Managing emissions demand response event generation
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Solution Overview
Problem
Utility companies face challenges in consistently managing electricity demand while reducing carbon emissions due to variance in consumer demand and cleaner electricity availability, often relying on polluting sources when cleaner energy is insufficient.
Innovation Solution
A cloud-based HVAC control system that receives emissions rate forecasts to generate emissions demand response events, adjusting thermostat setpoints to shift electricity usage to times when cleaner energy sources are available, using preemptive and deferred events to minimize carbon emissions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Object-affected harmful factors
If HVAC systems operate continuously to meet consumer demand, then consumer comfort is maintained, but carbon emissions increase due to reliance on polluting energy sources
Solution Approach 1:
The system performs preliminary cooling or heating of spaces before high emissions periods occur by adjusting thermostat setpoints in advance. This pre-conditioning allows the HVAC system to reduce or suspend operation during peak emissions times while maintaining consumer comfort, thereby reducing carbon emissions without sacrificing ease of operation
Solution Approach 2:
The system dynamically adjusts thermostat setpoints based on real-time emissions rate forecasts and demand response events. Rather than operating continuously at fixed settings, the HVAC system adapts its operation to emissions conditions, creating dynamic trade-offs between emissions reduction and comfort that resolve the contradiction between reducing harmful factors and maintaining ease of operation
2Object-affected harmful factors
If thermostat setpoints are adjusted frequently to optimize emissions, then carbon emissions are reduced, but user discomfort increases
Solution Approach 1:
The system applies partial adjustments to thermostat setpoints rather than extreme changes, optimizing emissions reduction while maintaining acceptable temperature ranges for user comfort. By using moderate setpoint changes and selective adjustment timing, the system achieves sufficient emissions reduction without excessive action that would compromise temperature stability and user comfort
Solution Approach 2:
The system incorporates user feedback mechanisms to monitor comfort levels and adjust future setpoint changes accordingly. This feedback loop allows the system to learn user preferences and tolerance thresholds, refining its emissions optimization strategy to maintain temperature stability while still achieving carbon reduction goals
3Object-affected harmful factors
If demand response events are extended to maximize emissions reduction, then carbon emissions decrease, but electricity consumption during low cleaner energy availability increases
Solution Approach 1:
The system converts the challenge of extended demand response events into an opportunity by using pre-conditioning strategies. Instead of extending events during high emissions periods when clean energy is unavailable, the system uses these periods to perform beneficial pre-cooling or pre-heating, storing thermal energy in buildings' thermal mass. This approach reduces emissions by shifting load to times when cleaner energy is available, while the building's thermal inertia maintains comfort during the extended event period
Data Source
AI summary
Techniques for performing an emissions demand response event are described. In an example, a cloud-based HVAC control server system receives an emissions rate forecast for a predefined future time period. Using the emissions rate forecast, a plurality of emissions differential values are created for a plurality of points in time during the predefined future time period. The emissions differential values represent a change in predicted emissions over time. Based on the plurality of emissions differential values and a predefined maximum number of emissions demand response events, an emissions demand response event is generated during the predefined future time period. The cloud-based HVAC control server system then causes a thermostat to control an HVAC system in accordance with the generated emissions demand response event.