CN-121998191-A - Real-time data acquisition and analysis system and method for power load

CN121998191ACN 121998191 ACN121998191 ACN 121998191ACN-121998191-A

Abstract

The invention relates to the technical field of power load analysis methods, in particular to a power load real-time data acquisition and analysis system and method, wherein the power load real-time data acquisition and analysis method comprises the steps of acquiring power load real-time data through an internet of things terminal deployed at a user side and uploading the power load real-time data through a preset communication protocol; the method comprises the steps of receiving uploaded power load real-time data, cleaning and standardizing the power load real-time data, aggregating the power load real-time data according to preset dimensions and storing the power load real-time data, analyzing and real-time safety pre-warning load characteristics based on historical and real-time aggregated data, applying the load analysis and prediction data to strategy support before power spot transaction, analyzing and strategy optimization after the transaction based on market clearing and settlement results, and carrying out multi-terminal visual display and interface output on whole process data, analysis results and transaction views.

Inventors

FU HONGRUN
XING CHENG
XIANG ZHENG
HE ZHAORAN

Assignees

大唐重庆能源营销有限公司

Dates

Publication Date: 20260508
Application Date: 20260127

Claims (7)

1. The method for collecting and analyzing the real-time data of the power load is characterized by comprising the following steps of: acquiring power load real-time data through an Internet of things terminal deployed on a user side, and uploading the data through a preset communication protocol; The method comprises the steps of receiving uploaded power load real-time data, cleaning and standardizing the power load real-time data, and storing the power load real-time data after aggregation according to preset dimensions; based on the history and real-time aggregation data, carrying out load characteristic analysis and real-time safety pre-warning; Applying load analysis and prediction data to the pre-power spot trade strategic support; Based on market clearing and settlement results, performing post-transaction analysis and policy optimization; And carrying out multi-terminal visual display and interface output on the whole process data, the analysis result and the transaction view.
2. The method for collecting and analyzing real-time data of power load according to claim 1, wherein in the step of collecting real-time data of power load through an internet of things terminal disposed at a user side and uploading through a preset communication protocol, The power load real-time data includes active power, three-phase current, voltage, and device temperature.
3. The method for collecting and analyzing real-time data of power load according to claim 2, wherein in the step of collecting real-time data of power load through an internet of things terminal disposed at a user side and uploading the data through a preset communication protocol The preset communication protocol is an MQTT protocol or an HTTPS protocol, and the data transmission process implements integrity check and communication state monitoring.
4. The method for collecting and analyzing real-time data of power load according to claim 3, wherein the specific steps of receiving the uploaded real-time data of power load, cleaning and standardizing the real-time data of power load, and storing the data after aggregation according to a preset dimension include: Cleaning the received original power load real-time data, and removing abnormal values and invalid records; performing time stamp alignment and dimension unification standardization processing on the cleaned power load real-time data; And carrying out aggregation calculation on the standardized data according to the resource type, the user type and the time granularity, generating an aggregated data view and storing the aggregated data view in a time sequence database.
5. The method for real-time data collection and analysis of electrical load according to claim 4, wherein the specific steps of load characteristic analysis and real-time safety pre-warning based on historical and real-time aggregate data comprise: drawing a typical daily, weekly and monthly load curve based on the aggregated data, and identifying load peak-valley time periods and trends; Setting a multilevel safety threshold of power, current and temperature, monitoring the out-of-limit state in real time and triggering early warning; and constructing a user electricity consumption image based on the historical electricity consumption behaviors, and providing load prediction and energy efficiency evaluation suggestions.
6. The method of claim 5, wherein the step of applying load analysis and prediction data to pre-power-spot-transaction strategic support comprises: Based on the historical electric quantity and the real-time load curve of the user, predicting and summarizing electricity purchasing demands of future years, months and green electricity; Integrating and visually displaying the price and supply-demand ratio of the medium-term market and the spot market of the electric power and the system load market data; fusing real-time load, weather, market boundary and historical price data, and utilizing a machine learning algorithm training model to predict future time-sharing nodes and area clear electricity prices; And combining the load prediction, the electricity price prediction result and the medium-and-long-term contract holding warehouse to generate or simulate a recommended daily declaration curve and spot transaction strategies with different risk levels, and predicting each cost and income.
7. A real-time data acquisition and analysis system for electric load, which adopts the method for acquiring and analyzing the real-time data for electric load according to any one of claims 1 to 6, The system comprises a data acquisition and transmission module, a data processing and aggregation module, a load analysis and early warning module, a transaction decision support module, a transaction multi-disc optimization module and a visual output module, wherein the data acquisition and transmission module, the data processing and aggregation module, the load analysis and early warning module, the transaction decision support module, the transaction multi-disc optimization module and the visual output module are connected in sequence; the data acquisition and transmission module is used for acquiring power load real-time data through an internet of things terminal deployed at a user side and uploading the data through a preset communication protocol; The data processing aggregation module is used for receiving the uploaded power load real-time data, cleaning and standardizing the power load real-time data, and storing the power load real-time data after aggregation according to a preset dimension; the load analysis early warning module is used for carrying out load characteristic analysis and real-time safety early warning based on historical and real-time aggregation data; the transaction decision support module is used for applying load analysis and prediction data to strategy support before power spot transaction; The transaction multi-disc optimizing module is used for carrying out analysis and strategy optimization after transaction based on market clearing and settlement results; The visual output module is used for carrying out multi-terminal visual display and interface output on the whole process data, the analysis result and the transaction view.

Description

Real-time data acquisition and analysis system and method for power load Technical Field The invention relates to the technical field of power load analysis methods, in particular to a system and a method for collecting and analyzing real-time data of a power load. Background Under the background of energy structure transformation and electric power market deepening and reform, the virtual power plant is used as a new form of aggregation distributed energy participation system regulation and control and market transaction, and the core technology is to accurately sense, intelligently analyze and cooperatively control massive and heterogeneous flexible resources. The method comprises the steps of carrying out deep characteristic analysis on the power load of the aggregation user and carrying out effective real-time safety early warning, and is an important foundation for guaranteeing stable operation of the system, excavating resource value and avoiding market risks. The traditional power load analysis management mode has obvious limitation, and is difficult to describe the fine power consumption behavior and the rapid load fluctuation of a user due to the fact that manual meter reading or low-frequency acquisition is often relied on in a data layer, the data granularity is coarse, the real-time performance is poor, and the early warning timeliness and accuracy are lacking due to the fact that threshold value warning is generally adopted in the prior art but static stiffness is set in threshold value setting, warning information is isolated and dispersed, and abnormal working conditions such as equipment overload, parameter out-of-limit and communication interruption are not predicted actively in the safety management layer. Disclosure of Invention The invention aims to provide a system and a method for collecting and analyzing real-time data of power load, which can analyze the power load characteristics and perform real-time safety early warning, and improve the timeliness and accuracy of early warning. To achieve the above object, in a first aspect, the present invention provides a method for collecting and analyzing real-time data of an electrical load, including: acquiring power load real-time data through an Internet of things terminal deployed on a user side, and uploading the data through a preset communication protocol; The method comprises the steps of receiving uploaded power load real-time data, cleaning and standardizing the power load real-time data, and storing the power load real-time data after aggregation according to preset dimensions; based on the history and real-time aggregation data, carrying out load characteristic analysis and real-time safety pre-warning; Applying load analysis and prediction data to the pre-power spot trade strategic support; Based on market clearing and settlement results, performing post-transaction analysis and policy optimization; And carrying out multi-terminal visual display and interface output on the whole process data, the analysis result and the transaction view. Wherein, in the step of collecting the real-time data of the power load through the terminal of the internet of things deployed at the user side and uploading the data through a preset communication protocol, The power load real-time data includes active power, three-phase current, voltage, and device temperature. Wherein, in the step of collecting the real-time data of the power load through the terminal of the internet of things deployed at the user side and uploading the data through a preset communication protocol The preset communication protocol is an MQTT protocol or an HTTPS protocol, and the data transmission process implements integrity check and communication state monitoring. The method for storing the power load real-time data comprises the specific steps of receiving the uploaded power load real-time data, cleaning and standardizing the power load real-time data, and storing the power load real-time data after aggregation according to preset dimensions, wherein the specific steps comprise: Cleaning the received original power load real-time data, and removing abnormal values and invalid records; performing time stamp alignment and dimension unification standardization processing on the cleaned power load real-time data; And carrying out aggregation calculation on the standardized data according to the resource type, the user type and the time granularity, generating an aggregated data view and storing the aggregated data view in a time sequence database. The specific steps of carrying out load characteristic analysis and real-time safety pre-warning based on the historical and real-time aggregation data comprise the following steps: drawing a typical daily, weekly and monthly load curve based on the aggregated data, and identifying load peak-valley time periods and trends; Setting a multilevel safety threshold of power, current and temperature, monitoring the out-of-limit state in r