Aggregate Thermal Model for Building Flexibility and Privacy
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Solution Overview
Problem
Current methods for utilizing building flexibility in power systems face challenges due to the large number of buildings, leading to computational and communication burdens, and they lack effective privacy preservation for user data.
Innovation Solution
A privacy-preserved method for constructing an aggregate thermal dynamic model of buildings, which involves establishing a thermal dynamic model of one building region, aggregating it, performing parameter estimation using the least square method with a regular term, and employing a transformation-based encryption method and secure aggregation protocol (SAP) for privacy preservation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If direct information interaction between the energy system and numerous building users is implemented, then the flexibility resources of buildings can be utilized for power system operation, but the computational and communication burden increases significantly
Solution Approach 1:
The patent aggregates individual building thermal dynamic models into a single aggregate model that represents multiple buildings collectively. This merging approach allows the energy system to interact with buildings as a unified entity rather than individually, significantly reducing computational and communication complexity while preserving the ability to utilize building flexibility resources for power system operation.
Solution Approach 2:
The patent segments the parameter estimation process into two distinct phases: offline parameter estimation using historical data to build the aggregate model, and online parameter estimation using real-time data for control. This segmentation allows complex computations to be performed offline when computational resources are abundant, while online operations require minimal computation, thus resolving the contradiction between flexibility utilization and computational burden.
2Adaptability or versatility
If current methods for mining building flexibility are used, then building flexibility can be utilized, but user privacy information is at risk of leakage
Solution Approach 1:
The patent introduces an aggregate thermal dynamic model as an intermediary between individual building data and the energy system. This intermediary processes and aggregates individual building information, allowing flexibility mining while preventing direct exposure of user privacy data. The aggregate model serves as a mediator that extracts useful flexibility characteristics without revealing sensitive individual building information.
Solution Approach 2:
The patent creates an aggregate model that copies and synthesizes the essential thermal dynamic characteristics of multiple individual buildings without copying actual user data. This synthetic aggregate model preserves the flexibility properties needed for power system operation while containing no sensitive personal information, thus enabling flexibility mining without privacy leakage.
3Ease of manufacture
If parameter estimation is performed without regularization, then the model can be constructed, but sparsity problems arise in the aggregation coefficients
Solution Approach 1:
The patent changes the parameter estimation approach by introducing regularization terms (L1 and L2 norms) to the objective function. This parameter modification transforms the estimation problem to produce sparse and stable aggregation coefficients, improving both the interpretability and accuracy of the model while maintaining ease of construction through standard optimization techniques.
Data Source
AI summary
Disclosed is a privacy-preserved method for constructing an aggregate thermal dynamic model of buildings, including the steps of: establishing a thermal dynamic model of one building region, and establishing an aggregate thermal dynamic model of buildings based on an aggregation equation; performing parameter estimation using a least square method based on a measurement equation, introducing a regular term to solve a sparsity problem, and obtaining a parameter estimation model in a compact form for the aggregate thermal dynamic model of buildings; and establishing a privacy-preserved parameter estimation method for the aggregate thermal dynamic model of buildings. Based on the technique, the aggregate modeling is performed on numerous buildings by a building load aggregator to participate in the operation and control of an energy system while preserving privacy of building users, promoting the mining of thermal inertia of buildings and enhancing the flexibility of operation and regulation of a power system.