Electric power metering system for realizing day-ahead load curve optimization

Abstract

The invention discloses an electric power metering system for realizing day-ahead load curve optimization, and relates to the technical field of electric power metering, and the technical scheme is characterized in that a plurality of intelligent electric meters are used for collecting output data of a power plant and power utilization data of a user side, and uploading the output data and the power utilization data to a data processing center in real time; the data processing center is used for preprocessing the output data and the power utilization data, transmitting the preprocessed data to the dispatching center, and synchronizing electricity price information to the intelligent electric meter according to an interval electricity price strategy formulated by the dispatching center; and the dispatching center is used for generating a day-ahead dispatching curve according to the preprocessed data, and formulating an interval electricity price strategy for a time period when the electricity abandoning rate exceeds a set value according to the day-ahead dispatching curve. The electricity price of the whole time period of the next day is updated in time, the electricity price information is synchronously transmitted to the intelligent electric meter on the user side, consumption of new energy is promoted, the economic benefit of a power system is improved, and energy waste is reduced.

Description

Technical Field

[0001] The present invention relates to the technical field of electric power metering, and more particularly to an electric power metering system for optimizing a day-ahead load curve. Background Art

[0003] At present, despite the continuous progress of metering technology for power equipment, there are still certain limitations in metering accuracy, which leads to data errors. In addition, electricity prices cannot be updated in real time, and the overall intelligence and informatization are insufficient, which makes data collection, processing and analysis inefficient and difficult to cope with rapidly changing market demands. These problems have brought many challenges to the accuracy and effectiveness of solutions. It can be seen that the high precision and strong real-time performance of power metering equipment are of great significance to the accuracy of electric energy metering and strategy formulation.

[0004] Therefore, how to study and design an electricity metering system that can overcome the above-mentioned defects and optimize the day-ahead load curve is an issue that we urgently need to solve. Summary of the invention

[0005] In order to address the deficiencies in the prior art, the purpose of the present invention is to provide an electricity metering system that realizes the optimization of the day-ahead load curve. Based on the analysis of the power abandonment rate, the load demand for the next day and the output of the power plant are reasonably predicted and analyzed in the day-ahead stage to generate an optimized output curve. According to this analysis, an electricity pricing strategy is formulated, the electricity price for the whole period of the next day is updated in time, and the electricity price information is synchronously transmitted to the smart meter on the user side, which encourages users to actively use electricity during the period with higher power abandonment rate, thereby promoting the consumption of new energy, improving the economic benefits of the power system, and reducing energy waste.

[0006] The above technical objectives of the present invention are achieved through the following technical solutions: A power metering system for optimizing the day-ahead load curve, comprising:

[0007] Multiple smart meters are used to collect power plant output data and user-side electricity consumption data, and upload the output data and electricity consumption data to the data processing center in real time;

[0008] The data processing center is used to pre-process the output data and power consumption data, transmit the pre-processed data to the dispatching center, and synchronize the power price information to the smart meter according to the interval power price strategy formulated by the dispatching center;

[0009] The dispatch center is used to generate the day-ahead dispatch curve based on the preprocessed data, and formulate interval electricity price strategies for the time periods when the power abandonment rate exceeds the set value based on the day-ahead dispatch curve.

[0010] Furthermore, the process of preprocessing the output data and the power consumption data is specifically as follows:

[0011] Receive power consumption data, output data and fault information uploaded by smart meters, and process the received data in a unified format to ensure consistency of timestamps and data units;

[0012] Filter invalid data, missing data and obviously abnormal items in the received data;

[0013] Classify data by time dimension, user category and power source, and distinguish between elastic load and inelastic load for statistics;

[0014] And, the sorted data is stored in a distributed database and tagged with timestamp, user ID, and electricity price information.

[0015] Furthermore, the expression of the interval electricity price strategy is specifically:

[0016]

[0017] Among them, e(t) is the interval electricity price of the next day; P average is the average of the daily interval electricity price yesterday; α is the adjustment coefficient, which is used to control the sensitivity of electricity price to the power abandonment rate. The larger α is, the stronger the response of electricity price to the power abandonment rate, and vice versa. P discord (t) is the amount of renewable energy power generation abandoned during the forecast period; P total (t) is the total power generation of renewable energy during the forecast period; L base (t) is the benchmark load during the forecast period; L actual (t) is the actual load during the forecast period; q is the percentage of profit obtained by the power grid.

[0018] Furthermore, the adjustment coefficient is dynamically adjusted according to the deviation between the actual power abandonment rate and the target power abandonment rate. The specific expression is:

[0019]

[0020] Among them, α base is the reference value of the adjustment coefficient, which is used to set the minimum sensitivity; k1 is the adjustment parameter, which is used to indicate the influence of the abandonment rate deviation on the adjustment coefficient; R discard (t) is the actual power abandonment rate; R target The target power abandonment rate.

[0021] Furthermore, the profit percentage of the power grid is dynamically set according to the flexibility of user load adjustment and the power grid operation efficiency. The specific expression is:

[0022]

[0023] Among them, qbase is the benchmark value of the power grid profit percentage, which is used to set the expected initial profit ratio of the power grid; k 2 is an adjustment parameter, which indicates the influence of the proportion of user elastic load to total load on q; L elastic (t) is the elastic load; L total (t) is the total load.

[0024] Furthermore, the smart meter comprises:

[0025] The information collection module is used to collect electric energy information including current, voltage and active power, and collect the power consumption data on the user side and the output data of the power plant;

[0026] The data communication module is used to receive the interval electricity price strategy from the data processing center and transmit the power information on the user side back to the data processing center;

[0027] The data processing module is used to process the collected electric energy information and analyze the interval electricity price strategy issued by the data processing center;

[0028] The network interaction module is used to connect the smart meter to the Internet so that the relevant information of the interval electricity price strategy can be synchronized with the network equipment on the user side;

[0029] The display module is used to display the electricity price information for the entire period of the next day, and to display the electricity price fluctuations for each period of the day in real time.

[0030] Furthermore, the data communication module also includes:

[0031] During the information transmission process, data is automatically sent according to the predetermined time interval or based on the trigger conditions.

[0032] Furthermore, the data processing module also includes:

[0033] The meter calculates the electricity consumption and charges according to the preset rate or algorithm, calculates the electric energy, power factor and maximum demand parameters, and stores the processed data in the memory inside the meter.

[0034] Furthermore, the information acquisition module integrates a high-precision, high-sampling-rate electric energy information acquisition device, and the electric energy information acquisition device is an ADE9153A or CS5463 high-precision electric energy metering chip;

[0035] The data communication module integrates NB-IoT, LoRa, Zigbee or Wi-Fi communication tools;

[0036] The data processing module adopts the STM32 series microcontroller, based on the Cortex-M core, and supports efficient real-time data processing;

[0037] The network interaction module uses an ESP32 Wi-Fi module to support wireless connection with user devices;

[0038] The display module adopts an OLED display screen.

[0039] Furthermore, the power plant includes a traditional thermal power plant and a new energy power plant.

[0040] Compared with the prior art, the present invention has the following beneficial effects:

[0041] 1. The present invention provides an electric power metering system for optimizing the day-ahead load curve. Based on the analysis of the power abandonment rate, the load demand of the next day and the output of the power plant are reasonably predicted and analyzed in the day-ahead stage to generate an optimized output curve. According to the analysis, an electric power pricing strategy is formulated to timely update the electricity price for the whole period of the next day, and the electricity price information is synchronously transmitted to the smart meter on the user side, so as to encourage users to actively use electricity during the period with high power abandonment rate, thereby promoting the consumption of new energy, improving the economic benefits of the power system, and reducing energy waste.

[0042] 2. The present invention dynamically adjusts the adjustment coefficient by the deviation between the actual power abandonment rate and the target power abandonment rate, and can adaptively control the sensitivity of the electricity price to the power abandonment rate according to the actual situation, so as to achieve a better optimization effect of the day-ahead load curve;

[0043] 3. The present invention also dynamically sets the profit percentage of the power grid according to the flexibility of user load adjustment and the power grid operation efficiency, further enhancing the initiative and flexibility of users in participating in power market dispatch. BRIEF DESCRIPTION OF THE DRAWINGS

[0044] The drawings described herein are used to provide a further understanding of the embodiments of the present invention, constitute a part of this application, and do not constitute a limitation of the embodiments of the present invention. In the drawings:

[0045] Figure 1 is a system block diagram in an embodiment of the present invention;

[0046] Figure 2 It is a workflow diagram in an embodiment of the present invention;

[0047] Figure 3 is a functional block diagram of a user-side smart meter in an embodiment of the present invention;

[0048] Figure 4 It is a schematic diagram of the structure of a user-side smart meter in an embodiment of the present invention. DETAILED DESCRIPTION

[0049] In order to make the objectives, technical solutions and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with embodiments and drawings. The exemplary embodiments of the present invention and their description are only used to explain the present invention and are not intended to limit the present invention.

[0050] It should be noted that when a component is referred to as being "fixed to" or "disposed on" another component, it can be directly on the other component or indirectly on the other component. When a component is referred to as being "connected to" another component, it can be directly or indirectly connected to the other component.

[0051] It should be understood that the orientation or position relationship indicated by terms such as "length", "width", "up", "down", "front", "back", "left", "right", "vertical", "horizontal", "top", "bottom", "inside" and "outside" are based on the orientation or position relationship shown in the drawings, and are only for the convenience of describing the present invention and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, be constructed and operate in a specific orientation, and therefore cannot be understood as a limitation on the present invention.

[0052] 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 the indicated technical features. Therefore, the features defined as "first" and "second" may explicitly or implicitly include one or more of the features. In the description of the present invention, the meaning of "plurality" is two or more, unless otherwise clearly and specifically defined.

[0053] Embodiment: A power metering system for optimizing the day-ahead load curve, such as Figure 1 As shown, it includes smart meters, data processing center and dispatching center.

[0054] The power plant is composed of a thermal power plant and a new energy power plant, which jointly provide electricity supply to users; the smart meter collects the output of new energy and thermal power plants and records the historical electricity consumption data on the user side, and uploads this information to the data processing center in real time; the data processing center interacts with the smart meter, receives historical data from the smart meter, organizes and analyzes the collected data, and transmits it to the dispatch center. At the same time, according to the interval electricity price strategy formulated by the dispatch center, the real-time electricity price information is synchronized to the smart meter; based on the historical data of the power plant and user load, the dispatch center analyzes the power abandonment rate in each time period, and predicts the load curve, adjusts the unit output and dynamically adjusts the electricity price strategy accordingly, to form a scientific and reasonable interval electricity price plan, which is synchronized to the data processing center; the load includes elastic load and inelastic load, and users can achieve load curve smoothing and reduce peak-valley differences by shifting and redistributing the elastic load.

[0055] The specific process of preprocessing output data and electricity consumption data is as follows: receiving electricity consumption data, output data and fault information uploaded by smart meters, and formatting the received data in a unified manner to ensure the consistency of timestamps and data units; filtering invalid data, missing data and obviously abnormal entries in the received data; classifying the data according to time dimension, user category and power source, and distinguishing between elastic loads and inelastic loads for statistics; and storing the sorted data in a distributed database, and attaching timestamps, user IDs, and electricity price information tags.

[0056] Specifically, the data processing center uses smart meters to collect the output of renewable energy and thermal power plants, and the historical electricity consumption data on the load side. The data processing center transmits the collected data to the dispatching center. The dispatching center analyzes and predicts based on the historical operation data of the load and power plant, and generates a day-ahead dispatching curve to plan the power plant output; secondly, based on the day-ahead dispatching curve, for periods with high power abandonment rates, economic inducement measures are taken, that is, the electricity price strategy is adjusted to stimulate users' flexible load transfer and smooth the daily output curve; then, the dispatching center regenerates the day-ahead dispatching curve based on the smoothed output curve, and conducts robust analysis to reserve sufficient spare capacity; finally, the formulated interval price is transmitted to the data processing center and synchronized to each user's meter. Users can adjust the next day's flexible load and smooth the load curve based on the given day-ahead price range.

[0057] The dispatch center analyzes and predicts the historical operating data of loads and power plants, generates a day-ahead dispatch curve, analyzes periods with higher power abandonment rates, establishes a power metering and pricing method for the power system, and uses a price-driven approach to incentivize the transfer or adjustment of flexible loads on the user side, thereby smoothing the load curve and improving the economic benefits of the system. In addition, the interval electricity price plan is generated in the day-ahead stage and synchronized to the smart meter measured by the user. Its advantages lie in advance planning, optimized resource allocation, reduced costs and market volatility risks, and improved stability and reliability of the power system.

[0058] like Figure 2 As shown in the figure, in the day-ahead stage, the dispatching center predicts the load change and output capacity of the new energy power plant for the next day based on the analysis of the historical operating data of the load and the power plant, and further conducts a time period analysis of the power abandonment rate. For the periods with higher power abandonment rates, preferential electricity price policies are designed and implemented to encourage users to increase electricity consumption during these periods and encourage users to flexibly adjust their electricity consumption behavior according to electricity price signals, that is, to transfer flexible loads from peak electricity consumption periods to valley periods, promote the optimization of resource allocation, promote the consumption of new energy, and reduce the abandonment of renewable energy such as wind energy and solar energy. In addition, by smoothing the load output curve and reducing the peak-valley difference, the stability and reliability of the power system are enhanced.

[0059] The specific expression of the interval electricity price strategy is:

[0060]

[0061] Among them, e(t) is the interval electricity price of the next day; P average is the average of the daily interval electricity price yesterday; α is the adjustment coefficient, which is used to control the sensitivity of electricity price to the power abandonment rate. The larger α is, the stronger the response of electricity price to the power abandonment rate, and vice versa. P discord (t) is the amount of renewable energy power generation abandoned during the forecast period; P total (t) is the total power generation of renewable energy during the forecast period; L base (t) is the benchmark load during the forecast period; L actual (t) is the actual load during the forecast period; q is the percentage of profit obtained by the power grid.

[0062] The above-mentioned electricity price measurement expression is directly related to the curve flattening and the power abandonment rate. Specifically, the higher the power abandonment rate in the time period of the load output curve, the lower the electricity price in the interval period will be based on this. In addition, the degree of curve flattening will also be analyzed. If the difference between the peak and valley of the curve is too large, the electricity price will be processed to encourage users to use electricity when the load is low. The present invention aims to dynamically adjust the electricity price, encourage users to increase electricity consumption during periods when new energy accounts for a high proportion, optimize the load curve and reduce power abandonment. The formula integrates multiple factors such as unstable power generation, load level and market benchmark price to achieve economical and efficient operation of the power system.

[0063] The day-ahead interval electricity price is dynamically adjusted according to the predicted power abandonment and load conditions. The electricity price adjustment is based on the average electricity price of the previous day. The relevant calculation formula is shown in formula (1):

[0064]

[0065] In formula (1), P average is the average of yesterday’s daily interval electricity prices, i is yesterday’s interval period, P ago (i) is the electricity price during yesterday’s time period.

[0066] In order to better absorb new energy and smooth the load curve, the benchmark electricity price P average Dynamic adjustment is performed, and the adjustment size is determined by the electricity price adjustment factor β(t). The specific interval electricity price is shown in formula (2):

[0067] e(t)=P average *β(t) (2)

[0068] In formula (2), β(t) takes into account factors such as power abandonment rate, load level and the sensitivity of the controlled electricity price to the power abandonment rate.

[0069] Specifically, as shown in formula (3):

[0070]

[0071] In formula (3), α is the adjustment coefficient, P discard (t) is the amount of renewable energy power generation abandoned during the forecast period, P total (t) is the total power generation of renewable energy during the forecast period, L base (t) is the benchmark load during the forecast period, L actual (t) is the actual load during the forecast period, and q is the percentage of profit earned by the power grid; where α is used to control the sensitivity of electricity price to power abandonment rate. The larger α is, the stronger the response of electricity price to power abandonment rate is, and vice versa. The amount of renewable energy power generation P abandoned during the forecast period is discard (t) and the total power generation of renewable energy in this period P total The ratio of (t) is the predicted power abandonment rate R of the new energy in this interval. discard (t), through the reference load L base (t) and actual load L actual The electricity price is adjusted by the ratio of (t). During the period of high power abandonment rate, if the actual load is high, the electricity price is adjusted downward to encourage users to use electricity when the load is low and to smooth the load curve. q is determined by the local revenue benefits. The power grid can use q to make further electricity price adjustments to promote users to transfer loads to achieve better economic benefits.

[0072] As an optional implementation, the adjustment coefficient may adopt a fixed value.

[0073] As another optional implementation, the adjustment coefficient is dynamically adjusted according to the deviation between the actual power abandonment rate and the target power abandonment rate. The specific expression is:

[0074]

[0075] Among them, α b ase is the reference value of the adjustment coefficient, used to set the minimum sensitivity; k 1 is the adjustment parameter, which is used to indicate the influence of the abandonment rate deviation on the adjustment coefficient; R discard (t) is the actual power abandonment rate; R target The target power abandonment rate. It is the ratio of the current power abandonment rate to the target power abandonment rate, indicating the degree of deviation of the power abandonment rate from the target value. If ΔR>1, it means that the current power abandonment rate is higher than the target value, and the adjustment coefficient α increases, thereby enhancing the sensitivity of electricity prices to the power abandonment rate, guiding users to transfer loads, and reducing the power abandonment rate; if ΔR≤1, it means that the power abandonment rate is within a reasonable range, and the adjustment coefficient α remains at a low level to maintain the stability of electricity prices and avoid unnecessary fluctuations.

[0076] As an optional implementation, the profit percentage obtained by the power grid may be a fixed value.

[0077] As another optional implementation, the profit percentage of the power grid is dynamically set according to the flexibility of user load adjustment and the power grid operation efficiency. The specific expression is:

[0078]

[0079] Among them, q base is the benchmark value of the grid profit percentage, which is used to set the expected initial profit ratio of the grid; k2 is an adjustment parameter, which indicates the influence of the proportion of user elastic load to total load on q; L elastic (t) is the elastic load; L total (t) is the total load. is the total load ratio of the elastic load station. According to the different elastic load ratios, the adjustment strategy of the grid profit percentage q is as follows: when the total load ratio of the elastic load station is lower than 20%, the elastic load ratio is low, and most user loads are inelastic loads, which are difficult to adjust. At this time, the user's electricity consumption behavior is weakly responsive to the adjustment of electricity prices. In order to make up for the inefficiency of scheduling optimization, it is appropriate to set a higher grid profit percentage q to ensure the grid's revenue; when the total load ratio of the elastic load station is between 20% and 50%, the elastic load ratio is moderate, and some users have the ability to adjust their loads. At this time, the electricity price adjustment can guide some users' loads from peak periods to lower peaks. Shifting to the off-peak period has a certain load smoothing effect. In this case, while ensuring the profit of the power grid, the sensitivity of electricity prices can be appropriately increased to balance economic benefits and scheduling optimization effects. When the total load ratio of the elastic load station is higher than 50%, the elastic load ratio is relatively high, and most user loads have the ability to adjust. The electricity price strategy has a significant guiding effect on user behavior and can effectively achieve the load smoothing goal. In periods with high power abandonment rates, the potential for load shifting is large, and the power grid scheduling efficiency is significantly improved. The power grid profit percentage q can be appropriately reduced to further encourage users to use electricity during off-peak periods and make full use of low-cost new energy.

[0080] The present invention improves the smart electric meter on the user side, adds functions such as receiving and parsing electricity price strategy information, displaying the total time period electricity price of the next day, real-time electricity price display, and network equipment interaction. To ensure that the dispatching center can predict the load change of the next day and the output capacity of the new energy power plant based on the analysis of the historical operation data of the load and the power plant, further conduct a time period analysis of the power abandonment rate, and implement preferential electricity price policies, the present invention is synchronized with the user-side electric meter in the day-ahead stage. To ensure the smooth reception and efficient use of electricity price information, the present invention constructs a stable data transmission channel with the network equipment to ensure the real-time and accuracy of the information, so that users can flexibly adjust or rearrange their load usage plans based on the next day's electricity price information, thereby achieving cost savings and optimization of energy use, and enhancing the initiative and flexibility of users in participating in power market scheduling.

[0081] The smart electric meter recorded in the present invention has the following functions: pre-displaying the electricity price information for the whole time period of the next day, displaying the electricity price situation for each time period of the day, network connection, data analysis, electric energy metering, active and reactive load records, fault alarm, safety reporting, basic communication, power freezing, meter clearing, historical data storage and the like.

[0082] like Figure 3 As shown, the smart electric meter recorded in the present invention has the following modules: an information acquisition module, which is used to collect electric energy information including current, voltage and active power, and collects electricity consumption data on the user side and output data of the power plant; a data communication module, which is used to receive the interval electricity price strategy from the data processing center, and transmit the electric energy information on the user side back to the data processing center; a data processing module, which is used to process the collected electric energy information, and parse the interval electricity price strategy issued by the data processing center; a network interaction module, which is used to realize the connection between the smart electric meter and the Internet, so that the relevant information of the interval electricity price strategy can be synchronized with the network equipment on the user side; a display module, which is used to display the electricity price information for the whole time period of the next day, and to display the electricity price fluctuations in each time period of the day in real time.

[0083] Specifically, the information acquisition module collects voltage, current and other signals in the power grid through sensors or sampling circuits, converts the collected analog signals into digital signals, provides basic data for subsequent power calculations, ensures the accuracy and stability of signal conversion, and supports efficient power consumption monitoring; the data communication module transmits the electric energy information measured by the electric meter to the data processing center to realize centralized management and analysis of the electric energy data. During the information transmission process, the module can automatically send data at predetermined time intervals or based on trigger conditions to ensure synchronous update of data and maintain the real-time and accuracy of information transmission. At the same time, it can also receive electricity price strategy information from the data processing center to ensure that users obtain the latest electricity price adjustment information; the data processing module receives data from the acquisition module and pre-processes it (filtering, denoising, etc.) to improve the accuracy and reliability of the data, and performs power calculation and fee settlement according to the preset rate or algorithm, and calculates electric energy, power factor, and the most The data processing module stores the processed data in the memory inside the meter for subsequent data query, display and remote transmission. In addition, the module is also responsible for receiving and parsing the next day's electricity price strategy transmitted by the data processing center, and generating corresponding electricity price information; the network interaction module realizes the connection between the smart meter and the user's network equipment through the Internet, and transmits the user's electricity consumption, electricity price and other information to the user's network equipment. Through this function, the user can obtain electricity price information in time without geographical restrictions, and adjust the electricity strategy according to the change of electricity price, so as to optimize the electricity cost; the display module can display the user's current electricity consumption and remaining electricity in real time or at a fixed time, and display the electricity price information of the whole time period of the next day, and at the same time display the electricity price fluctuation of each time period of the day in real time, so as to help users understand their own electricity consumption in time. Through the intuitively displayed data, users can adjust the electricity strategy, reasonably plan the electricity demand, realize economical electricity consumption, smooth the load curve, and enhance the stability of the power system.

[0084] In order to more effectively optimize the load curve and absorb new energy, the dispatch center needs to have a more accurate understanding of the electricity usage in the power grid, and users also need to grasp the electricity price strategy in a timely manner in order to smoothly transfer the load. Based on these needs, the present invention improves the smart meter on the user side. The traditional electric energy meter can only measure the power consumption, while the improved smart meter on the user side is added with an information collection module for collecting the user's electricity consumption and the power generation of the power plant, a data communication module for realizing two-way data transmission between the data center and the user, a data processing module with stronger data processing capabilities to quickly parse the received electricity price information, a network interaction module for realizing the connection between the smart meter and the Internet to transmit data to the network equipment, and a display module that can clearly display the electricity price information. Among them, the data communication module needs to integrate advanced communication tools such as NB-IoT, LoRa, Zigbee or Wi-Fi to ensure stable remote communication capabilities in different scenarios and adapt to different distances, power consumption and data volume requirements, so as to communicate with the power grid management system (such as AMI, advanced metering infrastructure) in real time and receive electricity price data; the data processing module adopts the STM32 series microcontroller, based on the Cortex-M core, supports efficient real-time data processing, and is widely used in smart meters and power monitoring systems with high precision and low power consumption; the network interaction module adopts the ESP32 Wi-Fi module, supports wireless connection with user devices, is commonly used in power data transmission in smart home systems, supports low-power operation, and realizes remote monitoring of the meter status; the display module adopts an OLED display screen with high contrast and low power consumption. It can clearly display data such as power consumption and electricity price in a compact space, and is also suitable for low-power application scenarios under long-term display state.

[0085] like Figure 4 As shown in the figure, the functions of its specific components are shown in the figure, including a display screen for displaying power usage, electricity price information, working status, active and reactive load records, alarm information, etc.; active indicator light and reactive indicator light, when active or reactive consumption is generated in the circuit, the light is on, otherwise the light is off; trip indicator light, when a circuit fault causes tripping, the trip indicator light will light up; data indicator light, when the smart meter is performing normal data collection and transmission, the light is off, otherwise the light is on, which is convenient for users or maintenance personnel to understand the meter fault in time; alarm indicator light, when the meter encounters a fault or the electricity fee is insufficient, the alarm indicator light will light up; buzzer, when the trip indicator light or the alarm indicator light is on, the buzzer will alarm to remind the user that a circuit fault has occurred; meter function switching key group, by pressing these keys, the display screen content can be switched, including power information, the current full-time electricity price information, alarm information and the next day's full-time electricity price information, etc., so that users can view relevant data conveniently.

[0086] Working principle: Based on the analysis of power abandonment rate, the present invention makes reasonable prediction and analysis of the load demand and output of the power plant for the next day in the day-ahead stage, generates an optimized output curve, formulates electricity pricing strategy according to this analysis, timely updates the electricity price for the whole period of the next day, and synchronously transmits the electricity price information to the smart meter on the user side, so as to encourage users to actively use electricity during the period with higher power abandonment rate, thereby promoting the consumption of new energy, improving the economic benefits of the power system, and reducing energy waste; in addition, the present invention dynamically adjusts the adjustment coefficient according to the deviation between the actual power abandonment rate and the target power abandonment rate, and can adaptively control the sensitivity of the electricity price to the power abandonment rate according to the actual situation, so as to achieve better optimization effect of the day-ahead load curve; in addition, the present invention also dynamically sets the profit percentage of the power grid according to the flexibility of user load adjustment and the efficiency of power grid operation, so as to further enhance the initiative and flexibility of users in participating in power market dispatch.

[0087] Those skilled in the art will appreciate that the embodiments of the present application may be provided as methods, systems, or computer program products. Therefore, the present application may adopt the form of a complete hardware embodiment, a complete software embodiment, or an embodiment in combination with software and hardware. Moreover, the present application may adopt the form of a computer program product implemented in one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) that include computer-usable program code.

[0088] The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0089] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0090] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0091] The specific implementation methods described above further illustrate the objectives, technical solutions and beneficial effects of the present invention in detail. It should be understood that the above description is only a specific implementation method of the present invention and is not intended to limit the scope of protection of the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the scope of protection of the present invention.

Claims

1. An electric power metering system for optimizing the day-ahead load curve, characterized in that: include: Multiple smart meters are used to collect power plant output data and user-side electricity consumption data, and upload the output data and electricity consumption data to the data processing center in real time; The data processing center is used to pre-process the output data and power consumption data, transmit the pre-processed data to the dispatching center, and synchronize the power price information to the smart meter according to the interval power price strategy formulated by the dispatching center; The dispatch center is used to generate the day-ahead dispatch curve based on the preprocessed data, and formulate interval electricity price strategies for the time periods when the power abandonment rate exceeds the set value based on the day-ahead dispatch curve.

2. The power metering system for optimizing the day-ahead load curve according to claim 1 is characterized in that: The process of preprocessing the output data and the power consumption data is specifically as follows: Receive power consumption data, output data and fault information uploaded by smart meters, and process the received data in a unified format to ensure consistency of timestamps and data units; Filter invalid data, missing data and obviously abnormal items in the received data; Classify data by time dimension, user category and power source, and distinguish between elastic load and inelastic load for statistics; And, the sorted data is stored in a distributed database and tagged with timestamp, user ID, and electricity price information.

3. The power metering system for optimizing the day-ahead load curve according to claim 1 is characterized in that: The expression of the interval electricity price strategy is specifically: Among them, e(t) is the interval electricity price of the next day; P average is the average of the daily interval electricity price yesterday; α is the adjustment coefficient, which is used to control the sensitivity of electricity price to the power abandonment rate. The larger α is, the stronger the response of electricity price to the power abandonment rate, and vice versa. P discord (t) is the amount of renewable energy power generation abandoned during the forecast period; P total (t) is the total power generation of renewable energy during the forecast period; L base (t) is the benchmark load during the forecast period; L actual (t) is the actual load during the forecast period; q is the percentage of profit obtained by the power grid.

4. The power metering system for optimizing the day-ahead load curve according to claim 3 is characterized in that: The adjustment coefficient is dynamically adjusted according to the deviation between the actual power abandonment rate and the target power abandonment rate. The specific expression is: Among them, α base is the reference value of the adjustment coefficient, which is used to set the minimum sensitivity; k1 is the adjustment parameter, which is used to indicate the influence of the abandonment rate deviation on the adjustment coefficient; R discard (t) is the actual power abandonment rate; R target The target power abandonment rate.

5. The power metering system for optimizing the day-ahead load curve according to claim 3 is characterized in that: The profit percentage of the power grid is dynamically set according to the flexibility of user load adjustment and the power grid operation efficiency. The specific expression is: Among them, q base is the benchmark value of the grid profit percentage, which is used to set the expected initial profit ratio of the grid; k2 is an adjustment parameter, which indicates the influence of the proportion of user elastic load to total load on q; L elastic (t) is the elastic load; L total (t) is the total load.

6. The power metering system for optimizing the day-ahead load curve according to claim 1 is characterized in that: The smart electric meter comprises: The information collection module is used to collect electric energy information including current, voltage and active power, and collect the power consumption data on the user side and the output data of the power plant; The data communication module is used to receive the interval electricity price strategy from the data processing center and transmit the power information on the user side back to the data processing center; The data processing module is used to process the collected electric energy information and analyze the interval electricity price strategy issued by the data processing center; The network interaction module is used to connect the smart meter to the Internet so that the relevant information of the interval electricity price strategy can be synchronized with the network equipment on the user side; The display module is used to display the electricity price information for the entire period of the next day, and to display the electricity price fluctuations for each period of the day in real time.

7. The power metering system for optimizing the day-ahead load curve according to claim 6 is characterized in that: The data communication module also includes: During the information transmission process, data is automatically sent according to the predetermined time interval or based on the trigger conditions.

8. The power metering system for optimizing the day-ahead load curve according to claim 6 is characterized in that: The data processing module also includes: The meter calculates the electricity consumption and charges according to the preset rate or algorithm, calculates the electric energy, power factor and maximum demand parameters, and stores the processed data in the memory inside the meter.

9. The power metering system for optimizing the day-ahead load curve according to claim 6 is characterized in that: The information acquisition module integrates a high-precision, high-sampling-rate electric energy information acquisition device, and the electric energy information acquisition device is an ADE9153A or CS5463 high-precision electric energy metering chip; The data communication module integrates NB-IoT, LoRa, Zigbee or Wi-Fi communication tools; The data processing module adopts the STM32 series microcontroller, based on the Cortex-M core, and supports efficient real-time data processing; The network interaction module uses an ESP32 Wi-Fi module to support wireless connection with user devices; The display module adopts an OLED display screen.

10. The power metering system for optimizing the day-ahead load curve according to claim 1, characterized in that: The power plants include traditional thermal power plants and new energy power plants.

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