Logo
ScienceStack
Table of Contents
Fetching ...
Sign In

A Comparative Study of Distributed Feedback Optimizing Control Architectures

Risvan Dirza, Hari Prasad Varadarajan, Vegard Aas, Sigurd Skogestad, Dinesh Krishnamoorthy

TL;DR

This article provides a comparative study of different distributed feedback-optimizing control architectures using two case studies and compares the different feedback-optimizing control approaches using simulations.

Abstract

This paper 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 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, and estimate parameters locally, as well as provide local fast response, allowing different closed-loop time constants for each local subsystem. This paper 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 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 lab-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. %The pros and cons of the different approaches are discussed.

A Comparative Study of Distributed Feedback Optimizing Control Architectures

TL;DR

This article provides a comparative study of different distributed feedback-optimizing control architectures using two case studies and compares the different feedback-optimizing control approaches using simulations.

Abstract

This paper 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 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, and estimate parameters locally, as well as provide local fast response, allowing different closed-loop time constants for each local subsystem. This paper 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 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 lab-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. %The pros and cons of the different approaches are discussed.

Paper Structure

This paper contains 17 sections, 19 equations, 14 figures, 4 tables.

Table of Contents

  1. Introduction
  2. Problem Formulation for Distributed Feedback-optimizing Control
  3. Control structure architecture using price-based coordination
  4. Control structure architecture using price-based coordination with constraint override
  5. Control structure architecture using opportunity cost based coordination
  6. Case Study 1: Demand response in a district-level energy hub
  7. Case Study II - Optimal resource allocation in a subsea production network
  8. Problem setup
  9. Experimental Results
  10. Discussion
  11. Controller placement and information exchange
  12. Timescale separation
  13. Pairing
  14. Coupling constraints and local constraints
  15. Gradient estimation
  16. ...and 2 more sections

Figures (14)

  • Figure 1: Schematic representation of the distributed feedback optimizing control architecture based on dual decomposition (price-based coordination). Gray box indicates the information boundary of each subsystem.
  • Figure 2: Schematic representation of the distributed feedback optimizing control architecture based on dual decomposition (price-based coordination) with active constraint control override in the fast timescale. Gray box indicates the information boundary of each subsystem.
  • Figure 3: Schematic representation of the distributed feedback optimizing control architecture based on primal decomposition (opportunity cost based coordination). Grey box indicates the information boundary of each subsystem.
  • Figure 4: Schematic representation of a district-level micro energy hub powered by a common solar energy source coupled with battery storage
  • Figure 5: RC-thermal model of a single house.
  • ...and 9 more figures

Theorems & Definitions (7)

  • Remark 1
  • Remark 2
  • Remark 3
  • Remark 4
  • Remark 5
  • Remark 6
  • Remark 7