Industrial plants coordinate operation while keeping data private
Privacy-Preserving Coordinated Operation of Multi-Player Industrial Network Using Secure Aggregation
Computational Engineering, Finance, and Science
Summary
Managing production in chemical plants together can save money by adjusting to changing electricity prices. But sharing detailed plans between companies raises privacy concerns. The authors developed a method that lets these plants work together using secure computing tricks so no one has to reveal their secret data. Their experiments showed this approach can save nearly 20% in costs while keeping every plant better off than if they worked alone.
What this means in practice
- •For industrial plant operators: Coordinate production schedules across multiple chemical plants to reduce electricity costs without sharing sensitive operational data.
- •For energy management teams: Implement privacy-preserving demand response programs that allow multiple industrial sites to jointly optimize energy usage securely.
- •For smart grid solution providers: Develop secure coordination services that enable industrial customers to share aggregated operational data while protecting proprietary information.$Commercial implications: Enables creation of privacy-focused grid optimization products tailored to industrial clients who must protect sensitive data.
Authors
Akshdeep Singh Ahluwalia, Zachary Wilson, Jeffrey E. Arbogast, Can Li
Abstract
Electrified chemical industries with operational flexibility can reduce operating costs by shifting production and distribution decisions in response to time-varying electricity prices. However, chemical plants operate within process networks where coordinated demand response can exploit flexibility across multiple stakeholders. Centralized coordination requires access to stakeholders' local scheduling models and proprietary operational data, often incompatible with data-privacy requirements. Distributed optimization with an independent central coordinator (ICC) avoids direct model sharing, but iterative exchange of coupling variables can still reveal private model parameters. We propose a privacy-preserving distributed coordination framework for coordinated demand response in industrial networks. The framework integrates secure aggregation with an ICC-based alternating direction method of multipliers (ADMM) algorithm, so plant-level messages are numerically masked and become useful to the ICC only after aggregation. We test the framework on a multi-plant industrial gas network in which three air-separation units jointly schedule production and shipments to shared customer regions. To support stable participation, we incorporate a two-phase revenue-sharing mechanism that reallocates savings so every plant improves relative to its decentralized status quo. In a 31-day rolling-horizon simulation with synthetic data representing heterogeneous electricity prices and demand, the coordinated policy reduces total network cost by 19.77% relative to decentralized operation and achieves a full-month cost within 3.08% of a centralized social-welfare-maximization benchmark. We further quantify a conservative worst-case collusion mode, showing how unmasked iterates and auxiliary information can expose private objective parameters.