Dynamic Building Load Curtailment for Peak Demand Control
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Solution Overview
Problem
Existing energy management systems face challenges in dynamically adjusting energy consumption to minimize peak demand, often requiring extensive tuning and retuning due to seasonal changes, leading to potential occupant disruption if not properly calibrated.
Innovation Solution
A dynamic load curtailment algorithm that learns optimum energy consumption conditions for a building, employing separate control schemes for HVAC and non-HVAC loads, using historical peak demand measurements to predict and strategically curtail loads within user-configured parameters, ensuring efficient operation while minimizing peak demand.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Use of energy by moving object
If load curtailment is implemented using schedule or threshold basis, then energy consumption is reduced, but extensive tuning and retuning are required due to seasonal changes
Solution Approach 1:
The system performs self-tuning by automatically learning occupancy patterns and thermal characteristics of the building over time. The controller adapts setback schedules and temperature setpoints based on historical data without requiring manual intervention or retuning for seasonal changes, thereby reducing energy consumption while eliminating tuning complexity.
Solution Approach 2:
The load curtailment system transitions from static schedule/threshold-based control to dynamic adaptive control. The system continuously learns and adjusts to changing building conditions, occupancy patterns, and seasonal variations, enabling energy reduction without requiring manual retuning while maintaining comfort within learned parameters.
2Ease of operation
If load curtailment is implemented without proper tuning, then system operation is simplified, but occupant disruption occurs due to excessive curtailment
Solution Approach 1:
The system automatically learns acceptable comfort parameters and occupancy patterns through continuous monitoring of building responses to curtailment actions. This self-learning capability enables the system to operate without manual tuning while reliably maintaining occupant comfort by adapting to actual building characteristics and occupancy behaviors.
Solution Approach 2:
The system implements closed-loop feedback by monitoring building temperature responses, occupancy patterns, and comfort conditions. This feedback enables the controller to learn optimal curtailment strategies that maintain reliability and occupant comfort while simplifying operation, as the system automatically adjusts based on observed building behavior and occupancy feedback.
3Measurement precision
If manual tuning is used to optimize curtailment, then curtailment accuracy is improved, but time and effort for tuning and retuning are increased
Solution Approach 1:
The system performs self-tuning by automatically learning building thermal characteristics, occupancy patterns, and optimal curtailment strategies through continuous operation. This eliminates the need for manual tuning and retuning while maintaining high curtailment accuracy, as the system adapts to building-specific behaviors and seasonal changes autonomously over time.
Solution Approach 2:
The system performs preliminary learning during initial operation to establish baseline building characteristics and occupancy patterns. This preliminary action enables accurate curtailment from the outset without requiring subsequent manual tuning, as the system proactively learns and adapts to building-specific conditions during normal operation.
Data Source
AI summary
A system that dynamically learns the optimum energy consumption operating condition for a building and monitors/controls energy consuming equipment to keep the peak demand interval at a minimum. The algorithm employs two separate control schemes, one for HVAC loads and one for non-HVAC loads, and uses historical peak demand measurements in its real-time limiting strategy. The algorithm continuously attempts to reduce peak demand within user configured parameters. When a new peak is inevitable, the algorithm removes and/or introduces loads to limit the new peak magnitude and places the operating conditions within the user configured parameters. The algorithm can examine the previous seven days of metering information to identify a peak demand interval, use real-time load information to predict the demand peak of the upcoming interval, and curtail loads in order to limit the demand peak so as not to set a new peak.