Active Demand Management for EV Charging and Peak Load Control
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Solution Overview
Problem
Existing energy management systems struggle to dynamically manage energy demand in residential and commercial settings, particularly with the increasing penetration of electric vehicles, as they often rely on rigid rules and fail to anticipate sudden increases in demand, leading to potential overloads and inefficiencies.
Innovation Solution
An active demand management system that uses a cloud-based platform to dynamically prioritize and manage energy distribution across networked devices, including electric vehicles, by forecasting demand based on historical data, real-time smart meter data, and user inputs, adjusting device priorities in real-time to maintain energy efficiency and user comfort without requiring physical infrastructure upgrades.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If rigid rules are used for demand management, then system simplicity is maintained, but the system cannot anticipate sudden demand increases leading to overloads and inefficiencies
Solution Approach 1:
The patent implements dynamic demand management by transitioning from rigid static rules to adaptive algorithms that continuously adjust device priorities based on real-time conditions. The system dynamically forecasts demand, identifies at-risk devices, and modifies operational parameters on-the-fly to prevent overloads while maintaining system reliability.
Solution Approach 2:
The system performs preliminary actions by forecasting energy demand before actual consumption occurs. It proactively identifies devices at risk of causing overloads and adjusts their priorities or operational parameters in advance, preventing rather than merely reacting to demand issues.
2Productivity
If real-time dynamic control is implemented, then energy efficiency and responsiveness are improved, but system complexity increases
Solution Approach 1:
The patent implements self-service mechanisms where the demand management system automatically forecasts demand, identifies problematic devices, and adjusts priorities without requiring manual intervention or complex external control infrastructure. The system serves itself by using its own data and algorithms to optimize energy distribution in real-time.
Solution Approach 2:
The system incorporates continuous feedback loops where real-time energy consumption data is monitored, compared against forecasts, and used to dynamically adjust device priorities. This feedback mechanism enables automatic adaptation to changing conditions, improving efficiency while keeping control logic manageable through iterative optimization.
3Reliability
If forecasted demand exceeds threshold, then devices must be powered off to prevent overload, but this reduces user convenience and comfort
Solution Approach 1:
The patent applies local quality by differentiating device priorities rather than applying uniform load shedding. Instead of powering off devices indiscriminately, the system selectively manages specific devices based on their individual priority levels, operational status, and contribution to forecasted demand, maintaining user convenience for high-priority devices while preventing overload.
Solution Approach 2:
The system performs partial action by adjusting priorities of only those devices necessary to bring forecasted demand below thresholds. Rather than powering off all devices or making blanket restrictions, it applies targeted priority adjustments to specific at-risk devices, minimizing impact on user convenience while achieving overload prevention.
Data Source
AI summary
Active demand management system and methods are disclosed herein. An example method for active demand management includes, facilitating dynamic and adaptive control of energy consumption within a network of devices. The method involves computing a rule set based on certain conditions, assessing the current operational state of devices, and integrating user inputs and preferences. By evaluating forecasted demand against a predetermined threshold, the method generates a control strategy to sequentially deactivate devices in order of prioritized importance, ensuring demand does not surpass the set threshold. Subsequently, energy-related commands are dispatched to the devices to implement the devised plan, optimizing energy distribution and preventing overload, thereby achieving efficient demand management.