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Inventions
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Advanced Nonlinear Control and Optimization for Renewable Energy Systems, Smart...
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Advanced Nonlinear Control and Optimization for Renewable Energy Systems, Smart Grids and Electric Vehicles

A special issue of Inventions (ISSN 2411-5134). This special issue belongs to the section "Inventions and Innovation in Electrical Engineering/Energy/Communications".

Deadline for manuscript submissions: 30 August 2026 | Viewed by 1559

Special Issue Editor

Dr. Aziz Watil

E-Mail Website
Guest Editor
LERMA Laboratory, School of Aerospace and Automotive Engineering, International University of Rabat, Rabat 11100, Morocco
Interests: nonlinear control; energy management and optimization; renewable energy integration; electric and hybrid vehicle technologies

Special Issue Information

Dear Colleagues,

The integration of renewable energy systems and electric vehicles (EVs) into modern smart grids presents both tremendous opportunities and complex control challenges. Achieving high efficiency, stability, and reliability in such interconnected systems requires advanced control strategies that can handle nonlinearities, uncertainties, and dynamic interactions between components.

This Special Issue invites the submission of original research and review articles focused on the innovation and application of nonlinear control and optimization techniques in renewable energy systems, smart grids, and electric vehicle applications. Topics of interest include, but are not limited to, the following:

  • Nonlinear, adaptive, and robust control methods for power systems;
  • Control and optimization of EV integration in smart grids;
  • Advanced energy management strategies for renewable systems;
  • Intelligent control of power converters and electric drives;
  • Optimization techniques for system performance, stability, and efficiency;
  • Applications of model predictive control, sliding mode control, fuzzy logic, artificial neural networks (ANNs), Q-learning, and other advanced nonlinear and data-driven control methods;
  • Online and offline optimization techniques for system performance, stability, and efficiency;
  • Real-world implementation in renewable energy and smart grid environments.

We welcome contributions that advance both theoretical developments and practical applications, aiming to enhance the performance, reliability, and sustainability of future energy systems.

Dr. Aziz Watil
Guest Editor

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 250 words) can be sent to the Editorial Office for assessment.

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. Inventions 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 1800 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

  • nonlinear control
  • model predictive control
  • sliding mode control
  • backstepping
  • fuzzy logic
  • artificial neural networks (ANNs)
  • Q-learning
  • renewable energy systems
  • power management algorithms
  • model predictive control
  • renewable energy systems
  • smart grids
  • electric vehicles

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Further information on MDPI's Special Issue policies can be found here.

Published Papers (3 papers)

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Research

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23 pages, 1732 KB  
Article
Adaptive Nonlinear Control and State Estimation for Energy Management in Standalone Photovoltaic–Battery Systems
by Nabil Elaadouli, Ilyass ElMyasse, Abdelmounime ElMagri, Rachid Lajouad, Mishari Metab Almalki and Mahmoud A. Mossa
Inventions 2026, 11(3), 49; https://doi.org/10.3390/inventions11030049 - 18 May 2026
Viewed by 80
Abstract
This paper presents an adaptive nonlinear control and state observation framework for energy management in standalone photovoltaic (PV) systems integrated with battery energy storage. A unified nonlinear dynamic model is developed to describe the interactions between the PV generator, the DC/DC buck converter, [...] Read more.
This paper presents an adaptive nonlinear control and state observation framework for energy management in standalone photovoltaic (PV) systems integrated with battery energy storage. A unified nonlinear dynamic model is developed to describe the interactions between the PV generator, the DC/DC buck converter, and the lithium-ion battery. Based on this model, a multi-mode control strategy is designed to ensure efficient and safe operation under varying environmental and loading conditions. The proposed scheme incorporates maximum power point tracking (MPPT) to maximize photovoltaic energy extraction, along with constant current (CC) and constant voltage (CV) charging modes to guarantee battery safety and longevity. To address uncertainties and unmeasured states, an adaptive nonlinear observer is developed for real-time estimation of the battery open-circuit voltage and state of charge. The observer design is supported by Lyapunov-based stability analysis, ensuring boundedness and convergence of the estimation error in the presence of modeling uncertainties and external disturbances. An energy management algorithm is further introduced to coordinate the transition between operating modes according to the estimated system states and battery constraints. The effectiveness and robustness of the proposed control and observation strategy are validated through detailed simulations in MATLAB/Simulink under varying solar irradiance conditions. The results demonstrate accurate maximum power tracking, reliable state estimation, and safe battery charging performance, highlighting the potential of the proposed approach for advanced autonomous PV–battery systems. Full article
(This article belongs to the Special Issue Advanced Nonlinear Control and Optimization for Renewable Energy Systems, Smart Grids and Electric Vehicles)
33 pages, 4991 KB  
Article
Temperature–Power Adaptive Control Strategy for Multi-Electrolyzer Systems
by Yuxin Xu and Yan Dong
Inventions 2026, 11(2), 41; https://doi.org/10.3390/inventions11020041 - 21 Apr 2026
Viewed by 261
Abstract
Driven by renewable energy, the operating temperatures of alkaline water electrolyzers (AWEs) exhibit significant dynamic variations. Conventional control strategies rely on fixed startup parameters, causing dispatch plans to deviate from actual physical states, which leads to transient over-temperature or startup failures. To address [...] Read more.
Driven by renewable energy, the operating temperatures of alkaline water electrolyzers (AWEs) exhibit significant dynamic variations. Conventional control strategies rely on fixed startup parameters, causing dispatch plans to deviate from actual physical states, which leads to transient over-temperature or startup failures. To address this issue, this paper proposes a dual-layer optimization strategy for multi-electrolyzer systems based on temperature–power adaptation. First, a thermo-electro-hydrogen coupling model is established to quantitatively reveal the dynamic relationship among the initial temperature, startup power, and transition time. This relationship is utilized to construct a dynamic startup boundary, overcoming the limitations of traditional static constraints. Within the proposed framework, the upper layer utilizes a Mixed-Integer Linear Programming (MILP) model to formulate state-switching and baseline power allocation plans derived from short-term forecasts. Concurrently, the lower layer employs the Mongoose Optimization Algorithm (MOA) for real-time rolling optimization, enabling the system to actively perceive temperature variations and adaptively schedule power allocation. Simulations across typical seasonal scenarios validate the strategy’s superiority. In a typical spring scenario, compared to the traditional Daisy Chain and Rotation Control strategies, as well as the Equal Allocation strategy, the proposed approach reduces total startup time and energy consumption by 59.2% and 54.6%, respectively. Furthermore, it increases wind power accommodation rates by 17.7% and 14.2%, and total hydrogen production by 20.0% and 14.9%, respectively. These superior renewable energy utilization and production efficiencies are robustly maintained across typical seasonal scenarios. By actively perceiving actual temperatures for adaptive scheduling, the proposed strategy ultimately ensures synergy and reliability between the control strategy and actual operational constraints under fluctuating conditions. Full article
(This article belongs to the Special Issue Advanced Nonlinear Control and Optimization for Renewable Energy Systems, Smart Grids and Electric Vehicles)
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Review

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33 pages, 2201 KB  
Review
Machine Learning Models for Non-Intrusive Load Monitoring: A Systematic Review and Meta-Analysis
by Herman Cristiano Jaime, Adler Diniz de Souza, Raphael Carlos Santos Machado and Otávio de Souza Martins Gomes
Inventions 2026, 11(2), 29; https://doi.org/10.3390/inventions11020029 - 19 Mar 2026
Cited by 1 | Viewed by 763
Abstract
Non-Intrusive Load Monitoring (NILM) systems are increasingly applied in residential and commercial environments to disaggregate energy consumption without requiring additional hardware sensors. The integration of Machine Learning (ML) techniques has enhanced the accuracy and efficiency of load identification and classification in smart meter-based [...] Read more.
Non-Intrusive Load Monitoring (NILM) systems are increasingly applied in residential and commercial environments to disaggregate energy consumption without requiring additional hardware sensors. The integration of Machine Learning (ML) techniques has enhanced the accuracy and efficiency of load identification and classification in smart meter-based systems. This study presents a systematic review and meta-analysis aimed at identifying, classifying, and quantitatively evaluating ML models applied to NILM. Searches were conducted in the IEEE Xplore and Scopus databases, restricted to peer-reviewed publications from 2017 to 2024. Thirty studies met the eligibility criteria and were included in the quantitative synthesis using a random-effects meta-analysis model (DerSimonian–Laird estimator). The primary effect measure was the F1-score. Statistical analyses were performed using R (version 4.5.0) and Python (version 3.10.0), including heterogeneity assessment and subgroup analyses according to model type. Hybrid models, such as SVDT-KNN-MLP, LE-CRNN, and RBFNN-MOGA, achieved the highest pooled F1-scores, although supported by a limited number of studies. Traditional approaches, including CNN, KNN, and Random Forest, demonstrated consistently strong performance and broader validation, whereas Boosted Trees and RNN-based models showed lower or more variable results. Substantial heterogeneity was observed across studies, highlighting the need for dataset standardization, reproducible evaluation frameworks, and further validation of emerging hybrid architectures in diverse operational scenarios. This study contributes by providing a quantitative synthesis of machine learning models applied to NILM using a structured PRISMA-based methodology and subgroup analysis by model architecture. Unlike previous narrative reviews, this work integrates scientometric analysis with meta-analytic performance aggregation, offering a consolidated and comparative evidence base for future NILM research. Full article
(This article belongs to the Special Issue Advanced Nonlinear Control and Optimization for Renewable Energy Systems, Smart Grids and Electric Vehicles)
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