Managing emissions demand response event generation
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Solution Overview
Problem
Utility companies face challenges in balancing electricity demand with reducing carbon emissions due to varying consumer demand and cleaner electricity supply, necessitating emissions demand response (EDR) events to shift load to cleaner energy sources, but existing systems struggle with user discomfort and inefficiencies.
Innovation Solution
A cloud-based HVAC control server system generates emissions demand response events by analyzing emissions rate forecasts to adjust thermostat setpoints, optimizing event generation and execution to minimize user discomfort while maximizing carbon reduction, using constraints and user-specific data to balance comfort and emissions reduction.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Object-generated harmful factors
If emissions demand response events are implemented to shift load to cleaner energy sources, then carbon emissions are reduced, but user comfort deteriorates due to thermostat setpoint adjustments
Solution Approach 1:
The system performs preliminary cooling or heating before emissions-intensive periods by adjusting thermostat setpoints in advance. This pre-conditioning allows the HVAC system to meet cooling/heating demands during high-emission periods without active operation, thereby reducing emissions while maintaining user comfort through predictive timing rather than reactive adjustments.
Solution Approach 2:
The system dynamically adjusts thermostat setpoints based on real-time emissions rate forecasts and varying user comfort preferences. Rather than fixed adjustments, the control strategy adapts setpoint magnitude and timing continuously, allowing optimal balance between emissions reduction and comfort maintenance under changing conditions.
2Object-generated harmful factors
If thermostat setpoints are adjusted frequently to optimize emissions reduction, then carbon emissions decrease, but system complexity increases
Solution Approach 1:
The system incorporates feedback loops that continuously monitor emissions rate forecasts, actual HVAC performance, and user comfort responses. This feedback enables automated adjustment of setpoint strategies, where the system learns from past events and refines future predictions, reducing the need for complex manual control while maintaining optimization.
Solution Approach 2:
The control system autonomously manages emissions demand response events without requiring user intervention for each adjustment. It automatically interprets emissions forecasts, determines optimal setpoint changes, executes adjustments, and monitors outcomes, thereby simplifying the user interface while handling complexity internally through self-directed optimization.
3Object-generated harmful factors
If emissions demand response events are extended in duration to maximize carbon reduction, then emissions decrease, but loss of time increases due to prolonged user discomfort
Solution Approach 1:
The system applies preliminary anti-action by pre-cooling or pre-heating spaces before emissions-intensive periods begin. This anticipatory conditioning creates a thermal buffer that passiveively maintains comfort during high-emission periods without requiring prolonged active HVAC operation, thereby shortening the effective duration of user-experienced discomfort while achieving emissions reduction.
Data Source
AI summary
Techniques for performing an emissions demand response event are described. In an example, a power control server system receives an emissions rate forecast for a predefined future time period. Using the emissions rate forecast, an emissions rate event is identified during the predefined future time period. Based on the plurality of emissions rate event, an emissions demand response event is generated during the predefined future time period. The power control server system then causes a power controller to modify an energy consumption by an electronic device in accordance with the generated emissions demand response event.