Demand-Response Load Control Using Occupancy-Based Opt-Out Decisions
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Solution Overview
Problem
Existing demand-response programs face challenges as users often opt out due to negative impacts on their facilities, leading to inefficiencies in load balancing, particularly when weather conditions or prolonged events necessitate frequent load adjustments.
Innovation Solution
A system that autonomously determines resource availability and participation in demand-response events based on occupancy, usage, and weather conditions, using algorithms to select a subset of resources for load adjustment, and notifies operators of decisions to participate or opt out, minimizing user input.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional demand-response programs require user participation in load curtailment, then grid load balancing is improved, but facility operation and user comfort deteriorate
Solution Approach 1:
The system performs preliminary actions by predicting resource availability in advance of DR events and pre-negotiating curtailment amounts with the system operator. This allows the facility to prepare for potential curtailments without immediately impacting operations, resolving the contradiction by enabling load balancing participation while maintaining facility reliability through advance planning and selective resource commitment.
Solution Approach 2:
The facility autonomously determines its own resource availability and makes participation decisions without requiring user intervention. The system automatically assesses which resources can be curtailed based on facility needs, enabling load balancing participation while protecting critical operations, thus improving grid contribution without sacrificing facility reliability.
2Productivity
If demand-response events are called frequently due to weather conditions, then grid demand management is improved, but user convenience deteriorates
Solution Approach 1:
The system automatically determines resource availability and makes participation decisions without requiring user input for each DR event. This self-service approach allows the facility to respond to frequent DR events efficiently while maintaining normal operations, resolving the contradiction by enabling demand management participation without compromising user convenience.
Solution Approach 2:
The system performs preliminary assessments of resource availability and pre-negotiates curtailment parameters before DR events occur. This advance preparation enables the facility to respond quickly to frequent weather-related DR events without requiring user intervention, improving demand management efficiency while maintaining user convenience through automated decision-making.
3Reliability
If users are allowed to opt out of demand-response events, then facility operation is protected, but program participation and load control effectiveness deteriorate
Solution Approach 1:
The system segments the facility's resources into curtable and non-curtailable categories, allowing selective participation in DR events. By identifying specific resources that can be curtailed without impacting critical operations, the system enables program participation while protecting facility reliability, resolving the contradiction between participation effectiveness and operational protection.
Solution Approach 2:
The facility autonomously identifies and commits specific curtable resources to DR programs without requiring user opt-out decisions. This self-service approach ensures that only appropriate resources are curtailed, maintaining facility operation reliability while maximizing program participation effectiveness through automated, intelligent resource selection.
4Extent of automation
If automated decision-making is implemented for DR participation, then user intervention is minimized, but system complexity increases
Solution Approach 1:
The system implements automated decision-making that autonomously determines resource availability and makes DR participation decisions without user intervention. This self-service automation resolves the contradiction by minimizing user involvement while managing system complexity through intelligent algorithms that automatically assess facility needs and grid conditions to make appropriate participation decisions.
Data Source
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.