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, as they need to balance customer comfort with predictable load shedding, and existing solutions often result in fluctuating energy consumption patterns that affect both comfort and load management.
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 predictable load reduction while maintaining customer comfort through targeted thermostat adjustments.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional thermostat control is used to manage electrical load during high energy consumption periods, then customer comfort may be maintained, but predictable load shedding cannot be achieved
Solution Approach 1:
The system performs pre-cooling of buildings before anticipated peak demand periods by lowering thermostat setpoints in advance. This stores cooling capacity in the building thermal mass, allowing HVAC systems to be reduced or shut off during peak periods while maintaining comfort, thus achieving predictable load shedding without sacrificing customer comfort.
Solution Approach 2:
The system dynamically adjusts thermostat setpoints and HVAC operation based on real-time grid conditions, weather forecasts, and building-specific thermal characteristics. This dynamic control enables the system to maintain comfort during non-peak periods while achieving predictable load reduction during peak periods, resolving the contradiction between reliability and ease of operation.
2Productivity
If load management is implemented during high energy consumption periods, then electrical load can be controlled, but energy consumption patterns become fluctuating and unpredictable
Solution Approach 1:
By shifting energy consumption to off-peak periods through pre-cooling and advance HVAC scheduling, the system creates a more stable and predictable overall energy consumption pattern. The load is moved rather than simply reduced, maintaining stability while achieving better peak period control.
Solution Approach 2:
The system continuously monitors building temperature, outdoor conditions, grid demand signals, and HVAC performance to dynamically adjust control strategies. This feedback loop ensures that load management actions maintain stable energy consumption patterns while achieving productivity goals for load control during peak periods.
3Loss of energy
If thermostat adjustments are made to achieve load reduction, then energy consumption can be reduced during peak periods, but customer comfort may be compromised
Solution Approach 1:
The system performs pre-cooling before peak demand periods by lowering thermostat setpoints and running HVAC systems at elevated capacity. This stores thermal energy in building mass, allowing the system to reduce or suspend HVAC operation during peak periods while maintaining comfortable temperatures, thus reducing peak energy consumption without compromising comfort.
Solution Approach 2:
The system changes thermostat setpoint parameters dynamically based on the timing relative to peak demand events. During pre-cooling phases, setpoints are lowered below normal comfort ranges; during peak reduction phases, setpoints are adjusted to maintain comfort while minimizing energy use. These parameter changes enable energy reduction without permanent comfort compromise.
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.