Managing emissions demand response event generation
Find Innovative SolutionsGenerate Solutions
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 electricity consumption to cleaner energy sources, but existing systems struggle with user discomfort and inefficiencies.
Innovation Solution
A cloud-based HVAC control system generates and manages EDR events by forecasting emissions rates, adjusting thermostat setpoints, and applying constraints to minimize user discomfort, using a cloud-based power control server system to optimize EDR events based on emissions differentials, user preferences, and event scoring.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Object-generated harmful factors
If EDR events are implemented to shift electricity consumption to cleaner energy sources, then carbon emissions are reduced, but user comfort deteriorates
Solution Approach 1:
The system performs preliminary cooling or heating before EDR events by adjusting thermostat setpoints in advance. This pre-conditioning allows the HVAC system to be turned off or reduced during high-emission periods while maintaining acceptable temperature ranges, thereby reducing carbon emissions without significantly impacting user comfort.
Solution Approach 2:
The system dynamically adjusts thermostat setpoints based on real-time emissions data, forecasted emissions, and user comfort preferences. The control strategy adapts continuously between different operating modes (normal, pre-cooling, pre-heating, EDR event) to optimize the balance between emissions reduction and comfort maintenance.
2Object-generated harmful factors
If frequent EDR events are generated to maximize emissions reduction, then carbon emissions are reduced more effectively, but user annoyance increases
Solution Approach 1:
The system incorporates feedback mechanisms by monitoring user comfort preferences, historical comfort data, and real-time environmental conditions. This feedback loop allows the system to learn user tolerance levels and adjust EDR event frequency and intensity accordingly, preventing excessive events that would cause annoyance while maintaining effective emissions reduction.
Solution Approach 2:
The system changes multiple parameters including thermostat setpoints, EDR event duration, event frequency, and pre-conditioning timing based on emissions forecasts and user preferences. By dynamically adjusting these parameters, the system optimizes the balance between emissions reduction effectiveness and user comfort acceptance.
3Object-generated harmful factors
If aggressive thermostat control is used to maximize emissions reduction during EDR events, then carbon emissions are reduced, but system complexity increases
Solution Approach 1:
The control system is segmented into distinct operational modes (normal operation, pre-cooling, pre-heating, EDR event execution) with specific control strategies for each mode. This segmentation simplifies the overall control logic by breaking down the complex emissions optimization problem into manageable, well-defined operational states with clear transition criteria.
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.