Demand response technology utilizing a simulation engine to perform thermostat-based demand response simulations
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Solution Overview
Problem
Grid operators face challenges in managing electrical load during periods of high energy consumption, needing to balance customer comfort with predictable load shedding while achieving specific energy reduction objectives.
Innovation Solution
An energy management server connects with customer and energy provider systems to implement a control strategy for HVAC systems, using historical data and simulation engines to optimize demand response events, ensuring consistent comfort and load reduction while aligning with firm load dispatch objectives.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If grid operators implement traditional thermostat control strategies, then customer comfort may be maintained, but predictable load shedding is difficult to achieve
Solution Approach 1:
The system performs pre-cooling of buildings before demand response events by lowering thermostat setpoints in advance. This stores thermal energy in the building structure (walls, floors, furniture), allowing the building to maintain comfortable temperatures during the event without active HVAC operation, thus achieving both comfort and predictable load reduction
Solution Approach 2:
The system dynamically adjusts thermostat setpoint temperatures based on building thermal characteristics, outdoor conditions, and demand response event parameters. By optimizing these temperature parameters using simulation models, the system achieves predictable load shedding while maintaining comfort within acceptable ranges
2Productivity
If grid operators reduce HVAC energy consumption to achieve load reduction objectives, then firm load dispatch goals are met, but customer comfort criteria may be compromised
Solution Approach 1:
The system uses simulation models to determine optimal thermostat setpoint parameters that achieve the required load reduction while maintaining comfort. By carefully selecting temperature parameters (e.g., pre-cooling temperature, event temperature, post-event temperature), the system meets firm load dispatch objectives without compromising customer comfort below acceptable thresholds
Solution Approach 2:
The system incorporates feedback from building thermal models and actual performance data to refine control strategies. This allows continuous optimization of the balance between load reduction and comfort maintenance, ensuring that firm load dispatch objectives are met while preserving customer satisfaction
3Loss of energy
If grid operators use simulation engines to optimize demand response events, then energy consumption is reduced with predictable load shapes, but system complexity increases
Solution Approach 1:
The system uses thermal simulation models that create virtual copies of building thermal behavior. These models replicate the thermal dynamics of actual buildings, allowing operators to test and optimize control strategies in silico before implementation, achieving energy reduction goals without the complexity of real-time complex control systems
Solution Approach 2:
The simulation engine performs preliminary optimization of demand response strategies before events occur. By pre-calculating optimal control sequences using building-specific thermal models, the system achieves predictable energy reduction patterns while the actual implementation remains relatively simple, reducing real-time computational complexity
Data Source
AI summary
Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for performing a thermostat-based demand response event. In one aspect, a method includes accessing, for sites, historical readings of HVAC activity, indoor temperature, and outdoor temperature and building a model for each of the sites using the historical readings of HVAC activity, indoor temperature, and outdoor temperature. The method also includes using a simulation engine to achieve a target load shed and load reduction shape for a thermostat-based demand response event, and performing the thermostat-based demand response event based on results of the simulation engine.