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Applied Optimization in Clean and Renewable Energy: New Trends

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A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Energy Science and Technology".

Deadline for manuscript submissions: closed (31 March 2021) | Viewed by 46920

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Prof. Ugo Fiore

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Department of Management and Quantitative Studies, Parthenope University, Napoli, Italy
Interests: data science; optimization; security
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Prof. Dr. Elias Munapo

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School of Economics & Decision Sciences, North West University, Mahikeng 2745, South Africa
Interests: optimization; applied computational mathematics; OR; stastical simulation
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Dr. Pandian Vasant

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MERLIN Research Centre, Faculty of Electrical and Electronic Engineering, Ton Duc Thang University, Ho Chi Minh City 700000, Vietnam
Interests: artificial intelligence; computing in mathematics; natural science; engineering and medicine algorithms
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Dr. Joshua Thomas

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UOW Malaysia, KDU Penang University College, George Town, Malaysia
Interests: intelligent systems techniques; deep learning algorithms; data science; visual analytics; scheduling and timetabling
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Dr. Vladimir Panchenko

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1. Department of Theoretical and Applied Mechanics, Russian University of Transport, 127994 Moscow, Russia
2. Laboratory of Systems of Non-Traditional Energy, Federal Scientific Agroengineering Center VIM, 109456 Moscow, Russia
Interests: renewable energy; agroengineering technologies; autonomous power supply; electric transport; three-dimensional modeling
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Special Issue Information

Dear Colleagues,

In recent years, the environmental pollution become a crisis and the world is facing. The direction of research is towards the utilization of renewable energy which will help in fullfilling the energy demand and also to mitigate the environmental problems. E.g. biomass. Evolutionary algorithms are a collection of the start-of-the-art theoretical research, design challenges, and applications in the field of computer science. Multi-objective optimization is concerned with mathematical optimization problems involving more than one objective function to optimized simultanesouly. It had been applied in many fields of science, including engineering, economics, and logistics where the optimal decisions need to be made trade-offs between the conflicting objectives. Nowadays, a large amount of unstructured heterogenous data powered the demand to extract useful insights in an automatic, reliable and scalable way. Machine learning, which aims to construct algorithms that can learn from and make predictions on data intelligently, has attracted increasing attention in the recent years and has been successfully applied to many data analytical tasks, such as image processing, face recognition, video surveillance, document summarization, etc. Since a lot of machine learning algorithms formulate the learning tasks as linear, quadratic or semi-definite mathematical programming problems, optimization becomes a crucial tool and plays a key role in machine learning and multimedia data analysis tasks. On the other hand, data science, data analytics are not simply the consumers of optimization technology but a rapidly evolving interdisciplinary research field that is itself promoting new optimization ideas, models, and solutions.

This special issue aims to seek the high-quality papers from academics and industry-related researchers in the areas of applied mathematics, renewable energy systems, machine learning, artificial intelligence, pattern recognition, data mining, multimedia processing, and big data to show the most recently advanced methods, e.g. deep neural networks and learning systems, in optimization and machine learning for parallel data computations.

Prof. Ugo Fiore
Prof. Dr. Elias Munapo
Dr. Pandian Vasant
Dr. J. Joshua Thomas
Dr. Vladimir Panchenko
Guest Editors

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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. Applied Sciences is an international peer-reviewed open access semimonthly journal published by MDPI.

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Keywords

Energy and power systems
Intelligent systems
Evolutionary Computation
Distributed/Parallel Algorithms in Machine Learning
Graph based learning
Imbalanced Data Learning
Particle Swarm Optimization
Artificial Intelligence
Reinforcement Learning
Robotics
Deep Learning Algorithms
Machine translation
Natural Language Processing
Fuzzy Logic
Dynamic Programming
Multi-Objective Optimization
Solar Energy
Applied Statistics
Operational Research
Trasportation research
Optimization
Industry 4.0
Smart Cities
5G Network
Sustainable computing

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Published Papers (13 papers)

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Editorial

Jump to: Research

3 pages, 187 KiB

Open AccessEditorial

Applied Optimization in Clean and Renewable Energy: New Trends

by Ugo Fiore, Elias Munapo, Pandian Vasant, Joshua Thomas and Vladimir Panchenko

Appl. Sci. 2022, 12(13), 6572; https://doi.org/10.3390/app12136572 - 29 Jun 2022

Cited by 2 | Viewed by 1411

Abstract

In recent years, more and more countries have paid close attention to energy and environmental issues [...] Full article

(This article belongs to the Special Issue Applied Optimization in Clean and Renewable Energy: New Trends)

Research

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16 pages, 2852 KiB

Open AccessArticle

The Start-Up of Continuous Biohydrogen Production from Cheese Whey: Comparison of Inoculum Pretreatment Methods and Reactors with Moving and Fixed Polyurethane Carriers

by Elza R. Mikheeva, Inna V. Katraeva, Andrey A. Kovalev, Dmitriy A. Kovalev, Alla N. Nozhevnikova, Vladimir Panchenko, Ugo Fiore and Yuri V. Litti

Appl. Sci. 2021, 11(2), 510; https://doi.org/10.3390/app11020510 - 6 Jan 2021

Cited by 24 | Viewed by 3760

Abstract

This article presents the results of the start-up of continuous production of biohydrogen from cheese whey (CW) in an anaerobic filter (AF) and anaerobic fluidized bed (AFB) with a polyurethane carrier. Heat and acid pretreatments were used for the inactivation of hydrogen-scavengers in the inoculum (mesophilic and thermophilic anaerobic sludge). Acid pretreatment was effective for thermophilic anaerobic sludge to suppress methanogenic activity, and heat treatment was effective for mesophilic anaerobic sludge. Maximum specific yields of hydrogen, namely 178 mL/g chemical oxygen demand (COD) and 149 mL/g COD for AFB and AF, respectively, were obtained at the hydraulic retention time (HRT) of 4.5 days and organic load rate (OLR) of 6.61 kg COD/(m³ day). At the same time, the maximum hydrogen production rates of 1.28 and 1.9 NL/(L day) for AF and AFB, respectively, were obtained at the HRT of 2.02 days and OLR of 14.88 kg COD/(m³ day). At the phylum level, the dominant taxa were Firmicutes (65% in AF and 60% in AFB), and at the genus level, Lactobacillus (40% in AF and 43% in AFB) and Bifidobacterium (24% in AF and 30% in AFB). Full article

(This article belongs to the Special Issue Applied Optimization in Clean and Renewable Energy: New Trends)

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15 pages, 1518 KiB

Open AccessArticle

Application of Polyacrylamide Flocculant for Stabilization of Anaerobic Digestion under Conditions of Excessive Accumulation of Volatile Fatty Acids

by Anna A. Nikitina, Artem A. Ermoshin, Elena A. Zhuravleva, Andrey A. Kovalev, Dmitriy A. Kovalev, Vladimir Panchenko and Yuri V. Litti

Appl. Sci. 2021, 11(1), 100; https://doi.org/10.3390/app11010100 - 24 Dec 2020

Cited by 7 | Viewed by 3171

Abstract

Excessive accumulation of volatile fatty acids (VFA) is one of the major factors destabilizing methanogenic digestion of organic wastes in anaerobic bioreactors. Existing methods of stabilization of this process are mostly expensive and labor-intensive, often requiring removal of a considerable portion of acidified biomass from the bioreactor. We propose a method for methanogenesis restoration in such soured reactors by the addition of a cationic polyacrylamide flocculant (PAM) at 20 mg/g total solids. After flocculant addition, mixing should be minimized to prolong the existence of the floccules formed in the presence of the flocculant. While partial microbial degradation of the polyacrylamide flocculant was observed during the thermophilic anaerobic process, complete PAM mineralization did not occur. Significant inhibition of anaerobic processes, primarily in the activity of syntrophic propionate-oxidizing bacteria, was observed at PAM concentrations above 40 mg/g total solids. Full article

(This article belongs to the Special Issue Applied Optimization in Clean and Renewable Energy: New Trends)

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14 pages, 8360 KiB

Open AccessArticle

A Novel Variable Step Size Incremental Conductance Method with an Adaptive Scaling Factor

by Man-Tsai Chuang, Yi-Hua Liu and Song-Pei Ye

Appl. Sci. 2020, 10(15), 5214; https://doi.org/10.3390/app10155214 - 29 Jul 2020

Cited by 10 | Viewed by 2767

Abstract

In this paper, a novel variable step size (VSS) incremental conductance (INC) method with an adaptive scaling factor is proposed. The proposed technique utilizes the model-based state estimation method to calculate the irradiance level and then determine an appropriate scaling factor accordingly to enhance the capability of maximum power point tracking (MPPT). The fast and accurate tracking can be achieved by the presented method without the need for extra irradiance and temperature sensors. Only the voltage-and-current sets of any two operating points on the characteristic curve are needed to estimate the irradiance level. By choosing a proper scaling factor, the performance of the conventional VSS INC method can be improved. To validate the studied algorithm, a 600 W prototyping circuit is constructed and the performances are demonstrated experimentally. Compared to conventional VSS INC methods under the tested conditions, the tracking time is shortened by 31.8%. The tracking accuracy is also improved by 2.1% and 3.5%, respectively. Besides, tracking energy loss is reduced by 43.9% and 29.9%, respectively. Full article

(This article belongs to the Special Issue Applied Optimization in Clean and Renewable Energy: New Trends)

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17 pages, 1247 KiB

Open AccessArticle

Neural Network Approach to MPPT Control and Irradiance Estimation

by Žarko Zečević and Maja Rolevski

Appl. Sci. 2020, 10(15), 5051; https://doi.org/10.3390/app10155051 - 22 Jul 2020

Cited by 36 | Viewed by 4537

Abstract

Photovoltaic (PV) modules require maximum power point tracking (MPPT) algorithms to ensure that the amount of power extracted is maximized. In this paper, we propose a low-complexity MPPT algorithm that is based on the neural network (NN) model of the photovoltaic module. Namely, the expression for the output current of the NN model is used to derive the analytical, iterative rules for determining the maximal power point (MPP) voltage and irradiance estimation. In this way, the computational complexity is reduced compared to the other NN-based MPPT methods, in which the optimal voltage is predicted directly from the measurements. The proposed algorithm cannot instantaneously determine the optimal voltage, but it contains a tunable parameter for controlling the trade-off between the tracking speed and computational complexity. Numerical results indicate that the relative error between the actual maximum power and the one obtained by the proposed algorithm is less than 0.1%, which is up to ten times smaller than in the available algorithms. Full article

(This article belongs to the Special Issue Applied Optimization in Clean and Renewable Energy: New Trends)

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18 pages, 6367 KiB

Open AccessArticle

Hierarchical Pitch Control for Small Wind Turbines Based on Fuzzy Logic and Anticipated Wind Speed Measurement

by Ernesto Chavero-Navarrete, Mario Trejo-Perea, Juan Carlos Jáuregui-Correa, Roberto Valentín Carrillo-Serrano, Guillermo Ronquillo-Lomeli and José Gabriel Ríos-Moreno

Appl. Sci. 2020, 10(13), 4592; https://doi.org/10.3390/app10134592 - 2 Jul 2020

Cited by 14 | Viewed by 2944

Abstract

Bringing electricity to areas of difficult terrain is a complicated task, so it is convenient to generate power using local natural resources, such as wind, through a small horizontal-axis wind turbine (S-HAWT). However, at the rotor height of these wind turbines, the wind is often turbulent due to obstacles such as trees and buildings. For a turbine to function properly in these conditions, the action of the wind force on the rotor must be smoothed out by controlling the pitch angle. A commercial derivative-integral-proportional (PID)-type pitch controller works well when system dynamics are stable, but not when there are disturbances in the system. This paper proposes a hierarchical fuzzy logic controller (HFLC) to solve the nonlinear system effects produced by atypical winds. The methodology includes a statistical analysis of wind variability at the installation site, which determines the functions of belonging and its hierarchy. In addition, installing an anemometer in front of the turbine allows an advanced positioning of the blades in the presence of wind gusts. The algorithm was implemented in an S-HAWT, and a comparison was made to quantify the performance difference between the proposed control strategy and a conventional PID controller. Full article

(This article belongs to the Special Issue Applied Optimization in Clean and Renewable Energy: New Trends)

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20 pages, 4101 KiB

Open AccessArticle

A Hybrid Model to Simulate Test Cases of Electrical Power Systems

by Roman Rodriguez-Aguilar

Appl. Sci. 2020, 10(10), 3531; https://doi.org/10.3390/app10103531 - 20 May 2020

Cited by 1 | Viewed by 2540

Abstract

The development of solution algorithms for power system problems is based on hypothetical test systems and test cases. These systems are very scarce, and the degree of variability is relatively low. The constant development of the economic analysis in electrical power systems denotes the need to obtain standardized systems and cases. In this study, the creation of standardized test cases based on a hybrid model using Lévy alpha stable distributions and generalized additive models is proposed. The objective of the work is to present a methodological proposal for the creation of test environments for optimization models based on general information about the operation of particular power systems. The simulation of random values based on Lévy alpha stable distributions lets capturing the series impulsivity and demand peaks, and the use of generalized additive models permits capturing non-linearity in the behavior of the demand for electrical energy. The hybrid model will tolerate simulating as many instances as necessary, with a coherent behavior attached to the reality of the operation of the analyzed electrical systems. Full article

(This article belongs to the Special Issue Applied Optimization in Clean and Renewable Energy: New Trends)

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21 pages, 2940 KiB

Open AccessArticle

Power Capacity Optimization in a Photovoltaics-Based Microgrid Using the Improved Artificial Bee Colony Algorithm

by Huijuan Zhang, Zi Xie, Hsiung-Cheng Lin and Shaoyong Li

Appl. Sci. 2020, 10(9), 2990; https://doi.org/10.3390/app10092990 - 25 Apr 2020

Cited by 14 | Viewed by 2916

Abstract

Although the combined cooling, heating and power (CCHP) microgrid is feasible for achieving a high energy utilization efficiency, the fluctuation of energy sources, such as a photovoltaic system and multiple loads, may affect the safety, economics and stability in CCHP microgrid operation. For this reason, this paper establishes a mathematical model using a multi-objective optimization mechanism for resolving the influence of economy and energy allocation in the mixed photovoltaic type CCHP microgrid. It is based on analytic hierarchy process (AHP) to determine the individual weight of objective function optimization for the multi-objective power capacity allocation. The improved artificial bee colony (IABC) based on the whale search and dynamic selection probability can achieve an optimization solution, reaching a stable operation state and reasonable capacity configuration in the microgrid system. The performance results confirm that the proposed algorithm is superior to others in both convergence speed and accuracyfor the capacity allocation of the CCHP microgrid. Full article

(This article belongs to the Special Issue Applied Optimization in Clean and Renewable Energy: New Trends)

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13 pages, 2329 KiB

Open AccessArticle

Surrogacy-Based Maximization of Output Power of a Low-Voltage Vibration Energy Harvesting Device

by Marcin Kulik, Mariusz Jagieła and Marian Łukaniszyn

Appl. Sci. 2020, 10(7), 2484; https://doi.org/10.3390/app10072484 - 4 Apr 2020

Cited by 6 | Viewed by 2278

Abstract

The coreless microgenerators implemented in electromagnetic vibration energy harvesting devices usually suffer from power deficiency. This can be noticeably improved by optimizing the distribution of separate turns within the armature winding. The purposeful optimization routine developed in this work is based on numerical identification of the turns that contribute most to the electromotive force and the elimination of those with the least contribution in order to reduce the internal impedance of the winding. The associated mixed integer nonlinear programming problem is solved comparatively using three approaches employing surrogate models based on kriging. The results show very good performance of the strategy based on a sequentially refined kriging in terms of the ability to accurately localize extremum and reduction of the algorithm execution time. As a result of optimization, the output power of the system increased by some 300 percent with respect to the initial configuration. Full article

(This article belongs to the Special Issue Applied Optimization in Clean and Renewable Energy: New Trends)

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13 pages, 3490 KiB

Open AccessArticle

An Optimized Covering Spheroids by Spheres

by Alexander Pankratov, Tatiana Romanova, Igor Litvinchev and Jose Antonio Marmolejo-Saucedo

Appl. Sci. 2020, 10(5), 1846; https://doi.org/10.3390/app10051846 - 7 Mar 2020

Cited by 13 | Viewed by 2794

Abstract

Covering spheroids (ellipsoids of revolution) by different spheres is studied. The research is motivated by packing non-spherical particles arising in natural sciences, e.g., in powder technologies. The concept of an

ε

-cover is introduced as an outer multi-spherical approximation of the spheroid with [...] Read more.

ε

-cover is introduced as an outer multi-spherical approximation of the spheroid with the proximity

ε

. A fast heuristic algorithm is proposed to construct an optimized

ε

-cover giving a reasonable balance between the value of the proximity parameter

ε

and the number of spheres used. Computational results are provided to demonstrate the efficiency of the approach. Full article

(This article belongs to the Special Issue Applied Optimization in Clean and Renewable Energy: New Trends)

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14 pages, 1269 KiB

Open AccessArticle

Optimization of the Distribution Network Using an Emerging Technology

by Emanuel Jesús Ulin Hernández, Jania Astrid Saucedo Martínez and José Antonio Marmolejo Saucedo

Appl. Sci. 2020, 10(3), 857; https://doi.org/10.3390/app10030857 - 26 Jan 2020

Cited by 9 | Viewed by 4219

Abstract

Unmanned Aerial Vehicles (UAVs) are a technology that has recently been incorporated in the distribution of products, which in this study, are packages. It can improve the distribution system in environments where there is significant traffic congestion. Furthermore, UAV can help to deliver small packages between warehouses, by using them as an alternative means of distribution. The incursion is of an emerging technology, in this case the use of drones, for a new delivery system in order to improve a university distribution system, in the view of the fact that in recent years, companies have focused on the use of logistics operations for the improvement of productivity and delivery times. The study presents a mathematical model, based on the Problem of the Traveling Salesman (TSP) for the planning of a route to increase the efficiency in the distribution process at Ciudad Universitaria, an Universidad Autónoma de Nuevo León (UANL) campus that contemplates the use of the emerging technology of UAVs, and the traditional method of using trucks. The model considers restrictions on the use of drones, such as the limitation of travel times and maximum distance. Full article

(This article belongs to the Special Issue Applied Optimization in Clean and Renewable Energy: New Trends)

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16 pages, 540 KiB

Open AccessArticle

Demand Prediction Using a Soft-Computing Approach: A Case Study of Automotive Industry

by Tomas Eloy Salais-Fierro, Jania Astrid Saucedo-Martinez, Roman Rodriguez-Aguilar and Jose Manuel Vela-Haro

Appl. Sci. 2020, 10(3), 829; https://doi.org/10.3390/app10030829 - 24 Jan 2020

Cited by 13 | Viewed by 5123

Abstract

According to the literature review performed, there are few methods focused on the study of qualitative and quantitative variables when making demand projections by using fuzzy logic and artificial neural networks. The purpose of this research is to build a hybrid method for integrating demand forecasts generated from expert judgements and historical data and application in the automotive industry. Demand forecasts through the integration of variables; expert judgements and historical data using fuzzy logic and neural network. The methodology includes the integration of expert and historical data applying the Delphi method as a means of collecting fuzzy date. The result according to proposed methodology shows how fuzzy logic and neural networks is an alternative for demand planning activity. Machine learning techniques are techniques that generate alternatives for the tools development for demand forecasting. In this study, qualitative and quantitative variables are integrated through the implementation of fuzzy logic and time series artificial neural networks. The study aims to focus in manufacturing industry factors in conjunction time series data. Full article

(This article belongs to the Special Issue Applied Optimization in Clean and Renewable Energy: New Trends)

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15 pages, 909 KiB

Open AccessArticle

Optimization of the Storage Location Assignment and the Picker-Routing Problem by Using Mathematical Programming

by Johanna Bolaños Zuñiga, Jania Astrid Saucedo Martínez, Tomas Eloy Salais Fierro and José Antonio Marmolejo Saucedo

Appl. Sci. 2020, 10(2), 534; https://doi.org/10.3390/app10020534 - 10 Jan 2020

Cited by 24 | Viewed by 5994

Abstract

The order picking process involves a series of activities in response to customer needs, such as the selection or programming of orders (batches), and the selection of different items from their storage location to shipment. These activities are accomplished by a routing policy that determines the picker sequence for retrieving the items from the storage location. Therefore, the order picking problem has been plenty investigated; however, in previous research, the proposed models were based on demand fulfilling, putting aside factors such as the product weight—which is an important criterion—at the time of establishing routes. In this article, a mathematical model is proposed; it takes into account the product’s weight derived from a case study. This model is relevant, as no similar work was found in the literature that improves the order picking by making simultaneous decisions on the storage location assignment and the picker-routing problem, considering precedence constraints based on the product weight and the characteristics of the case study, as the only location for each product in a warehouse with a general layout. Full article

(This article belongs to the Special Issue Applied Optimization in Clean and Renewable Energy: New Trends)

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