Appliance State Return Control After Utility Demand Response
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Solution Overview
Problem
Current energy management systems require manual operation of appliances during off-peak hours to reduce electricity costs, which is inconvenient and lacks advanced control beyond simple on/off switching, and different utilities use varying methods to communicate peak demand times, leading to inefficiencies in load management.
Innovation Solution
A home energy management system with an appliance controller that receives signals from utilities to adjust power-consuming functions based on threshold variables, allowing for more granular control and flexibility, including modes beyond on/off, and enabling communication through various protocols to manage energy usage dynamically.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of energy
If manual operation of appliances during off-peak hours is required, then energy cost savings are achieved, but consumer convenience deteriorates and operational complexity increases
Solution Approach 1:
The appliance controller automatically monitors utility signals, determines peak and off-peak periods, and adjusts appliance operation without requiring manual consumer intervention. The system serves itself by autonomously making scheduling decisions based on utility pricing signals, thereby achieving energy cost savings while maintaining consumer convenience.
Solution Approach 2:
The system continuously receives utility pricing signals as feedback and automatically adjusts appliance operation in response. The controller monitors the utility signals, compares them against threshold variables, and dynamically schedules appliance tasks to operate during off-peak periods, creating a closed-loop feedback system that achieves cost savings without manual intervention.
2Loss of energy
If simple on/off switching is used for demand response, then energy control is achieved, but system functionality and consumer acceptability deteriorate
Solution Approach 1:
The appliance controller dynamically adjusts appliance operation parameters based on real-time utility pricing signals. Instead of simple binary on/off control, the system continuously adapts its operation schedule, task priorities, and resource allocation in response to changing utility rates, thereby maintaining both energy control and system functionality.
Solution Approach 2:
The system changes operational parameters such as task scheduling timing, power consumption levels, and operational modes based on utility pricing signals. By adjusting these parameters dynamically rather than using fixed on/off switching, the system achieves energy control while preserving adaptability and consumer acceptability.
3Adaptability or versatility
If multiple communication protocols are supported, then adaptability to different utilities is improved, but device complexity increases
Solution Approach 1:
The appliance controller includes an intermediary communication layer that translates between different utility communication protocols and the appliance's internal control system. This intermediary module handles protocol conversion, allowing the appliance to work with multiple utility companies using different communication methods without increasing overall system complexity.
Solution Approach 2:
The communication interface is designed with universal functionality to handle multiple communication protocols through a single integrated module. This multi-functional approach allows the same hardware and software infrastructure to support various utility communication methods, achieving adaptability without proportionally increasing device complexity.
4Loss of energy
If automatic appliance operation during off-peak hours is implemented, then energy cost savings are achieved, but control precision requirements increase
Solution Approach 1:
The system replaces manual interpretation of utility signals with automated electronic processing. The controller uses programmed logic and algorithms to automatically interpret utility pricing signals, compare them against threshold variables, and determine optimal scheduling decisions, thereby achieving the required precision through computational rather than manual means.
Solution Approach 2:
The appliance controller pre-configures threshold variables and decision-making rules before utility signals arrive. By having predetermined criteria and automated processing logic in place, the system can quickly and accurately interpret incoming utility signals without requiring complex real-time analysis, thereby achieving precision through preparation.
Data Source
AI summary
In another aspect of the disclosure, a method of controlling an appliance is provided comprising establishing settings on an appliance related to threshold variables, wherein the settings include the threshold variables for determining a reaction of the appliance in response to reaching one or more of the threshold variables. The method further comprises sending a signal from an associated utility to the appliance, wherein the appliance includes a controller in signal communication with the associated utility. The controller receives and processes a signal from the associated utility, and converts and compares the signal to the threshold variables. The method still further comprises changing the operating of the appliance from a first state of operation to a second state of operation, wherein in the second state of operation one or more power consuming functions of the appliance are based on the comparison of the signal to the threshold variables and, returning the appliance to the first state of operation.