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Power Grids’ Future Perspective in the Mainstream E-mobility Era and under the Umbrella of Sustainable Development

A special issue of Sustainability (ISSN 2071-1050). This special issue belongs to the section "Sustainable Transportation".

Deadline for manuscript submissions: closed (31 December 2023) | Viewed by 6860

Special Issue Editors

1. Research Promotion Unit, Co-creation Management Department, University of the Ryukyus, Okinawa 903-0213, Japan
2. Visting Researcher, Department of Electrical and Electronics Engineering, Faculty of Engineering, University of the Ryukyus, Okinawa 903-0213, Japan
Interests: sustainable energy; renewable power generation; power system optimization; power distribution system; sustainable e-mobility; energy efficiency; energy saving; distributed generation; battery storage
Energy Systems (Chubu Electric Power) Funded Research Division, IMaSS (Institute of Materials and Systems for Sustainability), Nagoya University, Nagoya, Japan
Interests: energy strategy; energy policy; sustainable development; environmental science; strategic management; renewable energy deployment; energy technologies; socioeconomic studies; Smart Grid; IoT
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Special Issue Information

Dear Colleagues,

A broad paradigm transition from internal combustion engines to electric vehicles and electric mass transportation is currently underway. As people, businesses, and public transport operators adopt e-mobility as a primary transport technology, it will be necessary to invest in infrastructures and technologies that support and enable this transition. The most prominent is how power will be provided to charge the millions of new electric vehicles (EVs) that will soon take to the roads. Power system behavior and performance must also be taken into consideration, as well as the enormous transitions between downstream networks and upstream generating levels. Furthermore, introducing clean energy resources and bi-directional energy flow from grid to vehicles and vehicles to gird considered to be huge promising for well energy supply and demand management fulfilling both technical as well as environmental concerns. Though several relevant projects have been initiated into practice, still more and more advanced research is expected to be applied for higher reliability and knowledge-based research. As a result, researchers in this extensive field need to focus on the importance of more decentralized renewable energy penetrations, grid modernization, smart grids, energy management, and deployment of smart technologies into conventional networks current framework and structure.

Thus, this Special Issue welcomes original research and review papers related to e-mobility, electric mass transportation and sustainable approaches, modernizing power systems, smart grids, energy management, massive renewable energy integration, and other topics which are connected to relevant Sustainable Development Goals (SDGs).

Additionally, this Special Issue offers a more concentrated forum for scholars in this study field and areas related to it to develop and utilize the greatest potential of these current challenges. Articles on, but not limited to, the following themes are urged to be submitted for this Special Issue:

  • Optimizations and energy management towards sustainable power grids;
  • Integrating renewable energy sources into power systems for a sustainable environment;
  • Optimal operation and control of power distribution networks in presence of EVs;
  • Environmental impacts of transportation electrification;
  • Evaluation methods for electrified transportation systems;
  • Electric vehicle charging infrastructure planning;
  • Urban transport electrification and energy consumption;
  • New transport systems concepts and future perspectives;
  • Electric vehicles and the environment;
  • Theoretical developments in the planning and operation of sustainable EVs;
  • Energy storage devices for sustainable EVs’ demand;
  • Energy infrastructure for electrical transportation;
  • Modeling and analysis of modern and future sustainable electric vehicles energy systems;
  • Smart energy management in electric vehicles for energy saving;
  • Energy savings through optimized EV charging in bidirectional energy flow V2G and G2V.

Dr. Mikaeel Ahmadi
Dr. Mir Sayed Shah Danish
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. Sustainability 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

  • sustainable mobility
  • power system optimal planning
  • sustainable development goals
  • sustainable transportation
  • power quality
  • power system reliability
  • smart grid
  • power system technologies
  • zero-energy building (ZEB)
  • energy efficiency
  • sustainability
  • renewable energy
  • electric vehicle
  • storage system
  • energy management
  • environmental and economic impacts
  • energy storage systems
  • green storage solutions
  • distributed generations
  • optimization

Published Papers (4 papers)

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Research

33 pages, 4169 KiB  
Article
Electric Vehicle Charging Station Power Supply Optimization with V2X Capabilities Based on Mixed-Integer Linear Programming
by Antonio Josip Šolić, Damir Jakus, Josip Vasilj and Danijel Jolevski
Sustainability 2023, 15(22), 16073; https://doi.org/10.3390/su152216073 - 17 Nov 2023
Cited by 2 | Viewed by 937
Abstract
The European Union is committed to both lowering greenhouse gas emissions and promoting the adoption of electric vehicles (EVs) on its roads. To achieve these goals, it is imperative to speed up the development of the charging infrastructure as well as to ensure [...] Read more.
The European Union is committed to both lowering greenhouse gas emissions and promoting the adoption of electric vehicles (EVs) on its roads. To achieve these goals, it is imperative to speed up the development of the charging infrastructure as well as to ensure the effective integration of the charging infrastructure into distribution networks. Given that EV charging costs significantly contribute to the total cost of owning an EV, it is important to hedge against rising electricity prices and ensure affordable charging for the end users. Connecting solar power plants and battery storage to the electric vehicle charging stations (EVCSs) serves as a measure of hedging against potential future electricity price increases but also as an option that can contribute to reducing impact on the distribution network loading. In addition to this, connecting EVCS through grid connections of existing consumers (office/residential buildings, shopping malls, etc.) can reduce grid connection costs for EVCS but also contribute to electricity cost reduction for both EVCS and existing end consumers. Additionally, by integrating advanced charging strategies like the vehicle-to-everything (V2X) approach, the overall charging costs can be reduced even further. This paper focuses on optimizing the power supply and operation of EVCS by considering strategic investments in grid connection, photovoltaic plants, and battery energy storage. The research explores the potential savings derived from reduced energy/charging costs, along with the reduction in peak power expenses for different power supply options. In addition to this, the research explores the effect of different EV charging strategies as well as EVCS grid connection on optimal investments and total system costs. The combined investment and energy management problem is focused on determining the optimal EVCS power supply and operation while minimizing total investment and operation expenditures over the project lifetime. The underlying optimization problems for different supply scenarios are cast as mixed-integer linear programming problems that can be solved efficiently. The results show the influence of different grid connection options and EV charging strategies on the joint operation and costs of EVCS and existing buildings. Full article
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20 pages, 3283 KiB  
Article
Maximizing Annual Energy Yield in a Grid-Connected PV Solar Power Plant: Analysis of Seasonal Tilt Angle and Solar Tracking Strategies
by Hameedullah Zaheb, Habibullah Amiry, Mikaeel Ahmadi, Habibullah Fedayi, Sajida Amiry and Atsushi Yona
Sustainability 2023, 15(14), 11053; https://doi.org/10.3390/su151411053 - 14 Jul 2023
Cited by 2 | Viewed by 940
Abstract
Harnessing the abundant solar resources holds great potential for sustainable energy generation. This research paper delves into a comprehensive analysis of seasonal tilt and solar tracking strategy scenarios for a 15 MW grid-connected PV solar power plant situated in Kandahar province, Afghanistan. The [...] Read more.
Harnessing the abundant solar resources holds great potential for sustainable energy generation. This research paper delves into a comprehensive analysis of seasonal tilt and solar tracking strategy scenarios for a 15 MW grid-connected PV solar power plant situated in Kandahar province, Afghanistan. The study investigates the impact of fixed tilt, seasonal tilt, SAHST (single-axis horizontal solar tracking), and SAVST (single-axis vertical solar tracking) on energy yield, considering technical, economic, and environmental aspects. In the first scenario, a fixed tilt angle of 31 degrees was employed. The second scenario explored the use of seasonal tilt angles, with a summer tilt angle of 15 degrees and a winter tilt angle of 30 degrees. The third scenario analyzed SAHST. Finally, the fourth scenario focused on implementing SAVST. SAVST proved to be an exceptional solution, showcasing a remarkable increase in annual energy yield, and generating an additional 6680 MWh/year, 6336 MWh/year, and 5084 MWh/year compared to fixed, seasonal, and SAHST scenarios, respectively. As a result, surplus energy yielded an income of USD 554,440.00 per year compared to fixed tilt. However, the investment cost for the solar tracking system amounted to USD 1,451,932, accompanied by an annual operation and maintenance cost of 0.007 USD/W/year. The analysis revealed a promising payback period of 3 years, confirming the economic feasibility of this investment. The findings underscore the effectiveness of different strategies for optimizing solar power generation in the Kandahar region. Notably, the installation of SAVST emerged as an influential solution, significantly increasing power production. These research outcomes bear practical implications for solar tracking strategies for addressing the load challenges faced by Kandahar province and offer valuable insights for the operators and operation of solar power plants in similar regions. Full article
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16 pages, 4412 KiB  
Article
AI-Enabled Energy Policy for a Sustainable Future
by Mir Sayed Shah Danish and Tomonobu Senjyu
Sustainability 2023, 15(9), 7643; https://doi.org/10.3390/su15097643 - 06 May 2023
Cited by 4 | Viewed by 2575
Abstract
The present time is a seminal decade for the transition of the energy sector through the deployment of green energy and the optimization of efficiencies using the power of automation and artificial intelligence (AI), which demands competitive policies to handle multidimensional endeavors via [...] Read more.
The present time is a seminal decade for the transition of the energy sector through the deployment of green energy and the optimization of efficiencies using the power of automation and artificial intelligence (AI), which demands competitive policies to handle multidimensional endeavors via a single platform. The failure of energy policies can have far-reaching socioeconomic consequences when policies do not meet the energy and climate goals throughout the lifecycle of the policy. Such shortcomings are reported to be due to inadequate incentives and poor decision making that needs to promote fairness, equality, equity, and inclusiveness in energy policies and project decision making. The integration of AI in energy sectors poses various challenges that this study aims to analyze through a comprehensive examination of energy policy processes. The study focuses on (1) the decision-making process during the development stage, (2) the implementation management process for the execution stage, (3) the integration of data science, machine learning, and deep learning in energy systems, and (4) the requirements of energy systems in the context of substantiality. Synergistically, an emerging blueprint of policy, data science and AI, engineering practices, management process, business models, and social approaches that provides a multilateral design and implementation reference is propounded. Finally, a novel framework is developed to develop and implement modern energy policies that minimize risks, promote successful implementation, and advance society’s journey towards net zero and carbon neutral objectives. Full article
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17 pages, 1223 KiB  
Article
Power System Voltage Stability Margin Estimation Using Adaptive Neuro-Fuzzy Inference System Enhanced with Particle Swarm Optimization
by Oludamilare Bode Adewuyi, Komla A. Folly, David T. O. Oyedokun and Emmanuel Idowu Ogunwole
Sustainability 2022, 14(22), 15448; https://doi.org/10.3390/su142215448 - 21 Nov 2022
Viewed by 1379
Abstract
In the current era of e-mobility and for the planning of sustainable grid infrastructures, developing new efficient tools for real-time grid performance monitoring is essential. Thus, this paper presents the prediction of the voltage stability margin (VSM) of power systems by the critical [...] Read more.
In the current era of e-mobility and for the planning of sustainable grid infrastructures, developing new efficient tools for real-time grid performance monitoring is essential. Thus, this paper presents the prediction of the voltage stability margin (VSM) of power systems by the critical boundary index (CBI) approach using the machine learning technique. Prediction models are based on an adaptive neuro-fuzzy inference system (ANFIS) and its enhanced model with particle swarm optimization (PSO). Standalone ANFIS and PSO-ANFIS models are implemented using the fuzzy ‘c-means’ clustering method (FCM) to predict the expected values of CBI as a veritable tool for measuring the VSM of power systems under different loading conditions. Six vital power system parameters, including the transmission line and bus parameters, the power injection, and the system voltage derived from load flow analysis, are used as the ANFIS model implementation input. The performances of the two ANFIS models on the standard IEEE 30-bus and the Nigerian 28-bus systems are evaluated using error and regression analysis metrics. The performance metrics are the root mean square error (RMSE), mean absolute percentage error (MAPE), and Pearson correlation coefficient (R) analyses. For the IEEE 30-bus system, RMSE is estimated to be 0.5833 for standalone ANFIS and 0.1795 for PSO-ANFIS; MAPE is estimated to be 13.6002% for ANFIS and 5.5876% for PSO-ANFIS; and R is estimated to be 0.9518 and 0.9829 for ANFIS and PSO-ANFIS, respectively. For the NIGERIAN 28-bus system, the RMSE values for ANFIS and PSO-ANFIS are 5.5024 and 2.3247, respectively; MAPE is 19.9504% and 8.1705% for both ANFIS and PSO-ANFIS variants, respectively, and the R is estimated to be 0.9277 for ANFIS and 0.9519 for ANFIS-PSO, respectively. Thus, the PSO-ANFIS model shows a superior performance for both test cases, as indicated by the percentage reduction in prediction error, although at the cost of a higher simulation time. Full article
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