Submit to Energies Review for Energies Propose a Special Issue

Journal Menu

Journal Browser

Advances in Sustainable Power and Energy Systems

Print Special Issue Flyer
Special Issue Editors
Special Issue Information
Keywords
Benefits of Publishing in a Special Issue
Related Special Issue
Published Papers

A special issue of Energies (ISSN 1996-1073). This special issue belongs to the section "F5: Artificial Intelligence and Smart Energy".

Deadline for manuscript submissions: closed (25 March 2025) | Viewed by 5402

Share This Special Issue

Special Issue Editors

Prof. Dr. Inga Zicmane

E-Mail Website
Guest Editor

Faculty of Power and Electrical Engineering, Riga Technical University 1658 Riga, Latvia
Interests: power energy; sustainability; renewables; power system reliability
Special Issues, Collections and Topics in MDPI journals

Dr. Svetlana Beryozkina

E-Mail Website
Guest Editor

College of Engineering and Technology, American University of the Middle East, Egaila 54200, Kuwait
Interests: power and energy systems; renewable energy; sustainability; smart grids
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The importance of artificial intelligence (AI) in the energy sector cannot be overstated, as it plays a key role in addressing pressing challenges and unlocking significant opportunities for the industry. These involve the use of sophisticated computational algorithms and methodologies to optimize various aspects of energy production, distribution, consumption, and management. In this regard, artificial intelligence tools such as machine learning, neural networks, and optimization techniques are used to analyze big datasets from energy systems, including power plants, smart grids, and renewable energy objects, to make informed decisions and improve operational efficiency. Integrating AI into the energy industry improves energy systems’ reliability and stability while minimizing costs and environmental impact. By harnessing the power of artificial intelligence, energy companies can overcome challenges, adapt to changing market dynamics, and contribute to a more sustainable and resilient energy future.

This Special Issue aims to showcase the most recent advances related to the application of different artificial intelligence techniques in the control and optimization of smart grids employing alternative and renewable energy sources and the development smart grid structures that dynamically adapt to changing supply-and-demand dynamics, improving the overall system resilience and stability.

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

Grid management systems based on artificial intelligence;
Energy distribution network optimization by adapting to changing demand and integrating different energy sources;
AI to smooth the integration of renewable energy sources, such as solar and wind power, into the grid by predicting weather conditions and adjusting energy production accordingly;
AI for energy efficiency and demand management;
AI-based innovations in the energy sector;
AI-based energy management systems;
AI for data-driven decision making by analyzing massive datasets from sensors, meters, and other sources in real time;
AI to improve asset management and the predictive maintenance of energy infrastructure.

Prof. Dr. Inga Zicmane
Dr. Svetlana Beryozkina
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 100 words) can be sent to the Editorial Office for announcement on this website.

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

artificial intelligence
smart energy system
operational efficiency
energy system modeling
optimization
demand management
integration of renewable energy sources

Benefits of Publishing in a Special Issue

Ease of navigation: Grouping papers by topic helps scholars navigate broad scope journals more efficiently.
Greater discoverability: Special Issues support the reach and impact of scientific research. Articles in Special Issues are more discoverable and cited more frequently.
Expansion of research network: Special Issues facilitate connections among authors, fostering scientific collaborations.
External promotion: Articles in Special Issues are often promoted through the journal's social media, increasing their visibility.
e-Book format: Special Issues with more than 10 articles can be published as dedicated e-books, ensuring wide and rapid dissemination.

Further information on MDPI's Special Issue policies can be found here.

Related Special Issue

Advances in Sustainable Power and Energy Systems: 2nd Edition in Energies

Published Papers (9 papers)

Download All Papers

Order results

Result details

Show export options Show export options

Select all

Export citation of selected articles as:

Research

23 pages, 5167 KiB

Open AccessArticle

Optimal and Sustainable Operation of Energy Communities Organized in Interconnected Microgrids

by Epameinondas K. Koumaniotis, Dimitra G. Kyriakou and Fotios D. Kanellos

Energies 2025, 18(8), 2087; https://doi.org/10.3390/en18082087 - 18 Apr 2025

Viewed by 141

Abstract

Full dependence on the main electrical grid carries risks, including high electricity costs and increased power losses due to the distance between power plants and consumers. An energy community consists of distributed generation resources and consumers within a localized area, aiming to produce electricity economically and sustainably while minimizing long-distance power transfers and promoting renewable energy integration. In this paper, a method for the optimal and sustainable operation of energy communities organized in interconnected microgrids is developed. The microgrids examined in this work consist of residential buildings, plug-in electric vehicles (PEVs), renewable energy sources (RESs), and local generators. The primary objective of this study is to minimize the operational costs of the energy community resulting from the electricity exchange with the main grid and the power production of local generators. To achieve this, microgrids efficiently share electric power, regulate local generator production, and leverage energy storage in PEVs for power management, reducing the need for traditional energy storage installation. Additionally, this work aims to achieve net-zero energy exchange with the main grid, reduce greenhouse gas (GHG) emissions, and decrease power losses in the distribution lines connecting microgrids, while adhering to numerous technical and operational constraints. Detailed simulations were conducted to prove the effectiveness of the proposed approach. Full article

(This article belongs to the Special Issue Advances in Sustainable Power and Energy Systems)

► Show Figures

Figure 1

14 pages, 4117 KiB

Open AccessArticle

Advanced Voltage Stability Assessment in Renewable-Powered Islanded Microgrids Using Machine Learning Models

by Muhammad Jamshed Abbass, Robert Lis and Waldemar Rebizant

Energies 2025, 18(8), 2047; https://doi.org/10.3390/en18082047 - 16 Apr 2025

Viewed by 100

Abstract

The assessment of voltage stability within a microgrid is essential to ensure that all buses in the system can maintain the required voltage levels. Recent research has focused on developing modern voltage stability estimation equipment rather than identifying optimal locations for integrating inverter-based resources (IBRs) within the network. This study analyzes and evaluates voltage stability in power systems with increasing levels of IBRs using modal analysis methodologies that consider active power (PV) and reactive power (QV). It examines the impact of load flow when integrating IBRs into the weakest-and strongest-load buses. Additionally, this study introduces a support vector machine (SVM) approach to assessing voltage stability in a microgrid. The results indicate that the proposed SVM approach achieved an optimal accuracy of 95.10%. Using the IEEE 14-bus scheme, the methodology demonstrated the effective and precise determination of the voltage stability category of the system. Furthermore, the analysis was conducted using the modified DES power system. The core contribution of this research lies in evaluating and identifying the locations that are the most and least favorable for integrating IBRs within the simplified DES power system network, utilizing modal analysis for both QV and solar photovoltaics (SPVs). The results of the load flow analysis suggest that integrating IBR is significantly more beneficial in the most substantial bus, as it minimally impacts other load buses assessed as the least reliable bus within the system. Full article

(This article belongs to the Special Issue Advances in Sustainable Power and Energy Systems)

► Show Figures

Figure 1

25 pages, 15178 KiB

Open AccessArticle

Priority Load Management for Improving Supply Reliability of Critical Loads in Healthcare Facilities Under Highly Unreliable Grids

by Ndukwe Henry Ibiam, Fadi Kahwash and Jubaer Ahmed

Energies 2025, 18(6), 1343; https://doi.org/10.3390/en18061343 - 9 Mar 2025

Viewed by 624

Abstract

Many developing countries suffer from unreliable grids and rolling blackouts on a daily basis. Losing electricity in healthcare facilities can be detrimental to human life and the required health services. Thus, it is often necessary to keep critical loads operational even if the grid experiences a blackout. Such support is usually provided using battery storage or diesel generators. In the system design phase, it is often unknown how the priority-based load management will impact the battery life, sizing of the optimal battery, or operational cost in the long run. This paper presents a comprehensive analysis of a priority load management strategy for healthcare facilities in areas of highly unreliable grids. A grid-connected battery backup system is used for the evaluation. To operate the system, a priority-based dispatch algorithm is developed, which classifies medical loads into three tiers based on their criticality. Synthetic medical facility load profiles and blackout patterns are constructed to test the algorithm. The battery model was enhanced with the introduction of aging calculations spanning multiple years. It was found that the priority-based algorithm improved the reliability served to the most critical loads at the expense of the least critical. The load priority strategy slowed the battery pack degradation over time and reduced the number of replacement cycles, which is financially favorable in the long run. Finally, some insights for designing such a backup system are provided. Full article

(This article belongs to the Special Issue Advances in Sustainable Power and Energy Systems)

► Show Figures

Figure 1

16 pages, 4833 KiB

Open AccessArticle

Collaborative Forecasting of Multiple Energy Loads in Integrated Energy Systems Based on Feature Extraction and Deep Learning

by Zhe Wang, Jiali Duan, Fengzhang Luo and Xiaoyu Qiu

Energies 2025, 18(5), 1048; https://doi.org/10.3390/en18051048 - 21 Feb 2025

Viewed by 320

Abstract

Accurate load forecasting is crucial for the safe, stable, and economical operation of integrated energy systems. However, directly applying single models to predict coupled cooling, heating, and electric loads under complex influencing factors often yields unsatisfactory results. This paper proposes a collaborative load forecasting method based on feature extraction and deep learning. First, the complete ensemble empirical mode decomposition with adaptive noise algorithm decomposes load data, and a dynamic time warping-based k-medoids clustering algorithm reconstructs subsequences aligned with system load components. Second, a correlation analysis identifies the key influencing factors for model input. Then, a multi-task parallel learning framework combining a regression convolutional neural network and long short-term memory networks is developed to predict reconstructed subsequences. Case studies demonstrate that the proposed model achieves mean absolute percentage errors (MAPE) of 2.24%, 2.75%, and 1.69% for electricity, cooling, and heating loads on summer workdays, with mean accuracy (MA) values of 97.76%, 97.25%, and 98.31%, respectively. For winter workdays, the MAPE values are 2.92%, 1.66%, and 2.87%, with MA values of 97.08%, 98.34%, and 97.13%. Compared to traditional single-task models, the weighted mean accuracy (WMA) improves by 2.01% and 2.33% in summer and winter, respectively, validating its superiority. This method provides a high-precision tool for the planning and operation of integrated energy systems. Full article

(This article belongs to the Special Issue Advances in Sustainable Power and Energy Systems)

► Show Figures

Figure 1

22 pages, 7186 KiB

Open AccessArticle

Enhancing Renewable Energy Integration and Implementing EV Charging Stations for Sustainable Electricity in Crete’s Supermarket Chain

by Emmanuel Karapidakis, Marios Nikologiannis, Marini Markaki, Georgios Kouzoukas and Sofia Yfanti

Energies 2025, 18(3), 754; https://doi.org/10.3390/en18030754 - 6 Feb 2025

Cited by 2 | Viewed by 702

Abstract

In current times, sustainability is paramount, and businesses are increasingly adopting renewable energy sources (RESs) and electric vehicle (EV) charging infrastructure to minimise their environmental impact and operational costs. Such a transition can prove challenging to multi-location businesses since each chain store functions under different constraints; therefore, the implementation of a corporate policy requires adaptations. The increased electricity demand associated with EV charging stations and their installation cost could prove to be a significant financial burden. Therefore, this study aims to investigate and develop strategies for effectively incorporating RES and EV charging stations into the operations of a supermarket chain in Crete. Monthly electricity consumption data, parking availability, and premise dimensions were collected for 20 supermarkets under the same brand. To achieve a more tailored approach to custom energy system sizing, the integration of energy storage coupled with a photovoltaic (PV) system was investigated, using the Moth–Flame Optimiser (MFO) to maximise the Net Present Value (NPV) of 20 years. The algorithm managed to locate optimal solutions that yield profitable installations for all supermarkets by installing the necessary number of PV units. Manual exploration around the solutions led to the optimal integration of energy storage systems with a total upfront cost of EUR 856,477.00 and a total profit for the entire brand equal to EUR 6,426,355.14. Full article

(This article belongs to the Special Issue Advances in Sustainable Power and Energy Systems)

► Show Figures

Figure 1

24 pages, 6256 KiB

Open AccessArticle

Effective and Local Constraint-Aware Load Shifting for Microgrid-Based Energy Communities

by Dimitra G. Kyriakou, Fotios D. Kanellos, George J. Tsekouras and Konstantinos A. Moungos

Energies 2025, 18(2), 343; https://doi.org/10.3390/en18020343 - 14 Jan 2025

Cited by 1 | Viewed by 719

Abstract

The rising energy demand, coupled with increased integration of distributed energy resources (DERs) and fluctuating renewable generation, underscores the need for effective load management within energy communities. This paper addresses these challenges by implementing effective, constraint-aware load shifting within microgrid-based energy communities. Specifically, the goal of this study is to flatten the electrical load profile of a High-Voltage (HV)/Medium-Voltage (MV) power transformer. The load of a central power transformer includes (a) the diverse, fluctuating electrical and thermal demands of buildings within the energy community and (b) the load of the area supplied by the substation excluding the energy community loads. To achieve a flattened load profile, we apply time shifting to both electrical and heating, ventilation, and air conditioning (HVAC) loads of the energy community, allowing for a redistribution of energy consumption over time. This approach entails shifting non-critical loads, particularly those related to HVAC and other building operations, to off-peak periods. The methodology considers critical operational constraints, such as maintaining occupant thermal comfort, ensuring compliance with building codes, and adhering to technical specifications of HVAC and electrical systems and microgrid organized energy communities. Detailed simulations were conducted to prove the effectiveness of this constraint-aware load-shifting approach. Full article

(This article belongs to the Special Issue Advances in Sustainable Power and Energy Systems)

► Show Figures

Figure 1

25 pages, 1490 KiB

Open AccessArticle

Adaptive Hosting Capacity Forecasting in Distribution Networks with Distributed Energy Resources

by Md Tariqul Islam, M. Jahangir Hossain, Md. Ahasan Habib and Muhammad Ahsan Zamee

Energies 2025, 18(2), 263; https://doi.org/10.3390/en18020263 - 9 Jan 2025

Cited by 2 | Viewed by 780

Abstract

The sustainable integration of distributed energy resources (DER) into distribution networks requires accurate forecasting of hosting capacity. The network and DER variables alone do not capture the full range of external influences on DER integration. Traditional models often overlook the dynamic impacts of these exogenous factors, leading to suboptimal predictions. This study introduces a Sensitivity-Enhanced Recurrent Neural Network (SERNN) model, featuring a sensitivity gate within the neural network’s memory cell architecture to enhance responsiveness to time-varying variables. The sensitivity gate dynamically adjusts the model’s response based on external conditions, allowing for improved capture of input variability and temporal characteristics of the distribution network and DER. Additionally, a feedback mechanism within the model provides inputs from previous cell states into the forget gate, allowing for refined control over input selection and enhancing forecasting precision. Through case studies, the model demonstrates superior accuracy in hosting capacity predictions compared to baseline models like LSTM, ConvLSTM, Bidirectional LSTM, Stacked LSTM, and GRU. Study shows that the SERNN achieves a mean absolute error (MAE) of 0.2030, a root mean square error (RMSE) of 0.3884 and an R-squared value of 0.9854, outperforming the best baseline model by 48 per cent in MAE and 71 per cent in RMSE. Additionally, Feature engineering enhances the model’s performance, improving the R-squared value from 0.9145 to 0.9854. The sensitivity gate also impacts the model’s performance, lowering MAE to 0.2030 compared to 0.2283 without the sensitivity gate, and increasing the R-squared value from 0.9152 to 0.9854. Incorporating exogenous factors such as the time of day as a sensitivity gate input, further improves responsiveness, making the model more adaptable to real-world conditions. This advanced SERNN model offers a reliable framework for distribution network operators, supporting intelligent planning and proactive DER management. Ultimately, it provides a significant step forward in hosting capacity analysis, enabling more efficient and sustainable DER integration within next-generation distribution networks. Full article

(This article belongs to the Special Issue Advances in Sustainable Power and Energy Systems)

► Show Figures

Figure 1

18 pages, 2508 KiB

Open AccessArticle

Linear Quadratic Gaussian Integral Control for Secondary Voltage Regulation

by Elio Chiodo, Pasquale Di Palma, Maurizio Fantauzzi, Davide Lauria, Fabio Mottola and Domenico Villacci

Energies 2025, 18(1), 4; https://doi.org/10.3390/en18010004 - 24 Dec 2024

Cited by 1 | Viewed by 512

Abstract

In this paper, the voltage regulation in power systems is addressed from the perspective of the modern paradigm of control logic supported by phasor measurement units. The information available from measurements is used to better adapt the regulation actions to the actual operation point of the system. The use of the online measurement data allows for identifying the sensitivity matrix and for improving the regulation performances with respect to the fast load variations that increasingly affect modern power systems. With the aim of estimating the sensitivity matrices, a preliminary action is necessary to reconstruct the phases of the network voltages, which are assumed not to be provided by the phasor measurement units. This allows for obtaining a model-free adaptive control method. It is then shown how the regulation problem can be formulated in terms of a linear quadratic Gaussian problem, properly considering the load modeling in terms of the stochastic Ornstein–Uhlenbeck process. This control strategy has the advantage of avoiding dangerous oscillations of power flows, as demonstrated through the results of some simulations on a classical test network. Particularly, the advantage of the proposed approach is shown in the presence of different levels of load disturbances. Full article

(This article belongs to the Special Issue Advances in Sustainable Power and Energy Systems)

► Show Figures

Figure 1

19 pages, 4428 KiB

Open AccessArticle

Co-Movement Among Electricity Consumption, Economic Growth and Financial Development in Portugal, Italy, Greece, and Spain: A Wavelet Analysis

by Cosimo Magazzino, Syed Kafait Hussain Naqvi and Lorenzo Giolli

Energies 2024, 17(24), 6338; https://doi.org/10.3390/en17246338 - 16 Dec 2024

Viewed by 728

Abstract

The aim of this paper is to examine the connections among time-frequency dependencies associated with electrical power consumption (EPC), economic growth, and financial development (FD) in Portugal, Italy, Greece, and Spain during the period 1970–2014. Using monthly data collected from the World Bank (WB) and Federal Reserve Bank of St. Louis (FRED), the wavelet analysis is applied, which allows for assessing the co-movement between these variables. As a first step, a classical time-domain approach is used to alternatively test the connection, including unit-root tests and cointegration. To achieve a comprehensive understanding of the relationships between EPC, economic growth, and FD, we employ Wavelet Transform Coherency (WTC) and Partial Wavelet Coherency (PWC) to explore both their temporal and phase-based dynamics. The main findings show that EPC leads FD, but in the short term, and periods dominated by economic stagnations and political crises. Otherwise, FD drives EPC in the medium term, under economic expansion periods. In both cases, economic growth is crucial, being a strong binding force of the interaction between EPC and FD. The difference in the applied results provides alternative policy implications, justifying the use of the wavelet approach. Full article

(This article belongs to the Special Issue Advances in Sustainable Power and Energy Systems)

► Show Figures

Journal Menu

Journal Browser

Advances in Sustainable Power and Energy Systems

Share This Special Issue

Special Issue Editors

Special Issue Information

Keywords

Benefits of Publishing in a Special Issue

Related Special Issue

Published Papers (9 papers)

Research

Further Information

Guidelines

MDPI Initiatives

Follow MDPI