Systems, apparatus and methods for managing demand-response programs and events
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Solution Overview
Problem
Existing demand-response programs face challenges in efficiently managing peak electricity demand, as current methods like direct load control and temperature setback control often result in customer discomfort and lack of control, failing to optimize energy shifting and participant selection effectively.
Innovation Solution
The implementation of intelligent, network-connected thermostats that assess physical parameters and user preferences to determine suitability for demand response events, shifting energy consumption from peak to off-peak times through a demand response event implementation profile, while preventing tampering attempts.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If direct load control is used to reduce peak demand, then energy consumption during peak periods is reduced, but customer comfort and control are compromised
Solution Approach 1:
The system performs pre-cooling of residences before anticipated peak demand periods by lowering thermostat setpoints in advance. This stores cooling capacity in the building thermal mass, allowing the cooling system to operate at reduced capacity during peak periods while maintaining comfort. The intelligent thermostat assesses physical parameters like thermal mass and insulation to determine optimal pre-cooling strategies.
2Adaptability or versatility
If intelligent thermostats assess physical parameters and user preferences, then participant selection for demand response events is optimized, but system complexity increases
Solution Approach 1:
The intelligent thermostat assesses multiple parameters including residence thermal mass, insulation quality, cooling system capacity, outdoor temperature conditions, and user comfort preferences. These parameters are processed to generate a suitability score that determines optimal participation in demand response events. The system dynamically adjusts operational parameters like pre-cooling setpoints and duration based on real-time conditions, achieving high adaptability through software-based parameter optimization rather than hardware complexity.
Data Source
AI summary
Apparatus, systems, methods, and related computer program products for managing demand-response programs and events. The systems disclosed include an energy management system in operation with an intelligent, network-connected thermostat located at a structure. The thermostat controls an HVAC system to cool the structure using a demand response event implementation profile over the demand response event period. The thermostat can also receive a requested change to the setpoint temperatures defined by the demand response event implementation profile and access a determination of an impact on energy shifting that would result if the requested change is incorporated into the demand response event implementation profile. This determination can be communicated to the energy consumer.