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A special issue of Processes (ISSN 2227-9717). This special issue belongs to the section "Energy Systems".

Deadline for manuscript submissions: closed (15 September 2023) | Viewed by 19603

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Prof. Dr. Mohan Lal Kolhe

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Faculty of Engineering and Science, University of Agder, P.O. Box 422, 4604 Kristiansand, Norway
Interests: clean energy technologies; renewable energy systems; electrical energy engineering; energy efficiency; energy economics; techno-economic operation of energy systems; renewable energy technologies integration; smart grids; micro grids; electric vehicles; energy storage
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Special Issue Information

Dear Colleagues,

The 5th International Conference on Electrical Engineering and Green Energy (CEEGE) will be one of the most recognizable conferences for sustainable energy technologies. It will be held at Berlin, Germany from June 8 to 11, 2022. From the very beginning, this conference has been a unique occasion for disseminating and sharing the latest advancements in clean energy and electrical energy technologies. The development of clean technologies is now progressing, and hence the conference will focus on providing an opportunity to technologists, scientists, industrialists, environmentalists and experts to showcase their novel and energy-efficient clean energy technologies. The goal of the conference is to address energy- and environment-related challenges, especially those facing the whole world. This conference is going to provide networking opportunities for global collaborations to develop suitable solutions for diverse applications and user groups in sustainable development. The scientific program will include overviews, state-of-the-art lectures and controversial debates, and interactive education sessions, featuring symposia, breakout sessions and oral presentation sessions for abstracts. We sincerely hope that the blend of pleasant weather, warm hospitality and revitalizing social evenings will make the scientific environment richer. Selected peer-review articles will be recommended for publication in a Special Issue of the journal Processes (ISBN: ISSN 2227-9717), which is indexed in well-established databases/archives, e.g., SCI, Scopus, etc.

In this Special Issue, we seek innovative research and case studies that demonstrate the applications and advancements in clean energy technologies for sustainable development. The following topics are the main focus for this Special Issue on sustainable energy systems:

Renewable energy technologies, solar engineering, wind engineering, hydrogen energy technologies (electrolysis and fuel cells), energy efficiency, energy management systems, sustainable electrical energy systems, intelligent operation of energy processes, smart grids, micro-grids, e-mobility, techno-economics of energy processes, electro-mechanical energy conversion processes, electro-chemical energy conversion processes, modelling and control of sustainable energy systems and processes, etc.

Prof. Dr. Mohan Lal Kolhe
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. Processes is an international peer-reviewed open access monthly 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

renewable energy technologies
solar engineering
wind engineering
hydrogen energy technologies (electrolysis and fuel cells)
energy efficiency
energy management systems
sustainable electrical energy systems
intelligent operation of energy processes
smart grids
micro-grids
e-mobility
techno-economics of energy processes
electro-mechanical energy conversion processes
electro-chemical energy conversion processes
modelling and control of sustainable energy systems and processes

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Published Papers (9 papers)

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Research

25 pages, 3537 KiB

Open AccessArticle

Optimizing Short-Term Photovoltaic Power Forecasting: A Novel Approach with Gaussian Process Regression and Bayesian Hyperparameter Tuning

by Md. Samin Safayat Islam, Puja Ghosh, Md. Omer Faruque, Md. Rashidul Islam, Md. Alamgir Hossain, Md. Shafiul Alam and Md. Rafiqul Islam Sheikh

Processes 2024, 12(3), 546; https://doi.org/10.3390/pr12030546 - 11 Mar 2024

Cited by 3 | Viewed by 1265

Abstract

The inherent volatility of PV power introduces unpredictability to the power system, necessitating accurate forecasting of power generation. In this study, a machine learning (ML) model based on Gaussian process regression (GPR) for short-term PV power output forecasting is proposed. With its benefits in handling nonlinear relationships, estimating uncertainty, and generating probabilistic forecasts, GPR is an appropriate approach for addressing the problems caused by PV power generation’s irregularity. Additionally, Bayesian optimization to identify optimal hyper-parameter combinations for the ML model is utilized. The research leverages solar radiation intensity data collected at 60-min and 30-min intervals over periods of 1 year and 6 months, respectively. Comparative analysis reveals that the data set with 60-min intervals performs slightly better than the 30-min intervals data set. The proposed GPR model, coupled with Bayesian optimization, demonstrates superior performance compared to contemporary ML models and traditional neural network models. This superiority is evident in 98% and 90% improvements in root mean square errors compared to feed-forward neural network and artificial neural network models, respectively. This research contributes to advancing accurate and efficient forecasting methods for PV power output, thereby enhancing the reliability and stability of power systems. Full article

(This article belongs to the Special Issue Recent Advances in Sustainable Electrical Energy Technologies)

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27 pages, 5489 KiB

Open AccessArticle

Atomic Orbital Search Algorithm for Efficient Maximum Power Point Tracking in Partially Shaded Solar PV Systems

by Md Tahmid Hussain, Mohd Tariq, Adil Sarwar, Shabana Urooj, Amal BaQais and Md. Alamgir Hossain

Processes 2023, 11(9), 2776; https://doi.org/10.3390/pr11092776 - 17 Sep 2023

Cited by 4 | Viewed by 2095

Abstract

The efficient extraction of solar PV power is crucial to maximize utilization, even in rapidly changing environmental conditions. The increasing energy demands highlight the importance of solar photovoltaic (PV) systems for cost-effective energy production. However, traditional PV systems with bypass diodes at their output terminals often produce multiple power peaks, leading to significant power losses if the optimal combination of voltage and current is not achieved. To address this issue, algorithms capable of finding the highest value of a function are employed. Since the PV power output is a complex function with multiple local maximum power points (LMPPs), conventional algorithms struggle to handle partial shading conditions (PSC). As a result, nature-inspired algorithms, also known as metaheuristic algorithms, are used to maximize the power output of solar PV arrays. In this study, we introduced a novel metaheuristic algorithm called atomic orbital search for maximum power point tracking (MPPT) under PSC. The primary motivation behind this research is to enhance the efficiency and effectiveness of MPPT techniques in challenging scenarios. The proposed algorithm offers several advantages, including higher efficiency, shorter tracking time, reduced output variations, and improved duty ratios, resulting in faster convergence to the maximum power point (MPP). To evaluate the algorithm’s performance, we conducted extensive experiments using Typhoon HIL and compared it with other existing algorithms commonly employed for MPPT. The results clearly demonstrated that the proposed atomic orbital search algorithm outperformed the alternatives in terms of rapid convergence and efficient MPP tracking, particularly for complex shading patterns. This makes it a suitable choice for developing an MPP tracker applicable in various settings, such as industrial, commercial, and residential applications. In conclusion, our research addresses the pressing need for effective MPPT methods in solar PV systems operating under challenging conditions. The atomic orbital search algorithm showcases its potential in significantly improving the efficiency and performance of MPPT, ultimately contributing to the optimization of solar energy extraction and utilization. Full article

(This article belongs to the Special Issue Recent Advances in Sustainable Electrical Energy Technologies)

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30 pages, 46115 KiB

Open AccessArticle

Renewable Energy Role in Climate Stabilization and Water Consumption Minimization in Jordan

by Ayman Al-Quraan, Hiba Darwish and Ahmad M. A. Malkawi

Processes 2023, 11(8), 2369; https://doi.org/10.3390/pr11082369 - 7 Aug 2023

Cited by 3 | Viewed by 1869

Abstract

Climate change is one of the most essential phenomena studied by several researchers in the last few decades. The main reason this phenomenon occurs is greenhouse gases (GHG), chiefly CO2 emissions. About 30% of the created GHG emissions are achieved by electricity generation. This article investigates the role of renewable energy projects in Jordan, specifically wind and solar energy, in mitigating climate change and water consumption reduction using RETScreen software. It was found that the cumulative water consumption reduction from 2017 to 2021 due to the use of wind and solar projects is equal to 6.9491 × 109 gallons. Finally, the results show that the future dependence on renewable energy projects in Jordan to meet the growth in demand by the year 2030 reduces the expected increment in the climate temperature by 1.047 °C by that year. Full article

(This article belongs to the Special Issue Recent Advances in Sustainable Electrical Energy Technologies)

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21 pages, 6158 KiB

Open AccessArticle

Neuro-Fuzzy Based High-Voltage DC Model to Optimize Frequency Stability of an Offshore Wind Farm

by Muhammad Shoaib Bhutta, Tang Xuebang, Muhammad Faheem, Fahad M. Almasoudi, Khaled Saleem S. Alatawi and Huali Guo

Processes 2023, 11(7), 2049; https://doi.org/10.3390/pr11072049 - 9 Jul 2023

Cited by 7 | Viewed by 1666

Abstract

Lack of synchronization between high voltage DC systems linking offshore wind farms and the onshore grid is a natural consequence owing to the stochastic nature of wind energy. The poor synchronization results in increased system disturbances, grid contingencies, power loss, and frequency instability. Emphasizing frequency stability analysis, this research investigates a dynamic coordination control technique for a Double Fed Induction Generator (DFIG) consisting of OWFs integrated with a hybrid multi-terminal HVDC (MTDC) system. Line commutated converters (LCC) and voltage source converters (VSC) are used in the suggested control method in order to ensure frequency stability. The adaptive neuro-fuzzy inference approach is used to accurately predict wind speed in order to further improve frequency stability. The proposed HVDC system can integrate multiple distributed OWFs with the onshore grid system, and the control strategy is designed based on this concept. In order to ensure the transient stability of the HVDC system, the DFIG-based OWF is regulated by a rotor side controller (RSC) and a grid side controller (GSC) at the grid side using a STATCOM. The devised HVDC (MTDC) is simulated in MATLAB/SIMULINK, and the performance is evaluated in terms of different parameters, such as frequency, wind power, rotor and stator side current, torque, speed, and power. Experimental results are compared to a conventional optimal power flow (OPF) model to validate the performance. Full article

(This article belongs to the Special Issue Recent Advances in Sustainable Electrical Energy Technologies)

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19 pages, 8731 KiB

Open AccessArticle

Enhancing Power Generation Stability in Oscillating-Water-Column Wave Energy Converters through Deep-Learning-Based Time Delay Compensation

by Chan Roh

Processes 2023, 11(6), 1787; https://doi.org/10.3390/pr11061787 - 12 Jun 2023

Cited by 3 | Viewed by 1685

Abstract

Oscillating-water-column wave energy converters (OWC-WECs) are gaining attention for their high energy potential and environmental friendliness. However, their irregular input energy characteristics pose challenges to achieving stable power generation, particularly due to high peak power compared to average power. This study focuses on stable rating control to enable continuous power generation in the presence of irregular wave energy. It is difficult to precisely configure the existing rated power controllers due to physical time delays; this impacts system stability and utilization. To address this, we propose a rated power controller that compensates for system time delays using a deep learning algorithm. By predicting the valve control angle in advance and analyzing the input data for angle estimation, we successfully compensate for the physical time delay. The performance of the proposed rated power controller, incorporating the deep learning algorithm, is evaluated by analyzing the algorithm’s error rate. The results demonstrate that the proposed method improves power generation under various wave conditions by compensating for the unavoidable time delay of OWC-WECs, leading to a significant increase in annual power generation. In conclusion, the proposed method achieves approximately 31% higher annual power generation compared to the time delay controller. Full article

(This article belongs to the Special Issue Recent Advances in Sustainable Electrical Energy Technologies)

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17 pages, 1835 KiB

Open AccessArticle

Dynamic Optimization of Variable Load Process for Combined Heat and Power Unit Based on Sequential Quadratic Programming and Interior Point Method Alternating Solution Method

by Yuehua Huang, Qing Chen, Lei Zhang, Zihao Zhang, Xingtao Liu and Jintong Tu

Processes 2023, 11(6), 1660; https://doi.org/10.3390/pr11061660 - 30 May 2023

Cited by 1 | Viewed by 1024

Abstract

Aiming at the problem that the modeling and solving method of combined heat and power (CHP) unit variable load control process is challenging to meet the demand for efficient analysis of complex systems, this paper proposes a method based on sequential quadratic programming and interior point method (SQP-IPM) alternating solution for dynamic optimization of the CHP unit variable load process. Firstly, by constructing the CHP unit mechanism model, multi-variable coordination control constraints, and output variable process constraints, the dynamic optimization proposition of the CHP unit variable load control process is formed. Then, the large-scale nonlinear programming (NLP) problem formed by using the orthogonal configuration method to discrete the state and control variables is optimally solved using the IPM-SQP alternating solution method. Further, from the perspective of balancing the accuracy of the solution and computational efficiency, the flexible convergence depth control (CDC) strategy is introduced into the alternative solution method to improve the real-time performance of the algorithm. Finally, the variable load control process of 300MW extraction CHP unit is simulated to verify that the proposed method reduces the calculation time for 12 consecutive variable load scenarios by about 70%, effectively improving the real-time performance of scenario applications. Full article

(This article belongs to the Special Issue Recent Advances in Sustainable Electrical Energy Technologies)

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17 pages, 1943 KiB

Open AccessArticle

Economic Dispatch of Combined Heat and Power Plant Units within Energy Network Integrated with Wind Power Plant

by Paramjeet Kaur, Krishna Teerth Chaturvedi and Mohan Lal Kolhe

Processes 2023, 11(4), 1232; https://doi.org/10.3390/pr11041232 - 16 Apr 2023

Cited by 5 | Viewed by 1896

Abstract

Cogeneration, also known as a combined heat and power (CHP) system, produces both power and heat simultaneously. It reduces the operating costs and emissions by utilising waste heat from steam turbines and contributes to incapacitating the intermittency of renewable energy. The CHP-economic dispatch (CHP-ED) is needed to overcome the load dynamics as well as renewable intermittency. In this work, a CHP system connected with a wind power plant is considered for analysing the CHPED within a typical power system area. This study examines, the CHPED with and without a wind integrated energy network. The main objective of this work is to minimise the total operating cost, while meeting the generators’ constraints and prioritising the wind power output. The feasible operating region, valve point loading impact, and prohibited working regions of the CHP plants are taken while finding a CHPED solution with an integrated wind turbine. To find a CHPED solution, an optimisation algorithm was applied and the algorithm was based on selecting the best and worst scenarios. A typical 48-unit structure was used for validating the considered technique’s success for CHPED with/without a wind power plant. In our investigation, we found that operational costs were significantly reduced with a wind energy system. The presented methodology will be useful for the CHPED process of the decentralised CHP units for promoting further integration of the wind turbines and other distributed clean energy resources. Full article

(This article belongs to the Special Issue Recent Advances in Sustainable Electrical Energy Technologies)

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12 pages, 1094 KiB

Open AccessFeature PaperArticle

Priority Wise Electric Vehicle Charging for Grid Load Minimization

by Sayali Ashok Jawale, Sanjay Kumar Singh, Pushpendra Singh and Mohan Lal Kolhe

Processes 2022, 10(9), 1898; https://doi.org/10.3390/pr10091898 - 19 Sep 2022

Cited by 7 | Viewed by 3153

Abstract

The number of Electric vehicle (EV) users is expected to increase in the future. The driving profile of EV users is unpredictable, necessitating the design of charging scheduling protocols for EV charging stations servicing multiple EVs. A large EV charging load affects the grid in terms of peak load demand. Electric vehicle charging stations with solar panels can help to reduce the grid impact of EV charging events. With reference to the increasing number of EVs, new technology needs to be developed for charging station and management to create a stable system for users, and electric utilities. The load of a total EV charge can affect the grid, degrading quality and system stability. In this paper, a charging station scheduling strategy is proposed based on the game theoretic approach. In the proposed strategy, with respect to the grid load demand minimization, charging stations have scheduled EV charging times to prevent sudden peak load on the grid the proposed game theory strategy is sudden peak load on the grid. The proposed game theory strategy is defined on the basis of priority so that both grid operators and EV users can maximize their profit by setting priorities for charging and discharging. This work provides a strategy for grid peak load minimization. Full article

(This article belongs to the Special Issue Recent Advances in Sustainable Electrical Energy Technologies)

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13 pages, 2480 KiB

Open AccessArticle

Thermal Safety Evaluation of Silane Polymer Compounds as Electrolyte Additives for Silicon-Based Anode Lithium-Ion Batteries

by Chuan-Zhu Zhang, Lin-Jie Xie, Yan Tang, You Li, Jun-Cheng Jiang and An-Chi Huang

Processes 2022, 10(8), 1581; https://doi.org/10.3390/pr10081581 - 11 Aug 2022

Cited by 16 | Viewed by 3099

Abstract

The capacity fading and thermal safety issues caused by the volume effect of Si-based anodes and unstable solid electrolyte interphase (SEI) films during long-term cycling limit its large-scale application. In this study, silane polymer compound (2-cyanoethyl) triethoxysilane (TCN) was selected as an electrolyte additive to improve the reversibility and thermal safety of Si-based anode lithium-ion batteries (LIBs). TCN prevented the thermal interaction between the vitiated anode and electrolyte, and the onset temperature of the thermal reaction increased from 122.22 to 127.07 °C, as demonstrated by the results of thermogravimetric analysis and differential scanning calorimetry. The thermal stability of lithiated anodes containing various electrolytes was then assessed using a range of thermo-kinetic models. The results revealed that the activation energy of Si-based lithiated anodes increased from 68.46 to 91.32 kJ/mol, while the thermal hazard greatly decreased. Additionally, the electrochemical test and characterization results showed that TCN helped generate a stable SEI coating with more Li₂CO₃ components, which improved the cells’ cycle stability. This study provides a new reference for the growth of LIBs with high security and energy density. Full article

(This article belongs to the Special Issue Recent Advances in Sustainable Electrical Energy Technologies)

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