Smart Grid Power Storage Control for Renewable Imbalance
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Solution Overview
Problem
Existing power consumption management systems struggle to optimize power consumption and storage effectively, particularly in electrical power grids with fluctuating renewable energy sources, due to the complexity of handling power imbalances and user preferences.
Innovation Solution
A power management system utilizing smart power consumption meters, machine learning algorithms, and optimization algorithms to analyze and forecast power consumption and production, applying user-defined rules to automatically manage power distribution and storage, optimizing power usage based on cost, capacity, and user preferences.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If conventional power consumption management systems are used, then power consumption data can be collected, but the system cannot effectively optimize power consumption and storage due to complexity of handling power imbalances
Solution Approach 1:
The system segments the power management problem into distinct components: power consumption analysis, power production forecasting, storage optimization, and user preference management. Each component is handled by specific modules that process particular aspects of the data, making the overall complex system manageable and effective.
Solution Approach 2:
The patent introduces an intermediary optimization system that acts as a mediator between power consumption data, power production forecasts, and control decisions. This intermediary layer processes the complex data and generates optimized control signals, simplifying the management of power imbalances without requiring direct complex interactions between all system components.
2Loss of information
If power consumption data is collected from smart meters, then data availability increases, but the ability to manage and determine power consumption and production data remains insufficient
Solution Approach 1:
The system performs preliminary actions by forecasting power production and analyzing consumption patterns before making optimization decisions. This advance processing of data allows the system to prepare optimized control strategies in advance, making the actual management and determination of power data more efficient and less complex when decisions need to be executed.
3Productivity
If optimization algorithms are applied to manage power imbalances, then power consumption optimization improves, but computational complexity increases
Solution Approach 1:
The optimization algorithms are applied with local quality by focusing computational resources on specific critical aspects of power management rather than uniformly processing all data. The system identifies key decision points and applies optimization specifically where needed, improving power consumption optimization performance while reducing overall computational complexity and resource requirements.
Data Source
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AI summary
Systems and methods of managing power distribution in a portion of an electrical power grid with at least one power storage, including: receiving at least one power consumption rule from at least one consumer of the power grid, analyzing power consumption data from at least one power consumption meter connected to the power grid, applying the at least one power consumption rule on the analyzed power consumption data, based on forecasted data, and managing power consumption for the at least one consumer, based on the result of the at least one power consumption rule, and also based on a power capacity status of the at least one power storage.