Controlling an HVAC system in association with a demand-response event
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Solution Overview
Problem
Current demand-response programs for HVAC systems during peak demand periods often result in customer discomfort and inefficiencies due to one-size-fits-all approaches, lacking personalized comfort and energy optimization, and are prone to communication errors that can undermine their effectiveness.
Innovation Solution
An intelligent, network-connected thermostat that identifies demand-response events and determines an optimized control trajectory for HVAC systems, minimizing energy consumption, occupant discomfort, and energy rate deviations by generating a setpoint temperature profile and adjusting HVAC control characteristics based on user preferences and system data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If utility companies implement demand-response programs with direct control of cooling systems, then peak demand is reduced, but consumer comfort and control are compromised
Solution Approach 1:
The patent segments the control approach by dividing consumers into different groups based on their demand-response participation preferences and characteristics. This allows the utility company to apply different control strategies to different segments, reducing peak demand through willing participants while maintaining consumer control for those who prioritize it, thus resolving the contradiction between peak demand reduction and consumer control.
Solution Approach 2:
The patent implements dynamic control trajectories that are adjusted in real-time based on grid conditions, consumer preferences, and environmental factors. The control strategy is not static but adapts dynamically to balance peak demand reduction with consumer comfort and control, allowing the system to optimize between these competing objectives during demand-response events.
2Device complexity
If utility companies use one-size-fits-all demand-response control strategies, then implementation is simplified, but consumer comfort and program effectiveness are reduced
Solution Approach 1:
The patent applies local quality by tailoring control strategies to individual consumers' preferences, thermal characteristics of their residences, and local conditions. Each consumer receives a customized control trajectory that respects their comfort requirements while contributing to peak demand reduction, thereby improving program effectiveness and consumer comfort without requiring overly complex centralized control.
Solution Approach 2:
The patent changes key parameters such as setpoint temperature profiles, duty cycle percentages, and pre-cooling durations based on consumer preferences and grid conditions. By dynamically adjusting these parameters for different consumers and situations, the system achieves effective peak demand reduction while maintaining consumer comfort, resolving the contradiction between simplicity and effectiveness.
3Loss of energy
If cooling systems are controlled during demand-response events to reduce peak demand, then energy consumption during peak periods is reduced, but consumer comfort is compromised
Solution Approach 1:
The patent implements pre-cooling as a preliminary action before demand-response events begin, lowering the temperature of residences in advance when grid conditions allow. This stored cooling capacity enables the system to reduce or suspend cooling during peak demand periods while maintaining consumer comfort, thus reducing peak period energy consumption without causing discomfort.
Solution Approach 2:
The patent incorporates feedback mechanisms that continuously monitor residence temperature, consumer comfort preferences, and grid conditions. This feedback allows the control system to adjust cooling trajectories in real-time, ensuring that peak demand reduction actions do not push temperatures beyond consumer comfort thresholds, thereby resolving the contradiction between energy reduction and comfort maintenance.
Data Source
AI summary
A control system includes an energy management system in operation with intelligent, network-connected thermostats located in structures. The thermostats are operable to control heating, ventilation, and air conditioning (HVAC) systems. Control during a demand response (DR) event period may be performed based on an optimal control trajectory of the HVAC system, where the control trajectory is optimal in that it minimizes a cost function.