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24 pages, 1304 KiB

Open AccessArticle

Distributed Control of an Ill-Conditioned Non-Linear Process Using Control Relevant Excitation Signals

by Yusuf Abubakar Sha’aban

Processes 2023, 11(12), 3320; https://doi.org/10.3390/pr11123320 - 29 Nov 2023

Cited by 2 | Viewed by 1315

Efficient control schemes for ill-conditioned systems, such as the high-purity distillation column, can be challenging and costly to design and implement. In this paper, we propose a distributed control scheme that utilizes well-designed excitation signals to identify the system. Unlike traditional systems, we found that a summation of correlated and uncorrelated signals can yield better excitation of the plant. Our proposed distributed model predictive control (MPC) scheme uses a shifted input sequence to address loop interactions and reduce the computational load. This approach deviates from traditional schemes that use iteration, which can increase complexity and computational load. We initially tested the proposed method on the linear model of a highly coupled 2 × 2 process and compared its performance with decentralized proportional-integral-derivative (PID) controllers and centralized MPC. Our results show improved performance over PID controllers and similar results to centralized MPC. Furthermore, we compared the performance of the proposed approach with a centralized MPC on a nonlinear model of a distillation column. The results for the second study also demonstrated comparable performance between the two controllers with the decentralised control slightly outperforming the centralised MPC in some cases. These findings are promising and may be of interest to practitioners that are more comfortable with tuning decentralised loops. Full article

(This article belongs to the Special Issue Advances in the Control of Complex Dynamic Systems)

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14 pages, 4632 KiB

Open AccessArticle

Generic Framework for the Optimal Implementation of Flexibility Mechanisms in Large-Scale Smart Grids

by Alejandro J. del Real, Andrés Pastor and Jaime Durán

Energies 2021, 14(23), 8063; https://doi.org/10.3390/en14238063 - 2 Dec 2021

Cited by 1 | Viewed by 1751

Abstract

This paper aims to provide the smart grid research community with an open and accessible general mathematical framework to develop and implement optimal flexibility mechanisms in large-scale network applications. The motivation of this paper is twofold. On the one hand, flexibility mechanisms are currently a hot topic of research, which is aimed to mitigate variation and uncertainty of electricity demand and supply in decentralised grids with a high aggregated share of renewables. On the other hand, a large part of such related research is performed by heuristic methods, which are generally inefficient (such methods do not guarantee optimality) and difficult to extrapolate for different use cases. Alternatively, this paper presents an MPC-based (model predictive control) framework explicitly including a generic flexibility mechanism, which is easy to particularise to specific strategies such as demand response, flexible production and energy efficiency services. The proposed framework is benchmarked with other non-optimal control configurations to better show the advantages it provides. The work of this paper is completed by the implementation of a generic use case, which aims to further clarify the use of the framework and, thus, to ease its adoption by other researchers in their specific flexibility mechanism applications. Full article

(This article belongs to the Topic Advances in Energy Market and Power System Modelling and Optimization)

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