Demand Response Load Control Using Occupancy-Based Opt-Out Decisions
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Solution Overview
Problem
Traditional demand-response programs often require users to make sacrifices that can be harmful to their operations, leading to non-participation in demand-response events, especially when repeated, due to the lack of personalized control over resource allocation based on occupancy and usage patterns.
Innovation Solution
A system that uses resource-availability schedules, adjusted by user input and predictive algorithms, to automatically decide participation in or opt out of demand-response events, considering occupancy, usage, and weather conditions, allowing for precise control of electrical load adjustments.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of energy
If traditional demand-response programs require users to make sacrifices (curtailment of electrical load), then electrical demand on the grid is reduced, but user operations may be harmed and participation reliability decreases
Solution Approach 1:
The system performs preliminary actions by proactively identifying and scheduling curtailment activities before DR events are called. Resource-availability schedules are created in advance, predicting which resources can be curtailed without harming operations, so when a DR event occurs, the system can immediately execute pre-planned curtailments rather than making hasty decisions under pressure.
Solution Approach 2:
The system implements feedback mechanisms where user input and actual operational impacts are continuously monitored and used to adjust resource-availability schedules. The system learns from past DR events and user responses, refining its predictions about which curtailments are safe to execute, thereby improving both the reliability of participation and the accuracy of impact assessment over time.
2Reliability
If users are given control over resource allocation based on occupancy and usage patterns, then participation reliability improves, but system complexity increases
Solution Approach 1:
The system enables self-service by allowing facilities to autonomously manage their own resource-availability schedules based on their operational patterns and constraints. The automated scheduling system empowers facilities to make intelligent curtailment decisions independently, reducing the need for complex centralized control while improving participation reliability through localized knowledge of operational requirements.
Solution Approach 2:
The system manages complexity by dynamically adjusting parameters in resource-availability schedules, such as curtailment thresholds, time windows, and resource priorities, based on occupancy and usage patterns. Rather than requiring complex structural changes, the system achieves intelligent control through flexible parameter adjustment that adapts to different facility types and operational requirements.
3Loss of energy
If curtailment is implemented without considering occupancy and usage patterns, then electrical load is reduced quickly, but harmful impacts on facility operations increase
Solution Approach 1:
The system applies local quality by creating customized resource-availability schedules for different facilities and even different resources within facilities, based on their specific occupancy and usage patterns. Rather than implementing uniform curtailment across all facilities, the system tailors curtailment strategies to local conditions, ensuring that curtailments are applied where they will have minimal operational impact while still achieving load reduction goals.
Solution Approach 2:
The system performs preliminary analysis of occupancy and usage patterns before implementing curtailments. Resource-availability schedules are proactively created that identify which resources can be safely curtailed based on predicted facility conditions, ensuring that curtailments are executed at times and locations where they will not harm operations while still achieving the necessary electrical load reduction.
Data Source
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AI summary
A method is provided for controlling electrical load on a power grid from a load facility using demand response. The method includes accessing memory storing computer-readable program code for decision analysis of a specified time interval for a demand-response (DR) event. The method also includes executing the computer-readable program code, via a processor, to cause an apparatus to at least make a decision to participate in or opt out of the DR event. This includes the apparatus receiving values of variables that describe occupancy and usage of the load facility for one or more time intervals. The apparatus applies the values to an algorithm that maps the variables to a decision to participate in or opt out of the DR event for the specified time interval. And the apparatus automatically notifies an operator responsible for the DR event of the decision at least when the decision is to opt out.