Special Issue "Sustainable Energy Systems Planning, Integration and Management"

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

Deadline for manuscript submissions: closed (31 July 2019).

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A printed edition of this Special Issue is available here.

Special Issue Editors

Dr. Amjad Anvari-Moghaddam
E-Mail Website
Guest Editor
Associate Professor, Department of Energy Technology, Aalborg University, Aalborg 9220, Denmark
Interests: power system operation and planning; microgrids and active energy networks; energy markets and analytics; operations research and its applications to energy systems
Special Issues and Collections in MDPI journals
Dr. Behnam Mohammadi-ivatloo
E-Mail Website
Guest Editor
Department of Electrical and Computer Engineering, University of Tabriz, Tabriz, Iran
Interests: smart energy systems; energy hub; water–energy nexus; renewable energy
Dr. Somayeh Asadi
E-Mail Website
Guest Editor
Department of Architectural Engineering, Pennsylvania State University, University Park, PA 16802, USA
Tel. +1-814-865-3013
Interests: design of energy efficient buildings; smart grid; food–water–energy nexus; integrated advanced sustainable materials and nanomaterials; life cycle assessment
Special Issues and Collections in MDPI journals
Prof. Dr. Kim Guldstrand Larsen
E-Mail Website
Guest Editor
Department of Computer Science, Aalborg University, 9220 Aalborg East, Denmark
Interests: embedded systems; computational sustainability; socio-technical systems in ICT; energy timed automata
Prof. Dr. Mohammad Shahidehpour
E-Mail Website
Guest Editor
Armour College of Engineering, Illinois Institute of Technology, Chicago, IL 60616, USA
Interests: Energy Systems Operation and Planning, Microgrids, Energy Hubs, Sustainable Energy Integration

Special Issue Information

Dear Colleagues,

Energy systems worldwide are undergoing a major transformation as a consequence of the transition towards the widespread use of clean and sustainable energy sources. Basically, this involves massive changes in technical and organizational levels together with tremendous technological upgrades in different sectors ranging from the energy generation and transmission systems down to the distribution systems. These actions constitute a huge science and engineering challenges and demands for expert knowledge in the field to create solutions for a sustainable energy system that is economically, environmentally and socially viable, while meeting high security requirements.

This Special Issue will cover these promising and dynamic areas of research and development and will allow the gathering of contributions in sustainable energy systems planning, integration and management. This Special Issue also seeks papers to report advances on any aspect of these developments. Manuscripts should be unpublished and report significant advancements.

Dr. Amjad Anvari-Moghaddam
Dr. Behnam Mohammadi-ivatloo
Dr. Somayeh Asadi
Prof. Dr. Kim Guldstrand Larsen
Prof. Dr. Mohammad Shahidehpour
Guest Editors

Manuscript Submission Information

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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.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1800 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-based generation systems
  • Sustainable energy integration especially for dispersed and small capacity sources or even small networks (e.g., microgrids)
  • Micro- and nano-energy systems and technologies
  • Dynamic optimization with uncertainly in sustainable energy systems
  • Optimal operations and control of sustainable energy systems
  • Innovative perspectives on systems of sustainable consumption and production
  • Economic and environment friendly dispatch with renewables
  • Energy harvesting
  • Intelligent methods in sustainable power and energy systems
  • Real-time emissions and environmental impact analysis
  • Zero waste
  • Energy efficient and zero energy buildings
  • Sustainability metrics in energy systems
  • Sustainable computing systems and applications
  • Energy conversion, conservation and management
  • Hybrid/combined/integrated energy systems for multi-generation
  • Energy systems modelling for sustainable planning
  • Energy efficiency and technology
  • Sustainable/Green energy
  • Design and development of sustainable energy supply systems
  • Multi-energy systems

Published Papers (15 papers)

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Editorial

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Open AccessEditorial
Sustainable Energy Systems Planning, Integration, and Management
Appl. Sci. 2019, 9(20), 4451; https://doi.org/10.3390/app9204451 - 20 Oct 2019
Abstract
Energy systems worldwide are undergoing a major transformation as a consequence of the transition towards the widespread use of clean and sustainable energy sources [...] Full article

Research

Jump to: Editorial

Open AccessArticle
Energy Consumption Optimization Model of Multi-Type Bus Operating Organization Based on Time-Space Network
Appl. Sci. 2019, 9(16), 3352; https://doi.org/10.3390/app9163352 - 15 Aug 2019
Cited by 1
Abstract
As a new type of green bus, the pure electric bus has obvious advantages in energy consumption and emission reduction compared with the traditional fuel bus. However, the pure electric bus has a mileage range constraint and the amount of charging infrastructure cannot [...] Read more.
As a new type of green bus, the pure electric bus has obvious advantages in energy consumption and emission reduction compared with the traditional fuel bus. However, the pure electric bus has a mileage range constraint and the amount of charging infrastructure cannot meet the demand, which makes the scheduling of the electric bus driving plans more complicated. Meanwhile, many routes are operated with mixing pure electric buses and traditional fuel buses. As mentioned above, we focus on the operating organization problem with the multi-type bus (pure electric buses and traditional fuel buses), aiming to provide guidance for future application of electric buses. We take minimizing the energy consumption of vehicles, the waiting and traveling time of passengers as the objectives, while considering the constraints of vehicle full load limitation, minimal departure interval, mileage range and charging time window. The energy consumption based multi-type bus operating organization model was formulated, along with the heuristic algorithm to solve it. Then, a case study in Beijing was performed. The results showed that, the optimal mixing ratio of electric bus and fuel bus vary according to the variation of passenger flow. In general, each fuel bus could be replaced by two pure electric buses. Moreover, in the transition process of energy structure in public transport, the vehicle scale keeps increasing. The parking yard capacity and the amount of charging facilities are supposed to be further expanded. Full article
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Open AccessArticle
Optimal Strategy to Select Load Identification Features by Using a Particle Resampling Algorithm
Appl. Sci. 2019, 9(13), 2622; https://doi.org/10.3390/app9132622 - 28 Jun 2019
Cited by 1
Abstract
This paper proposes a robust strategy to select the load identification features, which is based on particle resampling to promote the performance for the successive load identification. Firstly, the sliding window incorporated with the bilateral cumulative sum control chart (CUSUM) method is utilized [...] Read more.
This paper proposes a robust strategy to select the load identification features, which is based on particle resampling to promote the performance for the successive load identification. Firstly, the sliding window incorporated with the bilateral cumulative sum control chart (CUSUM) method is utilized to obtain the load event. Then, the minimum inner-class variance, using the time-serial data, is introduced to judge the happened time as precise as possible, thus marking the changing point of the state of load for the following feature extraction. Due to the fluctuating data of current and voltage sampled by the monitoring device, the particle resampling method, containing the importance principle, is applied to find the steady and effectiveness point, ensuring that the obtained features have the desired fit with its actual features. The fitness measurement is then carried out by using the 2-D fuzzy theory. Finally, the proposed method was tested on the real household measurements in the labs. The result demonstrates an improvement in obtaining the desired load features when applied to the real household for the following load identification. Full article
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Open AccessArticle
A Novel Method Based on Neural Networks for Designing Internal Coverings in Buildings: Energy Saving and Thermal Comfort
Appl. Sci. 2019, 9(10), 2140; https://doi.org/10.3390/app9102140 - 25 May 2019
Cited by 3
Abstract
Although several papers define energy saving and thermal comfort optimization with internal coverings materials, none of them deal with predictive models to improve design in building constructions. Thus, artificial intelligence (AI) procedures were applied in this paper. In particular, neural networks (NNs) were [...] Read more.
Although several papers define energy saving and thermal comfort optimization with internal coverings materials, none of them deal with predictive models to improve design in building constructions. Thus, artificial intelligence (AI) procedures were applied in this paper. In particular, neural networks (NNs) were designed for indoor ambiences with internal covering materials in different buildings, were trained and employed to predict indoor ambiences (indoor temperature and relative humidity as a function of weather conditions), and, based on these procedures, local thermal comfort conditions and energy consumption, due to the type of internal covering permeability level, were calculated. Results from this original methodology showed a better acceptability of indoor ambiences when permeable coating materials were used, in agreement with previous research works. At the same time, with permeable coverings, a lower energy consumption of 20% in the heating, ventilation, and air conditioning (HVAC) systems was needed to reach more comfortable conditions during the summer season in the first hours of occupation. Finally, all these results suggest an original methodology to optimize indoor ambiences based on the design of internal coverings by NN. Full article
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Open AccessFeature PaperArticle
Risk-Constrained Optimal Chiller Loading Strategy Using Information Gap Decision Theory
Appl. Sci. 2019, 9(9), 1925; https://doi.org/10.3390/app9091925 - 10 May 2019
Cited by 6
Abstract
This paper presents a novel framework for economic cooling load dispatch in conventional water-cooled chillers. Moreover, information gap decision theory (IGDT) is applied to the optimal chiller loading (OCL) problem to find the optimum operating point of the test system in three decision-making [...] Read more.
This paper presents a novel framework for economic cooling load dispatch in conventional water-cooled chillers. Moreover, information gap decision theory (IGDT) is applied to the optimal chiller loading (OCL) problem to find the optimum operating point of the test system in three decision-making modes: (a) risk-neutral approach, (b) risk-aversion or robustness approach, and (c) risk-taker or opportunistic approach. In the robustness mode of the IGDT-based OCL problem, the system operator enters a desired energy cost value in order to find the most appropriate loading points for the chillers so that the total electricity procurement cost over the study horizon is smaller than or equal to this critical value. Meanwhile, the cooling load increase is maximized to the highest possible level to find the most robust performance of the benchmark grid with respect to the overestimated load. Similarly, the risk-taker optimization method finds the on/off status and the partial load ratio (PLR) of the chillers in order to keep the total energy cost as low as the given cost function. In addition, the minimum value of cooling load decrease can be found while satisfying the refrigeration capacity of the chiller and the load-generation balance constraint. Thus, a mixed-integer non-linear programming problem is solved using the branch and reduce optimization (BARON) tool of the generalized algebraic mathematical modeling system (GAMS) for a five-chiller plant, to demonstrate that IGDT is able to find a good solution in robustness/risk-taker OCL problem. Full article
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Open AccessArticle
Meteorological Variables’ Influence on Electric Power Generation for Photovoltaic Systems Located at Different Geographical Zones in Mexico
Appl. Sci. 2019, 9(8), 1649; https://doi.org/10.3390/app9081649 - 20 Apr 2019
Cited by 3
Abstract
In this study, the relation among different meteorological variables and the electrical power from photovoltaic systems located at different selected places in Mexico were presented. The data was collected from on-site real-time measurements from Mexico City and the State of Sonora. The statistical [...] Read more.
In this study, the relation among different meteorological variables and the electrical power from photovoltaic systems located at different selected places in Mexico were presented. The data was collected from on-site real-time measurements from Mexico City and the State of Sonora. The statistical estimation by the gradient descent method demonstrated that solar radiation, outdoor temperature, wind speed, and daylight hour influenced the electric power generation when it was compared with the real power of each photovoltaic system. According to our results, 97.63% of the estimation results matched the real data for Sonora and 99.66% the results matched for Mexico City, achieving overall errors less than 7% and 2%, respectively. The results showed an acceptable performance since a satisfactory estimation error was achieved for the estimation of photovoltaic power with a high determination coefficient R2. Full article
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Open AccessArticle
Robust Planning of Energy and Environment Systems through Introducing Traffic Sector with Cost Minimization and Emissions Abatement under Multiple Uncertainties
Appl. Sci. 2019, 9(5), 928; https://doi.org/10.3390/app9050928 - 05 Mar 2019
Cited by 2
Abstract
Motor vehicles have been identified as a growing contributor to air pollution, such that analyzing the traffic policies on energy and environment systems (EES) has become a main concern for governments. This study developed a dual robust stochastic fuzzy optimization—energy and environmental systems [...] Read more.
Motor vehicles have been identified as a growing contributor to air pollution, such that analyzing the traffic policies on energy and environment systems (EES) has become a main concern for governments. This study developed a dual robust stochastic fuzzy optimization—energy and environmental systems (DRSFO-EES) model for sustainable planning EES, while considering the traffic sector through integrating two-stage stochastic programming, robust two-stage stochastic optimization, fuzzy possibilistic programming, and robust fuzzy possibilistic programming methods into a framework, which can be used to effectively tackle fuzzy and stochastic uncertainties as well as their combinations, capture the associated risks from fuzzy and stochastic uncertainties, and thoroughly analyze the trade-offs between system costs and reliability. The proposed model can: (i) generate robust optimized solutions for energy allocation, coking processing, oil refining, heat processing, electricity generation, electricity power expansion, electricity importation, energy production, as well as emission mitigation under multiple uncertainties; (ii) explore the impacts of different vehicle policies on vehicular emission mitigation; (iii) identify the study of regional atmospheric pollution contributions of different energy activities. The proposed DRSFO-EES model was applied to the EES of the Beijing-Tianjin-Hebei (BTH) region in China. Results generated from the proposed model disclose that: (i) limitation of the number of light-duty passenger vehicles and heavy-duty trucks can effectively reduce vehicular emissions; (ii) an electric cars’ policy is enhanced by increasing the ratio of its power generated from renewable sources; and (iii) the air-pollutant emissions in the BTH region are expected to peak around 2030, because the energy mix of the study region would be transformed from one dominated by coal to one with a cleaner pattern. The DRSFO-EES model can not only provide scientific support for the sustainable managing of EES by cost-effective ways, but also analyze the desired policies for mitigating pollutant emissions impacts with a risk adverse attitude under multiple uncertainties. Full article
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Open AccessArticle
Effects of Configurations of Internal Walls on the Threshold Value of Operation Hours for Intermittent Heating Systems
Appl. Sci. 2019, 9(4), 756; https://doi.org/10.3390/app9040756 - 21 Feb 2019
Cited by 1
Abstract
The heating load of intermittent heating is not always lower than that of continuous heating for heat storage and release of internal walls. Therefore, the threshold value of daily operation hours exists, and is affected by the configuration of internal walls. A comparative [...] Read more.
The heating load of intermittent heating is not always lower than that of continuous heating for heat storage and release of internal walls. Therefore, the threshold value of daily operation hours exists, and is affected by the configuration of internal walls. A comparative study is performed between continuous and intermittent heating modes to investigate the threshold value of daily operation hours for different internal wall configurations by employing computational fluid dynamic (CFD) models. Meanwhile, field tests on the temperature distribution within a thermal mass was carried out to validate the simulation. The results show that the heating load index of intermittent heating is larger than that of continuous heating with increased amplitude ranging from 31.58% to 152.63%. The threshold value of daily operation hours is, respectively, 18.04 h, 15.80 h, 14.59 h, and 13.46 h for four internal wall configurations. Moreover, with the increase in the insulation level of internal walls, the threshold value of daily operation hours decreases. In addition, the results indicate that it is more economical to use continuous heating when the daily operation hours are more than the threshold values. Full article
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Open AccessArticle
Smart-Grid-Aware Load Regulation of Multiple Datacenters towards the Variable Generation of Renewable Energy
Appl. Sci. 2019, 9(3), 518; https://doi.org/10.3390/app9030518 - 03 Feb 2019
Cited by 2
Abstract
Recently, as renewable and distributed power sources boost, many such resources are integrated into the smart grid as a clean energy input. However, since the generation of renewable energy is intermittent and unstable, the smart grid needs to regulate the load to maintain [...] Read more.
Recently, as renewable and distributed power sources boost, many such resources are integrated into the smart grid as a clean energy input. However, since the generation of renewable energy is intermittent and unstable, the smart grid needs to regulate the load to maintain stability after integrating the renewable energy source. At the same time, with the development of cloud computing, large-scale datacenters are becoming potentially controllable loads for the smart grid due to their high energy consumption. In this paper, we propose an appropriate approach to dynamically adjust the datacenter load to balance the unstable renewable energy input into the grid. This could meet the demand response requirements by taking advantage of the variable power consumption of datacenters. We have examined the scenarios of one or more datacenters being integrated into the grid and adopted a stochastic algorithm to solve the problem we established. The experimental results illustrated that the dynamic load management of multiple datacenters could help the smart grid to reduce losses and thus save operational costs. Besides, we also analyzed the impact of the flexibility and the delay of datacenter actions, which could be applied to more general scenarios in realistic environments. Furthermore, considering the impact of the action delay, we employed a forecasting method to predict renewable energy generation in advance to eliminate the extra losses brought by the delay as much as possible. By predicting solar power generation, the improved results showed that the proposed method was effective and feasible under both sunny and cloudy/rainy/snowy weather conditions. Full article
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Open AccessArticle
Analysis of Heat Transfer and Thermal Environment in a Rural Residential Building for Addressing Energy Poverty
Appl. Sci. 2018, 8(11), 2077; https://doi.org/10.3390/app8112077 - 28 Oct 2018
Cited by 2
Abstract
Reducing energy consumption and creating a comfortable thermal indoor environment in rural residential buildings can play a key role in fighting global warming in China. As a result of economic development, rural residents are building new houses and modernizing existing buildings. This paper [...] Read more.
Reducing energy consumption and creating a comfortable thermal indoor environment in rural residential buildings can play a key role in fighting global warming in China. As a result of economic development, rural residents are building new houses and modernizing existing buildings. This paper investigated and analyzed a typical rural residential building in the Ningxia Hui Autonomous Region in Northwest China through field measurements and numerical simulation. The results showed that making full use of solar energy resources is an important way to improve the indoor temperature. Reasonable building layout and good thermal performance of the building envelope can reduce wind velocities and convective heat loss. Insulation materials and double-glazed windows should be used to reduce energy loss in new buildings, although it is an evolution process in creating thermally efficient buildings in rural China. This research provides a reference for the design and construction of rural residential buildings in Northwest China and similar areas for addressing energy poverty. Full article
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Open AccessArticle
Evaluation of a Smart System for the Optimization of Logistics Performance of a Pruning Biomass Value Chain
Appl. Sci. 2018, 8(10), 1987; https://doi.org/10.3390/app8101987 - 19 Oct 2018
Cited by 2
Abstract
The paper presents a report on the performance evaluation of a newly developed smart logistics system (SLS). Field tests were conducted in Spain, Germany, and Sweden. The evaluation focused on the performance of a smart box tool (used to capture information during biomass [...] Read more.
The paper presents a report on the performance evaluation of a newly developed smart logistics system (SLS). Field tests were conducted in Spain, Germany, and Sweden. The evaluation focused on the performance of a smart box tool (used to capture information during biomass transport) and a web-based information platform (used to monitor the flow of agricultural pruning from farms to end users and associated information flow). The tests were performed following a product usability testing approach, considering both qualitative and quantitative parameters. The detailed performance evaluation included the following: systematic analysis of 41 recordable parameters (stored in a spreadsheet database), analysis of feedback and problems encountered during the tests, and overall quality analysis applying the product quality model adapted from ISO/IEC FDIS 9126-1 standard. The data recording and storage and the capability to support product traceability and supply chain management were found to be very satisfactory, while assembly of smart box components (mainly the associated cables), data transferring intervals, and manageability could be improved. From the data retrieved during test activities, in more than 95% of the parameters within 41 columns, the expected values were displayed correctly. Some errors were observed, which might have been caused mainly by barriers that could hinder proper data recording and transfer from the smart box to the central database. These problems can be counteracted and the performance of the SLS can be improved so that it can be upgraded to be a marketable tool that can promote sustainable biomass-to-energy value chains. Full article
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Open AccessArticle
A New Hybrid Approach for Wind Speed Forecasting Applying Support Vector Machine with Ensemble Empirical Mode Decomposition and Cuckoo Search Algorithm
Appl. Sci. 2018, 8(10), 1754; https://doi.org/10.3390/app8101754 - 28 Sep 2018
Cited by 6
Abstract
Wind speed forecasting plays a crucial role in improving the efficiency of wind farms, and increases the competitive advantage of wind power in the global electricity market. Many forecasting models have been proposed, aiming to enhance the forecast performance. However, some traditional models [...] Read more.
Wind speed forecasting plays a crucial role in improving the efficiency of wind farms, and increases the competitive advantage of wind power in the global electricity market. Many forecasting models have been proposed, aiming to enhance the forecast performance. However, some traditional models used in our experiment have the drawback of ignoring the importance of data preprocessing and the necessity of parameter optimization, which often results in poor forecasting performance. Therefore, in order to achieve a more satisfying performance in forecasting wind speed data, a new short-term wind speed forecasting method which consists of Ensemble Empirical Mode Decomposition (EEMD) for data preprocessing, and the Support Vector Machine (SVM)—whose key parameters are optimized by the Cuckoo Search Algorithm (CSO)—is developed in this paper. This method avoids the shortcomings of some traditional models and effectively enhances the forecasting ability. To test the prediction ability of the proposed model, 10 min wind speed data from wind farms in Shandong Province, China, are used for conducting experiments. The experimental results indicate that the proposed model cannot only improve the forecasting accuracy, but can also be an effective tool in assisting the management of wind power plants. Full article
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Open AccessFeature PaperArticle
Smart System for the Optimization of Logistics Performance of the Pruning Biomass Value Chain
Appl. Sci. 2018, 8(7), 1162; https://doi.org/10.3390/app8071162 - 18 Jul 2018
Cited by 5
Abstract
Agricultural pruning biomass is one of the important resources in Europe for generating renewable energy. However, utilization of the agricultural residues requires development of efficient and effective logistics systems. The objective of this study was to develop smart logistics system (SLS) appropriate for [...] Read more.
Agricultural pruning biomass is one of the important resources in Europe for generating renewable energy. However, utilization of the agricultural residues requires development of efficient and effective logistics systems. The objective of this study was to develop smart logistics system (SLS) appropriate for the management of the pruning biomass supply chain. The paper describes the users’ requirement of SLS, defines the technical and functional requirements and specifications for the development of SLS, and determines relevant information/data to be documented and managed by the SLS. This SLS has four major components: (a) Smart box, a sensor unit that enables measurement of data such as relative humidity, temperature, geographic positions; (b) On-board control unit, a unit that performs route planning and monitors the recordings by the smart box; (c) Information platform, a centralized platform for data storing and sharing, and management of pruning supply chain and traceability; and (d) Central control unit, an interface linking the Information platform and On-board control unit that serves as a point of administration for the whole pruning biomass supply chain from harvesting to end user. The SLS enables the improvement of performance of pruning biomass supply chain management and product traceability leading to a reduction of product loss, increased coordination of resources utilisation and quality of solid biofuel supply, increased pruning marketing opportunity, and reduction of logistics cost. This SLS was designed for pruning biomass, but could also be adapted for any type of biomass-to-energy initiatives. Full article
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Open AccessArticle
A Hybrid Fuzzy Analysis Network Process (FANP) and the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) Approaches for Solid Waste to Energy Plant Location Selection in Vietnam
Appl. Sci. 2018, 8(7), 1100; https://doi.org/10.3390/app8071100 - 07 Jul 2018
Cited by 11
Abstract
Many research studies have applied the multi-criteria decision making (MCDM) approach to various fields of science and engineering, and this trend has been increasing for many years. One of the fields that the MCDM model has been employed is for location selection, yet [...] Read more.
Many research studies have applied the multi-criteria decision making (MCDM) approach to various fields of science and engineering, and this trend has been increasing for many years. One of the fields that the MCDM model has been employed is for location selection, yet very few studies consider this problem under fuzzy environmental conditions. In this research, the authors propose an MCDM approach, including fuzzy analysis network process (FANP), and the technique for order of preference by similarity to ideal solution (TOPSIS), for solid waste to energy plant location selection in Vietnam. In the first stage of this research, the ANP approach with fuzzy logic is applied to determine the weight of criteria. In the FANP model, the value of the criteria is provided by the experts so that the disadvantages of this model are that the input data, expressed in linguistic terms, depends on the experience of experts, and thus involves subjectivity. This is a reason why TOPSIS model was proposed for ranking alternatives in the final stage. Analysis shows that Hau Giang (Decision Making Unit 8 (DMU 8)) is the best location for building solid waste to energy plant, because it has the shortest geometric distance from the positive ideal solution (PIS) and the longest geometric distance from the negative ideal solution (NIS). The contribution of this research is a proposed hybrid FANP and TOPSIS approach for solid waste to energy plant location selection in Vietnam under fuzzy environmental conditions. This paper is also part of an evolution of a new hybrid model that is flexible and practical for decision makers. In addition, the research also provides a special, useful guideline in solid waste to energy plant location selection in many countries, as well as provides a guideline for location selection in other industries. Thus, this research makes significant contributions on both academic and practical fronts. Full article
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Open AccessArticle
Study of the Wave Energy Propagation Patterns in the Western Black Sea
Appl. Sci. 2018, 8(6), 993; https://doi.org/10.3390/app8060993 - 17 Jun 2018
Cited by 5
Abstract
The most relevant patterns of the wave energy propagation in the western side of the Black Sea were assessed in the present work. The emphasis was put on the western side because this is also the most energetic part of the Black Sea. [...] Read more.
The most relevant patterns of the wave energy propagation in the western side of the Black Sea were assessed in the present work. The emphasis was put on the western side because this is also the most energetic part of the Black Sea. The assessments performed relate some recent results provided by a numerical wave modeling system based on the spectrum concept. The SWAN model (acronym for Simulating Waves Nearshore) was considered. This was implemented over the entire sea basin and focused with increasing resolution in the geographical space towards the Romanian nearshore. Furthermore, some data assimilation techniques have also been implemented, such that the results provided are accurate and reliable. Special attention was paid to the high, but not extreme, winter wave energy conditions. The cases considered are focused on the coastal waves generated by distant storms, which means the local wind has not very high values in the targeted areas. This also takes into account the fact that the configuration of the environmental matrix in the Black Sea is currently subjected to significant changes mainly due to the climate change. From this perspective, the present work illustrates some of the most recent patterns of wave energy propagation in the western side of the Black Sea, considering eight different SWAN computational domains. According to most of the recent evaluations, the nearshore of the Black Sea is characterized by an average wave power lower than 6 kW/m. The results of the present work show that there is a real tendency of the wave energy enhancement. This tendency, especially concerns the western side of the basin, where in the high conditions considered, values of the wave power about 10 times greater than the average have been noticed. Full article
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