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Managing Sustainable Energy Systems: Challenges, Models and Opportunities for the Long-Term Energy Transition

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Energy Science and Technology".

Deadline for manuscript submissions: 20 November 2026 | Viewed by 1895

Special Issue Editors

Dr. Francesco Gabriele Galizia

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Guest Editor
Department of Industrial Engineering, Alma Mater Studiorum—University of Bologna, Viale del Risorgimento 2, 40136 Bologna, Italy
Interests: manufacturing systems; lean production; production systems; operations management
Special Issues, Collections and Topics in MDPI journals
Dr. Marco Bortolini

E-Mail Website
Guest Editor
Department of Industrial Engineering, Alma Mater Studiorum—University of Bologna, Viale del Risorgimento 2, 40136 Bologna, Italy
Interests: production management; green logistics; operations management; smart manufacturing; sustainability transition
Special Issues, Collections and Topics in MDPI journals
Prof. Mauro Gamberi

E-Mail Website
Guest Editor
Department of Industrial Engineering, Alma Mater Studiorum—University of Bologna, Viale del Risorgimento 2, 40136 Bologna, Italy
Interests: manufacturing systems; logistics; production planning and control; renewable energy; sustainable manufacturing
Special Issues, Collections and Topics in MDPI journals
Dr. Cristian Cafarella

E-Mail Website
Guest Editor Assistant
Department of Industrial Engineering, Alma Mater Studiorum—University of Bologna, Viale del Risorgimento 2, 40136 Bologna, Italy
Interests: energy systems; energy efficiency; renewable energy; energy system modeling; energy policy

Special Issue Information

Dear Colleagues,

The global energy transition requires a radical shift in how energy systems are thought about, designed, planned, and managed. The increasing penetration of renewable energy sources, the electrification of end-uses, digitalization, and smart grid systems are reshaping the structure of modern energy systems. These trends demand innovative paradigms, methods, strategies, and tools to ensure the sustainability, resilience, and efficiency of energy production, conversion, storage, and distribution systems.

This Special Issue aims to gather state-of-the-art research, methodologies, decision–support tools, and case studies that address the challenges and opportunities related to the long-term energy transition. We particularly encourage contributions that combine innovative approaches and strategies with a systemic view of sustainability, taking into account the interrelations among technoeconomic, environmental, and social factors. Potential topics of interest include, but are not limited to, the following:

  • Energy system modeling and scenario analysis;
  • Long-term and short-term energy planning;
  • Power systems’ optimization under uncertainty;
  • Integrated planning of multienergy systems;
  • Renewable energy integration and grid stability;
  • Distributed generation and energy communities;
  • Sector coupling and energy flexibility strategies;
  • Smart grids and demand-side management;
  • Lifecycle assessment and sustainability metrics for energy systems;
  • Planning under environmental and policy constraints;
  • Data-driven and AI-based approaches for energy modeling;
  • Energy system resilience and reliability;
  • Industrial decarbonization and energy efficiency.

Dr. Francesco Gabriele Galizia
Dr. Marco Bortolini
Prof. Mauro Gamberi
Guest Editors

Dr. Cristian Cafarella
Guest Editor Assistant

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. Applied Sciences 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 2400 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

  • energy systems
  • renewable energy
  • sustainability
  • energy planning
  • optimization
  • decarbonization
  • modeling

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Published Papers (3 papers)

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Research

21 pages, 1895 KB  
Article
A Three-Objective Optimization Model for Sustainable Power System Design: Balancing Costs, Emissions and Social Opposition
by Cristian Cafarella, Michele Ronchi, Francesco Gabriele Galizia, Marco Bortolini and Mauro Gamberi
Appl. Sci. 2026, 16(6), 2946; https://doi.org/10.3390/app16062946 - 18 Mar 2026
Viewed by 265
Abstract
The design of sustainable power systems requires planning tools that jointly account for economic, environmental, and social dimensions. However, multi-objective energy system models typically prioritize economic–environmental trade-offs, while the social dimension is still rarely included as an explicit optimization objective. Furthermore, many formulations [...] Read more.
The design of sustainable power systems requires planning tools that jointly account for economic, environmental, and social dimensions. However, multi-objective energy system models typically prioritize economic–environmental trade-offs, while the social dimension is still rarely included as an explicit optimization objective. Furthermore, many formulations adopt a low temporal resolution (e.g., annual time steps) and assume fully flexible power plants, potentially overlooking temporal variability and operational constraints. This paper presents a three-objective optimization model for sustainable power system design that minimizes (i) costs, (ii) greenhouse gas (GHG) emissions, and (iii) social opposition (i.e., the public resistance to certain energy technologies). Temporal variability and operational detail are preserved using weighted representative periods with intra-period time steps and a clustered unit commitment (CUC) formulation. The Pareto frontier is generated using the normalized normal constraint (NNC) method, highlighting the space of efficient economic, environmental, and social solutions. A case study focused on the Italian electricity system exemplifies the model application by providing the cost-optimal, emissions-optimal, and social-optimal solutions, together with trade-off solutions. Among the trade-off solutions, the selected best balance solution achieves a significant reduction in emissions (−20%) compared to the cost-optimal solution, with a limited cost increase (+5%) and a marginal increase in social opposition (+0.7%). Overall, the proposed model enables transparent quantification of multi-dimensional trade-offs to support decision-making in sustainable power system design. Full article
(This article belongs to the Special Issue Managing Sustainable Energy Systems: Challenges, Models and Opportunities for the Long-Term Energy Transition)
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22 pages, 10587 KB  
Article
Accelerating Optimal Building Control Through Reinforcement Learning with Surrogate Building Models
by Andres Sebastian Cespedes Cubides, Christian Friborg Laursen and Muhyiddine Jradi
Appl. Sci. 2026, 16(6), 2790; https://doi.org/10.3390/app16062790 - 13 Mar 2026
Viewed by 520
Abstract
Buildings account for a substantial share of global energy use, yet the adoption of advanced optimal control strategies remains limited due to high computational costs and the difficulty of safe deployment. This paper presents a fully Python-based, data-driven deep reinforcement learning (DRL) supervisory [...] Read more.
Buildings account for a substantial share of global energy use, yet the adoption of advanced optimal control strategies remains limited due to high computational costs and the difficulty of safe deployment. This paper presents a fully Python-based, data-driven deep reinforcement learning (DRL) supervisory control framework that leverages gray box surrogate modeling and Imitation Learning to overcome these barriers. The novelty of this work lies in the integration of an ontology-based Twin4Build surrogate model with Imitation Learning and Deep Reinforcement Learning, enabling efficient training of building control policies in a low-cost environment before transfer to a high-fidelity BOPTEST emulator. Results demonstrate that the trade-off of using a lower-accuracy surrogate accelerates training by a factor of 11 compared to high-fidelity models. Furthermore, the RL agent successfully learned load-shifting and peak-shaving strategies, eliminating start-up power spikes and achieving energy savings of up to 28.9%. Beyond substantial energy reductions, this pipeline yields a calibrated digital twin suitable for ongoing building services like anomaly detection, presenting a scalable path for real-world smart building optimization. Full article
(This article belongs to the Special Issue Managing Sustainable Energy Systems: Challenges, Models and Opportunities for the Long-Term Energy Transition)
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16 pages, 1176 KB  
Article
Deep Learning-Based Detection and Forecasting of Performance Losses in Solar PV Systems Using Multi-Sensor Data
by Erhan Baran
Appl. Sci. 2026, 16(4), 1709; https://doi.org/10.3390/app16041709 - 9 Feb 2026
Viewed by 608
Abstract
Photovoltaic (PV) systems are subject to nonlinear performance degradation caused by operational and environmental factors, which limits reliable energy production. Most existing studies focus on power output forecasting and fail to isolate intrinsic efficiency losses from meteorological variability. This study proposes a degradation-aware [...] Read more.
Photovoltaic (PV) systems are subject to nonlinear performance degradation caused by operational and environmental factors, which limits reliable energy production. Most existing studies focus on power output forecasting and fail to isolate intrinsic efficiency losses from meteorological variability. This study proposes a degradation-aware deep learning framework for predicting PV performance loss using multi-sensor time-series data. Performance degradation is formulated as a reference-based performance loss ratio derived from the deviation between observed power output and an ideal physics-informed reference model. A hybrid convolutional neural network (CNN) and long short-term memory (LSTM) architecture is employed to jointly capture local feature representations and long-term temporal degradation dynamics. Model evaluation is conducted using a synthetically generated yet physically consistent dataset, informed by real PV measurements to ensure real-world relevance. Experimental results demonstrate that the proposed CNN–LSTM model outperforms baseline approaches, including persistence, linear regression, and XGBoost, particularly in terms of mean absolute error (MAE) and normalized root mean square error (RMSE). Additional analyses confirm stable error behavior and temporal generalization, highlighting the suitability of the proposed approach for degradation-aware performance monitoring and predictive maintenance in PV systems. Full article
(This article belongs to the Special Issue Managing Sustainable Energy Systems: Challenges, Models and Opportunities for the Long-Term Energy Transition)
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