Integrating Building Thermal Flexibility Into Distribution System: A Privacy-Preserved Dispatch Approach

TL;DR

This paper presents a privacy-preserving dispatch method that enables the integration of building thermal flexibility into power distribution systems without compromising user privacy, enhancing system efficiency and reliability.

Contribution

It introduces a novel optimal dispatch model and a privacy-preserved algorithm based on transformation encryption, addressing privacy concerns in building-system integration.

Findings

The method accurately exploits building flexibility without revealing private data.

The approach maintains privacy against semi-honest adversaries and eavesdroppers.

Case studies confirm the method's efficiency and privacy-preserving capabilities.

Abstract

The inherent thermal storage capacity of buildings brings considerable thermal flexibility to the heating/cooling loads, which are promising demand response resources for power systems. It is widely believed that integrating the thermal flexibility of buildings into the distribution system can improve the operating economy and reliability of the system. However, the private information of the buildings needs to be transferred to the distribution system operator (DSO) to achieve a coordinated optimization, bringing serious privacy concerns to users. Given this issue, we propose a novel privacy-preserved optimal dispatch approach for the distribution system incorporating buildings. Using it, the DSO can exploit the thermal flexibility of buildings without accessing their private information, such as model parameters and indoor temperature profiles. Specifically, we first develop an optimal dispatch model for the distribution system integrating buildings, which can be extended to other storage-like flexibility resources. Second, we reveal that the privacy-preserved integration of buildings is a joint privacy preservation problem for both parameters and state variables and then design a privacy-preserved algorithm based on transformation-based encryption, constraint relaxation, and constraint extension techniques. Besides, we implement a detailed privacy analysis for the proposed method, considering both semi-honest adversaries and external eavesdroppers. Case studies demonstrate the accuracy, privacy-preserved performance, and computational efficiency of the proposed method.