2,332 research outputs found
A hierarchical MPC scheme for interconnected systems
This paper describes a hierarchical control scheme for interconnected
systems. The higher layer of the control structure is designed with robust
Model Predictive Control (MPC) based on a reduced order dynamic model of the
overall system and is aimed at optimizing long-term performance, while at the
lower layer local regulators acting at a higher frequency are designed for the
full order models of the subsystems to refine the control action. A simulation
experiment concerning the control of the temperature inside a building is
reported to witness the potentialities of the proposed approach
Two-Stage Consensus-Based Distributed MPC for Interconnected Microgrids
In this paper, we propose a model predictive control based two-stage energy
management system that aims at increasing the renewable infeed in
interconnected microgrids (MGs). In particular, the proposed approach ensures
that each MG in the network benefits from power exchange. In the first stage,
the optimal islanded operational cost of each MG is obtained. In the second
stage, the power exchange is determined such that the operational cost of each
MG is below the optimal islanded cost from the first stage. In this stage, a
distributed augmented Lagrangian method is used to solve the optimisation
problem and determine the power flow of the network without requiring a central
entity. This algorithm has faster convergence and same information exchange at
each iteration as the dual decomposition algorithm. The properties of the
algorithm are illustrated in a numerical case study
A distributed accelerated gradient algorithm for distributed model predictive control of a hydro power valley
A distributed model predictive control (DMPC) approach based on distributed
optimization is applied to the power reference tracking problem of a hydro
power valley (HPV) system. The applied optimization algorithm is based on
accelerated gradient methods and achieves a convergence rate of O(1/k^2), where
k is the iteration number. Major challenges in the control of the HPV include a
nonlinear and large-scale model, nonsmoothness in the power-production
functions, and a globally coupled cost function that prevents distributed
schemes to be applied directly. We propose a linearization and approximation
approach that accommodates the proposed the DMPC framework and provides very
similar performance compared to a centralized solution in simulations. The
provided numerical studies also suggest that for the sparsely interconnected
system at hand, the distributed algorithm we propose is faster than a
centralized state-of-the-art solver such as CPLEX
Decentralized energy management of power networks with distributed generation using periodical self-sufficient repartitioning approach
© 2019 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.In this paper, we propose a decentralized model predictive control (MPC) method as the energy management strategy for a large-scale electrical power network with distributed generation and storage units. The main idea of the method is to periodically repartition the electrical power network into a group of self-sufficient interconnected microgrids. In this regard, a distributed graph-based partitioning algorithm is proposed. Having a group of self-sufficient microgrids allows the decomposition of the centralized dynamic economic dispatch problem into local economic dispatch problems for the microgrids. In the overall scheme, each microgrid must cooperate with its neighbors to perform repartitioning periodically and solve a decentralized MPC-based optimization problem at each time instant. In comparison to the approaches based on distributed optimization, the proposed scheme requires less intensive communication since the microgrids do not need to communicate at each time instant, at the cost of suboptimality of the solutions. The performance of the proposed scheme is shown by means of numerical simulations with a well-known benchmark case. © 2019 American Automatic Control Council.Peer ReviewedPostprint (author's final draft
Modeling and supervisory control design for a combined cycle power plant
The traditional control strategy based on PID controllers may be unsatisfactory when dealing with processes with large time delay and constraints. This paper presents a supervisory model based constrained predictive controller (MPC) for a combined cycle power plant (CCPP). First, a non-linear dynamic model of CCPP using the laws of physics was proposed. Then, the supervisory control using the linear constrained MPC method was designed to tune the performance of the PID controllers by including output constraints and manipulating the set points. This scheme showed excellent tracking and disturbance rejection results and improved performance compared with a stand-alone PID controller’s scheme
Rate analysis of inexact dual first order methods: Application to distributed MPC for network systems
In this paper we propose and analyze two dual methods based on inexact
gradient information and averaging that generate approximate primal solutions
for smooth convex optimization problems. The complicating constraints are moved
into the cost using the Lagrange multipliers. The dual problem is solved by
inexact first order methods based on approximate gradients and we prove
sublinear rate of convergence for these methods. In particular, we provide, for
the first time, estimates on the primal feasibility violation and primal and
dual suboptimality of the generated approximate primal and dual solutions.
Moreover, we solve approximately the inner problems with a parallel coordinate
descent algorithm and we show that it has linear convergence rate. In our
analysis we rely on the Lipschitz property of the dual function and inexact
dual gradients. Further, we apply these methods to distributed model predictive
control for network systems. By tightening the complicating constraints we are
also able to ensure the primal feasibility of the approximate solutions
generated by the proposed algorithms. We obtain a distributed control strategy
that has the following features: state and input constraints are satisfied,
stability of the plant is guaranteed, whilst the number of iterations for the
suboptimal solution can be precisely determined.Comment: 26 pages, 2 figure
Resilient Distributed Energy Management for Systems of Interconnected Microgrids
In this paper, distributed energy management of interconnected microgrids,
which is stated as a dynamic economic dispatch problem, is studied. Since the
distributed approach requires cooperation of all local controllers, when some
of them do not comply with the distributed algorithm that is applied to the
system, the performance of the system might be compromised. Specifically, it is
considered that adversarial agents (microgrids with their controllers) might
implement control inputs that are different than the ones obtained from the
distributed algorithm. By performing such behavior, these agents might have
better performance at the expense of deteriorating the performance of the
regular agents. This paper proposes a methodology to deal with this type of
adversarial agents such that we can still guarantee that the regular agents can
still obtain feasible, though suboptimal, control inputs in the presence of
adversarial behaviors. The methodology consists of two steps: (i) the
robustification of the underlying optimization problem and (ii) the
identification of adversarial agents, which uses hypothesis testing with
Bayesian inference and requires to solve a local mixed-integer optimization
problem. Furthermore, the proposed methodology also prevents the regular agents
to be affected by the adversaries once the adversarial agents are identified.
In addition, we also provide a sub-optimality certificate of the proposed
methodology.Comment: 8 pages, Conference on Decision and Control (CDC) 201
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