Forecasting Alternative Power Production in Electrical Grids
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Solution Overview
Problem
Current power grid management systems lack the capability to effectively predict and optimize network load considering alternative power sources and consumption from dual consumers, as 'smart' meters only provide net measurements, not direct data on alternative power consumption or production.
Innovation Solution
A method and system utilizing timestamped data of weather conditions and individual grid power consumption to identify dual consumers connected to alternative power sources, employing a Forecasting Machine Learning Model to forecast alternative power production, and enabling management actions such as battery charging/discharging and thermostat control to balance grid supply and demand.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If smart meters are used to measure power consumption, then power consumption data becomes available to providers, but the meters only provide net measurements and cannot directly measure alternative power consumption or production
Solution Approach 1:
The patent introduces an intermediary system comprising a server and processing algorithms that act as a mediator between the smart meter's net measurement and the need for separate alternative power production data. The server receives net consumption data, combines it with weather condition data, and processes this information to estimate alternative power production, thereby recovering the lost information through computational analysis rather than direct measurement
Solution Approach 2:
The patent replaces the physical measurement capability (which would require separate meters for grid and alternative power) with a computational system. Instead of using additional physical measurement devices, the system uses data processing algorithms that analyze net consumption patterns alongside weather data to infer alternative power production, substituting mechanical measurement with intelligent computation
2Productivity
If vast amounts of power consumption data are collected from smart meters, then continuous monitoring capability is achieved, but the data cannot be effectively managed to determine power production
Solution Approach 1:
The patent creates a multi-functional data management system that handles multiple tasks: storing consumption data, collecting weather data, identifying dual consumers, estimating alternative power production, and enabling various management actions. This universal platform consolidates what would otherwise require separate systems for each function, reducing overall complexity while maintaining continuous monitoring capabilities
Solution Approach 2:
The system enables self-service through automated processing of the vast data volumes. The server automatically identifies dual consumers by analyzing consumption patterns, estimates alternative power production without manual intervention, and provides this information to providers, eliminating the need for complex manual data management processes
3Measurement precision
If dual consumers with alternative power sources are identified using weather condition relationships, then accurate power production forecasting is achieved, but the system complexity increases
Solution Approach 1:
The patent changes the parameters used for identification from direct measurement parameters to correlational parameters. Instead of requiring complex sensor arrays to directly measure alternative power production, the system uses weather condition parameters (solar irradiance, wind speed) and their correlation with consumption patterns to identify dual consumers and forecast production, simplifying the system while maintaining accuracy
Solution Approach 2:
The system implements feedback loops where consumption data and weather data are continuously analyzed together, with results fed back to refine identification of dual consumers and improve forecasting accuracy. This feedback mechanism allows the system to learn from patterns and improve over time without increasing hardware complexity
Data Source
AI summary
There is provided a technique of managing an electrical power grid. The technique comprises, by a computer: processing timestamped data informative of weather conditions and of individual grid power consumption by a plurality of consumers to identify dual consumers connected to alternative power sources with power generating dependable on the weather conditions; for the dual consumers, forecasting alternative power production by respective connected alternative power sources; and using the provided forecast to enable management action(s) with regard to power production in the electrical power grid (e.g. issuing command(s) related to charging/discharging one or more batteries connected to the grid, controlling thermostat set-point change in a set of points connected to the grid, etc.). Forecasting alternative power production can be provided using a trained Forecasting Machine Learning Model trained to forecast the alternative power production in accordance with a forecast of the one or more weather conditions in the geographical area.