Smart Energy Systems, 2nd Edition

Topic Information

Dear Colleagues,

There are some definitions for what a Smart Energy System is. Terms such as: cost-effective, sustainable, secure, renewable energy production, storage systems, demand-side response, electrical vehicles, energy efficiency, active users, and intelligent networking are often associated with the Smart Energy System concept. It is a broader term than Smart Grid, in the sense that it includes more sectors (electricity, heating, cooling, industry, buildings, transportation, and water) rather than focusing exclusively on the electricity sector. Smart Energy Systems are closely related to the ongoing energy transition toward a 100% renewable energy system.

We are pleased to invite the research community to submit review or regular research papers on, but not limited to, the following relevant topics related to Smart Energy Systems:

• Hydrogen systems;
• Storage technologies and systems;
• Demand side response;
• Electrical Vehicles;
• Planning, operation, control, and management;
• Modeling, simulation, and data management;
• Ancillary services;
• Power electronic converters and drives;
• Smart thermal grids;
• Smart gas grids;
• Smart electricity grids;
• Energy efficient systems;
• Virtual power plants;
• Renewable energy production and integration;
• Micro-Grids;
• Power-to-H2O (desalination and water purification);
• Hybrid AC/DC grids;
• Off-grid hybrid renewable systems;
• Artificial intelligence and optimization;
• Demand, Production, and weather forecast;
• Smart homes, cities, and communities;
• Efficient buildings and Net Zero Energy Buildings;
• Power quality;
• Protection systems and reliability;
• Sensors, communications, and intelligent networking;
• Smart metering;
• Virtual power lines;
• Security and privacy of data exchange;
• Life cycle assessment;
• Public policies;
• Local markets;
• Flexibility markets;
• TSO-DSO coordination;
• Education.

Prof.Dr. Hugo Morais
Prof.Dr. Rui Castro
Dr. Cindy Guzman
Topic Editors

Keywords

Participating Journals

Journal Name	Impact Factor	CiteScore	Launched Year	First Decision (median)	APC
Applied Sciences applsci	2.5	5.3	2011	18.4 Days	CHF 2400	Submit
Batteries batteries	4.6	4.0	2015	19.7 Days	CHF 2700	Submit
Buildings buildings	3.1	3.4	2011	15.3 Days	CHF 2600	Submit
Data data	2.2	4.3	2016	26.8 Days	CHF 1600	Submit
Electricity electricity	-	4.8	2020	27.9 Days	CHF 1000	Submit
Electronics electronics	2.6	5.3	2012	16.4 Days	CHF 2400	Submit
Energies energies	3.0	6.2	2008	16.8 Days	CHF 2600	Submit
Smart Cities smartcities	7.0	11.2	2018	28.4 Days	CHF 2000	Submit

Preprints.org is a multidisciplinary platform offering a preprint service designed to facilitate the early sharing of your research. It supports and empowers your research journey from the very beginning.

MDPI Topics is collaborating with Preprints.org and has established a direct connection between MDPI journals and the platform. Authors are encouraged to take advantage of this opportunity by posting their preprints at Preprints.org prior to publication:

1. Share your research immediately: disseminate your ideas prior to publication and establish priority for your work.
2. Safeguard your intellectual contribution: Protect your ideas with a time-stamped preprint that serves as proof of your research timeline.
3. Boost visibility and impact: Increase the reach and influence of your research by making it accessible to a global audience.
4. Gain early feedback: Receive valuable input and insights from peers before submitting to a journal.
5. Ensure broad indexing: Web of Science (Preprint Citation Index), Google Scholar, Crossref, SHARE, PrePubMed, Scilit and Europe PMC.

Published Papers (11 papers)

Order results

Content type Publication Date

Result details

Normal Extended Compact

Journals

All Applied Sciences Batteries Buildings Data Electricity Electronics Energies Smart Cities

Show export options expand_more Show export options expand_less

Select all

Export citation of selected articles as:

Plain Text BibTeX BibTeX (without abstracts) Endnote Endnote (without abstracts) Tab-delimited RIS

Error

Oops... you haven't selected anything for export.

Ok

clear

15 pages, 1302 KiB  

Open AccessData Descriptor

Experimental Parametric Forecast of Solar Energy over Time: Sample Data Descriptor

by Fernando Venâncio Mucomole, Carlos Augusto Santos Silva and Lourenço Lázaro Magaia

Data 2025, 10(3), 37; https://doi.org/10.3390/data10030037 - 17 Mar 2025

Viewed by 366

Abstract

Variations in solar energy when it reaches the Earth impact the production of photovoltaic (PV) solar plants and, in turn, the dynamics of clean energy expansion. This incentivizes the objective of experimentally forecasting solar energy by parametric models, the results of which are [...] Read more.

Variations in solar energy when it reaches the Earth impact the production of photovoltaic (PV) solar plants and, in turn, the dynamics of clean energy expansion. This incentivizes the objective of experimentally forecasting solar energy by parametric models, the results of which are then refined by machine learning methods (MLMs). To estimate solar energy, parametric models consider all atmospheric, climatic, geographic, and spatiotemporal factors that influence decreases in solar energy. In this study, data on ozone, evenly mixed gases, water vapor, aerosols, and solar radiation were gathered throughout the year in the mid-north area of Mozambique. The results show that the calculated solar energy was close to the theoretical solar energy under a clear sky. When paired with MLMs, the clear-sky index had a correlational order of 0.98, with most full-sun days having intermediate and clear-sky types. This suggests the potential of this area for PV use, with high correlation and regression coefficients in the range of 0.86 and 0.89 and a measurement error in the range of 0.25. We conclude that evenly mixed gases and the ozone layer have considerable influence on transmittance. However, the parametrically forecasted solar energy is close to the energy forecasted by the theoretical model. By adjusting the local characteristics, the model can be used in diverse contexts to increase PV plants’ electrical power output efficiency. Full article

(This article belongs to the Topic Smart Energy Systems, 2nd Edition)

►▼ Show Figures

Figure 1

17 pages, 954 KiB  

Open AccessArticle

Leveraging Explainable Artificial Intelligence in Solar Photovoltaic Mappings: Model Explanations and Feature Selection

by Eduardo Gomes, Augusto Esteves, Hugo Morais and Lucas Pereira

Energies 2025, 18(5), 1282; https://doi.org/10.3390/en18051282 - 5 Mar 2025

Cited by 1 | Viewed by 513

Abstract

This work explores the effectiveness of explainable artificial intelligence in mapping solar photovoltaic power outputs based on weather data, focusing on short-term mappings. We analyzed the impact values provided by the Shapley additive explanation method when applied to two algorithms designed for tabular [...] Read more.

This work explores the effectiveness of explainable artificial intelligence in mapping solar photovoltaic power outputs based on weather data, focusing on short-term mappings. We analyzed the impact values provided by the Shapley additive explanation method when applied to two algorithms designed for tabular data—XGBoost and TabNet—and conducted a comprehensive evaluation of the overall model and across seasons. Our findings revealed that the impact of selected features remained relatively consistent throughout the year, underscoring their uniformity across seasons. Additionally, we propose a feature selection methodology utilizing the explanation values to produce more efficient models, by reducing data requirements while maintaining performance within a threshold of the original model. The effectiveness of the proposed methodology was demonstrated through its application to a residential dataset in Madeira, Portugal, augmented with weather data sourced from SolCast. Full article

(This article belongs to the Topic Smart Energy Systems, 2nd Edition)

►▼ Show Figures

Figure 1

12 pages, 6417 KiB  

Open AccessData Descriptor

Dataset for Machine Learning: Explicit All-Sky Image Features to Enhance Solar Irradiance Prediction

by Joylan Nunes Maciel, Jorge Javier Gimenez Ledesma and Oswaldo Hideo Ando Junior

Data 2024, 9(10), 113; https://doi.org/10.3390/data9100113 - 29 Sep 2024

Viewed by 2148

Abstract

Prediction of solar irradiance is crucial for photovoltaic energy generation, as it helps mitigate intermittencies caused by atmospheric fluctuations such as clouds, wind, and temperature. Numerous studies have applied machine learning and deep learning techniques from artificial intelligence to address this challenge. Based [...] Read more.

Prediction of solar irradiance is crucial for photovoltaic energy generation, as it helps mitigate intermittencies caused by atmospheric fluctuations such as clouds, wind, and temperature. Numerous studies have applied machine learning and deep learning techniques from artificial intelligence to address this challenge. Based on the recently proposed Hybrid Prediction Method (HPM), this paper presents an original and comprehensive dataset with nine attributes extracted from all-sky images developed using image processing techniques. This dataset and analysis of its attributes offer new avenues for research into solar irradiance forecasting. To ensure reproducibility, the data processing workflow and the standardized dataset have been meticulously detailed and made available to the scientific community to promote further research into prediction methods for photovoltaic energy generation. Full article

(This article belongs to the Topic Smart Energy Systems, 2nd Edition)

►▼ Show Figures

Figure 1

27 pages, 1106 KiB  

Open AccessEditor’s ChoiceArticle

Forecasting Electric Vehicles’ Charging Behavior at Charging Stations: A Data Science-Based Approach

by Herbert Amezquita, Cindy P. Guzman and Hugo Morais

Energies 2024, 17(14), 3396; https://doi.org/10.3390/en17143396 - 10 Jul 2024

Cited by 2 | Viewed by 1579

Abstract

The rising adoption of electric vehicles (EVs), driven by carbon neutrality goals, has prompted the need for accurate forecasting of EVs’ charging behavior. However, this task presents several challenges due to the dynamic nature of EVs’ usage patterns, including fluctuating demand and unpredictable [...] Read more.

The rising adoption of electric vehicles (EVs), driven by carbon neutrality goals, has prompted the need for accurate forecasting of EVs’ charging behavior. However, this task presents several challenges due to the dynamic nature of EVs’ usage patterns, including fluctuating demand and unpredictable charging durations. In response to these challenges and different from previous works, this paper presents a novel and holistic methodology for day-ahead forecasting of EVs’ plugged-in status and power consumption in charging stations (CSs). The proposed framework encompasses data analysis, pre-processing, feature engineering, feature selection, the use and comparison of diverse machine learning forecasting algorithms, and validation. A real-world dataset from a CS in Boulder City is employed to evaluate the framework’s effectiveness, and the results demonstrate its proficiency in predicting the EVs’ plugged-in status, with XGBoost’s classifier achieving remarkable accuracy with an F1-score of 0.97. Furthermore, an in-depth evaluation of six regression methods highlighted the supremacy of gradient boosting algorithms in forecasting the EVs’ power consumption, with LightGBM emerging as the most effective method due to its optimal balance between prediction accuracy with a 4.22% normalized root-mean-squared error (NRMSE) and computational efficiency with 5 s of execution time. The proposed framework equips power system operators with strategic tools to anticipate and adapt to the evolving EV landscape. Full article

(This article belongs to the Topic Smart Energy Systems, 2nd Edition)

►▼ Show Figures

Figure 1

15 pages, 850 KiB  

Open AccessArticle

Virtual Power Plants: Challenges, Opportunities, and Profitability Assessment in Current Energy Markets

by Zahid Ullah, Arshad Arshad and Azam Nekahi

Electricity 2024, 5(2), 370-384; https://doi.org/10.3390/electricity5020019 - 12 Jun 2024

Cited by 4 | Viewed by 4163

Abstract

The arrival of virtual power plants (VPPs) marks important progress in the energy sector, providing optimistic solutions to the increasing need for energy flexibility, resilience, and improved energy systems’ integration. VPPs harness several characteristics to bring together distributed energy resources (DERs), resulting in [...] Read more.

The arrival of virtual power plants (VPPs) marks important progress in the energy sector, providing optimistic solutions to the increasing need for energy flexibility, resilience, and improved energy systems’ integration. VPPs harness several characteristics to bring together distributed energy resources (DERs), resulting in economic gains and improved power grid reliability. Nevertheless, VPPs encounter major challenges when it comes to engaging in energy markets, mainly because there is no all-encompassing policy and regulatory framework specifically designed to accommodate their unique characteristics. This underscores the necessity for research endeavours to develop more advanced methods and structures for the long-term viability of VPPs. To address this concern, the study advocates for the implementation of a multi-aspect framework (MAF) as a systematic approach to thoroughly examine each aspect of virtual power plants (VPPs). A STEEP (social, technological, environmental, economic, and political) analytical tool is utilized to evaluate the challenges, opportunities, and benefits of a VPP in the existing energy markets. The proposed approach highlights important factors and actions that need to be taken to tackle the challenges related to VPP’ entry into energy markets. This study suggests that further support is required to promote the fast and widespread adoption of long-term VPP implementations. For this reason, a more favourable policy and regulatory framework based on social, technological, economic, environmental, and policy considerations is necessary to realize the genuine contributions of VPPs. Full article

(This article belongs to the Topic Smart Energy Systems, 2nd Edition)

►▼ Show Figures

Figure 1

17 pages, 7371 KiB  

Open AccessArticle

Identification of the Key Issues and Technical Paths for Intelligent Operation of Water Source Heat Pump Energy Stations Applying Digital Twin Technology

by Jiaji Zhang, Qiankun Wang, Shuqiang Gui, Junli Zhou and Jinlong Sun

Appl. Sci. 2024, 14(12), 5094; https://doi.org/10.3390/app14125094 - 12 Jun 2024

Cited by 1 | Viewed by 1082

Abstract

To address the challenges posed by global climate change, developing green energy systems characterized by informatization, digitalization, and intelligence is crucial for achieving carbon neutrality. This article is a research report type paper on water source heat pump (WSHP) energy stations, aiming to [...] Read more.

To address the challenges posed by global climate change, developing green energy systems characterized by informatization, digitalization, and intelligence is crucial for achieving carbon neutrality. This article is a research report type paper on water source heat pump (WSHP) energy stations, aiming to use digital twin technology and other information technologies to resolve conflicts between clean energy development and efficient energy utilization. The primary objective of this study is to identify and analyze issues in traditional energy station operations and management systems. Based on this analysis, specific technical solutions are proposed, including pathways for technological research, methodologies, and content. The results provide a comprehensive theoretical framework for the intelligent transformation of energy station systems and essential technical support for the WSHP energy station project in the Hankou Binjiang International Business District. The findings have significant implications for the widespread adoption of WSHP energy stations and the achievement of national carbon neutrality goals. Full article

(This article belongs to the Topic Smart Energy Systems, 2nd Edition)

►▼ Show Figures

Figure 1

24 pages, 1376 KiB  

Open AccessArticle

Determination of the Optimal Level of Reactive Power Compensation That Minimizes the Costs of Losses in Distribution Networks

by Jerzy Andruszkiewicz, Józef Lorenc and Agnieszka Weychan

Energies 2024, 17(1), 150; https://doi.org/10.3390/en17010150 - 27 Dec 2023

Cited by 7 | Viewed by 1873

Abstract

The objective of the presented paper is to verify economically justified levels of reactive energy compensation in the distribution network in the new market conditions, including the extensive use of smart metering systems, new types of load, or distributed generation. The proposed methodology [...] Read more.

The objective of the presented paper is to verify economically justified levels of reactive energy compensation in the distribution network in the new market conditions, including the extensive use of smart metering systems, new types of load, or distributed generation. The proposed methodology is based on the minimization of annual costs of losses caused by the flow of reactive energy to the supplied loads through the equivalent resistance of the distribution system determined on the basis of statistical energy losses in this network. The costs of losses are compared to the costs of using compensating devices expressed by the levelized costs of reactive energy generation. The results are the relations describing the optimal annual average value of the tgφ factor to be maintained by customers to optimize the cost of loss of the distribution network caused by reactive energy flows. The dependence of the optimal tgφ value on the analyzed load and network parameters is also discussed. The resulting optimal tgφ levels should be considered in the tariffication process of services offered by distribution system operators to improve capacity and limit the costs of power network operation due to reactive energy transmission. Full article

(This article belongs to the Topic Smart Energy Systems, 2nd Edition)

►▼ Show Figures

Figure 1

37 pages, 9772 KiB  

Open AccessArticle

Smoothing Intermittent Output Power in Grid-Connected Doubly Fed Induction Generator Wind Turbines with Li-Ion Batteries

by Henok Ayele Behabtu, Majid Vafaeipour, Abraham Alem Kebede, Maitane Berecibar, Joeri Van Mierlo, Kinde Anlay Fante, Maarten Messagie and Thierry Coosemans

Energies 2023, 16(22), 7637; https://doi.org/10.3390/en16227637 - 17 Nov 2023

Cited by 5 | Viewed by 2398

Abstract

Wind energy is an increasingly important renewable resource in today’s global energy landscape. However, it faces challenges due to the unpredictable nature of wind speeds, resulting in intermittent power generation. This intermittency can disrupt power grid stability when integrating doubly fed induction generators [...] Read more.

Wind energy is an increasingly important renewable resource in today’s global energy landscape. However, it faces challenges due to the unpredictable nature of wind speeds, resulting in intermittent power generation. This intermittency can disrupt power grid stability when integrating doubly fed induction generators (DFIGs). To address this challenge, we propose integrating a Li-ion battery energy storage system (BESS) with the direct current (DC) link of grid-connected DFIGs to mitigate power fluctuations caused by variable wind speed conditions. Our approach entails meticulous battery modeling, sizing, and control methods, all tailored to match the required output power of DFIG wind turbines. To demonstrate how well our Li-ion battery solution works, we have developed a MATLAB/Simulink R2022a version model. This model enables us to compare situations with and without the Li-ion battery in various operating conditions, including steady-state and dynamic transient scenarios. We also designed a buck–boost bidirectional DC-DC converter controlled by a proportional integral controller for battery charging and discharging. The battery actively monitors the DC-link voltage of the DFIG wind turbine and dynamically adjusts its stored energy in response to the voltage level. Thus, DFIG wind turbines consistently generate 1.5 MW of active power, operating with a highly efficient power factor of 1.0, indicating there is no reactive power produced. Our simulation results confirm that Li-ion batteries effectively mitigate power fluctuations in grid-connected DFIG wind turbines. As a result, Li-ion batteries enhance grid power stability and quality by absorbing or releasing power to compensate for variations in wind energy production. Full article

(This article belongs to the Topic Smart Energy Systems, 2nd Edition)

►▼ Show Figures

Figure 1

14 pages, 1217 KiB  

Open AccessArticle

Distributed Platform for Offline and Online EV Charging Simulation

by Joaquim Perez, Filipe Quintal and Lucas Pereira

Electronics 2023, 12(21), 4401; https://doi.org/10.3390/electronics12214401 - 25 Oct 2023

Viewed by 1315

Abstract

Efforts to enhance electric vehicle (EV) charging processes have spurred the emergence of smart charging algorithms. However, these studies are intricate and costly, necessitating preliminary simulations to assess EV integration into power grids. Existing solutions to this issue tend to be limited to [...] Read more.

Efforts to enhance electric vehicle (EV) charging processes have spurred the emergence of smart charging algorithms. However, these studies are intricate and costly, necessitating preliminary simulations to assess EV integration into power grids. Existing solutions to this issue tend to be limited to academia and proprietary systems. To address this, we propose a user-friendly and intuitive simulation tool employing a decoupled and flexible architecture. This architecture, achieved through open design and containerized microservices, streamlines maintenance, extension, and scalability. We substantiated the validity of our solution by simulating the charging infrastructure from an H2020 Research Project. Furthermore, we integrated our solution with an external system that executes smart charging algorithms. The proposed system yielded the desired results, enabling the project team to evaluate both the integration and algorithms, even amidst the COVID-19 lockdown. Full article

(This article belongs to the Topic Smart Energy Systems, 2nd Edition)

►▼ Show Figures

Figure 1

21 pages, 5422 KiB  

Open AccessArticle

System Dynamics Model of Decentralized Household Electricity Storage Implementation: Case Study of Latvia

by Armands Gravelsins, Erlanda Atvare, Edgars Kudurs, Anna Kubule and Dagnija Blumberga

Smart Cities 2023, 6(5), 2553-2573; https://doi.org/10.3390/smartcities6050115 - 25 Sep 2023

Cited by 3 | Viewed by 2016

Abstract

Increasing renewable energy share in total energy production is a direction that leads toward the European Union’s aims of carbon neutrality by 2050, as well as increasing energy self-sufficiency and independence. Some of the main challenges to increasing renewable energy share while providing [...] Read more.

Increasing renewable energy share in total energy production is a direction that leads toward the European Union’s aims of carbon neutrality by 2050, as well as increasing energy self-sufficiency and independence. Some of the main challenges to increasing renewable energy share while providing an efficient and secure energy supply are related to the optimization and profitability of de-centralized energy production systems. Integration of energy storage systems in addition to decentralized renewable energy production, for example, by solar panels, leads to more effective electricity supply and smart energy solutions. The modeling of such a complex dynamic system can be performed using the system dynamics method. The main aim of this research is to build and validate the basic structure of the system dynamics model for PV and battery diffusion in the household sector. A system dynamics model predicting the implementation of battery storage in private households was created for the case study of Latvia. Modeling results reveal that under the right conditions for electricity price and investment costs and with the right policy interventions, battery storage technologies combined with PV panels have a high potential for utilization in the household sector. Model results show that in a baseline scenario with no additional policies, up to 21,422 households or 10.8% of Latvian households could have combined PV and battery systems installed in 2050. Moderate subsidy policy can help to increase this number up to 25,118. Full article

(This article belongs to the Topic Smart Energy Systems, 2nd Edition)

►▼ Show Figures

Figure 1

13 pages, 1775 KiB  

Open AccessData Descriptor

Exploring Spatial Patterns in Sensor Data for Humidity, Temperature, and RSSI Measurements

by Juan Botero-Valencia, Adrian Martinez-Perez, Ruber Hernández-García and Luis Castano-Londono

Data 2023, 8(5), 82; https://doi.org/10.3390/data8050082 - 29 Apr 2023

Cited by 2 | Viewed by 2816

Abstract

The Internet of Things (IoT) is one of the fastest-growing research areas in recent years and is strongly linked to the development of smart cities, smart homes, and factories. IoT can be defined as connecting devices, sensors, and physical objects that can collect [...] Read more.

The Internet of Things (IoT) is one of the fastest-growing research areas in recent years and is strongly linked to the development of smart cities, smart homes, and factories. IoT can be defined as connecting devices, sensors, and physical objects that can collect and transmit data across a network, enabling increased automation and better decision-making. In several IoT applications, humidity and temperature are some of the most used variables for adjusting system configurations and understanding their performance because they are related to various physical processes, human comfort, manufacturing processes, and 3D printing, among other things. In addition, one of the biggest problems associated with IoT is the excessive production of data, so it is necessary to develop methodologies to optimize the process of collecting information. This work presents a new dataset comprising almost 55 million values of temperature, relative humidity, and RSSI (Received Signal Strength Indicator) collected in two indoor spaces for longer than 3915 h at 10 s intervals. For each experiment, we captured the information from 13 previously calibrated sensors suspended from the ceiling at the same height and with a known relative position. The proposed dataset aims to contribute a benchmark for evaluating indoor temperature and humidity-controlled systems. The collected data allow the validation and improvement of the acquisition process for IoT applications. Full article

(This article belongs to the Topic Smart Energy Systems, 2nd Edition)

►▼ Show Figures

Figure 1

Show export options expand_more Show export options expand_less

Select all

Export citation of selected articles as:

Plain Text BibTeX BibTeX (without abstracts) Endnote Endnote (without abstracts) Tab-delimited RIS

 

Error

Oops... you haven't selected anything for export.

Ok

clear

Displaying articles 1-11