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

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

Deadline for manuscript submissions: 5 March 2026 | Viewed by 626

Special Issue Editor

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

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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

Published Papers (2 papers)

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Research

27 pages, 3154 KB  
Article
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
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 [...] Read more.
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
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34 pages, 7065 KB  
Article
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 395
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. [...] Read more.
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
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