Distributed model predictive control for energy distribution

Scherer, Helton Fernando; Pasamontes, M.; Álvarez, José Domingo; Guzmán, José Luís; Camponogara, Eduardo; Normey‐Rico, Julio E.

doi:10.23919/ecc.2013.6669323

Cited by 3 publications

(2 citation statements)

References 18 publications

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“…Further, r 1, m >0 and

r_{i, m} \geq 0

for all

i \in {scriptR}^{-} = scriptR ∖ {1}

and

m \in scriptℳ

, where the former condition will allow us to establish a one‐to‐one relationship between control increments and slack variables for the constraint of resource 1. This MPC problem was introduced in in an application of energy distribution for building comfort.…”

Section: Resource‐constrained Control Systemmentioning

confidence: 99%

“…Unlike the preceding work , the couplings among the subsystems become pairwise in the space of the slack variables, rather than having all subsystems coupled through the original resource constraint (). This pairwise structure emerging from the reformulation will lead to an effective problem decomposition for the application of distributed MPC.…”

Section: Resource‐constrained Control Systemmentioning

confidence: 99%

See 1 more Smart Citation

Distributed MPC for resource‐constrained control systems

Scherer

Camponogara

Normey‐Rico

et al. 2014

Optim Control Appl Methods

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This article presents a distributed model predictive control methodology to manage energy resources for a set of consumer subsystems. The objective of the controller is to optimally distribute the allowable energy to the subsystems. The proposed methodology yields a distributed solution that converges to the optimum that would be obtained by a centralized controller. This optimal performance is achieved by expressing the problem in terms of slack variables and the global coupling constraint as a set of local subsystem constraints, thereby favoring the application of distributed model predictive control. Hardware-in-the-loop experiments with an air-conditioning thermal solar plant are performed to show the good performance of the proposed distributed controller.This section presents the MPC formulation for resource-constrained systems, followed by a compact formulation and its reformulation in terms of slack variables that will be handy to develop a DMPC solution. Model predictive control formulationMPC encompasses the large class of control algorithms that make explicit use of a process model to obtain the control signal by minimizing an objective function over a given horizon [1]. Moreover, MPC strategies use a receding strategy whereby the horizon is displaced toward the future at each instant, but only the first control signal of the sequence calculated at each step is applied to the plant.

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“…Further, r 1, m >0 and