Privacy-Preserving Peak Load Management via Group Data Aggregation
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Solution Overview
Problem
Current peak load management in electricity systems requires collecting detailed load profiles, compromising privacy and security, and is computationally expensive, even with state-of-art optimization solvers.
Innovation Solution
A privacy-preserving method that aggregates load data from groups using a group agent, selectively provides aggregated information to system operators, and employs a Lagrange multiplier-based algorithm to manage peak loads efficiently, ensuring privacy and achieving near-optimal or optimal solutions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If detailed load profiles are collected from individuals for peak load management, then peak load management effectiveness is improved, but privacy and security are compromised
Solution Approach 1:
The patent merges individual load profiles at the group level, where only aggregated group load data is collected and processed. This combining approach maintains peak load management effectiveness while protecting individual privacy and security by eliminating the need to handle sensitive individual-level information.
Solution Approach 2:
The patent introduces group-level aggregation as an intermediary layer between individual consumers and the system operator. This intermediary mechanism processes and anonymizes data before transmission, allowing effective peak load management without direct exposure of individual privacy-sensitive information.
2Manufacturing precision
If detailed load profiles are collected for optimal peak load management, then solution optimality is improved, but computational cost increases
Solution Approach 1:
The patent extracts and processes only the essential aggregated load data at the group level, removing unnecessary individual-level computational processing. This extraction approach maintains solution optimality for peak load management while significantly reducing computational energy requirements by focusing only on necessary aggregated information.
Data Source
AI summary
The present exemplary embodiments relate to a privacy preserving approach to peak load management. It finds example application in conjunction with, for example, power systems, such as smart homes, smart buildings, microgrids, distribution systems and bulk systems, and energy management systems.