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Computational Modeling and Intelligent Simulation of Next-Generation Energy Systems

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

Deadline for manuscript submissions: 31 July 2026 | Viewed by 1080

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Special Issue Editors

Dr. Arya Abdollahi

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Guest Editor

Electrical and Information Engineering, Polytechnic University of Bari, 70125 Bari, Italy
Interests: smart and microgrids; energy management system; energy storage systems; hybrid renewable energy integration; machine learning in power and energy systems; power system dynamics and stability

Prof. Dr. Frank Werner

E-Mail Website
Guest Editor

Faculty of Mathematics, Otto-von-Guericke-University, D-39016 Magdeburg, Germany
Interests: scheduling; development of exact and approximate algorithms; stability investigations; discrete optimization; scheduling with interval processing times; complex investigations for scheduling problems; train scheduling; graph theory; logistics; supply chains; packing; simulation; applications
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Special Issue Information

Dear Colleagues,

The rapid transformation of today’s energy landscape, driven by renewable integration, distributed generation, electrification, and digitalization, requires advanced computational tools capable of capturing complex, multiscale, and dynamic behaviors. This Special Issue focuses on innovative approaches for the modeling and simulation of next-generation energy systems, including intelligent algorithms, high-performance computational frameworks, and data-driven techniques that enable accurate analysis, optimization, and control of modern power and energy systems.

We invite original research and review papers that explore novel computational models, intelligent simulation methodologies, and cross-disciplinary strategies for understanding and designing future energy systems. Topics of interest include mathematical modeling, AI-enhanced simulations, digital twins, optimization-based frameworks, and real-time simulation environments. Contributions addressing challenges in renewable energy systems, smart grids, energy storage, microgrids, and integrated energy networks are particularly welcome.

This Special Issue aims to bring together researchers from energy engineering, computer science, applied mathematics, and related fields to advance the state of the art in computational modeling and simulation for the next generation of sustainable, resilient, and intelligent energy systems.

Dr. Arya Abdollahi
Prof. Dr. Frank Werner
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-blind 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

computational modeling of energy systems
intelligent simulation methods
smart and microgrids
renewable energy integration
artificial intelligence in energy systems
digital twins for power systems
optimization and control in energy networks
high-performance simulation
multidomain and multiscale modeling
energy storage systems

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Published Papers (2 papers)

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Research

21 pages, 1596 KB

Open AccessArticle

Integration of Building Information Modelling and Economic Multi-Criteria Decision-Making with Neural Networks: Towards a Smart Renewable Energy Community

by Helena M. Ramos, Ana Paula Falcao, Praful Borkar, Oscar E. Coronado-Hernández, Francisco-Javier Sánchez-Romero and Modesto Pérez-Sánchez

Algorithms 2026, 19(5), 327; https://doi.org/10.3390/a19050327 - 23 Apr 2026

Viewed by 187

Abstract

This research introduces a novel methodology that combines Building Information Modelling (BIM) and Economic Multi-Criteria Decision-Making (EMCDM) with Neural Networks to optimize hybrid renewable energy systems in small communities. Its core aim is to improve sustainability, technical performance, and financial vokiability through integrated modelling and decision-making. The approach is applied to a hydropower site, evaluating five Scenarios (IDs 1–5) under a Community and Industry model. Financial benchmarks include a 10% Minimum Required Return and a 7-year payback period. ID3—hydropower, solar, and wind—proves most effective, with ANPV of €10,905 (wet) and €4501 (dry), and ROI of 155%/64%. Its ROIA/MRA Index peaks at 539%, and Payback/N ratios remain within acceptable limits (55%/96%). LCOE stays stable in average conditions (0.042–0.046 €/kWh), rising in dry years (0.07–0.10 €/kWh). Profitability differences primarily stem from demand and curtailment, rather than production costs. The NARX neural network reliably models SS% values from renewable inputs with low error across scenarios. The integrated BIM–EMCDM framework ensures transparent, sustainable, and risk-balanced energy system decisions for long-term autonomy. Full article

(This article belongs to the Special Issue Computational Modeling and Intelligent Simulation of Next-Generation Energy Systems)

35 pages, 4669 KB

Open AccessArticle

A Hybrid Physics-Informed ML Framework for Emission and Energy Flow Prediction in a Retrofitted Heavy-Duty Vehicle

by Talha Mujahid, Teresa Donateo and Pietropaolo Morrone

Algorithms 2026, 19(4), 317; https://doi.org/10.3390/a19040317 - 17 Apr 2026

Viewed by 361

Abstract

This study introduces a physics-informed machine learning framework for predicting transient emissions and energy variables in a retrofitted heavy-duty diesel vehicle. It merges data-driven modeling with physically derived features for reliable real-world analysis. A Random Forest regressor is trained on a public dataset (26 trips from one instrumented vehicle) to predict CO₂ and NO_x mass rates, exhaust temperature, exhaust mass flow rate, and fuel flow rate from synchronized multi-sensor inputs using past-only, time-lagged features. On held-out trips, exhaust temperature prediction achieves

R^{2}

= 0.9997 and

RMSE

= 0.53 g/s; for CO₂, with

R^{2}

= 0.9985 and

RMSE

= 0.38 g/s, comparable performance is reported for NO_x, exhaust flow, and fuel rate. The trained model is integrated into a simulation framework to enable the evaluation of alternative operating conditions and powertrain configurations. First, the impact of cold-start versus hot-start operation is assessed, showing cumulative emission penalties of up to +28% for CO₂ and +30% for NO_x. Second, the effect of hybridization is investigated by comparing the baseline thermal configuration with a hybrid electric architecture, resulting in estimated reductions of −12.2% in CO₂ and −10.5% in NO_x emissions. This tool excels in high-fidelity emission prediction and system-level energy analysis, aiding advanced powertrain assessments under realistic driving conditions. Full article

(This article belongs to the Special Issue Computational Modeling and Intelligent Simulation of Next-Generation Energy Systems)

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