A Comparative Study of Distributed Feedback-Optimizing Control Architectures

Abstract

This article considers the problem of steady-state real-time optimization (RTO) of interconnected systems with a common constraint that couples several units, for example, a shared resource. Such problems are often studied under the context of distributed optimization, where decisions are made locally in each subsystem and are coordinated to optimize the overall performance. Here, we use a distributed feedback-optimizing control framework, where the local systems and the coordinator problems are converted into feedback control problems. This is a powerful scheme that allows us to design feedback control loops, estimate parameters locally, and provide a local fast response, allowing different closed-loop time constants for each local subsystem. This article provides a comparative study of different distributed feedback-optimizing control architectures using two case studies. The first case study considers the problem of demand response (DR) in a residential energy hub powered by a common renewable energy source and compares the different feedback-optimizing control approaches using simulations. The second case study experimentally validates and compares the different approaches using a laboratory-scale experimental rig that emulates a subsea oil production network, where the common resource is the gas lift that must be optimally allocated among the wells.