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Algorithms
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Optimization in Renewable Energy Systems (2nd Edition)
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Optimization in Renewable Energy Systems (2nd Edition)

A special issue of Algorithms (ISSN 1999-4893). This special issue belongs to the section "Algorithms for Multidisciplinary Applications".

Deadline for manuscript submissions: 30 November 2026 | Viewed by 2817

Editors

Dr. Cristina Requejo

E-Mail Website
Guest Editor
School of Economics and Management, University of Lisbon, 31649-004 Lisboa, Portugal
Interests: applications of mixed integer linear programming in health care; green energy and smart grids; communications networks
Special Issues, Collections and Topics in MDPI journals
Dr. Adelaide Cerveira

E-Mail Website
Guest Editor
Department of Mathematics, School of Science and Technology, University of Trás-os-Montes e Alto Douro, 5000-801 Vila Real, Portugal
Interests: applications of mixed integer linear programming in renewable energy systems; forest management; power grids; power quality; project management; renewable energy sources; smart power grids
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

There is an urgent need to develop sustainable energy systems and encourage reducing our carbon footprint. Wind and solar power are driving a clean energy revolution, and renewable energy is booming as innovation reduces costs and begins to deliver on the promise of a clean energy future. Renewables are increasingly replacing “dirty” fossil fuels in the energy sector, offering the benefit of lower carbon emissions and other types of pollution.

The increasing use of solar and wind energy is giving rise to a great diversity of optimization problems, such as telecommunication infrastructures, electrical and transport networks, and support systems. In particular, these networks and systems are increasingly converging and becoming very large networks and systems.

This Special Issue aims to explore emerging concerns arising from the integration and transformation of the existing energy system and to give an overview of the wide spectrum of interesting algorithms, optimization problems, mixed integer linear programming models, and studies related to solution algorithms and their applications in renewable energy.

Manuscripts regarding new and innovative research proposals, algorithms and ideas of computer science, computational mathematics, optimization models, artificial intelligence, automation and control systems, theory, methods, interdisciplinary applications, data and information systems, and software engineering are particularly welcome to be submitted.

This Special Issue aims to explore the emerging concerns arising from integrating and transforming the existing energy system, with particular emphasis on algorithms applied to solving problems in this area.

Dr. Cristina Requejo
Dr. Adelaide Cerveira
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 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-anonymized peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Algorithms is an international peer-reviewed open access monthly 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

  • renewable energy
  • virtual power plants
  • algorithm engineering
  • approximation algorithms
  • iterative methods and algorithms
  • performance and testing of algorithms
  • optimization
  • operational research
  • machine learning
  • mathematical programming
  • combinatorial optimization
  • discrete mathematics and graph theory
  • metaheuristics and matheuristics
  • modelling
  • networks
  • communication and data networks
  • uncertainty data
  • production planning
  • scheduling
  • transport
  • timetabling

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Related Special Issue

Published Papers (4 papers)

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51 pages, 2361 KB  
Article
A Relation-Aware Multi-Driver Pipeline for Interpretable Low-Frequency Load Disaggregation Under Partial Observability
by Balázs András Tolnai, Zheng Grace Ma and Bo Nørregaard Jørgensen
Algorithms 2026, 19(7), 516; https://doi.org/10.3390/a19070516 (registering DOI) - 27 Jun 2026
Viewed by 67
Abstract
Non-intrusive load monitoring (NILM) estimates component-level energy use from aggregate measurements, but low-frequency data limit appliance signatures and make overlapping or weakly observed loads difficult to separate. This paper proposes a relation-aware multi-driver pipeline for interpretable low-frequency load attribution under partial observability. The [...] Read more.
Non-intrusive load monitoring (NILM) estimates component-level energy use from aggregate measurements, but low-frequency data limit appliance signatures and make overlapping or weakly observed loads difficult to separate. This paper proposes a relation-aware multi-driver pipeline for interpretable low-frequency load attribution under partial observability. The method does not require supervised component labels or predefined appliance models. It combines semantic feature typing, heterogeneous relation discovery, feature-family construction, mechanism-aware evidence modeling, conservative allocation, event-background separation, and role-based attribution. Only evidence-supported load is assigned to feature families, while unsupported variation is retained as unexplained demand or residual load. The method is evaluated in a simulated EV-focused building case and through measured-building validation on nine ADRENALIN buildings. In the EV case, the selected EV-aligned family achieved a correlation of 0.990 and an NMAE of 0.100 against the withheld EV reference, while heat-pump and base-load recovery was weaker, with NMAE values of 0.565 and 0.895. In the ADRENALIN validation, temperature-associated families achieved median NMAE values of 0.594 using the restricted feature set and 0.576 using the full feature set. Additional comparison, ablation, sensitivity, diagnostic, and runtime analyses show that the pipeline is most effective for dominant event-driven loads, remains limited for smoother or masked lower-magnitude components, and treats unexplained variation explicitly. The results demonstrate a practical framework for interpretable driver-based load attribution when component labels are unavailable or incomplete. Full article
(This article belongs to the Special Issue Optimization in Renewable Energy Systems (2nd Edition))
35 pages, 7791 KB  
Article
Experimental Evaluation of Microgrid Energy Management Using Surrogate-Assisted Optimization on PHIL and Smart Grid Systems
by Saiful Islam, Sanaz Mostaghim and Michael Hartmann
Algorithms 2026, 19(6), 454; https://doi.org/10.3390/a19060454 - 4 Jun 2026
Viewed by 203
Abstract
In this study, we present an integrated surrogate-assisted multi-objective optimization for an energy management system, ensuring physical feasibility with real system constraints. The hybrid framework incorporates knee-guided selective physical replay and a stochastic survival strategy to maintain both convergence and diversity of the [...] Read more.
In this study, we present an integrated surrogate-assisted multi-objective optimization for an energy management system, ensuring physical feasibility with real system constraints. The hybrid framework incorporates knee-guided selective physical replay and a stochastic survival strategy to maintain both convergence and diversity of the search process. The method is used to evaluate grid-forming and grid-following modes using the OPAL-RT and Lucas-Nülle platforms in three different stages to address the technical and economic performance, and the reliability of the system. The proposed method reduces 116 generated surrogate candidates to 7 physically feasible non-dominated solutions based on physical replay. In the direct evaluation stage without replay, the system achieves high renewable utilization (PV ≈ 97%), reliable load coverage (>99%), and minimal supply–demand mismatch (≈1 W), supported by controlled battery usage. In the extended EMS evaluation, the proposed method reduces the number of true evaluations from approximately 54,600 to 16,895 (≈69% reduction) while maintaining stable performance. Despite the reduction in the number of evaluations, the method preserves stable convergence behavior and a consistent Pareto spread (≈0.0124). Statistical tests, such as Wilcoxon (p≈1.9×10−6) and Friedman (p≈2.9×10−7), show a significant difference and consistent performance across runs. This demonstrates the framework’s ability to provide a compact, decision-relevant set of feasible operating solutions under real system constraints and its practical applicability to real-world EMS decision making. Full article
(This article belongs to the Special Issue Optimization in Renewable Energy Systems (2nd Edition))
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21 pages, 6402 KB  
Article
A New Method for Diagnosing Transformer Winding Faults Based on mRMR-RF Feature Selection and an Inverse Distance Weighted KNN Model
by Chenyang Wang, Huan Peng, Zirui Liu, Song Wang, Danyu Li, Fei Xie and Jian Yang
Algorithms 2026, 19(3), 241; https://doi.org/10.3390/a19030241 - 23 Mar 2026
Viewed by 404
Abstract
Accurately extracting deviation features in frequency response curves, which reflect winding deformation states, and selecting appropriate machine learning algorithms are critical for achieving a precise quantitative diagnosis of winding deformation based on frequency response analysis (FRA). To address the existing challenges in transformer [...] Read more.
Accurately extracting deviation features in frequency response curves, which reflect winding deformation states, and selecting appropriate machine learning algorithms are critical for achieving a precise quantitative diagnosis of winding deformation based on frequency response analysis (FRA). To address the existing challenges in transformer winding fault diagnosis, including the absence of a systematic feature evaluation framework for frequency response data and the limited recognition accuracy of machine learning models, a novel hybrid feature selection and diagnostic framework was developed. First, a high-dimensional feature pool comprising 25 numerical indices was extracted from experimental FRA curves. To eliminate feature redundancy and arbitrary selection, a hybrid mechanism integrating maximum-relevance, minimum-redundancy (mRMR) with random forest (RF) was developed to dynamically construct task-specific optimal feature subsets. Furthermore, an inverse-distance-weighted K-nearest neighbors (IKNN) model was introduced to enhance diagnostic sensitivity by accounting for feature-space distance variations. Experimental results obtained from a laboratory winding model demonstrate that the proposed mRMR-RF-IKNN model significantly outperforms traditional and optimized benchmarks across multiple macro-evaluation metrics. This study provides a systematic, intelligent screening mechanism that ensures high-precision identification of both the types and severity of faults in power transformers. Full article
(This article belongs to the Special Issue Optimization in Renewable Energy Systems (2nd Edition))
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19 pages, 892 KB  
Article
Optimizing Renewable Microgrid Performance Through Hydrogen Storage Integration
by Bruno Ribeiro, José Baptista and Adelaide Cerveira
Algorithms 2025, 18(10), 656; https://doi.org/10.3390/a18100656 - 17 Oct 2025
Cited by 1 | Viewed by 1501
Abstract
The global transition to a low-carbon energy system requires innovative solutions that integrate renewable energy production with storage and utilization technologies. The growth in energy demand, combined with the intermittency of these sources, highlights the need for advanced management models capable of ensuring [...] Read more.
The global transition to a low-carbon energy system requires innovative solutions that integrate renewable energy production with storage and utilization technologies. The growth in energy demand, combined with the intermittency of these sources, highlights the need for advanced management models capable of ensuring system stability and efficiency. This paper presents the development of an optimized energy management system integrating renewable sources, with a focus on green hydrogen production via electrolysis, storage, and use through a fuel cell. The system aims to promote energy autonomy and support the transition to a low-carbon economy by reducing dependence on the conventional electricity grid. The proposed model enables flexible hourly energy flow optimization, considering solar availability, local consumption, hydrogen storage capacity, and grid interactions. Formulated as a Mixed-Integer Linear Programming (MILP) model, it supports strategic decision-making regarding hydrogen production, storage, and utilization, as well as energy trading with the grid. Simulations using production and consumption profiles assessed the effects of hydrogen storage capacity and electricity price variations. Results confirm the effectiveness of the model in optimizing system performance under different operational scenarios. Full article
(This article belongs to the Special Issue Optimization in Renewable Energy Systems (2nd Edition))
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