Thermostat Demand Response Simulation for Predictable Load Shedding
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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 to customer and energy provider systems, implementing a control strategy for HVAC systems that reduces energy consumption while maintaining comfort criteria, using historical data and simulation engines to optimize demand response events and adjust thermostats to achieve a target load reduction shape.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If grid operators implement thermostat control to manage electrical load during high energy consumption periods, then load management capability is improved, but customer comfort may deteriorate
Solution Approach 1:
The system performs pre-cooling of buildings before peak load periods by lowering thermostat setpoints in advance. This stores cooling capacity in the building thermal mass, allowing the HVAC system to reduce or suspend operation during high demand periods while maintaining customer comfort. The simulation engine optimizes these pre-cooling strategies to achieve both load reduction and comfort preservation.
2Use of energy by moving object
If grid operators implement aggressive load shedding to achieve predictable load reduction, then energy consumption control is improved, but customer comfort deteriorates
Solution Approach 1:
The system creates virtual copies of building thermal models through simulation engines that replicate actual building behavior. These digital twins allow grid operators to test and optimize load reduction strategies in the virtual environment before implementing them in the physical system. This enables prediction of both energy savings and comfort impacts, allowing optimization of strategies that achieve load reduction targets while maintaining acceptable comfort levels.
3Productivity
If simulation engines are used to optimize demand response events across multiple sites, then energy consumption optimization is improved, but system complexity increases
Solution Approach 1:
The simulation engine serves multiple functions: it models individual building thermal dynamics, optimizes demand response strategies for single buildings, aggregates results across multiple sites, and predicts overall grid impact. This multi-functional capability allows the system to handle complex multi-site optimization without requiring separate specialized tools for each function, thereby managing system complexity while achieving comprehensive energy optimization.
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.