Method and system for limiting power consumption
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Solution Overview
Problem
Building management systems face challenges in balancing energy savings with maintaining thermal comfort, as reducing energy consumption can lead to occupant discomfort, and existing methods lack flexibility in managing power loads of climate control appliances effectively.
Innovation Solution
A method and system that limit power consumption of climate control appliances by setting a cap and using a projection-based decision mechanism to determine which appliances to activate, minimizing deviation from target environmental conditions, while allowing for dynamic adjustments and prioritization based on real-time sensor data and occupancy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of energy
If power consumption of climate control appliances is reduced to achieve energy savings, then energy cost decreases, but thermal comfort of building inhabitants deteriorates
Solution Approach 1:
The system segments the climate control appliance fleet into controllable individual units, each with its own power consumption profile. By dividing the total load into discrete controllable segments, the system can selectively manage which appliances operate to maintain comfort while limiting overall power consumption to stay below the cap.
Solution Approach 2:
The system dynamically adjusts the operation of climate control appliances based on real-time conditions including power cap constraints, occupancy patterns, and environmental factors. This dynamic control allows the system to optimize the balance between energy savings and thermal comfort by adapting appliance activation decisions to current system state and constraints.
2Power
If a strict power cap is imposed on climate control appliances to meet demand response requirements, then peak power consumption is limited, but the ability to maintain target environmental conditions deteriorates
Solution Approach 1:
The system changes operational parameters of climate control appliances including setpoint temperatures, operational schedules, and power levels to adapt to the imposed power cap. By adjusting these parameters dynamically, the system maintains environmental conditions within acceptable ranges while ensuring total power consumption remains below the cap constraint.
Solution Approach 2:
The system implements feedback mechanisms that continuously monitor both power consumption levels and environmental conditions. This feedback allows the control system to make real-time adjustments to appliance operation, ensuring that the power cap is not exceeded while minimizing deviations from target environmental conditions through closed-loop control.
3Productivity
If existing demand response strategies are applied to HVAC systems, then some energy management is achieved, but flexibility in managing individual appliance power loads is insufficient
Solution Approach 1:
The system divides the HVAC infrastructure into individually controllable appliance segments, each with its own power consumption characteristics and control capabilities. This segmentation enables flexible management of individual appliance loads while maintaining overall energy management efficiency through coordinated control of the segmented units.
Solution Approach 2:
The system employs dynamic control strategies that adapt to varying power cap constraints, occupancy patterns, and environmental conditions. This dynamic approach provides the flexibility needed to manage individual appliance loads effectively while maintaining energy management efficiency through real-time optimization of appliance operation schedules and power levels.
Data Source
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AI summary
This invention relates to methods and systems for limiting consumption, particularly power consumption, more particularly by appliances in a building, and is generally suitable for integration with building management systems. Embodiments of the invention provide arrangements in which the aggregated power load of a plurality of appliances is capped to a selected value (which may be arbitrary, or may be dictated by conditions) whilst seeking to minimize the deviation from target environmental conditions within the building through a combination of distributed decision making by the appliances themselves and centralized orchestration, which may be informed by real-time sensor readings and/or known properties of the building. The distributed decision-making by individual devices may be based on projected deviation from the target conditions after a period of activity or inactivity but with a central controller which determines which devices should be switched on.