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Algorithmic Approaches and Control Techniques for Energy Optimization in Power Converters and Grid Transmission

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

Deadline for manuscript submissions: 30 September 2026 | Viewed by 7031

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Special Issue Editor

Prof. Dr. Paolo Mercorelli

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Institute for Production Technology and Systems (IPTS), Leuphana Universität Lüneburg, 21335 Lüneburg, Germany
Interests: control systems; mechatronics; actuators; engine control; signal processing; wavelets; Kalman filter; optimal energy control
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Special Issue Information

Dear Colleagues,

It is widely recognized that effective management of energy resources is essential for both environmental sustainability and the efficient redistribution and reuse of resources. In contemporary energy systems, control strategies and optimization algorithms are pivotal. Energy optimization is a critical factor in various fields, including smart grids, microgrids, renewable energy systems, electric and hybrid vehicles, traditional engines, energy storage solutions, electric motors, and alternative actuators.

Whether large or small, these systems play a vital role in energy management. Converters (electrical, hydraulic, pneumatic, etc.), responsible for converting energy into motion, should be included in the broader effort toward achieving intelligent energy optimization due to their widespread use. This Special Issue of Energies will focus on the latest advancements in intelligent control techniques and algorithms in general, designed for optimal energy management in the aforementioned systems, or any system where intelligent control algorithms or techniques are key for optimizing energy efficiency and performance.

The specific topics of interest include (but are not limited to) the following:

Control techniques and energy optimization in any kind of converters (electrical, pneumatic, electromagnetic, mechanical, etc.);
Algorithms for optimal design of converters in terms of efficiency and performance;
Control techniques and algorithms for optimizing energy flow in smart grids;
Control techniques and algorithms for optimizing the operational conditions of wind turbines;
Energetic optimization in intelligent drive assistants for electrical and hybrid vehicles;
Techniques and algorithms for management in electrical batteries;
Techniques and algorithms to control combustion engines, such as algorithms for knock control, lambda control, camless systems, etc.

Prof. Dr. Paolo Mercorelli
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. 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

control techniques
energy optimization
power converters
algorithms
energy flow
smart grids
wind turbines
electrical and hybrid vehicles
electrical batteries
combustion engines

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Related Special Issue

Techniques of Control for Energy Optimization in Actuators, Motors and Power Generation Systems in Energies (19 articles)

Published Papers (7 papers)

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Research

26 pages, 1741 KB

Open AccessArticle

Model Predictive Control for Misalignment Compensation in Dynamic Wireless Charging of Electric Vehicles

by Md. Sadiqur Rahman, Sravan Kumar Dumpeti, Mohammadreza Davoodi and Mohd. Hasan Ali

Energies 2026, 19(11), 2640; https://doi.org/10.3390/en19112640 - 29 May 2026

Abstract

Dynamic wireless charging (DWC) of electric vehicles (EVs) offers a promising solution to mitigate range anxiety and enhance the feasibility of electrified transportation; however, achieving optimal power transfer requires precise alignment between the primary coil embedded in the roadway and the secondary coil mounted on the vehicle. In practice, lateral misalignment (LTM) frequently occurs, leading to reduced efficiency. Although conventional controllers can partially compensate for these losses, their performance degrades under significant misalignment, resulting in overshoot and steady-state error (SSE). To overcome these limitations, this paper proposes a model predictive control (MPC)-based approach to mitigate the effects of LTM and restore efficient power transfer. A comparative study between the proposed MPC and a conventional proportional–integral (PI) controller is conducted to assess performance and suitability. The MPC utilizes an optimization framework to determine optimal control actions over a prediction horizon, thereby minimizing SSE and reducing overshoot under varying misalignment conditions. The effectiveness of the proposed method is validated through MATLAB/Simulink simulations and experimental testing. The results demonstrate that the MPC maintains stable operation over a wide LTM range, achieving a maximum power transfer efficiency of 93% at zero misalignment, which decreases to 83% at severe misalignment (LTM = 0.5). Compared to the PI controller, the MPC improves average efficiency by approximately 8–12%, leading to improved robustness and smoother dynamic response. These results confirm the effectiveness of the proposed MPC approach in maintaining high efficiency and stable operation in misaligned DWC systems. Full article

(This article belongs to the Special Issue Algorithmic Approaches and Control Techniques for Energy Optimization in Power Converters and Grid Transmission)

21 pages, 1301 KB

Open AccessArticle

Control Design for Wind–Diesel Hybrid Power Systems Retrofitted with Fuel Cells

by José Luis Monroy-Morales, Rafael Peña-Alzola, Adwaith Sajikumar, David Campos-Gaona and Enrique Melgoza-Vázquez

Energies 2026, 19(6), 1573; https://doi.org/10.3390/en19061573 - 23 Mar 2026

Viewed by 457

Abstract

Interest in isolated electrical systems powered by renewable energy has driven the development of alternatives to traditional Wind–Diesel Systems (WDS) due to their unwanted emissions and regulatory constraints. In this context, clean and efficient hybrid architectures are needed to comply with regulations and ensure stable operation under variations in user load and wind generation. This paper proposes an integrated isolated hybrid system consisting of a fuel cell replacing the Diesel Generator (DG). To fulfil the role of the synchronous generator in the diesel-group, the fuel cell operates under a Grid-Forming (GFM) control scheme, acting as a virtual synchronous machine that establishes the system’s voltage and frequency. The main aim of the hybrid system is for the wind turbine to supply most of the active power to the loads, thereby minimising hydrogen consumption. A key challenge in these systems is maintaining power balance, particularly preventing reverse flows in the fuel cell system, which has less margin than the diesel generator. In this paper, a Dump Load (DL) quickly dissipates excess power and prevents reverse power conditions. Overall, the proposed system eliminates the need for diesel generation, thereby eliminating emissions while maintaining operational stability. Simulation results demonstrate the correct functioning of the system in the presence of significant variations in load and wind power generation. Full article

(This article belongs to the Special Issue Algorithmic Approaches and Control Techniques for Energy Optimization in Power Converters and Grid Transmission)

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29 pages, 14512 KB

Open AccessArticle

ANFIS-Based Controller and Associated Cybersecurity Issues with Hybrid Energy Storage Used in EV-Connected Microgrid System

by Md Nahin Islam and Mohd. Hasan Ali

Energies 2026, 19(4), 1103; https://doi.org/10.3390/en19041103 - 22 Feb 2026

Viewed by 696

Abstract

The increasing integration of electric vehicles (EVs) and renewable energy sources has accelerated the adoption of DC microgrids, where maintaining voltage stability and effective power sharing remains a critical challenge. Hybrid energy storage systems (HESS), combining batteries and supercapacitors, are commonly employed to address dynamic power variations. However, conventional proportional–integral (PI)-based control strategies for HESS can exhibit performance limitations under nonlinear and varying operating conditions. To overcome this drawback, this paper presents an adaptive neuro-fuzzy inference system (ANFIS)-based control strategy for HESS located in a DC microgrid, with comparative evaluation against both conventional PI and traditional Fuzzy Logic controller (FLC) schemes. The proposed approach is evaluated using a detailed MATLAB/Simulink R2024a model of a DC microgrid including EVs. Simulation results show that, under normal operating conditions, the ANFIS-based control demonstrates improved transient response, reduced voltage fluctuations, and effective coordination between the battery and supercapacitor during renewable power variations, compared to PI and FLC-controlled systems. In addition to nominal performance assessment, this work investigates the vulnerability of the ANFIS controller to cyber-attacks. Two representative attack scenarios, false data injection (FDI) and denial-of-service (DoS), are applied to critical measurement and control signals of HESS. Simulation results reveal that, although the DC-bus voltage regulation is largely maintained during attack intervals, cyber manipulation significantly disrupts the intended HESS power-sharing behavior. Full article

(This article belongs to the Special Issue Algorithmic Approaches and Control Techniques for Energy Optimization in Power Converters and Grid Transmission)

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28 pages, 2480 KB

Open AccessArticle

Virtual Synchronous Machine Testing and System Split Resilience: A Comparative Analysis with Grid-Following PV Inverters

by Ibrahim Okikiola Lawal, Horst Schulte and Ammar Salman

Energies 2026, 19(4), 1027; https://doi.org/10.3390/en19041027 - 15 Feb 2026

Viewed by 692

Abstract

The increasing penetration of converter-interfaced generation raises critical concerns for power system stability, especially during rapid transients and system split events that are not yet adequately addressed in current grid code compliance tests. This paper assesses the resilience of a Virtual Synchronous Machine (VSM) in comparison with a grid-following photovoltaic (PV) inverter through a combined framework of standardized benchmark tests and realistic system split scenarios. In benchmark testing, the VSM provided synthetic inertia by delivering a transient-power burst from a 0.30 p.u. setpoint to 0.545 p.u. (on a 20 MVA base, representing 54.5% of rated capacity) under a

- 0.4

Hz/s frequency ramp, corresponding to an equivalent inertia constant of approximately 15 s. With the limited frequency-sensitive mode–underfrequency (LFSM-U) function enabled, it sustained additional active power up to 0.61 p.u. once the frequency fell below 49.8 Hz. The PV inverter, by contrast, demonstrated compliance with conventional grid requirements: it curtailed power through LFSM-O during overfrequency conditions and injected 0.25 p.u. of reactive current during a fault ride-through (FRT) event at 1.129 p.u. voltage. In system split tests, the VSM absorbed surplus PV generation, stabilizing frequency after a transient rise to 52.8 Hz and containing voltage excursions beyond 1.2 p.u. During imbalance stress, it absorbed 1.266 MW against its 1.0 MW rating for approximately 2–3 s, corresponding to a 26.6% overload that falls within typical IGBT transient thermal capability but would require supervisory intervention (e.g., PV curtailment or load management) if sustained. These results demonstrate that while the PV inverter contributes valuable voltage support, only the grid-forming VSM maintains frequency stability and ensures secure islanded operation. The novelty of this study lies in integrating standardized compliance tests with system split scenarios, providing a comprehensive framework for evaluating grid-forming controls under both regulatory and resilience-oriented perspectives and informing the evolution of future grid codes. Full article

(This article belongs to the Special Issue Algorithmic Approaches and Control Techniques for Energy Optimization in Power Converters and Grid Transmission)

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14 pages, 2316 KB

Open AccessArticle

Experimental Characterization and Validation of a PLECS-Based Hardware-in-the-Loop (HIL) Model of a Dual Active Bridge (DAB) Converter

by Armel Asongu Nkembi, Danilo Santoro, Nicola Delmonte and Paolo Cova

Energies 2026, 19(2), 563; https://doi.org/10.3390/en19020563 - 22 Jan 2026

Viewed by 927

Abstract

Hardware-in-the-loop (HIL) simulation is an essential tool for rapid and cost-effective development and validation of power-electronic systems. The primary objective of this work is to validate and fine-tune a PLECS-based HIL model of a single dual active bridge (DAB) DC-DC converter, thereby laying the foundation for building more complex models (e.g., multiple converters connected in series or parallel). To this end, the converter is experimentally characterized, and the HIL model is validated across a wide range of operating conditions by varying the PWM phase-shift angle, voltage gain, switching frequency, and leakage inductance. Power transfer and efficiency are analyzed to quantify the influence of these parameters on converter performance. These experimental trends provide insight into the optimal modulation range and the dominant loss mechanisms of the DAB under single phase shift (SPS) control. A detailed comparison between HIL simulations and hardware measurements, based on transferred power and efficiency, shows close agreement across all the tested operating points. These results confirm the accuracy and robustness of the proposed HIL model, demonstrate the suitability of the PLECS platform for DAB development and control validation, and support its use as a scalable basis for more complex multi-converter studies, reducing design time and prototyping risk. Full article

(This article belongs to the Special Issue Algorithmic Approaches and Control Techniques for Energy Optimization in Power Converters and Grid Transmission)

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26 pages, 3154 KB

Open AccessFeature PaperArticle

Mitigating Load Shedding in South Africa Through Optimized Hybrid Solar–Battery Deployment: A Techno-Economic Assessment

by Ginevra Vittoria and Rui Castro

Energies 2025, 18(24), 6480; https://doi.org/10.3390/en18246480 - 10 Dec 2025

Viewed by 2465

Abstract

South Africa’s persistent electricity shortages and recurrent load shedding remain among the most pressing challenges to national economic growth and social stability. This paper presents a techno-economic framework to assess how optimized deployment of photovoltaic (PV) and battery energy storage systems (BESSs) can mitigate these disruptions under realistic grid and regulatory constraints. Despite recent operational improvements at Eskom—including a 10-month period without load shedding in 2024—energy insecurity persists due to aging coal assets, limited transmission capacity, and slow renewable integration. Using hourly demand and solar-resource data for 2023, combined with Eskom’s load-reduction records, a Particle Swarm Optimization (PSO) model identifies cost-optimal hybrid system configurations that minimize the Levelized Cost of Electricity (LCOE) while maximizing coverage of unserved energy. Three deployment scenarios are analyzed: (i) constrained regional grid capacity, (ii) flexible redistribution of capacity across six provinces, and (iii) unconstrained national deployment. Results indicate that constrained deployment covers about 86% of curtailed load at 1.88 USD kWh⁻¹, whereas flexible and unconstrained scenarios achieve over 99% coverage at ≈0.58 USD kWh⁻¹. The findings demonstrate that targeted PV–BESS expansion, coupled with selective grid reinforcement, can effectively eliminate load shedding and accelerate South Africa’s transition toward a resilient, low-carbon electricity system. Full article

(This article belongs to the Special Issue Algorithmic Approaches and Control Techniques for Energy Optimization in Power Converters and Grid Transmission)

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34 pages, 7065 KB

Open AccessArticle

Metaheuristic-Based Control Parameter Optimization of DFIG-Based Wind Energy Conversion Systems Using the Opposition-Based Search Optimization Algorithm

by Kavita Behara and Ramesh Kumar Behara

Energies 2025, 18(21), 5843; https://doi.org/10.3390/en18215843 - 5 Nov 2025

Viewed by 960

Abstract

Renewable wind energy systems widely employ doubly fed induction generators (DFIGs), where efficient converter control ensures grid-integrated power system stability and reliability. Conventional proportional–integral (PI) controller tuning methods often encounter challenges with nonlinear dynamics and parameter variations, resulting in reduced adaptability and efficiency. To address this, we present an owl search optimization (OSO)-based tuning strategy for PI controllers in DFIG back-to-back converters. Inspired by the hunting behavior of owls, OSO provides robust global search capabilities and resilience against premature convergence. The proposed method is evaluated in MATLAB/Simulink and benchmarked against particle swarm optimization (PSO), genetic algorithm (GA), and simulated annealing (SA) under step wind variations, turbulence, and grid disturbances. Simulation results demonstrate that OSO achieves superior performance, with 96.4% efficiency, reduced power losses (~40 kW), faster convergence (<400 ms), shorter settling time (<345 ms), and minimal oscillations (0.002). These findings establish OSO as a robust and efficient optimization approach for DFIG-based wind energy systems, delivering enhanced dynamic response and improved grid stability. Full article

(This article belongs to the Special Issue Algorithmic Approaches and Control Techniques for Energy Optimization in Power Converters and Grid Transmission)

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