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Selected Papers from 20 IEEE International Conference on Environment and Electrical Engineering (EEEIC 2020)

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A special issue of Energies (ISSN 1996-1073).

Deadline for manuscript submissions: closed (31 October 2020) | Viewed by 29498

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Prof. Dr. Rodolfo Araneo

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Department of Astronautical, Electrical and Energetic Engineering University of Rome La Sapienza Via Eudossiana 18, 00184 Rome, Italy
Interests: electromagnetic compatibility; energy harvesting; graphene electrodynamics; numerical and analytical techniques for modeling high-speed printed circuit boards; shielding; transmission lines; periodic structures; devices based on graphene
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Dear Colleagues,

The 20th IEEE International Conference on Environment and Electrical Engineering (IEEE EEEIC20) is an international forum for the exchange of ideas and information on energy systems. The Conference provides a unique opportunity for designers and people from industry to interact with manufacturers, energy utilities people, and university researchers, and to discuss a wide variety of topics related to energy systems and environmental issues. Ever-increasing awareness of environmental concerns and intensive international efforts to reduce emissions of greenhouse gases has stimulated the best contributions towards achieving the goals of renewable energy diversification and sustainable development. IEEE EEEIC 2020 is the 20th edition of the Conference and is one of Europe’s largest, longest-running professional networking and educational events. The conference has been technically co-sponsored by IEEE since 2008 and by EMCS, IAS, and PES since 2015.

Submissions related to any of the following topics are welcomed:

- Renewable-energy sources and storage

- Power systems and smart grids

- Energy-efficient systems

- Smart buildings

- Circuits, sensors, and actuators

- Materials

- Environmental phenomena and pollution

- Regulation and electricity markets

- Mobility

- Maintenance, operation, and safety

- Measurements

Thank you very much!

Prof. Dr. Rodolfo Araneo
Guest Editor

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.

Published Papers (12 papers)

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Research

14 pages, 4735 KiB

Open AccessArticle

A Low Power AC/DC Interface for Wind-Powered Sensor Nodes

by Mohammad Haidar, Hussein Chible, Corrado Boragno and Daniele D. Caviglia

Energies 2021, 14(7), 1823; https://doi.org/10.3390/en14071823 - 25 Mar 2021

Cited by 6 | Viewed by 1814

Abstract

Sensor nodes have been assigned a lot of tasks in a connected environment that is growing rapidly. The power supply remains a challenge that is not answered convincingly. Energy harvesting is an emerging solution that is being studied to integrate in low power applications such as internet of things (IoT) and wireless sensor networks (WSN). In this work an interface circuit for a novel fluttering wind energy harvester is presented. The system consists of a switching converter controlled by a low power microcontroller. Optimization techniques on the hardware and software level have been implemented, and a prototype is developed for testing. Experiments have been done with generated input signals resulting in up to 67% efficiency for a constant voltage input. Other experiments were conducted in a wind tunnel that showed a transient output that is compatible with the target applications. Full article

(This article belongs to the Special Issue Selected Papers from 20 IEEE International Conference on Environment and Electrical Engineering (EEEIC 2020))

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15 pages, 2425 KiB

Open AccessArticle

Estimation Model of Total Energy Consumptions of Electrical Vehicles under Different Driving Conditions

by Seyed Mahdi Miraftabzadeh, Michela Longo and Federica Foiadelli

Energies 2021, 14(4), 854; https://doi.org/10.3390/en14040854 - 06 Feb 2021

Cited by 18 | Viewed by 2424

Abstract

The ubiquitous influence of E-mobility, especially electrical vehicles (EVs), in recent years has been considered in the electrical power system in which CO₂ reduction is the primary concern. Having an accurate and timely estimation of the total energy demand of EVs defines the interaction between customers and the electrical power grid, considering the traffic flow, power demand, and available charging infrastructures around a city. The existing EV energy prediction methods mainly focus on a single electric vehicle energy demand; to the best of our knowledge, none of them address the total energy that all EVs consume in a city. This situation motivated us to develop a novel estimation model in the big data regime to calculate EVs’ total energy consumption for any desired time interval. The main contribution of this article is to learn the generic demand patterns in order to adjust the schedules of power generation and prevent any electrical disturbances. The proposed model successfully handled 100 million records of real-world taxi routes and weather condition datasets, demonstrating that energy consumptions are highly correlated to the weekdays’ traffic flow. Moreover, the pattern identifies Thursdays and Fridays as the days of peak energy usage, while weekend days and holidays present the lowest range. Full article

(This article belongs to the Special Issue Selected Papers from 20 IEEE International Conference on Environment and Electrical Engineering (EEEIC 2020))

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10 pages, 1099 KiB

Open AccessArticle

Storage Placement and Sizing in a Distribution Grid with High PV Generation

by Benjamin Matthiss, Arghavan Momenifarahani and Jann Binder

Energies 2021, 14(2), 303; https://doi.org/10.3390/en14020303 - 08 Jan 2021

Cited by 12 | Viewed by 2361

Abstract

With the increasing penetration of renewable resources into the low-voltage distribution grid, the demand for alternatives to grid reinforcement measures has risen. One possible solution is the use of battery systems to balance the power flow at crucial locations in the grid. Hereby, the optimal location and size of the system have to be determined in regard to investment and its effect on grid stability. In this paper, the optimal placement and sizing of battery storage systems for grid stabilization in a small low-voltage distribution grid in southern Germany with high PV penetration are investigated and compared to a grid heuristic reinforcement strategy. Full article

(This article belongs to the Special Issue Selected Papers from 20 IEEE International Conference on Environment and Electrical Engineering (EEEIC 2020))

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25 pages, 552 KiB

Open AccessArticle

A Review of the Enabling Methodologies for Knowledge Discovery from Smart Grids Data

by Fabrizio De Caro, Amedeo Andreotti, Rodolfo Araneo, Massimo Panella, Antonello Rosato, Alfredo Vaccaro and Domenico Villacci

Energies 2020, 13(24), 6579; https://doi.org/10.3390/en13246579 - 14 Dec 2020

Cited by 9 | Viewed by 1687

Abstract

The large-scale deployment of pervasive sensors and decentralized computing in modern smart grids is expected to exponentially increase the volume of data exchanged by power system applications. In this context, the research for scalable and flexible methodologies aimed at supporting rapid decisions in a data rich, but information limited environment represents a relevant issue to address. To this aim, this paper investigates the role of Knowledge Discovery from massive Datasets in smart grid computing, exploring its various application fields by considering the power system stakeholder available data and knowledge extraction needs. In particular, the aim of this paper is dual. In the first part, the authors summarize the most recent activities developed in this field by the Task Force on “Enabling Paradigms for High-Performance Computing in Wide Area Monitoring Protective and Control Systems” of the IEEE PSOPE Technologies and Innovation Subcommittee. Differently, in the second part, the authors propose the development of a data-driven forecasting methodology, which is modeled by considering the fundamental principles of Knowledge Discovery Process data workflow. Furthermore, the described methodology is applied to solve the load forecasting problem for a complex user case, in order to emphasize the potential role of knowledge discovery in supporting post processing analysis in data-rich environments, as feedback for the improvement of the forecasting performances. Full article

(This article belongs to the Special Issue Selected Papers from 20 IEEE International Conference on Environment and Electrical Engineering (EEEIC 2020))

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17 pages, 3397 KiB

Open AccessArticle

Convolutional Neural Network for Dust and Hotspot Classification in PV Modules

by Giovanni Cipriani, Antonino D’Amico, Stefania Guarino, Donatella Manno, Marzia Traverso and Vincenzo Di Dio

Energies 2020, 13(23), 6357; https://doi.org/10.3390/en13236357 - 02 Dec 2020

Cited by 28 | Viewed by 3159

Abstract

This paper proposes an innovative approach to classify the losses related to photovoltaic (PV) systems, through the use of thermographic non-destructive tests (TNDTs) supported by artificial intelligence techniques. Low electricity production in PV systems can be caused by an efficiency decrease in PV modules due to abnormal operating conditions such as failures or malfunctions. The most common performance decreases are due to the presence of dirt on the surface of the module, the impact of which depends on many parameters and conditions, and can be identified through the use of the TNDTs. The proposed approach allows one to automatically classify the thermographic images from the convolutional neural network (CNN) of the system, achieving an accuracy of 98% in tests that last a couple of minutes. This approach, compared to approaches in literature, offers numerous advantages, including speed of execution, speed of diagnosis, reduced costs, reduction in electricity production losses. Full article

(This article belongs to the Special Issue Selected Papers from 20 IEEE International Conference on Environment and Electrical Engineering (EEEIC 2020))

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16 pages, 1723 KiB

Open AccessArticle

Environmental and Economic Sustainability of Electric Vehicles: Life Cycle Assessment and Life Cycle Costing Evaluation of Electricity Sources

by Mattia Rapa, Laura Gobbi and Roberto Ruggieri

Energies 2020, 13(23), 6292; https://doi.org/10.3390/en13236292 - 29 Nov 2020

Cited by 15 | Viewed by 4326

Abstract

The electro-mobility of vehicles could solve the negative effects of road transport, by decreasing greenhouse gas emissions. However, some electric vehicles also have a negative impact on the environment related to the nature of electricity used. This paper aims to evaluate the electricity sources for electric vehicles using a Life Cycle Thinking approach. Life cycle assessment, using several midpoints and endpoint methods, highlighted that the most damaging sources were lignite and diesel, while hydropower, wind, and biomass were the most sustainable ones. Cumulative energy demand showed that biomass used the least energy (0.034 MJ eq.), but originates from 100% non-renewable sources. Lignite, which also comes from 100% non-renewable sources, used the most energy (17.791 MJ eq.). The lowest carbon footprints were for wind, biomass, and photovoltaic (<0.1 kg CO₂ eq). Municipal waste incineration and natural gas had a medium impact, while lignite, coal, peat, and diesel had a high impact (>1.0 kg CO₂ eq.). Considering life cycle costing, photovoltaic electricity generation was the most expensive (0.2107 USD/kWh) while natural gas the cheapest (0.0661 USD/kWh). Therefore, this study presents an integrated approach that may offer a valid tool for decision-makers, giving them the possibility to choose the electricity sources for electric vehicles. Full article

(This article belongs to the Special Issue Selected Papers from 20 IEEE International Conference on Environment and Electrical Engineering (EEEIC 2020))

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17 pages, 2921 KiB

Open AccessArticle

A Simulation Approach for Optimising Energy-Efficient Driving Speed Profiles in Metro Lines

by Mariano Gallo, Marilisa Botte, Antonio Ruggiero and Luca D’Acierno

Energies 2020, 13(22), 6038; https://doi.org/10.3390/en13226038 - 19 Nov 2020

Cited by 8 | Viewed by 1710

Abstract

We propose a model for optimising driving speed profiles on metro lines to reduce traction energy consumption. The model optimises the cruising speed to be maintained on each section between two stations; the functions that link the cruising speed to the travel time on the section and the corresponding energy consumption are built using microscopic railway simulation software. In addition to formulating an optimisation model and its resolution through a gradient algorithm, the problem is also solved by using a simulation model and the corresponding optimisation module, with which stochastic factors may be included in the problem. The results are promising and show that traction energy savings of over 25% compared to non-optimised operations may be achieved. Full article

(This article belongs to the Special Issue Selected Papers from 20 IEEE International Conference on Environment and Electrical Engineering (EEEIC 2020))

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12 pages, 1028 KiB

Open AccessArticle

A One-Month Monitoring of Exposure to Solar UV Radiation of a Group of Construction Workers in Tuscany

by Alberto Modenese, Fabriziomaria Gobba, Valentina Paolucci, Swen Malte John, Pietro Sartorelli and Marc Wittlich

Energies 2020, 13(22), 6035; https://doi.org/10.3390/en13226035 - 19 Nov 2020

Cited by 6 | Viewed by 1817

Abstract

Solar radiation exposure at work is a relevant heath risk in the construction sector. Our objective was to monitor for a full month the individual solar ultraviolet radiation (UVR) exposure of a group of three construction workers active in Siena (latitude = 43°19′ N), a town in Tuscany (Italy). We used personal electronic dosimeters “X-2012-10” (Gigahertz, Turkenfeld, Germany) to register the UV irradiance in the UVA and UVB/C regions separately and we consulted a specific database to retrieve the corresponding ambient erythemal UVR dose (cloud-free conditions). In spring, construction workers from central Italy received a quite variable UVR dose, between 0.9 standard erythemal doses (SED) and 15.6 SED/day, 5.7 on average. Considering the proportion with respect to the potential environmental exposure, personal exposure resulted between 2.7% and 31.2% of the ambient erythemal dose, with a mean value of 12.5%. Cumulatively, the three construction workers received in one working month a UVR dose of more than 120 SED. In a year, we estimated that a construction worker from Tuscany region is exposed to about 750 SED. This data demonstrates that construction workers in Italy are exposed to extremely high levels of solar UVR, with a relevant risk of developing adverse health effects related to the potential accumulation of UVR-induced damage in susceptible biological tissues, such as the skin and the eyes. Full article

(This article belongs to the Special Issue Selected Papers from 20 IEEE International Conference on Environment and Electrical Engineering (EEEIC 2020))

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18 pages, 5448 KiB

Open AccessArticle

Chattering Free Adaptive Sliding Mode Controller for Photovoltaic Panels with Maximum Power Point Tracking

by Hina Gohar Ali and Ramon Vilanova Arbos

Energies 2020, 13(21), 5678; https://doi.org/10.3390/en13215678 - 30 Oct 2020

Cited by 9 | Viewed by 2038

Abstract

Photovoltaic system is utilized to generate energy that relies upon the ecological conditions, for example, temperature, irradiance, and the load associated with it. Considering the non-linear component of photovoltaic (PV) array and the issue of low effectiveness because of the variable natural conditions, the Maximum Power Point Tracking (MPPT) method is required to extract the maximum power from the PV system. The adopted control is executed utilizing an Adaptive Sliding Mode Controller (ASMC) and the enhancement is actualized utilizing an Improved Pattern Search Method (IPSM). This work employs IPSM based optimization approach in order to command the underlying ASMC controller. The upper level decision determines the sliding surface for the adaptive controller. As a non-linear strategy, the stability of the adaptive controller is guaranteed by conducting a Liapunov analysis. On the practical side, MATLAB/Simulink is used as simulator for the controller implementation and coupling with PSIM in order to connect it with the PV system object of control. The simulation results validate that the proposed controller effectively improves the voltage tracking, system power with reduced chattering effect and steady-state error. The performance of the proposed control architectures is validated by comparing the proposals with that of the well-known and widely used Proportional Integral Derivative (PID) controller. That operated as a lower level controller for a Perturb & Observe (P&O) and Particle Swarm Optimization (PSO). Full article

(This article belongs to the Special Issue Selected Papers from 20 IEEE International Conference on Environment and Electrical Engineering (EEEIC 2020))

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20 pages, 5237 KiB

Open AccessArticle

Event-Driven Coulomb Counting for Effective Online Approximation of Li-Ion Battery State of Charge

by Saeed Mian Qaisar

Energies 2020, 13(21), 5600; https://doi.org/10.3390/en13215600 - 26 Oct 2020

Cited by 24 | Viewed by 2284

Abstract

Lithium-ion batteries are deployed in a range of modern applications. Their utilization is evolving with the aim of achieving a greener environment. Batteries are costly, and battery management systems (BMSs) ensure long life and proper battery utilization. Modern BMSs are complex and cause a notable overhead consumption on batteries. In this paper, the time-varying aspect of battery parameters is used to reduce the power consumption overhead of BMSs. The aim is to use event-driven processing to realize effective BMSs. Unlike the conventional approach, parameters of battery cells, such as voltages and currents, are no longer regularly measured at a predefined time step and are instead recorded on the basis of events. This renders a considerable real-time compression. An inventive event-driven coulomb counting method is then presented, which employs the irregularly sampled data information for an effective online state of charge (SOC) determination. A high energy battery model for electric vehicle (EV) applications is studied in this work. It is implemented by using the equivalent circuit modeling (ECM) approach. A comparison of the developed framework is made with conventional fixed-rate counterparts. The results show that, in terms of compression and computational complexities, the devised solution surpasses the second order of magnitude gain. The SOC estimation error is also quantified, and the system attains a ≤4% SOC estimation error bound. Full article

(This article belongs to the Special Issue Selected Papers from 20 IEEE International Conference on Environment and Electrical Engineering (EEEIC 2020))

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14 pages, 3822 KiB

Open AccessArticle

Conventional and Second Order Sliding Mode Control of Permanent Magnet Synchronous Motor Fed by Direct Matrix Converter: Comparative Study

by Abdelhakim Dendouga

Energies 2020, 13(19), 5093; https://doi.org/10.3390/en13195093 - 30 Sep 2020

Cited by 15 | Viewed by 1941

Abstract

The main objective of this work revolves around the design of second order sliding mode controllers (SOSMC) based on the super twisting algorithm (STA) for asynchronous permanent magnet motor (PMSM) fed by a direct matrix converter (DMC), in order to improve the effectiveness of the considered drive system. The SOSMC was selected to minimize the chattering phenomenon caused by the conventional sliding mode controller (SMC), as well to decrease the level of total harmonic distortion (THD) produced by the drive system. In addition, the literature has taken a great interest in the STA due to its robustness to modeling errors and to external disturbances. Furthermore, due to its low conduction losses, the space vector approach was designated as a switching law to control the DMC. In addition, the topology and design method of the damped passive filter, which allows improvement of the waveform and attenuation of the harmonics of the input current, have been detailed. Finally, to discover the strengths and weaknesses of the proposed control approach based on SOSMC, a comparative study between the latter and that using the conventional SMC was executed. The results obtained confirm the effectiveness of SOSMC over the conventional SMC under different operating conditions. Full article

(This article belongs to the Special Issue Selected Papers from 20 IEEE International Conference on Environment and Electrical Engineering (EEEIC 2020))

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16 pages, 5298 KiB

Open AccessArticle

Sequential Tasks Shifting for Participation in Demand Response Programs

by Mahsa Khorram, Pedro Faria, Zita Vale and Carlos Ramos

Energies 2020, 13(18), 4879; https://doi.org/10.3390/en13184879 - 17 Sep 2020

Cited by 6 | Viewed by 1573

Abstract

In this paper, the proposed methodology minimizes the electricity cost of a laundry room by means of load shifting. The laundry room is equipped with washing machines, dryers, and irons. Additionally, the optimization model handles demand response signals, respecting user preferences while providing the required demand reduction. The sequence of devices operation is also modeled, ensuring correct operation cycles of different types of devices which are not allowed to overlap or have sequence rules. The implemented demand response program specifies a power consumption limit in each period and offers discounts for energy prices as incentives. In addition, users can define the required number of operations for each device in specific periods, and the preferences regarding the operation of consecutive days. In the case study, results have been obtained regarding six scenarios that have been defined to survey about effects of different energy tariffs, power limitations, and incentives, in a laundry room equipped with three washing machines, two dryers, and one iron. A sensitivity analysis of the power consumption limit is presented. The results show that the proposed methodology is able to accommodate the implemented scenario, respecting user preferences and demand response program, minimizing energy costs. The final electricity price has been calculated for all scenarios to discuss the more effective schedule in each scenario. Full article

(This article belongs to the Special Issue Selected Papers from 20 IEEE International Conference on Environment and Electrical Engineering (EEEIC 2020))

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