HVAC Control Trajectory Optimization for Demand-Response Events
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Solution Overview
Problem
Existing load shedding techniques for HVAC systems during demand-response events often result in discomfort and inefficiency due to one-size-fits-all approaches, lack of consumer control, and inadequate consideration of individual thermal characteristics and comfort preferences.
Innovation Solution
An intelligent, network-connected thermostat that determines an optimized control trajectory for HVAC systems during demand-response events, balancing energy consumption, occupant discomfort, and energy consumption rate variations, allowing users to select comfort levels and generating personalized control strategies based on thermal retention, occupancy, and user habits.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of energy
If utility companies implement load shedding during peak demand periods, then energy consumption during peak periods is reduced, but consumer comfort deteriorates due to inadequate cooling
Solution Approach 1:
The system pre-cools residences before anticipated peak demand periods by lowering the setpoint temperature, storing cooling capacity in the building's thermal mass. This allows the HVAC system to be curtailed during peak periods while maintaining consumer comfort through the stored cooling effect.
Solution Approach 2:
The system continuously monitors residence temperature, outdoor conditions, and thermal characteristics to dynamically adjust control strategies. This feedback mechanism ensures that load shedding actions maintain consumer comfort while achieving peak demand reduction goals.
2Loss of energy
If utility companies use direct load control to cycle cooling systems, then energy consumption is reduced during peak periods, but consumer control and comfort deteriorate
Solution Approach 1:
The system empowers consumers to directly control their HVAC operation during demand-response events by providing them with a cost function that reflects their comfort preferences and thermal characteristics. Consumers independently optimize their control strategy without utility company micromanagement, maintaining both comfort and energy reduction goals.
Solution Approach 2:
The system transforms the control approach by changing from fixed utility-imposed cycling to dynamic consumer-controlled setpoint adjustment. Consumers modify the setpoint temperature parameter based on their comfort preferences and the provided cost function, achieving energy reduction while maintaining control and comfort.
3Device complexity
If utility companies apply uniform load shedding strategies to all consumers, then implementation complexity is reduced, but effectiveness deteriorates due to lack of individualization
Solution Approach 1:
The system tailors control strategies to each residence's specific thermal characteristics, occupancy patterns, and consumer comfort preferences. Each consumer receives a customized cost function and control approach rather than uniform treatment, significantly improving demand-response effectiveness while maintaining manageable complexity through automated personalization.
Solution Approach 2:
The system pre-characterizes each residence's thermal properties and consumer preferences before demand-response events. This preliminary personalization allows the system to generate customized control strategies quickly during events without requiring complex real-time adjustments, balancing individualization with operational simplicity.
4Loss of energy
If cooling systems are controlled during demand-response events to reduce peak demand, then energy consumption during peak periods is reduced, but energy consumption after the event increases as systems work to regain setpoint temperature
Solution Approach 1:
The system shifts cooling load to pre-event periods by pre-cooling residences and storing thermal energy in building mass. This temporal load shifting reduces peak period consumption while avoiding post-event rebound because the stored cooling capacity maintains comfort during and after the event without requiring intensive post-event recovery cooling.
Solution Approach 2:
The system maintains continuous cooling effectiveness by combining pre-event pre-cooling with during-event curtailment strategies that leverage thermal mass. This continuous approach eliminates the interruption-recovery cycle of traditional load shedding, reducing both peak consumption and post-event rebound while maintaining consumer comfort throughout the event period.
Data Source
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AI summary
A method of carrying out a demand response (DR) event by a control system comprises: identifying a DR event period for the DR event; determining an optimized control trajectory for a heating, ventilation, and air conditioning (HVAC) system, wherein the optimized control trajectory minimizes a cost function comprising a plurality of cost factors; controlling the HVAC system at a beginning of the DR event period in accordance with the optimized control trajectory for the HVAC system; determining, during the DR event period, whether a re-optimization of the optimized control trajectory is needed; performing, when the re-optimization is needed, the re-optimization; and controlling, after the re-optimization, the HVAC system in accordance with a control trajectory based on the re-optimization.