Networked Appliance Load Manager for Peak Demand Reduction
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Solution Overview
Problem
The existing power distribution grids face inefficiencies due to excess power generation capacity during peak demand periods, leading to increased costs for consumers as the capital cost of idled capacity is spread throughout the year, and there is a need to reduce peak power demands and variations in power consumption.
Innovation Solution
A system and method that utilize networked appliances with a load manager to dynamically adjust operating priorities and coordinate power consumption through a communication network, allowing for the allocation of power based on predicted loads and cost considerations to minimize peak demand and aggregate power consumption.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If excess power generating capacity is made available to meet peak power demand requirements, then the power grid can meet peak demand, but the capital cost of idled capacity is spread among consumers throughout the year, increasing the overall cost of power delivery
Solution Approach 1:
The system dynamically adjusts the operation of networked appliances based on real-time power consumption data and predicted loads. The load controller continuously monitors power usage and modifies appliance operation schedules to respond to changing grid conditions, transforming static appliance operation into a dynamic, adaptive system that can shift loads away from peak demand periods
Solution Approach 2:
The system implements a feedback mechanism where the first appliance reports power consumption to the load controller, which then uses this information to adjust the operation of the second appliance. This closed-loop feedback enables continuous optimization of power consumption patterns, allowing the system to learn from actual usage and improve load management over time
2Adaptability or versatility
If power generation capacity is idled for several months during off-peak seasons, then the grid can meet seasonal load variations, but the capital cost associated with the idled excess capacity increases the overall cost of power delivery
Solution Approach 1:
The system uses predicted load information to proactively schedule appliance operation before peak demand periods occur. By anticipating future power consumption needs and pre-coordinating appliance schedules, the system can shift loads to off-peak periods in advance, reducing the need for peak capacity while maintaining service quality
Solution Approach 2:
The system changes operational parameters of networked appliances based on power consumption conditions. The load controller modifies operating schedules, timing, and intensity of appliance operation to optimize power usage patterns, transforming fixed operational parameters into variable parameters that adapt to grid conditions and pricing signals
3Power
If networked appliances dynamically adjust operating priorities based on power consumption reports, then peak power demand is reduced, but the system complexity increases due to communication and coordination requirements
Solution Approach 1:
The system segments the load management function into distributed components: individual appliances report their own power consumption data, the load controller processes this information and makes scheduling decisions, and each appliance independently adjusts its operation. This segmentation distributes the complexity across multiple simple components rather than requiring one complex centralized controller, making the system more manageable and scalable
Data Source
AI summary
A system for controlling operation of a plurality of appliances includes first and second appliances. The first appliance is configured to report a power consumption via a network. A second appliance is configured to operate dependent on the power consumption reported by the first appliance.