Distributed fairness-guided optimization for coordinated demand response in multi-stakeholder process networks

• Optimization formulations for coordinated demand response across process networks. • Fair utility allocation with revenue sharing based on a Nash bargaining approach. • ADMM-based algorithms for privacy-preserving distributed optimization. • Computational experiments show efficacy of proposed models and algorithms. • Chlorine network case study demonstrates applicability to industrial problems. Demand response has become an essential operating paradigm for enabling high penetration of intermittent, renewable energy into the power grid. To maximize a process’s potential to perform demand response, it is important to not only consider that single process but also coordinate with the entire network of self-interested supplier and customer entities. In this work, we develop optimization formulations that enable the network-wide coordination of different processes’ operating schedules such that the benefits of coordination are shared fairly amongst stakeholders. We propose methods for solving the problems in a distributed manner that allow stakeholders to retain data privacy and avoid sharing their process models with one another. Computational studies are performed to analyze the difference in costs for different formulations and the difference in computational performance for different distributed algorithms. The applicability of the proposed approaches to problems of practical significance is further demonstrated through a chlorine network case study.