Optimal Resource Allocation using Distributed Feedback-based Real-time Optimization

Abstract

Abstract This paper considers the problem of steady-state optimal resource allocation in an industrial symbiosis, where different companies share common resources. Such optimal resource allocation problems are commonly studied in the context of distributed optimization to limit information sharing. One such framework is the Lagrangian decomposition approach, where the different subproblems are locally optimized for a given shadow price of the shared resource, which is updated by a master coordinator. In the traditional distributed RTO approach, this involves solving numerical optimization problems online for each subproblem, which can be computationally intensive. In order to avoid the need for solving numerical optimization problems, this paper proposes a distributed feedback-based real-time optimization framework, where each subproblem is locally optimized for a given shadow price using feedback controllers. The proposed feedback-based distributed RTO scheme is applied to an industrial symbiotic subsea oil production system, where the different wells are operated by different companies. The simulation results show that the proposed feedback-based distributed RTO scheme can optimally allocate the shared resources.