Dynamic adaptation of emissions demand response events
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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 sources are insufficient.
Innovation Solution
A cloud-based HVAC control system generates and modifies emissions demand response events by adjusting thermostat settings based on emissions rate forecasts, shifting electricity consumption to times when cleaner energy sources are available, and limiting user discomfort through constraints on event frequency and timing.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Object-generated harmful factors
If emissions demand response events are scheduled based on initial emissions rate forecasts, then carbon emissions can be reduced by shifting load to cleaner energy periods, but the effectiveness is reduced when forecasted emissions rates do not match actual conditions
Solution Approach 1:
The system dynamically modifies emissions demand response events based on updated emissions rate forecasts. When new forecast data indicates changed emissions conditions, the system adjusts event parameters such as start time, end time, and intensity to optimize carbon reduction effectiveness, transforming the static scheduling approach into a dynamic adaptation mechanism
Solution Approach 2:
The system implements a feedback loop where updated emissions rate forecasts are continuously obtained and used to modify ongoing or upcoming emissions demand response events. This feedback mechanism ensures that the events remain aligned with actual grid emissions conditions, improving reliability of carbon reduction outcomes
2Object-generated harmful factors
If emissions demand response events are frequently scheduled to maximize carbon reduction, then more carbon emissions can be reduced, but user discomfort increases due to frequent thermostat adjustments
Solution Approach 1:
The system modifies event parameters including intensity level, duration, and timing based on multiple forecast updates. By adjusting these parameters dynamically, the system can achieve effective carbon reduction while minimizing the magnitude and frequency of thermostat adjustments, thereby reducing user discomfort
Solution Approach 2:
The system applies partial action by implementing moderate thermostat adjustments during emissions demand response events rather than extreme changes. This approach achieves sufficient carbon reduction through cumulative effect of multiple moderate events rather than occasional extreme events, balancing environmental benefit with user comfort
3Object-generated harmful factors
If emissions demand response events are delayed to wait for more accurate emissions rate forecasts, then carbon reduction effectiveness improves, but time is lost and cleaner energy periods may be missed
Solution Approach 1:
The system performs preliminary scheduling of emissions demand response events based on initial emissions rate forecasts before more accurate data is available. This preliminary action allows the system to proactively shift load to cleaner energy periods while retaining the ability to modify events later based on updated forecasts, avoiding complete delays
Solution Approach 2:
The system dynamically adjusts event timing by comparing updated emissions rate forecasts with originally scheduled events. When forecasts indicate significant changes in emissions conditions, the system modifies start and end times to capture optimal carbon reduction opportunities, balancing proactive scheduling with adaptive timing
Data Source
AI summary
Techniques for performing an emissions demand response (EDR) event are described. In an example, a cloud-based HVAC control system may obtain a first emissions rate forecast and generate an EDR event with a start time and end time based on the first emissions rate forecast. The EDR event may then be transmitted to a thermostat and stored in a memory of the thermostat. At the start time, the thermostat may commence controlling an HVAC system according to the EDR event. After the start time and prior to the end time, the cloud-based HVAC control system may obtain a second emissions rate forecast and generate a modified EDR event with a modified end time. The modified EDR event may be transmitted to the thermostat before the end time and/or the modified end time whereupon the thermostat may control the HVAC system accordingly until the modified end time is reached.