Micro Demand Response Control for Real-Time Grid Load Balancing
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Solution Overview
Problem
Current power grid management systems face challenges in efficiently managing real-time energy demand fluctuations, leading to unnecessary power generation buffers that result in wasted energy and increased operational costs, as well as limitations in instantaneous demand response and geographical coordination.
Innovation Solution
A system and method that utilize cloud-computing based software to automatically adjust power usage across multiple facilities in real-time, allowing for targeted reduction or augmentation of energy demand based on real-time grid conditions, employing artificial intelligence to learn demand curves and coordinate equipment operation to minimize peak demand and maintain consistent energy consumption.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If a power generation buffer is maintained to absorb unexpected demand spikes, then grid reliability is improved, but energy waste and operational costs increase
Solution Approach 1:
The system continuously monitors actual power demand and automatically adjusts generator output in real-time, eliminating the need for static power buffers. Feedback from demand sensors triggers immediate generator response, allowing the system to maintain reliability while avoiding energy waste from oversized buffering capacity.
Solution Approach 2:
The invention transitions from a static power buffer approach to a dynamic generator response system. Generators automatically adjust their output levels based on real-time demand conditions, enabling the system to adapt flexibly to demand fluctuations without maintaining excessive reserved capacity that would waste energy.
2Loss of energy
If manual demand response is implemented to lower instantaneous demand, then operational costs are reduced, but response time increases
Solution Approach 1:
The system enables automated self-adjustment of power generation without requiring manual intervention. Sensors detect demand changes and automatically trigger generator responses, eliminating the time delay associated with manual demand response while maintaining cost efficiency through optimized generator utilization.
Solution Approach 2:
The invention replaces manual mechanical control systems with automated electronic control and sensing systems. This substitution enables instantaneous detection and response to demand changes, dramatically reducing response time compared to manual operations while maintaining operational cost efficiency.
3Loss of energy
If prediction-based generator scheduling is used, then operational costs are reduced, but inability to respond to instantaneous demand spikes increases grid instability risk
Solution Approach 1:
The system combines prediction-based scheduling with real-time feedback monitoring. While prediction optimizes operational costs by pre-planning generator schedules, the feedback mechanism continuously monitors actual demand and automatically adjusts generator output to respond to unexpected demand spikes, maintaining grid stability without sacrificing cost efficiency.
Data Source
AI summary
A system and method for dynamically and automatically adjusting the load on a power grid through micro adjustments of equipment coupled to the consumer side of the power grid. The system allowing for the automatic adjustment of equipment to either decrease or increase instantaneous power demand on the grid in response to peak demands and demand valleys to smooth the demand curve on the power grid. The system able to balance demand within the grid to adjust demand within various different portions of the power grid and allowing for reducing the power buffer supplied by electric utilities to reduce waste and carbon emissions.