Energy Orchestration Control for Price-Responsive Load Shifting
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Solution Overview
Problem
Current energy management systems fail to effectively optimize energy consumption and production, often leading to inefficient use, improper scheduling, and inconvenience, as they do not adequately consider user preferences, environmental factors, or volatile energy pricing, and lack effective communication between end-users and utilities.
Innovation Solution
The system employs non-linear generic models and modeling parameter sets to optimize energy systems by shifting loads to lower-priced or lower-carbon periods, using sensors and external data to simulate and predict energy system behavior, and generate control schedules that balance user comfort and cost savings.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of energy
If demand management systems turn off end-user devices during demand peaks, then utility load is reduced and blackouts are avoided, but user comfort deteriorates and inconvenience increases
Solution Approach 1:
The system performs preliminary actions by pre-cooling or pre-heating buildings before peak demand periods. Thermal energy storage systems are charged in advance during off-peak hours, allowing HVAC systems to be reduced or shut off during demand peaks while maintaining comfort through stored thermal energy.
Solution Approach 2:
Thermal energy storage systems act as intermediaries between the HVAC system and the building environment. These storage systems decouple the cooling/heating production from the consumption, allowing the HVAC system to operate at reduced capacity during peak periods while the storage system maintains building comfort.
2Ease of operation
If thermostats create temperature schedules for buildings, then user comfort is maintained during occupancy, but energy cost savings are lost by not considering volatile energy pricing
Solution Approach 1:
The system dynamically adjusts temperature schedules based on real-time energy pricing signals and forecasted demand. Rather than using fixed schedules, the thermostat continuously optimizes setpoints by considering varying energy costs, renewable energy availability, and building thermal characteristics to minimize costs while maintaining comfort.
Solution Approach 2:
The system incorporates feedback loops that monitor energy costs, consumption patterns, and building response to temperature changes. This feedback enables the thermostat to learn and adapt optimal schedules, adjusting future operations based on actual performance and changing energy pricing conditions.
3Loss of information
If sensors are deployed throughout energy systems to measure consumption and environmental factors, then data availability increases, but effective communication and coordination between devices and systems deteriorates
Solution Approach 1:
The system employs universal communication protocols and standardized data formats that enable diverse sensors and devices to interoperate seamlessly. A common platform aggregates data from multiple sources (temperature sensors, energy meters, weather stations) and presents unified information to control systems, eliminating communication silos.
Solution Approach 2:
Centralized or distributed control platforms serve as intermediaries between sensors and actuator devices. These platforms process sensor data, apply optimization algorithms, and generate coordinated control signals, simplifying the communication architecture and enabling effective system-wide coordination.
4Device complexity
If energy systems operate without considering renewable energy availability, then system simplicity is maintained, but opportunity for reducing carbon footprint and optimizing energy sources is lost
Solution Approach 1:
The system performs preliminary actions by pre-scheduling energy-intensive operations during periods of high renewable energy availability. Forecasting tools predict solar and wind generation patterns, allowing the system to advance non-critical loads to coincide with renewable peaks, thereby maximizing renewable utilization without requiring complex real-time control.
Data Source
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.