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Predictive Control: A Modernized Control Approach for High Performance Electrical Energy Systems (Theory and Practice)

A special issue of Energies (ISSN 1996-1073). This special issue belongs to the section "F: Electrical Engineering".

Deadline for manuscript submissions: closed (20 March 2024) | Viewed by 7585

Special Issue Editors

Dr. Mahmoud A. Mossa

E-Mail Website
Guest Editor
1. Electrical Engineering Department, Faculty of Engineering, Minia University, Minia 61111, Egypt
2. Department of Industrial Engineering, University of Padova, Via Gradenigo 6/a, 35131 Padova, Italy
Interests: control theory; model predictive control; adaptive control; elecrtric drives; power electronics; power management; integration of renewable energy sources; optimization
Prof. Dr. Pierluigi Siano

grade E-Mail Website
Guest Editor
Department of Management and Innovation Systems, University of Salerno, 84084 Salerno, Italy
Interests: smart grids; energy management; power systems; demand response
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The need for new effective control approaches as alternatives to the classic control techniques is receiving a great deal of interest from the research community. One of the most promising control approaches is predictive control (PC), which has been extensively used in industrial applications (mainly chemical industries). The orientation of the PC approach towards electrical energy systems (i.e., power systems and machine drives) has seen a dramatic increase over the last two decades. The main features which distinguish PC from the traditional techniques are the simplicity of control configuration, the ability to handle system nonlinearity without incorporating modulators or linear controllers (i.e., PI controllers), and the ability to handle several control objectives at the same time. All of these merits motivate us as researchers to deepen the knowledge of this promising approach—analyzing its operation, modifying its configuration, and testing its performance for different applications. This Special Issue will publish original manuscripts presenting recent advances in the predictive control of electrical energy systems, with a special focus on topics including but not limited to the following:

  • The application of predictive control to electrical power systems (i.e., frequency control, reliability, power quality).
  • Utilization of predictive control to manage the operation of utility-scale converters (HVDC, solid-state transformers, FACTS, etc.).
  • Utilization of predictive control in the integration process of renewable energy systems to utility grids.
  • Application of predictive control in microgrids (AC, DC and hybrid).
  • Predictive control for smart grids.
  • Predictive control in autonomous systems.
  • The application of predictive control to variable-speed electric machine drives and power electronic converters (e.g., DC/AC and multi-phase AC/AC converters).
  • Novel formulations of predictive control for rotating machine drives (three-phase and multi-phase) and linear machine drives.
  • Design of fault-tolerant predictive control algorithms for autonomous driving vehicles.
  • Designing of predictive control algorithms for the post-fault operation of electric machine drives.
  • Design and implementation challenges for predictive control: computation time, cost function formulation, weighting factor selection, parameter variation, delay compensation, variable switching frequency, etc.
  • The application of predictive control in industrial applications such as electric vehicles, more electric aircrafts, naval ships, etc.

Dr. Mahmoud A. Mossa
Prof. Dr. Pierluigi Siano
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Energies is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • power system control
  • predictive control algorithms
  • autonomous systems
  • smart grid control
  • electric drives

Published Papers (3 papers)

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Research

27 pages, 861 KiB  
Article
Operating Hydrogen-Based Energy Storage Systems in Wind Farms for Smooth Power Injection: A Penalty Fees Aware Model Predictive Control
by Valerio Mariani, Federico Zenith and Luigi Glielmo
Energies 2022, 15(17), 6307; https://doi.org/10.3390/en15176307 - 29 Aug 2022
Cited by 5 | Viewed by 1668
Abstract
Smooth power injection is one of the possible services that modern wind farms could provide in the not-so-far future, for which energy storage is required. Indeed, this is one among the three possible operations identified by the International Energy Agency (IEA)-Hydrogen Implementing Agreement [...] Read more.
Smooth power injection is one of the possible services that modern wind farms could provide in the not-so-far future, for which energy storage is required. Indeed, this is one among the three possible operations identified by the International Energy Agency (IEA)-Hydrogen Implementing Agreement (HIA) within the Task 24 final report, that may promote their integration into the main grid, in particular when paired to hydrogen-based energy storages. In general, energy storage can mitigate the inherent unpredictability of wind generation, providing that they are deployed with appropriate control algorithms. On the contrary, in the case of no storage, wind farm operations would be strongly affected, as well as their economic performances since the penalty fees wind farm owners/operators incur in case of mismatches between the contracted power and that actually delivered. This paper proposes a Model Predictive Control (MPC) algorithm that operates a Hydrogen-based Energy Storage System (HESS), consisting of one electrolyzer, one fuel cell and one tank, paired to a wind farm committed to smooth power injection into the grid. The MPC relies on Mixed-Logic Dynamic (MLD) models of the electrolyzer and the fuel cell in order to leverage their advanced features and handles appropriate cost functions in order to account for the operating costs, the potential value of hydrogen as a fuel and the penalty fee mechanism that may negatively affect the expected profits generated by the injection of smooth power. Numerical simulations are conducted by considering wind generation profiles from a real wind farm in the center-south of Italy and spot prices according to the corresponding market zone. The results show the impact of each cost term on the performances of the controller and how they can be effectively combined in order to achieve some reasonable trade-off. In particular, it is highlighted that a static choice of the corresponding weights can lead to not very effective handling of the effects given by the combination of the system conditions with the various exogenous’, while a dynamic choice may suit the purpose instead. Moreover, the simulations show that the developed models and the set-up mathematical program can be fruitfully leveraged for inferring indications on the devices’ sizing. Full article
(This article belongs to the Special Issue Predictive Control: A Modernized Control Approach for High Performance Electrical Energy Systems (Theory and Practice))
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13 pages, 2376 KiB  
Article
Decentralized Model-Predictive Control of a Coupled Wind Turbine and Diesel Engine Generator System
by Milad Shojaee, Fatemeh Mohammadi Shakiba and S. Mohsen Azizi
Energies 2022, 15(9), 3349; https://doi.org/10.3390/en15093349 - 04 May 2022
Cited by 4 | Viewed by 1358
Abstract
It is highly critical that renewable energy-based power generation units provide continuous and high-quality electricity. This requirement is even more pronounced in standalone wind–diesel systems where the wind power is not always constant or available. Moreover, it is desired that the extracted power [...] Read more.
It is highly critical that renewable energy-based power generation units provide continuous and high-quality electricity. This requirement is even more pronounced in standalone wind–diesel systems where the wind power is not always constant or available. Moreover, it is desired that the extracted power be maximized in such a way that less fuel is consumed from the diesel engine. This paper proposes a novel method to design decentralized model-predictive controllers to control the frequency and power of a single standalone generation system, which consists of a wind turbine subsystem mechanically coupled with a diesel engine generator subsystem. Two decentralized model-predictive controllers are designed to regulate the frequency and active power, while the mechanical coupling between the two subsystems is considered, and no communication links exist between the two controllers. Simulation results show that the proposed decentralized controllers outperform the benchmark decentralized linear-quadratic Gaussian (LQG) controllers in terms of eliminating the disturbances from the wind and load power changes. Furthermore, it is demonstrated that the proposed control strategy has an acceptable robust performance against the concurrent variations in all parameters of the system as compared to the LQG controllers. Full article
(This article belongs to the Special Issue Predictive Control: A Modernized Control Approach for High Performance Electrical Energy Systems (Theory and Practice))
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42 pages, 89442 KiB  
Article
Dynamic Performance Enhancement of a Renewable Energy System for Grid Connection and Stand-Alone Operation with Battery Storage
by Mahmoud A. Mossa, Olfa Gam and Nicola Bianchi
Energies 2022, 15(3), 1002; https://doi.org/10.3390/en15031002 - 29 Jan 2022
Cited by 14 | Viewed by 2012
Abstract
This paper introduces a new formulated control scheme for enhancing the dynamic performance of a wind driven surface permanent magnet synchronous generator. The designed control scheme is based on predictive control theory, in which the shortcomings of previous predictive controllers are avoided. To [...] Read more.
This paper introduces a new formulated control scheme for enhancing the dynamic performance of a wind driven surface permanent magnet synchronous generator. The designed control scheme is based on predictive control theory, in which the shortcomings of previous predictive controllers are avoided. To visualize the effectiveness of the proposed control scheme, the performance of the generator was dynamically evaluated under two different operating regimes: grid connection and standalone operation in which a battery storage system was used to enhance the power delivery to the isolated loads. In addition, a detailed performance comparison between the proposed controller and traditional predictive controllers was carried out. The traditional control topologies used for comparison were the model predictive direct power control, model predictive direct torque control, and model predictive current control. A detailed description of each control scheme is introduced illustrating how it is configured to manage the generator operation. Furthermore, to achieve the optimal exploitation of the wind energy and limit the power in case of exceeding the nominal wind speed, maximum power point tracking and blade pitch angle controls were adopted. A detailed performance comparison effectively outlined the features of each controller, confirming the superiority of the proposed control scheme over other predictive controllers. This fact is illustrated through its simple structure, low ripples, low computation burdens and low current harmonics obtained with the proposed control scheme. Full article
(This article belongs to the Special Issue Predictive Control: A Modernized Control Approach for High Performance Electrical Energy Systems (Theory and Practice))
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