Managing emissions demand response event intensity
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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 forecasts emissions rates and generates demand response events to shift electricity consumption to times when cleaner energy sources are available, adjusting thermostat settings to reduce peak demand during high emissions periods.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Object-generated harmful factors
If demand response events are implemented to reduce carbon emissions, then emissions reduction is improved, but consumer comfort may deteriorate due to thermostat adjustments
Solution Approach 1:
The system dynamically adjusts thermostat setpoints based on real-time emissions rate conditions and consumer comfort preferences. The controller continuously monitors emissions rates and modifies HVAC operation dynamically during demand response events, allowing flexible balancing of emissions reduction and comfort maintenance rather than using fixed adjustment rules
Solution Approach 2:
The system changes thermostat setpoint parameters during demand response events based on emissions rate conditions. By adjusting temperature setpoints dynamically and allowing consumer preference input, the system modifies operational parameters to achieve emissions reduction while attempting to maintain acceptable comfort levels
2Object-generated harmful factors
If thermostat settings are adjusted during demand response events, then emissions reduction is improved, but energy consumption patterns change which may affect system reliability
Solution Approach 1:
The system uses feedback from emissions rate monitoring to continuously adjust thermostat operation. By receiving real-time emissions rate data and consumer comfort feedback, the controller adapts its control strategy to maintain system reliability while achieving emissions reduction goals, preventing excessive deviations that could compromise HVAC system performance
3Object-generated harmful factors
If demand response events are scheduled during high emissions periods, then emissions reduction effectiveness is improved, but prediction accuracy may deteriorate due to forecast uncertainty
Solution Approach 1:
The system performs preliminary scheduling of demand response events based on forecasted high emissions periods. By proactively scheduling events during predicted high emissions times and using real-time emissions rate monitoring to confirm and adjust event execution, the system addresses forecast uncertainty through advance planning combined with adaptive real-time verification
Data Source
AI summary
Techniques for performing an emissions demand response event are described. In an example, a cloud-based HVAC control server system obtains an emissions rate forecast for a predefined future time period. Using the emissions rate forecast, a future emissions rate event during the predefined future time period is identified. The future emissions rate event comprises an indication of predicted magnitude and a time period when a predicted emissions rate will be at an increased or decreased level. A confidence value indicating a certainty of the future emissions rate event occurring as predicted is determined. Based on the identified future emissions rate event and the confidence value, an emissions demand response event having a start time and an end time during the future emissions rate event is generated. 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.