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Alternative Sources of Energy Modeling, Automation, Optimal Planning and Operation

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

Deadline for manuscript submissions: closed (30 April 2022) | Viewed by 24279

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Prof. Dr. George S. Stavrakakis

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Laboratory of Circuits,Sensors and Renewable Energy Sources, Electrical & Computer Engineering Department, Kounoupidiana Campus, Technical University of Crete, GR73100 Chania, Crete, Greece
Interests: alternative (renewable) energy sources modeling and automation; production systems automation; decision support systems; systems safety and reliability analysis; real-time industrial processes fault monitoring and diagnosis; building energy management systems
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Dear Colleagues,

Formulation of computer implemented models of alternative (renewable) sources of energy (ASE or RES) will help in the proper allocation of widely available renewable energy sources, but they are also absolutely necessary to design and implement efficient automation for optimal operation of ASE (RES) plants and installations. Detailed simulation of alternative sources of energy devices and integrated power plants may be a very cost-effective solution, and very often, several subsystems of an integrated ASE (RES) power plant might be inappropriate, difficult to find, and/or very expensive. Further, ASE (RES) potential forecasts are essential to the integration of renewable power generation in electricity markets operations, since markets ought to be cleared in advance, while market participants shall then make decisions even before that happens. This Special Issue, entitled “Alternative Sources of Energy Modeling and Automation”, was proposed for the international journal Energies, which is an SSCI and SCIE journal (Impact Factor of 2.676 for 2017), to cover original research and scientific contributions related to the abovementioned topics, including the most usual ASE (RES) small electric power plants, such as wind, small hydro, geothermal, biomass, tidal, photovoltaic, fuel cells, batteries, hybrid plants, etc. As an example, in order to design integrated smart power grids based on ASE (RES) with battery-based storage, very good ASE (RES) component behavioral model and automation of the integrated system is necessarily required.

Papers selected for this Special Issue will be subject to a rigorous peer review procedure, with the aim of rapid and wide dissemination of research results, developments, and applications.

I am writing to invite you to submit your original work to this Special Issue. I look forward to receiving your outstanding research.

Prof. Dr. George S. Stavrakakis
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

Alternative/renewable sources of energy (ASE/RES) modeling and computer simulation
Alternative /renewable sources of energy (ASE/RES) automation
Solar thermal energy computer modeling and automation
Photovoltaic (PV) energy computer modeling, automation, optimal planning and operation, especially applied to autonomous PV installations
Fuel Cells, hydrogen, batteries, and energy storage in general modeling, automation, optimal planning and operation
Wind energy modeling, automation, optimal planning and operation
Biomass, biofuels, biogas, gazification, bioenergy in general, modeling and automation
Tidal systems models and automation
Geothermal systems models and automation
Waste to energy modeling, automation, optimal planning and operation
Hybrid ASE/RES models and automation
ASE/RES and hybrid power system optimization
ASE/RES and smart micro grids integration optimization
Alternative/renewable sources of energy (ASE/RES) forecasting and optimal planning
ASE/RES models and automation based on artificial neural networks, fuzzy logic, neuro-fuzzy methods, machine learning and Artificial Intelligence (AI) methods
Optimization methods applied in ASE/RES planning and/or optimal operation and grid integration
Decision support systems (DSS) applied in ASE/RES planning and/or optimal operation and grid integration

Published Papers (10 papers)

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Research

30 pages, 368 KiB

Open AccessArticle

Information and Public Knowledge of the Potential of Alternative Energies

by Galvão Meirinhos, Mariano Malebo, António Cardoso, Rui Silva and Reiville Rêgo

Energies 2022, 15(13), 4928; https://0-doi-org.brum.beds.ac.uk/10.3390/en15134928 - 05 Jul 2022

Cited by 3 | Viewed by 1609

Abstract

The objective of this research project is to study the economic development model of the Angolan economy in order to analyze the adoption of an alternative strategy capable of leveraging the economy, based essentially on alternative energies, and therefore, to demonstrate and prove the need to diversify Angola’s economic model, highlighting the benefits of a diversified versus a non-diversified economy with respect to sustainability. The first stage of the design of this empirical study involved establishing a focus group in order to construct and adjust a data collection instrument in the form of a questionnaire to be applied to a broader set of managers and informed professionals with a critical view of the country’s future and the models and alternatives to economic development and diversification of the economy on a sustainable basis. Energy plays a fundamental role in Angola’s economic and social development. Excessive dependency on the oil sector and inefficient production due to high costs, combined with changes in global environmental and energy policies, make it essential to reflect on the evolution of the country’s energy sector, equating a different economic development model, the diversification of the economy, and the exploration of other sources of energy, such as biofuels. Renewable energies emerge as a safe, healthy, environmentally friendly and economically viable energy alternative that could bring the Angolan economy closer to that of developed countries. Biofuels have become popular and have begun to be seen as a valid alternative to fossil fuels because they have lower production costs and they cause less impact on nature. Furthermore, since they are biodegradable, they can be commercialized at a lower cost from renewable sources. According to the respondents, the research results show that the best energy alternatives to reduce oil dependency are solar energy, biodiesel, hydraulic energy, and bioethanol. An assessment of the attractiveness and potential of biofuels show that the best alternative is bioethanol, followed by biodiesel. Full article

(This article belongs to the Special Issue Alternative Sources of Energy Modeling, Automation, Optimal Planning and Operation)

32 pages, 2042 KiB

Open AccessArticle

Scalability and Replicability for Smart Grid Innovation Projects and the Improvement of Renewable Energy Sources Exploitation: The FLEXITRANSTORE Case

by Georgios Fotis, Christos Dikeakos, Elias Zafeiropoulos, Stylianos Pappas and Vasiliki Vita

Energies 2022, 15(13), 4519; https://0-doi-org.brum.beds.ac.uk/10.3390/en15134519 - 21 Jun 2022

Cited by 30 | Viewed by 3125

Abstract

In this paper, detailed scalability and replicability plans have been developed to facilitate the adoption of innovation technologies in the pan-EU market. Smart grid development must enable both information and power exchange between suppliers and customers, thanks to the enormous innovation in intelligent communication, monitoring, and management systems. Implementing physical infrastructure alone is not enough, but a smart grid must include new business models and new regulations. In recent years, the number, participants, and scope of smart grid initiatives have increased, with different goals and results. FLEXITRANSTORE project integrates hardware and software solutions in all areas of the transmission system and wholesale markets, unleashing the potential for full flexibility of power systems and promoting the penetration of renewable energy sources and pan-EU markets. Full deployment of these demonstrated solutions requires a reasonable level of scalability and replicability to prevent project demonstrators from continuing local experimental exercises. Scalability and replicability are fundamental requirements for successful scaling-up and replication. Therefore, scalability and replicability enable or at least reduce barriers to the growth and reuse of project demonstrator results. Full article

(This article belongs to the Special Issue Alternative Sources of Energy Modeling, Automation, Optimal Planning and Operation)

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25 pages, 1780 KiB

Open AccessArticle

One-Day-Ahead Solar Irradiation and Windspeed Forecasting with Advanced Deep Learning Techniques

by Konstantinos Blazakis, Yiannis Katsigiannis and Georgios Stavrakakis

Energies 2022, 15(12), 4361; https://0-doi-org.brum.beds.ac.uk/10.3390/en15124361 - 15 Jun 2022

Cited by 9 | Viewed by 1880

Abstract

In recent years, demand for electric energy has steadily increased; therefore, the integration of renewable energy sources (RES) at a large scale into power systems is a major concern. Wind and solar energy are among the most widely used alternative sources of energy. However, there is intense variability both in solar irradiation and even more in windspeed, which causes solar and wind power generation to fluctuate highly. As a result, the penetration of RES technologies into electricity networks is a difficult task. Therefore, more accurate solar irradiation and windspeed one-day-ahead forecasting is crucial for safe and reliable operation of electrical systems, the management of RES power plants, and the supply of high-quality electric power at the lowest possible cost. Clouds’ influence on solar irradiation forecasting, data categorization per month for successive years due to the similarity of patterns of solar irradiation per month during the year, and relative seasonal similarity of windspeed patterns have not been taken into consideration in previous work. In this study, three deep learning techniques, i.e., multi-head CNN, multi-channel CNN, and encoder–decoder LSTM, were adopted for medium-term windspeed and solar irradiance forecasting based on a real-time measurement dataset and were compared with two well-known conventional methods, i.e., RegARMA and NARX. Utilization of a walk-forward validation forecast strategy was combined, firstly with a recursive multistep forecast strategy and secondly with a multiple-output forecast strategy, using a specific cloud index introduced for the first time. Moreover, the similarity of patterns of solar irradiation per month during the year and the relative seasonal similarity of windspeed patterns in a timeseries measurements dataset for several successive years demonstrates that they contribute to very high one-day-ahead windspeed and solar irradiation forecasting performance. Full article

(This article belongs to the Special Issue Alternative Sources of Energy Modeling, Automation, Optimal Planning and Operation)

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15 pages, 5488 KiB

Open AccessArticle

Detection of Demagnetization Faults in Axial Flux Permanent-Magnet Synchronous Wind Generators

by Apostolos Lamprokostopoulos, Epameinondas Mitronikas and Alexandra Barmpatza

Energies 2022, 15(9), 3220; https://0-doi-org.brum.beds.ac.uk/10.3390/en15093220 - 28 Apr 2022

Cited by 4 | Viewed by 2065

Abstract

A new method for detecting demagnetization faults in axial flux permanent magnet synchronous wind generators is presented in this study. Demagnetization faults occur in the case of total or partial loss of the magnetic properties of one or more permanent magnets of the machine. Fault signatures appearing in the current or voltage signal due to a demagnetization fault can often be confused with those produced by eccentricity faults, making the discrimination between the two types of faults difficult. The proposed methodology is based on the analysis of the instant power spectrum of the generator, combined with an estimator to derive the permanent magnet flux, based on the machine equations. Short-Time Fourier Transform is proposed as the means for spectrum analysis to ensure performance during variations of the generator speed. Results derived from the experimental tests are presented, which show that the proposed methodology is capable of detecting demagnetization faults and distinguishing them from eccentricity ones under a wide variety of operating conditions. Full article

(This article belongs to the Special Issue Alternative Sources of Energy Modeling, Automation, Optimal Planning and Operation)

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21 pages, 10033 KiB

Open AccessArticle

A Deep Learning and GIS Approach for the Optimal Positioning of Wave Energy Converters

by Georgios Batsis, Panagiotis Partsinevelos and Georgios Stavrakakis

Energies 2021, 14(20), 6773; https://0-doi-org.brum.beds.ac.uk/10.3390/en14206773 - 17 Oct 2021

Cited by 1 | Viewed by 1742

Abstract

Renewable Energy Sources provide a viable solution to the problem of ever-increasing climate change. For this reason, several countries focus on electricity production using alternative sources. In this paper, the optimal positioning of the installation of wave energy converters is examined taking into account geospatial and technical limitations. Geospatial constraints depend on Land Use classes and seagrass of the coastal areas, while technical limitations include meteorological conditions and the morphology of the seabed. Suitable installation areas are selected after the exclusion of points that do not meet the aforementioned restrictions. We implemented a Deep Neural Network that operates based on heterogeneous data fusion, in this case satellite images and time series of meteorological data. This fact implies the definition of a two-branches architecture. The branch that is trained with image data provides for the localization of dynamic geospatial classes in the potential installation area, whereas the second one is responsible for the classification of the region according to the potential wave energy using wave height and period time series. In making the final decision on the suitability of the potential area, a large number of static land use data play an important role. These data are combined with neural network predictions for the optimizing positioning of the Wave Energy Converters. For the sake of completeness and flexibility, a Multi-Task Neural Network is developed. This model, in addition to predicting the suitability of an area depending on seagrass patterns and wave energy, also predicts land use classes through Multi-Label classification process. The proposed methodology is applied in the marine area of the city of Sines, Portugal. The first neural network achieves 98.7% Binary Classification accuracy, while the Multi-Task Neural Network 97.5% in the same metric and 93.5% in the F1 score of the Multi-Label classification output. Full article

(This article belongs to the Special Issue Alternative Sources of Energy Modeling, Automation, Optimal Planning and Operation)

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19 pages, 417 KiB

Open AccessArticle

An Overview of Probabilistic Dimensioning of Frequency Restoration Reserves with a Focus on the Greek Electricity Market

by Anthony Papavasiliou

Energies 2021, 14(18), 5719; https://0-doi-org.brum.beds.ac.uk/10.3390/en14185719 - 10 Sep 2021

Cited by 6 | Viewed by 1602

Abstract

The dynamic dimensioning of frequency restoration reserves based on probabilistic criteria is becoming increasingly relevant in European power grid operations, following the guidelines of European legislation. This article compares dynamic dimensioning based on k-means clustering to static dimensioning on a case study of the Greek electricity market. It presents a model of system imbalances which aims to capture various realistic features of the stochastic behavior of imbalances, including skewed distributions, the dependencies of the imbalance distribution on various imbalance drivers, and the contributions of idiosyncratic noise to system imbalances. The imbalance model was calibrated in order to be consistent with historical reserve requirements in the Greek electricity market. The imbalance model was then employed in order to compare dynamic dimensioning based on probabilistic criteria to static dimensioning. The analysis revealed potential benefits of dynamic dimensioning for the Greek electricity market, which include a reduction in average reserve requirements and the preservation of a constant risk profile due to the adaptive nature of probabilistic dimensioning. Full article

(This article belongs to the Special Issue Alternative Sources of Energy Modeling, Automation, Optimal Planning and Operation)

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22 pages, 8302 KiB

Open AccessArticle

Supporting the Clean Electrification for Remote Islands: The Case of the Greek Tilos Island

by John K. Kaldellis

Energies 2021, 14(5), 1336; https://0-doi-org.brum.beds.ac.uk/10.3390/en14051336 - 01 Mar 2021

Cited by 17 | Viewed by 2767

Abstract

Many islands around the world present considerable energy supply problems, while their energy mixture is controlled by oil products. Meanwhile, several of these isolated islands enjoy excellent RES potential that support actions to maximize RES integration. In order to ameliorate energy supply security and energy autonomy of the Aegean Archipelagos Greek islands, an integrated solution is deployed based on the exploitation of the existing RES potential in conjunction with the application of an appropriate energy storage scheme, as well as complementary smart-grid elements. The proposed solution has been applied for the Greek Tilos island in the framework of the Tilos-Horizon 2020 program. In this context, the implementation of the integrated Tilos energy solution under the current local legislative frame is a great success story introducing several important innovative characteristics in the European market, like the combined operation of a wind turbine and a PV installation, the application of new technology battery energy storage, the installation of a DSM network/platform and the development of a large number of reliable forecasting algorithms. The innovative integrated solution is a real-world working operating example offering knowledge and proving that the solution deployed could be equally well applied in various other remote islands throughout the European territory with very promising results. Full article

(This article belongs to the Special Issue Alternative Sources of Energy Modeling, Automation, Optimal Planning and Operation)

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27 pages, 2297 KiB

Open AccessArticle

Hybridizing Lead–Acid Batteries with Supercapacitors: A Methodology

by Xi Luo, Jorge Varela Barreras, Clementine L. Chambon, Billy Wu and Efstratios Batzelis

Energies 2021, 14(2), 507; https://0-doi-org.brum.beds.ac.uk/10.3390/en14020507 - 19 Jan 2021

Cited by 17 | Viewed by 4456

Abstract

Hybridizing a lead–acid battery energy storage system (ESS) with supercapacitors is a promising solution to cope with the increased battery degradation in standalone microgrids that suffer from irregular electricity profiles. There are many studies in the literature on such hybrid energy storage systems (HESS), usually examining the various hybridization aspects separately. This paper provides a holistic look at the design of an HESS. A new control scheme is proposed that applies power filtering to smooth out the battery profile, while strictly adhering to the supercapacitors’ voltage limits. A new lead–acid battery model is introduced, which accounts for the combined effects of a microcycle’s depth of discharge (DoD) and battery temperature, usually considered separately in the literature. Furthermore, a sensitivity analysis on the thermal parameters and an economic analysis were performed using a 90-day electricity profile from an actual DC microgrid in India to infer the hybridization benefit. The results show that the hybridization is beneficial mainly at poor thermal conditions and highlight the need for a battery degradation model that considers both the DoD effect with microcycle resolution and temperate impact to accurately assess the gain from such a hybridization. Full article

(This article belongs to the Special Issue Alternative Sources of Energy Modeling, Automation, Optimal Planning and Operation)

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17 pages, 6256 KiB

Open AccessArticle

Parallel PV Configuration with Magnetic-Free Switched Capacitor Module-Level Converters for Partial Shading Conditions

by Georgios Kampitsis, Efstratios Batzelis, Remco van Erp and Elison Matioli

Energies 2021, 14(2), 456; https://0-doi-org.brum.beds.ac.uk/10.3390/en14020456 - 15 Jan 2021

Cited by 2 | Viewed by 1893

Abstract

In this paper, a module-level photovoltaic (PV) architecture in parallel configuration is introduced for maximum power extraction, under partial shading (PS) conditions. For the first time, a non-regulated switched capacitor (SC) nX converter is a used at the PV-side conversion stage, whose purpose is just to multiply the PV voltage by a fixed ratio and accordingly reduce the input current. All the control functions, including the maximum power point tracking, are transferred to the grid-side inverter. The voltage-multiplied PV modules (VMPVs) are connected in parallel to a common DC-bus, which offers expandability to the system and eliminates the PS issues of a typical string architecture. The advantage of the proposed approach is that the PV-side converter is relieved of bulky capacitors, filters, controllers and voltage/current sensors, allowing for a more compact and efficient conversion stage, compared to conventional per-module systems, such as microinverters. The proposed configuration was initially simulated in a 5 kW residential PV system and compared against conventional PV arrangements. For the experimental validation, a 10X Gallium Nitride (GaN) converter prototype was developed with a flat conversion efficiency of 96.3% throughout the power range. This is particularly advantageous, given the power production variability of PV generators. Subsequently, the VMPV architecture was tested on a two-module 500 W_P prototype, exhibiting an excellent power extraction efficiency of over 99.7% under PS conditions and minimal DC-bus voltage variation of 3%, leading to a higher total system efficiency compared to most state-of-the-art configurations. Full article

(This article belongs to the Special Issue Alternative Sources of Energy Modeling, Automation, Optimal Planning and Operation)

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23 pages, 5413 KiB

Open AccessArticle

Markov Chain Simulation of Coal Ash Melting Point and Stochastic Optimization of Operation Temperature for Entrained Flow Coal Gasification

by Jinchun Zhang, Shiheng Guan, Jinxiu Hou, Zichuan Zhang, Zhaoqian Li, Xiangzhong Meng and Chao Wang

Energies 2019, 12(22), 4245; https://0-doi-org.brum.beds.ac.uk/10.3390/en12224245 - 07 Nov 2019

Viewed by 1978

Abstract

In the entrained flow coal gasification process, the gas production is critically affected by the operating temperature (OT) and coal ash melting point (AMP), and the AMP is one of key factors for the determinations of OT. Considering the fact that coal is a typical nonhomogeneous substance and the coal ash composition varies from batch to batch, this paper proposes the application of the Markov Chain (MC) method in simulation of the random AMP series and the stochastic optimization of OT based on MC simulation for entrained flow coal gasification. The purpose of this paper is to provide a more accurate optimal OT decision method for entrained flow coal gasification practice. In this paper, the AMP was regarded as a random variable, and the random process method, Markov Chain, was used to describe the random AMP series of feed coal. Firstly, the MC simulation model about AMP was founded according to an actual sample data, 200 sets of AMP data from an industrial gasification plant under three simulation schemes (the sample data were individually divided into 16, eight and four state groups,). The comparisons between the simulation results and the actual values show that the founded MC simulation model descries the AMP series very well. Then, a stochastic programming model based on MC simulation for OT optimization was developed. Finally, this stochastic programming optimization model was optimized by genetic algorithm (GA). Comparing with the conventional OT optimization method, the proposed stochastic OT optimization model integrated MC simulation can ascertain a more accurate OT for guiding the coal gasification practice. Full article

(This article belongs to the Special Issue Alternative Sources of Energy Modeling, Automation, Optimal Planning and Operation)

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