Controlling an HVAC system using an optimal setpoint schedule during 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 undermine their effectiveness.
Innovation Solution
An intelligent, network-connected thermostat that determines an optimized control trajectory for HVAC systems by minimizing a cost function combining energy consumption, occupant discomfort, and energy consumption deviations, allowing for personalized setpoint temperature profiles and dynamic control adjustments based on user preferences and system characteristics.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If utility companies implement demand-response events 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 demand-response program into two distinct control modes: utility-controlled mode for peak demand reduction and consumer-controlled mode for comfort maintenance. The system automatically transitions between these modes based on real-time conditions, allowing both utility objectives and consumer preferences to be satisfied without direct utility control during the event.
Solution Approach 2:
The system performs preliminary action by pre-cooling the residence before the demand-response event begins and storing this thermal energy in the building's thermal mass. This advance preparation allows the HVAC system to be curtailed during the event while maintaining comfortable temperatures, thus reducing peak demand without compromising consumer comfort or control.
2Device complexity
If utility companies use one-size-fits-all demand-response control, then implementation is simplified, but consumer comfort and program effectiveness are reduced
Solution Approach 1:
The patent implements local quality by allowing each consumer to customize their demand-response experience through individual comfort parameters, temperature thresholds, and preference settings. The system tailors the control strategy to each residence's specific thermal characteristics, occupancy patterns, and consumer preferences, ensuring optimal comfort and effectiveness for each participant rather than applying a uniform approach.
Solution Approach 2:
The system dynamically adapts the control strategy based on real-time conditions including outdoor temperature, residence thermal response, and consumer comfort feedback. The controller continuously monitors and adjusts control parameters during the demand-response event, transitioning between pre-cooling, curtailment, and recovery phases to maintain comfort while achieving demand reduction goals.
3Loss of energy
If cooling systems are controlled during demand-response events, then energy consumption during peak periods is reduced, but consumer comfort is compromised
Solution Approach 1:
The system performs preliminary pre-cooling of the residence before the demand-response event begins, storing thermal energy in the building's thermal mass (walls, floors, furniture). This advance cooling creates a thermal buffer that maintains comfortable indoor temperatures during the curtailment period, reducing energy consumption during peak periods without causing consumer discomfort.
Solution Approach 2:
The patent converts the potential harm of curtailment-induced discomfort into a benefit by using the curtailment period itself for recovery and re-cooling. The system strategically times the resumption of cooling to recover from any temperature rise while outdoor temperatures are still favorable, turning what could be a discomfort period into an energy-efficient cooling opportunity.
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.