HVAC Demand Response Control Using Reduced-Capacity Efficiency Mode
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Solution Overview
Problem
Traditional demand response strategies for resource consumption devices, such as HVAC systems, rely on setbacks that reduce customer comfort and may increase overall resource consumption during and after peak demand periods, as they prioritize maintaining a set point over efficiency.
Innovation Solution
Implementing a system that allows resource consumption devices to switch to a reduced capacity mode during demand response events, prioritizing efficient resource consumption over satisfying operational demands, thereby maintaining comfort and reducing overall consumption.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of energy
If a setback setting is implemented during demand response events, then resource consumption is reduced during peak demand periods, but customer comfort deteriorates and overall resource consumption may increase during recovery periods
Solution Approach 1:
The system performs preliminary action by pre-cooling or pre-heating spaces before anticipated peak demand periods when energy prices are lower. This allows the HVAC system to be reduced or shut off during high-price periods while maintaining comfort, thereby reducing peak demand consumption without compromising customer comfort or requiring significant recovery consumption.
Solution Approach 2:
The system dynamically adjusts HVAC operation based on real-time or forecasted energy prices, load conditions, and thermal comfort requirements. Instead of static setback settings, the system continuously optimizes operation timing and intensity, allowing flexible response to changing conditions and eliminating the need for fixed uncomfortable setback temperatures.
2Productivity
If a setback setting is implemented during demand response events, then operational demand is reduced, but overall resource consumption increases due to recovery period demands
Solution Approach 1:
The system shifts energy consumption to off-peak periods before demand response events rather than deferring it to recovery periods. By pre-conditioning spaces when energy prices and demand are lower, the system reduces both peak period consumption and avoids the need for intensive recovery consumption, thereby reducing overall resource consumption.
Solution Approach 2:
The system incorporates feedback from energy price signals, load forecasts, and actual consumption data to continuously optimize operation timing. This feedback mechanism allows the system to learn from past performance and adjust pre-conditioning strategies to minimize overall consumption while achieving demand response objectives, rather than using fixed setback rules that increase recovery consumption.
Data Source
AI summary
Architectures or techniques are presented that can prioritize operating a consumption device in a manner that is efficient in terms of consumption of a resource over satisfying a specified demand assigned to the consumption device. This re-prioritizing can be performed in response to a price of the resource exceeding a threshold.