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Computing for Sustainable Energy
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Computing for Sustainable Energy

A special issue of Energies (ISSN 1996-1073). This special issue belongs to the section "A: Sustainable Energy".

Deadline for manuscript submissions: closed (31 March 2022) | Viewed by 9064

Special Issue Editor

Dr. Marco Gerards

E-Mail Website
Guest Editor
Faculty of Electrical Engineering, Mathematics and Computer Science, University of Twente, 7522 NB Enschede, The Netherlands
Interests: algorithms; complexity theory; data science; demand-side management; game theory; optimization; machine learning

Special Issue Information

Dear Colleagues,

Due to global warming, it is essential to prepare our energy system for a high level of renewable energy penetration and an increased electrification of transportation and heating. This requires a shift from top–down demand-driven energy generation towards a bottom–up distributed system where demand adapts to local (renewable) generation. This shift requires a smart grid that is supported by a high volume of measurements, efficient algorithms, and a modern communication infrastructure.

With the development of the smart grid, many computational challenges occur. Decisions for operating the network, controlling batteries, and smart appliances will be automated by ICT systems that optimally control the equipment and the smart grid as a whole. For this new way of steering the energy system, new efficient optimization, machine learning, and control algorithms are needed. To be successful, the algorithm designer needs to take into account efficiency, computational complexity, required communication volumes, data quality, privacy and security, etc. For this Special Issue, we invite submissions that address these and related issues.

Dr. Marco Gerards
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.

Keywords

  • Algorithmic game theory for smart grids
  • Data science for smart grids
  • Decentralized energy management
  • Demand side management
  • Demand response
  • Machine learning for energy applications
  • Optimization algorithms for smart grids

 

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Published Papers (2 papers)

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Research

16 pages, 5000 KiB  
Article
Exploring Wind Speed for Energy Considerations in Eastern Jerusalem-Palestine Using Machine-Learning Algorithms
by Saeed Salah, Husain R. Alsamamra and Jawad H. Shoqeir
Energies 2022, 15(7), 2602; https://doi.org/10.3390/en15072602 - 2 Apr 2022
Cited by 24 | Viewed by 2772
Abstract
Wind energy is one of the fastest growing sources of energy worldwide. This is clear from the high volume of wind power applications that have been increased in recent years. However, the uncertain nature of wind speed induces several challenges towards the development [...] Read more.
Wind energy is one of the fastest growing sources of energy worldwide. This is clear from the high volume of wind power applications that have been increased in recent years. However, the uncertain nature of wind speed induces several challenges towards the development of efficient applications that require a deep analysis of wind speed data and an accurate wind energy potential at a site. Therefore, wind speed forecasting plays a crucial rule in reducing this uncertainty and improving application efficiency. In this paper, we experimented with several forecasting models coming from both machine-learning and deep-learning paradigms to predict wind speed in a metrological wind station located in East Jerusalem, Palestine. The wind speed data were obtained, modelled, and forecasted using six machine-learning techniques, namely Multiple Linear Regression (MLR), lasso regression, ridge regression, Support Vector Regression (SVR), random forest, and deep Artificial Neural Network (ANN). Five variables were considered to develop the wind speed prediction models: timestamp, hourly wind speed, pressure, temperature, and direction. The performance of the models was evaluated using four statistical error measures: Root Mean Square Error (RMSE), Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE), and coefficient of determination (R2). The experimental results demonstrated that the random forest followed by the LSMT-RNN outperformed the other techniques in terms of wind speed prediction accuracy for the study site. Full article
(This article belongs to the Special Issue Computing for Sustainable Energy)
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23 pages, 7649 KiB  
Article
Modelling, Design and Control of a Standalone Hybrid PV-Wind Micro-Grid System
by Ayman Al-Quraan and Muhannad Al-Qaisi
Energies 2021, 14(16), 4849; https://doi.org/10.3390/en14164849 - 9 Aug 2021
Cited by 59 | Viewed by 5581
Abstract
The problem of electrical power delivery is a common problem, especially in remote areas where electrical networks are difficult to reach. One of the ways that is used to overcome this problem is the use of networks separated from the electrical system through [...] Read more.
The problem of electrical power delivery is a common problem, especially in remote areas where electrical networks are difficult to reach. One of the ways that is used to overcome this problem is the use of networks separated from the electrical system through which it is possible to supply electrical energy to remote areas. These networks are called standalone microgrid systems. In this paper, a standalone micro-grid system consisting of a Photovoltaic (PV) and Wind Energy Conversion System (WECS) based Permanent Magnet Synchronous Generator (PMSG) is being designed and controlled. Fuzzy logic-based Maximum Power Point Tracking (MPPT) is being applied to a boost converter to control and extract the maximum power available for the PV system. The control system is designed to deliver the required energy to a specific load, in all scenarios. The excess energy generated by the PV panel is used to charge the batteries when the energy generated by the PV panel exceeds the energy required by the load. When the electricity generated by the PV panels is insufficient to meet the load’s demands, the extra power is extracted from the charged batteries. In addition, the controller protects the battery banks in all conditions, including normal, overcharging, and overdischarging conditions. The controller should handle each case correctly. Under normal operation conditions (20% < State of Charge (SOC) < 80%), the controller functions as expected, regardless of the battery’s state of charge. When the SOC reaches 80%, a specific command is delivered, which shuts off the PV panel and the wind turbine. The PV panel and wind turbine cannot be connected until the SOC falls below a safe margin value of 75% in this controller. When the SOC goes below 20%, other commands are sent out to turn off the inverter and disconnect the loads. The electricity to the inverter is turned off until the batteries are charged again to a suitable value. Full article
(This article belongs to the Special Issue Computing for Sustainable Energy)
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