of second power consumption power data of the first circuit in a second historical time period, where the second historical time period is any one of the plurality of historical time periods except the first historical time period, the start time of the first historical time period is the same as the start time of the second historical time period, the end time of the first historical time period is the same as the end time of the second historical time period, and the second power consumption power data is the historical power consumption power data corresponding to the second historical time period in the historical power consumption data; Calculating the power consumption power data fitting degree between the first historical time period and the second historical time period, specifically calculated by the following formula: ； Among them, R is the fitting degree of the electricity consumption power data, X i is the i-th first electricity consumption power data, Y i is the i-th second electricity consumption power data, P(X i ) is the fitting error value of multiple said first electricity consumption power data, Δ Y is the average value of multiple said second electricity consumption power data; Judging whether the power consumption power data fitting degree is less than or equal to a preset fitting degree threshold. If the power consumption power data fitting degree is less than or equal to the preset fitting degree threshold, then determining that the plurality of first power consumption power data and the plurality of second power consumption power data can be used to calculate the power consumption power data; 3. The intelligent control method for power consumption and supply coordination according to claim 1, wherein The calculating the power consumption power data of the target user in different future time periods according to the historical power consumption data specifically includes: ； Among them, P is the power consumption data, α is the weight parameter, Y t is the t-th historical power consumption data, Z t is the adjustment data corresponding to the t-th historical power consumption data.

4. The intelligent control method for power consumption and supply coordination according to claim 1, wherein Before determining the total power consumption cost of the target user according to the duration of each of the time periods, the power consumption power data corresponding to each of the time periods, and the power consumption cost corresponding to each of the time periods, the method further includes: Obtaining the target duration and the target power consumption of a target time period, where the target time period is any one of the plurality of time periods, and the target power consumption is the power consumption power data corresponding to the target time period among the plurality of power consumption power data; Calculating the target power consumption of the target time period according to the target power consumption and the target duration; Obtain the target electricity consumption cost for the target time period, where the target electricity consumption cost is the electricity consumption cost corresponding to the target time period among multiple time periods; Calculate the target electricity consumption cost for the target time period based on the target electricity consumption and the target electricity consumption cost.

5. A method for intelligent control of power supply and demand coordination according to claim 1, characterized in that, Determining the total electricity consumption cost of the target user according to the duration of each of the time periods, the electricity consumption power data corresponding to each of the time periods, and the electricity consumption cost corresponding to each of the time periods specifically includes: ； Where D is the total electricity cost, T j is the duration of the j-th time period, P j is the electricity power data corresponding to the j-th time period, Q j is the electricity cost corresponding to the j-th time period.

6. The intelligent control method for power consumption and supply coordination according to claim 1, wherein After determining whether the prepaid cost of the target user is greater than or equal to the total electricity consumption cost, the method further includes: If it is determined that the prepaid cost is less than the total electricity consumption cost, then determine the total electricity supply according to the prepaid cost, the duration of each of the time periods, the electricity consumption power data corresponding to each of the time periods, and the electricity consumption cost corresponding to each of the time periods.

7. A method for intelligent control of power supply and demand coordination according to claim 1, characterized in that After determining whether the prepaid cost of the target user is greater than or equal to the total electricity consumption cost, and if the prepaid cost is greater than or equal to the total electricity consumption cost, then determine the total electricity supply according to the duration of each of the time periods and the electricity consumption power data corresponding to each of the time periods, the method further includes: Obtain the total electricity consumption costs of multiple users in the target area, where the multiple users include the target user; Determine the total electricity supply of each user according to the magnitude relationship between the total electricity consumption cost of each user and the prepaid cost of the user; Determine the total power generation according to the total electricity supply of multiple users.

8. An intelligent control device for coordinated power consumption and supply, characterized in that, Includes an acquisition module (201), a calculation module (202), and a judgment module (203), where: The acquisition module (201) is configured to acquire the historical electricity consumption data of the target user, and the historical electricity consumption data includes the historical electricity consumption power data of each circuit in different historical time periods; The calculation module (202) is configured to calculate the electricity consumption power data of the target user in different future time periods according to the historical electricity consumption data; The calculation module (202) is configured to determine the total electricity consumption cost of the target user according to the duration of each of the time periods, the electricity consumption power data corresponding to each of the time periods, and the electricity consumption cost corresponding to each of the time periods; The judgment module (203) is configured to judge whether the prepaid cost of the target user is greater than or equal to the total electricity consumption cost. If the prepaid cost is greater than or equal to the total electricity consumption cost, then determine the total electricity supply according to the duration of each of the time periods and the electricity consumption power data corresponding to each of the time periods.

9. An electronic device, characterized in that, Includes a processor (301), a memory (305), a user interface (303), and a network interface (304). The memory (305) is used to store instructions. Both the user interface (303) and the network interface (304) are used to communicate with other devices. The processor (301) is used to execute the instructions stored in the memory (305) so that the electronic device executes the method according to any one of claims 1-7.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores instructions, and when the instructions are executed, the method according to any one of claims 1-7 is executed.

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