Orchestrated energy
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Solution Overview
Problem
Existing energy management systems fail to effectively coordinate between customer and utility goals, often leading to inefficient energy use, discomfort, and high costs due to inadequate scheduling and communication among energy-consuming devices.
Innovation Solution
An orchestrated energy facility that uses non-linear generic models, modeling parameter sets, and optimization algorithms to calibrate and simulate energy systems, generating control schedules that optimize energy consumption and production based on specific goals and constraints, including temperature, cost, and environmental impact.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of energy
If demand management systems turn off end-user devices during demand peaks, then utility demand is reduced, but user comfort is compromised and users receive no advance notice
Solution Approach 1:
The system performs preliminary actions by pre-cooling or pre-heating buildings before peak demand periods using stored energy in thermal mass. This allows the HVAC system to be reduced or shut off during peak periods without compromising comfort, as the thermal mass maintains acceptable temperature ranges. Users receive advance notice and control through programmable thermostats that allow them to set preferences beforehand.
Solution Approach 2:
The system implements feedback mechanisms where users receive advance notice of scheduled shutoffs and can adjust their temperature preferences. The programmable thermostats provide feedback loops that monitor building conditions and utility demand signals, automatically adjusting operations to balance demand reduction with user comfort preferences.
2Ease of operation
If thermostats create temperature schedules for buildings, then user comfort is maintained, but energy cost savings are ignored due to volatile pricing
Solution Approach 1:
The system transitions from static temperature schedules to dynamic scheduling that responds to real-time utility pricing signals and demand conditions. The programmable thermostats automatically adjust temperature setpoints based on varying energy costs, shifting operations to off-peak periods when energy is cheaper while maintaining user comfort within acceptable ranges defined by user preferences.
Solution Approach 2:
The system changes operational parameters (temperature setpoints, timing of HVAC operation) based on external pricing signals. Instead of fixed schedules, the thermostat dynamically modifies these parameters in response to utility pricing and demand conditions, optimizing the balance between comfort and cost.
3Measurement precision
If sensors are deployed throughout energy systems to measure consumption and environmental conditions, then measurement capability is improved, but effective communication and coordination between sensors is insufficient
Solution Approach 1:
The system merges sensor data collection with actionable control functions in integrated programmable thermostats. Rather than having isolated sensors that merely measure, the system combines measurement capabilities with communication interfaces and control algorithms that process utility signals and automatically implement optimized scheduling decisions.
Solution Approach 2:
The programmable thermostat acts as an intermediary that receives utility pricing and demand signals, processes sensor data from the building environment, and translates these inputs into coordinated control actions. This intermediary function enables effective communication between the utility system and end-use devices, transforming raw measurement data into actionable optimization decisions.
Data Source
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AI summary
A facility providing systems and methods for managing and optimizing energy consumption and/or production is provided. The facility provides techniques for optimizing energy-consuming and energy-producing systems to meet specified demands or goals in accordance with various constraints. The facility relies on models to generate an optimization for an energy system. In order to use generic models to simulate and optimize energy consumption for an energy system, the generic models are calibrated to properly represent or approximate conditions of the energy system during the optimization period. After the appropriate models have been calibrated for a given situation using one or more modeling parameter sets, the facility can simulate inputs and responses for the corresponding system. The facility uses the generated simulations to generate a plan or control schedule to be implemented by the energy system during the optimization period.