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, which existing technologies struggle to achieve effectively.
Innovation Solution
An energy management server connects customer and utility 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 achieve specific load reduction 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 preliminary simulations of demand response events using a simulation engine before actual implementation. The simulation engine models various thermostat control strategies and predicts their impact on load shedding and customer comfort, allowing operators to select optimal strategies in advance that achieve both predictable load reduction and comfort maintenance
Solution Approach 2:
The system uses historical HVAC activity, temperature, and energy consumption data to build site-specific models that provide feedback on how different control strategies will perform. This feedback mechanism allows the system to predict and optimize the balance between load shedding reliability and customer comfort before implementing control actions
2Loss of energy
If grid operators reduce HVAC energy consumption during demand response events, then load shedding is achieved, but customer comfort criteria may be compromised
Solution Approach 1:
The simulation engine evaluates multiple parameter configurations for thermostat control strategies, including temperature setpoints, timing schedules, and control duration. By optimizing these parameters through simulation, the system achieves significant HVAC energy consumption reduction while maintaining temperature ranges that satisfy customer comfort criteria
Solution Approach 2:
The system implements dynamic control strategies that adjust thermostat setpoints and control parameters in real-time based on simulated predictions of customer comfort impact. The control strategy evolves during the demand response event to maintain comfort criteria while maximizing energy consumption reduction
3Productivity
If grid operators use simulation engines to optimize demand response events, then firm load dispatch objectives are achieved, but system complexity increases
Solution Approach 1:
The system creates simplified digital copies (simulation models) of actual sites and HVAC systems that replicate their thermal and energy consumption characteristics. These virtual models allow the simulation engine to test and optimize control strategies without affecting real systems, achieving firm load dispatch objectives while keeping the actual deployment complexity manageable
Solution Approach 2:
The simulation engine serves multiple functions: it predicts load shedding outcomes, evaluates customer comfort impact, optimizes control strategies, and trains site-specific models. This multi-functional approach consolidates complexity into a single versatile tool rather than requiring separate systems for each function
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.