使用安全聚合的多参与者工业网络隐私保护协调运行
Privacy-Preserving Coordinated Operation of Multi-Player Industrial Network Using Secure Aggregation
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中文总结 AI 辅助
针对多参与者工业网络协调需求响应中的隐私泄露问题，提出融合安全聚合与ADMM的分布式协调框架，并引入两阶段收益分享机制，在31天模拟中总成本较分散运行降低19.77%，接近集中式最优。
中文摘要 AI 辅助
具有运行灵活性的电气化化工行业可以通过响应时变电价调整生产和分配决策来降低运营成本。然而，化工厂在过程网络中运行，协调的需求响应可以利用多个利益相关者之间的灵活性。集中式协调需要访问利益相关者的本地调度模型和专有运营数据，这通常与数据隐私要求不兼容。使用独立中央协调器（ICC）的分布式优化避免了直接模型共享，但耦合变量的迭代交换仍可能泄露私有模型参数。我们提出了一种用于工业网络中协调需求响应的隐私保护分布式协调框架。该框架将安全聚合与基于ICC的交替方向乘子法（ADMM）算法相结合，使得工厂级消息在数值上被掩蔽，并且仅在聚合后才对ICC有用。我们在一个多工厂工业气体网络上测试了该框架，其中三个空气分离单元联合调度生产和向共享客户区域的运输。为了支持稳定参与，我们纳入了一个两阶段收益分享机制，该机制重新分配节省的成本，使每个工厂相对于其分散的现状都有所改善。在为期31天的滚动时域模拟中，使用代表异构电价和需求的合成数据，协调策略相对于分散运行将总网络成本降低了19.77%，并实现了与集中式社会福利最大化基准相比在3.08%以内的全月成本。我们进一步量化了一种保守的最坏情况共谋模式，展示了未掩蔽的迭代和辅助信息如何暴露私有目标参数。
英文摘要
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.
发表机构
- Davidson School of Chemical Engineering, Purdue University（普渡大学戴维森化工学院）
- Air Liquide Innovation Campus, Air Liquide（液化空气创新园区，液化空气）
- Digital & AI, Air Liquide（液化空气数字与人工智能部门）
机构由 AI 辅助整理，请以论文原文为准。
