Demand-Response User Pooling for Faster Collective Grid Responsivity
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Solution Overview
Problem
Conventional demand-response programs face challenges in achieving optimal responsivity due to individual users' inability to timely reduce energy consumption during peak hours, leading to decreased compliance and instability in the energy supply grid.
Innovation Solution
A machine learning-based demand-response user pooling model identifies and groups users with homogeneous offtake obligations, allowing them to respond collectively to demands, thereby improving responsivity and energy conservation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If individual users respond independently to demand-response requests, then each user maintains operational independence, but the overall responsivity and compliance of the demand-response program deteriorates
Solution Approach 1:
The patent combines multiple individual users into a unified demand-response pool where users collectively respond to demand-response requests. This merging approach maintains individual user operational independence while achieving improved overall responsivity through coordinated collective action, directly resolving the contradiction between user independence and program effectiveness
2Quantity of substance
If more users are included in the demand-response program, then the total energy reduction capacity increases, but the complexity of coordinating and managing user responses increases
Solution Approach 1:
The patent introduces an automated coordination system that acts as an intermediary between the utility provider and individual users in the demand-response pool. This intermediary automatically manages user selection, response coordination, and performance tracking, enabling the system to handle large numbers of users without proportionally increasing management complexity
3Measurement precision
If users are selected based on detailed analysis of their energy consumption patterns, then the precision of matching users to appropriate demand-response requests improves, but the computational resources and time required for user selection increases
Solution Approach 1:
The patent performs preliminary analysis and pre-grouping of users based on their energy consumption patterns, load profiles, and responsiveness characteristics before actual demand-response events occur. This advance preparation creates pre-configured user pools that can be quickly deployed during peak demand periods, achieving high matching precision without time-consuming real-time analysis
Data Source
AI summary
Methods, computer program products, and systems are presented. The methods include, for instance: extracting from historical data and demand response agreements, attributes relevant to responsivities of demand response programs; and training a demand-response (DR) user pooling model as a machine learning model with training datasets including the attributes from the extracting,