Showing papers in "Energies in 2019"
TL;DR: In this paper, the authors analyzed fossil fuel energy consumption, fossil fuel depletion, and their relationship with other variables, such as energy dependence and share of renewable energy in gross final energy consumption.
Abstract: The use of fossil fuels as the main source of energy for most countries has caused several negative environmental impacts, such as global warming and air pollution. Air pollution causes many health problems, causing social and economic negative effects. Worldwide efforts are being made to avoid global warming consequences through the establishment of international agreements that then lead to local policies adapted to the development of each signing nation. In addition, there is a depletion of nonrenewable resources which may be scarce or nonexistent in future generations. The preservation of resources, which is a common goal of the Circular Economy strategy and of sustainable development, is not being accomplished nowadays. In this work, the calculation of indicators and mathematical and statistical analysis were applied to clarify and evidence the trends, provide information for the decision-making process, and increase public awareness. The fact that European countries do not possess abundant reserves of fossil fuels will not change, but the results of this analysis can evolve in the future. In this work, fossil fuel energy consumption, fossil fuel depletion, and their relationship with other variables, such as energy dependence and share of renewable energy in gross final energy consumption, were analyzed for 29 European countries. Furthermore, it was possible to conclude that many European countries still depend heavily on fossil fuels. Significant differences were not found in what concerns gross inland consumption per capita when the Kruskal–Wallis test was applied. It was possible to estimate that by 2050 (considering Jazz scenario) it will only remain approximately 14% of oil proven reserves, 72% of coal proven reserves and 18% of gas proven reserves. Given the small reserves of European countries on fossil fuels, if they need to use them, they will fast disappear.
TL;DR: In this paper, the authors present and compare key components of Li-ion batteries and describe associated battery management systems, as well as approaches to improve the overall battery efficiency, capacity, and lifespan.
Abstract: Over the past several decades, the number of electric vehicles (EVs) has continued to increase. Projections estimate that worldwide, more than 125 million EVs will be on the road by 2030. At the heart of these advanced vehicles is the lithium-ion (Li-ion) battery which provides the required energy storage. This paper presents and compares key components of Li-ion batteries and describes associated battery management systems, as well as approaches to improve the overall battery efficiency, capacity, and lifespan. Material and thermal characteristics are identified as critical to battery performance. The positive and negative electrode materials, electrolytes and the physical implementation of Li-ion batteries are discussed. In addition, current research on novel high energy density batteries is presented, as well as opportunities to repurpose and recycle the batteries.
TL;DR: A comprehensive review on the major blackouts and cascading events that have occurred in the last decade is introduced in this article, where a particular focus is given on the US power system outages and their causes since it is one of the leading power producers in the world.
Abstract: Power systems are the most complex systems and have great importance in modern life. They have direct impacts on the modernization, economic, political and social aspects. To operate such systems in a stable mode, several control and protection techniques are required. However, modern systems are equipped with several protection schemes with the aim of avoiding the unpredicted events and power outages, power systems are still encountering emergency and mal-operation situations. The most severe emergencies put the whole or at least a part of the system in danger. If the emergency is not well managed, the power system is likely to have cascading failures that might lead to a blackout. Due to the consequences, many countries around the world have research and expert teams who work to avoid blackouts on their systems. In this paper, a comprehensive review on the major blackouts and cascading events that have occurred in the last decade are introduced. A particular focus is given on the US power system outages and their causes since it is one of the leading power producers in the world and it is also due to the ready availability of data for the past events. The paper also highlights the root causes of different blackouts around the globe. Furthermore, blackout and cascading analysis methods and the consequences of blackouts are surveyed. Moreover, the challenges in the existing protective schemes and research gaps in the topic of power system blackout and cascading events are marked out. Research directions and issues to be considered in future power system blackout studies are also proposed.
TL;DR: There is an outstanding rise in the accuracy, robustness, precision and generalization ability of the ML models in energy systems using hybrid ML models.
Abstract: Machine learning (ML) models have been widely used in the modeling, design and prediction in energy systems. During the past two decades, there has been a dramatic increase in the advancement and application of various types of ML models for energy systems. This paper presents the state of the art of ML models used in energy systems along with a novel taxonomy of models and applications. Through a novel methodology, ML models are identified and further classified according to the ML modeling technique, energy type, and application area. Furthermore, a comprehensive review of the literature leads to an assessment and performance evaluation of the ML models and their applications, and a discussion of the major challenges and opportunities for prospective research. This paper further concludes that there is an outstanding rise in the accuracy, robustness, precision and generalization ability of the ML models in energy systems using hybrid ML models. Hybridization is reported to be effective in the advancement of prediction models, particularly for renewable energy systems, e.g., solar energy, wind energy, and biofuels. Moreover, the energy demand prediction using hybrid models of ML have highly contributed to the energy efficiency and therefore energy governance and sustainability.
TL;DR: In this paper, Li-ion batteries have attracted considerable attention in the EV industry owing to their high energy density, lifespan, nominal voltage, power density, and cost, and a smart battery management system is one of the essential components; it not only measures the states of battery accurately, but also ensures safe operation and prolongs the battery life.
Abstract: Energy storage system (ESS) technology is still the logjam for the electric vehicle (EV) industry. Lithium-ion (Li-ion) batteries have attracted considerable attention in the EV industry owing to their high energy density, lifespan, nominal voltage, power density, and cost. In EVs, a smart battery management system (BMS) is one of the essential components; it not only measures the states of battery accurately, but also ensures safe operation and prolongs the battery life. The accurate estimation of the state of charge (SOC) of a Li-ion battery is a very challenging task because the Li-ion battery is a highly time variant, non-linear, and complex electrochemical system. This paper explains the workings of a Li-ion battery, provides the main features of a smart BMS, and comprehensively reviews its SOC estimation methods. These SOC estimation methods have been classified into four main categories depending on their nature. A critical explanation, including their merits, limitations, and their estimation errors from other studies, is provided. Some recommendations depending on the development of technology are suggested to improve the online estimation.
TL;DR: In this article, the authors provide a survey of past and current strategies used to achieve a sustainable conversion of biomass to platform chemicals, such as 5-hydroxymethylfurfural (5-HMF), levulinic acid, furfurals, sugar alcohols, lactic acid, succinic acid, and phenols.
Abstract: The production of chemicals from biomass, a renewable feedstock, is highly desirable in replacing petrochemicals to make biorefineries more economical. The best approach to compete with fossil-based refineries is the upgradation of biomass in integrated biorefineries. The integrated biorefineries employed various biomass feedstocks and conversion technologies to produce biofuels and bio-based chemicals. Bio-based chemicals can help to replace a large fraction of industrial chemicals and materials from fossil resources. Biomass-derived chemicals, such as 5-hydroxymethylfurfural (5-HMF), levulinic acid, furfurals, sugar alcohols, lactic acid, succinic acid, and phenols, are considered platform chemicals. These platform chemicals can be further used for the production of a variety of important chemicals on an industrial scale. However, current industrial production relies on relatively old and inefficient strategies and low production yields, which have decreased their competitiveness with fossil-based alternatives. The aim of the presented review is to provide a survey of past and current strategies used to achieve a sustainable conversion of biomass to platform chemicals. This review provides an overview of the chemicals obtained, based on the major components of lignocellulosic biomass, sugars, and lignin. First, important platform chemicals derived from the catalytic conversion of biomass were outlined. Later, the targeted chemicals that can be potentially manufactured from the starting or platform materials were discussed in detail. Despite significant advances, however, low yields, complex multistep synthesis processes, difficulties in purification, high costs, and the deactivation of catalysts are still hurdles for large-scale competitive biorefineries. These challenges could be overcome by single-step catalytic conversions using highly efficient and selective catalysts and exploring purification and separation technologies.
TL;DR: In this article, the benefits of enhanced bioenergy production and solids reduction using co-substrates have attracted researchers to study the co-digestion technology and to better understand the effect of multi substrates on digester performance.
Abstract: Recent studies have shown that anaerobic co-digestion (AnCoD) is superior to conventional anaerobic digestion (AD). The benefits of enhanced bioenergy production and solids reduction using co-substrates have attracted researchers to study the co-digestion technology and to better understand the effect of multi substrates on digester performance. This review will discuss the results of such studies with the main focus on: (1) generally the advantages of co-digestion over mono-digestion in terms of system stability, bioenergy, and solids reduction; (2) microbial consortia diversity and their synergistic impact on biogas improvement; (3) the effect of digester mode, i.e., multi-stage versus single stage digestion on AnCoD. It is essential to note that the studies reported improvement in the synergy and diverse microbial consortia when using co-digestion technologies, in addition to higher biomethane yield when using two-stage mode. A good example would be the co-digestion of biodiesel waste and glycerin with municipal waste sludge in a two-stage reactor resulting in 100% increase of biogas and 120% increase in the methane content of the produced biogas with microbial population dominated by Methanosaeta and Methanomicrobium.
TL;DR: In this article, the authors compare the performance of two methods for the prediction of the power output of photovoltaic systems based on Artificial Neural Networks (ANN), which have been trained on the same dataset, thus enabling a much-needed homogeneous comparison.
Abstract: We compare the 24-hour ahead forecasting performance of two methods commonly used for the prediction of the power output of photovoltaic systems. Both methods are based on Artificial Neural Networks (ANN), which have been trained on the same dataset, thus enabling a much-needed homogeneous comparison currently lacking in the available literature. The dataset consists of an hourly series of simultaneous climatic and PV system parameters covering an entire year, and has been clustered to distinguish sunny from cloudy days and separately train the ANN. One forecasting method feeds only on the available dataset, while the other is a hybrid method as it relies upon the daily weather forecast. For sunny days, the first method shows a very good and stable prediction performance, with an almost constant Normalized Mean Absolute Error, NMAE%, in all cases (1% 2% and up to 5.3% for all the other cases). For cloudy days, the forecasting performance of both methods typically drops; the performance is rather stable for the method that does not use weather forecasts, while for the hybrid method it varies significantly for the days considered in the analysis.
TL;DR: An electricity theft detection system is proposed based on a combination of a convolutional neural network (CNN) and a long short-term memory (LSTM) architecture that can classify both the majority class and the minority class with good accuracy.
Abstract: Among an electricity provider’s non-technical losses, electricity theft has the most severe and dangerous effects. Fraudulent electricity consumption decreases the supply quality, increases generation load, causes legitimate consumers to pay excessive electricity bills, and affects the overall economy. The adaptation of smart grids can significantly reduce this loss through data analysis techniques. The smart grid infrastructure generates a massive amount of data, including the power consumption of individual users. Utilizing this data, machine learning and deep learning techniques can accurately identify electricity theft users. In this paper, an electricity theft detection system is proposed based on a combination of a convolutional neural network (CNN) and a long short-term memory (LSTM) architecture. CNN is a widely used technique that automates feature extraction and the classification process. Since the power consumption signature is time-series data, we were led to build a CNN-based LSTM (CNN-LSTM) model for smart grid data classification. In this work, a novel data pre-processing algorithm was also implemented to compute the missing instances in the dataset, based on the local values relative to the missing data point. Furthermore, in this dataset, the count of electricity theft users was relatively low, which could have made the model inefficient at identifying theft users. This class imbalance scenario was addressed through synthetic data generation. Finally, the results obtained indicate the proposed scheme can classify both the majority class (normal users) and the minority class (electricity theft users) with good accuracy.
TL;DR: In this article, the performance comparison between different smoothing methods has been presented, especially from a battery status perspective, and an analysis of the battery cycles and its deepness is performed based on the well-established rainflow cycle counting method.
Abstract: The high variability of solar irradiance, originated by moving clouds, causes fluctuations in Photovoltaic (PV) power generation, and can negatively impact the grid stability. For this reason, grid codes have incorporated ramp-rate limitations for the injected PV power. Energy Storage Systems (ESS) coordinated by ramp-rate (RR) control algorithms are often applied for mitigating these power fluctuations to the grid. These algorithms generate a power reference to the ESS that opposes the PV fluctuations, reducing them to an acceptable value. Despite their common use, few performance comparisons between the different methods have been presented, especially from a battery status perspective. This is highly important, as different smoothing methods may require the battery to operate at different regimes (i.e., number of cycles and cycles deepness), which directly relates to the battery lifetime performance. This paper intends to fill this gap by analyzing the different methods under the same irradiance profile, and evaluating their capability to limit the RR and maintain the battery State of Charge (SOC) at the end of the day. Moreover, an analysis into the ESS capacity requirements for each of the methods is quantified. Finally, an analysis of the battery cycles and its deepness is performed based on the well-established rainflow cycle counting method.
TL;DR: A comprehensive review of the application of phase change materials (PCMs) for solar energy use and storage is provided in this paper for solar power generation, water heating system, solar cookers, and solar dryers.
Abstract: Solar energy is a renewable energy source that can be utilized for different applications in today’s world. The effective use of solar energy requires a storage medium that can facilitate the storage of excess energy, and then supply this stored energy when it is needed. An effective method of storing thermal energy from solar is through the use of phase change materials (PCMs). PCMs are isothermal in nature, and thus offer higher density energy storage and the ability to operate in a variable range of temperature conditions. This article provides a comprehensive review of the application of PCMs for solar energy use and storage such as for solar power generation, water heating systems, solar cookers, and solar dryers. This paper will benefit the researcher in conducting further research on solar power generation, water heating system, solar cookers, and solar dryers using PCMs for commercial development.
TL;DR: In this article, the design and evaluation of different DC-DC converter topologies for battery electric vehicles (BEVs) and Plug-in Hybrid Electric Vehicles (PHEVs) are presented, analyzed and compared in terms of output power, component count, switching frequency, losses, effectiveness, reliability and cost.
Abstract: This article reviews the design and evaluation of different DC-DC converter topologies for Battery Electric Vehicles (BEVs) and Plug-in Hybrid Electric Vehicles (PHEVs). The design and evaluation of these converter topologies are presented, analyzed and compared in terms of output power, component count, switching frequency, electromagnetic interference (EMI), losses, effectiveness, reliability and cost. This paper also evaluates the architecture, merits and demerits of converter topologies (AC-DC and DC-DC) for Fast Charging Stations (FCHARs). On the basis of this analysis, it has found that the Multidevice Interleaved DC-DC Bidirectional Converter (MDIBC) is the most suitable topology for high-power BEVs and PHEVs (> 10kW), thanks to its low input current ripples, low output voltage ripples, low electromagnetic interference, bidirectionality, high efficiency and high reliability. In contrast, for low-power electric vehicles (<10 kW), it is tough to recommend a single candidate that is the best in all possible aspects. However, the Sinusoidal Amplitude Converter, the Z-Source DC-DC converter and the boost DC-DC converter with resonant circuit are more suitable for low-power BEVs and PHEVs because of their soft switching, noise-free operation, low switching loss and high efficiency. Finally, this paper explores the opportunity of using wide band gap semiconductors (WBGSs) in DC-DC converters for BEVs, PHEVs and converters for FCHARs. Specifically, the future roadmap of research for WBGSs, modeling of emerging topologies and design techniques of the control system for BEV and PHEV powertrains are also presented in detail, which will certainly help researchers and solution engineers of automotive industries to select the suitable converter topology to achieve the growth of projected power density.
TL;DR: The SWOT analysis conducted in the present study highlights that the implementation of the hydrogen economy depends decisively on the following main factors: legislative framework, energy decision makers, information and interest from the end beneficiaries, potential investors, and existence of specialists in this field.
Abstract: The climate changes that are becoming visible today are a challenge for the global research community. The stationary applications sector is one of the most important energy consumers. Harnessing the potential of renewable energy worldwide is currently being considered to find alternatives for obtaining energy by using technologies that offer maximum efficiency and minimum pollution. In this context, new energy generation technologies are needed to both generate low carbon emissions, as well as identifying, planning and implementing the directions for harnessing the potential of renewable energy sources. Hydrogen fuel cell technology represents one of the alternative solutions for future clean energy systems. This article reviews the specific characteristics of hydrogen energy, which recommends it as a clean energy to power stationary applications. The aim of review was to provide an overview of the sustainability elements and the potential of using hydrogen as an alternative energy source for stationary applications, and for identifying the possibilities of increasing the share of hydrogen energy in stationary applications, respectively. As a study method was applied a SWOT analysis, following which a series of strategies that could be adopted in order to increase the degree of use of hydrogen energy as an alternative to the classical energy for stationary applications were recommended. The SWOT analysis conducted in the present study highlights that the implementation of the hydrogen economy depends decisively on the following main factors: legislative framework, energy decision makers, information and interest from the end beneficiaries, potential investors, and existence of specialists in this field.
TL;DR: In this article, the authors provide a comprehensive review of the technical development of EVs and emerging technologies for their future application, including batteries, charging technology, electric motors and control, and charging infrastructure of EVs.
Abstract: To reduce the dependence on oil and environmental pollution, the development of electric vehicles has been accelerated in many countries. The implementation of EVs, especially battery electric vehicles, is considered a solution to the energy crisis and environmental issues. This paper provides a comprehensive review of the technical development of EVs and emerging technologies for their future application. Key technologies regarding batteries, charging technology, electric motors and control, and charging infrastructure of EVs are summarized. This paper also highlights the technical challenges and emerging technologies for the improvement of efficiency, reliability, and safety of EVs in the coming stages as another contribution.
TL;DR: In this paper, a literature review is presented to underline the strategies allowing to improve the sustainability of biodiesel sustainability, including substitution of homogeneous catalyzed processes, nowadays used in the biodiesel industry, by heterogeneous ones.
Abstract: Energy security and environmental concerns, related to the increasing carbon emissions, have prompted in the last years the search for renewable and sustainable fuels. Biodiesel, a mixture of fatty acids alkyl esters shows properties, which make it a feasible substitute for fossil diesel. Biodiesel can be produced using different processes and different raw materials. The most common, first generation, biodiesel is produced by methanolysis of vegetable oils using basic or acid homogeneous catalysts. The use of vegetable oils for biodiesel production raises serious questions about biodiesel sustainability. Used cooking oils and animal fats can replace the vegetable oils in biodiesel production thus allowing to produce a more sustainable biofuel. Moreover, methanol can be replaced by ethanol being totally renewable since it can be produced by biomass fermentation. The substitution of homogeneous catalyzed processes, nowadays used in the biodiesel industry, by heterogeneous ones can contribute to improve the biodiesel sustainability with simultaneous cost reduction. From the existing literature on biodiesel production, it stands out that several strategies can be adopted to improve the sustainability of biodiesel. A literature review is presented to underline the strategies allowing to improve the biodiesel sustainability.
TL;DR: A detailed review of NILM methods, focusing particularly on recent proposals and their applications, particularly in the areas of Home Energy Management Systems (HEMS) and Ambient Assisted Living (AAL), where the ability to determine the on/off status of certain devices can provide key information for making further decisions as mentioned in this paper.
Abstract: The ongoing deployment of smart meters and different commercial devices has made electricity disaggregation feasible in buildings and households, based on a single measure of the current and, sometimes, of the voltage. Energy disaggregation is intended to separate the total power consumption into specific appliance loads, which can be achieved by applying Non-Intrusive Load Monitoring (NILM) techniques with a minimum invasion of privacy. NILM techniques are becoming more and more widespread in recent years, as a consequence of the interest companies and consumers have in efficient energy consumption and management. This work presents a detailed review of NILM methods, focusing particularly on recent proposals and their applications, particularly in the areas of Home Energy Management Systems (HEMS) and Ambient Assisted Living (AAL), where the ability to determine the on/off status of certain devices can provide key information for making further decisions. As well as complementing previous reviews on the NILM field and providing a discussion of the applications of NILM in HEMS and AAL, this paper provides guidelines for future research in these topics.
TL;DR: The preliminary development of the data-driven prognostic, using a Deep Neural Networks (DNN) approach to predict the State of Health and the Remaining Useful Life of the lithium-ion battery is presented.
Abstract: Prognostic and health management (PHM) can ensure that a lithium-ion battery is working safely and reliably. The main approach of PHM evaluation of the battery is to determine the State of Health (SoH) and the Remaining Useful Life (RUL) of the battery. The advancements of computational tools and big data algorithms have led to a new era of data-driven predictive analysis approaches, using machine learning algorithms. This paper presents the preliminary development of the data-driven prognostic, using a Deep Neural Networks (DNN) approach to predict the SoH and the RUL of the lithium-ion battery. The effectiveness of the proposed approach was implemented in a case study with a battery dataset obtained from the NASA Ames Prognostics Center of Excellence (PCoE) database. The proposed DNN algorithm was compared against other machine learning algorithms, namely, Support Vector Machine (SVM), k-Nearest Neighbors (k-NN), Artificial Neural Networks (ANN), and Linear Regression (LR). The experimental results reveal that the performance of the DNN algorithm could either match or outweigh other machine learning algorithms. Further, the presented results could serve as a benchmark of SoH and RUL prediction using machine learning approaches specifically for lithium-ion batteries application.
TL;DR: In this paper, the authors used the Grey Wolf optimization (GWO) method coupled with an adaptive neuro-fuzzy inference system (ANFIS) to forecast the hydropower generation.
Abstract: Hydropower is among the cleanest sources of energy. However, the rate of hydropower generation is profoundly affected by the inflow to the dam reservoirs. In this study, the Grey wolf optimization (GWO) method coupled with an adaptive neuro-fuzzy inference system (ANFIS) to forecast the hydropower generation. For this purpose, the Dez basin average of rainfall was calculated using Thiessen polygons. Twenty input combinations, including the inflow to the dam, the rainfall and the hydropower in the previous months were used, while the output in all the scenarios was one month of hydropower generation. Then, the coupled model was used to forecast the hydropower generation. Results indicated that the method was promising. GWO-ANFIS was capable of predicting the hydropower generation satisfactorily, while the ANFIS failed in nine input-output combinations.
TL;DR: In this article, the evolution of in-flame soot species in a slow speed, buoyancy-driven diffusion flame is thoroughly studied with the implementation of the population balance approach in association with computational fluid dynamics (CFD) techniques.
Abstract: In this article, the evolution of in-flame soot species in a slow speed, buoyancy-driven diffusion flame is thoroughly studied with the implementation of the population balance approach in association with computational fluid dynamics (CFD) techniques. This model incorporates interactive fire phenomena, including combustion, radiation, turbulent mixing, and all key chemical and physical formation and destruction processes, such as particle inception, surface growth, oxidation, and aggregation. The in-house length-based Direct Quadrature Method of Moments (DQMOM) soot model is fully coupled with all essential fire sub-modelling components and it is specifically constructed for low-speed flames. Additionally, to better describe the combustion process of the parental fuel, ethylene, the strained laminar flamelet model, which considers detailed chemical reaction mechanisms, is adopted. Numerical simulation is validated against a self-conducted co-flow slot burner experimental measurement. A comprehensive assessment of the effect of adopting different nucleation laws, oxidation laws, and various fractal dimension and diffusivity values is performed. The results suggest the model employing Moss law of nucleation, modified NSC law of oxidation, and adopting a fractal dimension value of 2.0 and Schmidt number of 0.9 yields the simulation result that best agreed with experimental data.
TL;DR: In this article, the authors focused on using graphene oxide (GO) with the Φ = 0.2, 0.4 and 0.6 wt.% dispersed in paraffin, as phase change materials (PCMs) to improve the productivity of a solar still for desalination applications.
Abstract: Solar-driven water desalination technologies are rapidly developing with various links to other renewable sources. However, the efficiency of such systems severely depends on the design parameters. The present study focused on using graphene oxide (GO) with the Φ = 0.2, 0.4 and 0.6 wt.% dispersed in paraffin, as phase-change materials (PCMs), to improve the productivity of a solar still for desalination applications. The outcomes showed that by adding more graphene oxide to paraffin, the melting temperature got reduced. Solar still with GO/paraffin showed 25% productivity improvement in comparison with the solar still with only PCM. The obtained Nusselt number during the melting time also represented that free convection heat transfer into the melted region of the solar still has been enhanced by adding dispersed GO to the PCM, compared to the base paraffin. Also, increasing the hot wall temperature augments the Nusselt number. Finally, an empirical equation was derived to correlate the average Nusselt number as a function of Rayleigh number (Ra), the Stefan number (Ste), the subcooling factor (Sb), and the Fourier number (Fo). The obtained correlation depicted that Nusselt number enhancement has a reverse relation with Fourier number.
TL;DR: In this article, the authors proposed a learning-based approach for real-time scheduling of an MG considering the uncertainty of the load demand, renewable energy, and electricity price, which is modeled as a Markov Decision Process (MDP) with an objective of minimizing the daily operating cost.
Abstract: Driven by the recent advances and applications of smart-grid technologies, our electric power grid is undergoing radical modernization. Microgrid (MG) plays an important role in the course of modernization by providing a flexible way to integrate distributed renewable energy resources (RES) into the power grid. However, distributed RES, such as solar and wind, can be highly intermittent and stochastic. These uncertain resources combined with load demand result in random variations in both the supply and the demand sides, which make it difficult to effectively operate a MG. Focusing on this problem, this paper proposed a novel energy management approach for real-time scheduling of an MG considering the uncertainty of the load demand, renewable energy, and electricity price. Unlike the conventional model-based approaches requiring a predictor to estimate the uncertainty, the proposed solution is learning-based and does not require an explicit model of the uncertainty. Specifically, the MG energy management is modeled as a Markov Decision Process (MDP) with an objective of minimizing the daily operating cost. A deep reinforcement learning (DRL) approach is developed to solve the MDP. In the DRL approach, a deep feedforward neural network is designed to approximate the optimal action-value function, and the deep Q-network (DQN) algorithm is used to train the neural network. The proposed approach takes the state of the MG as inputs, and outputs directly the real-time generation schedules. Finally, using real power-grid data from the California Independent System Operator (CAISO), case studies are carried out to demonstrate the effectiveness of the proposed approach.
TL;DR: In this paper, an overview of the current technology related to the green diesel, from the classification and chemistry of the available biomass feedstocks to the possible production technologies and up to the final fuel properties and their effect in modern compression ignition internal combustion engines.
Abstract: The present investigation provides an overview of the current technology related to the green diesel, from the classification and chemistry of the available biomass feedstocks to the possible production technologies and up to the final fuel properties and their effect in modern compression ignition internal combustion engines. Various biomass feedstocks are reviewed paying attention to their specific impact on the production of green diesel. Then, the most prominent production technologies are presented such as the hydro-processing of triglycerides, the upgrading of sugars and starches into C15–C18 saturated hydrocarbons, the upgrading of bio-oil derived by the pyrolysis of lignocellulosic materials and the “Biomass-to-Liquid” (BTL) technology which combines the production of syngas (H2 and CO) from the gasification of biomass with the production of synthetic green diesel through the Fischer-Tropsch process. For each of these technologies the involved chemistry is discussed and the necessary operation conditions for the maximum production yield and the best possible fuel properties are reviewed. Also, the relevant research for appropriate catalysts and catalyst supports is briefly presented. The fuel properties of green diesel are then discussed in comparison to the European and US Standards, to petroleum diesel and Fatty Acid Methyl Esters (FAME) and, finally their effect on the compression ignition engines are analyzed. The analysis concludes that green diesel is an excellent fuel for combustion engines with remarkable properties and significantly lower emissions.
TL;DR: A review of the characteristics of MFC technology that make it revolutionary will be highlighted in this article, along with the importance of design and elements to reduce energy loss for scaling up the MFC system including the type of electrode, shape of the single reactor, electrical connection method, stack direction, and modulation.
Abstract: Microbial fuel cell (MFC) technology offers an alternative means for producing energy from waste products. In this review, several characteristics of MFC technology that make it revolutionary will be highlighted. First, a brief history presents how bioelectrochemical systems have advanced, ultimately describing the development of microbial fuel cells. Second, the focus is shifted to the attributes that enable MFCs to work efficiently. Next, follows the design of various MFC systems in use including their components and how they are assembled, along with an explanation of how they work. Finally, microbial fuel cell designs and types of main configurations used are presented along with the scalability of the technology for proper application. The present review shows importance of design and elements to reduce energy loss for scaling up the MFC system including the type of electrode, shape of the single reactor, electrical connection method, stack direction, and modulation. These aspects precede making economically applicable large-scale MFCs (over 1 m3 scale) a reality.
TL;DR: In this article, the authors presented an assessment of the cost performance of CO2 capture technologies when retrofitted to a cement plant: MEA-based absorption, oxyfuel, chilled ammonia based absorption (Chilled Ammonia Process), membrane assisted CO2 liquefaction, and calcium looping.
Abstract: This paper presents an assessment of the cost performance of CO2 capture technologies when retrofitted to a cement plant: MEA-based absorption, oxyfuel, chilled ammonia-based absorption (Chilled Ammonia Process), membrane-assisted CO2 liquefaction, and calcium looping. While the technical basis for this study is presented in Part 1 of this paper series, this work presents a comprehensive techno-economic analysis of these CO2 capture technologies based on a capital and operating costs evaluation for retrofit in a cement plant. The cost of the cement plant product, clinker, is shown to increase with 49 to 92% compared to the cost of clinker without capture. The cost of CO2 avoided is between 42 €/tCO2 (for the oxyfuel-based capture process) and 84 €/tCO2 (for the membrane-based assisted liquefaction capture process), while the reference MEA-based absorption capture technology has a cost of 80 €/tCO2. Notably, the cost figures depend strongly on factors such as steam source, electricity mix, electricity price, fuel price and plant-specific characteristics. Hence, this confirms the conclusion of the technical evaluation in Part 1 that for final selection of CO2 capture technology at a specific plant, a plant-specific techno-economic evaluation should be performed, also considering more practical considerations.
TL;DR: In this article, the authors investigated the relationship between economic, social, and environmental dimensions of sustainable development, and confirmed the Environmental Kuznets curve hypothesis for the EU and Ukraine.
Abstract: The paper investigates the relationships between economic, social, and environmental dimensions of sustainable development. GDP growth represents the main economic dimension, greenhouse gas (GHG) emissions and renewable energy consumption the environmental dimension, and corruption the social dimension of sustainable development. The investigation of these relationships is based on the concept of the Environmental Kuznets Curve hypothesis about the non-linear relationship between economic growth and environmental pollution. The authors used the panel data of EU countries and Ukraine for 2000–2016 years from the Eurostat database. The obtained results confirmed the Environmental Kuznets curve hypothesis for the EU and Ukraine. All the indicators were statistically significant at 1% and 5% levels. The findings proved that increasing renewable energy (RE) by 1% led to a decline of GHG in the interval (0.166103, 0.220551), and аn increase of the Control of Corruption Index by 1% provoked a decline of GHG by 0.88%. The conducted study enabled the authors to conclude that Ukraine needs to increase the GDP level per capita given the economy diversification and via the introduction of more effective and “clean” production technologies.
TL;DR: A review of the studies published since the year 2000 which have applied artificial neural networks for forecasting building energy use and demand, with a particular focus on reviewing the applications, data, forecasting models, and performance metrics used in model evaluations is presented in this article.
Abstract: During the past century, energy consumption and associated greenhouse gas emissions have increased drastically due to a wide variety of factors including both technological and population-based. Therefore, increasing our energy efficiency is of great importance in order to achieve overall sustainability. Forecasting the building energy consumption is important for a wide variety of applications including planning, management, optimization, and conservation. Data-driven models for energy forecasting have grown significantly within the past few decades due to their increased performance, robustness and ease of deployment. Amongst the many different types of models, artificial neural networks rank among the most popular data-driven approaches applied to date. This paper offers a review of the studies published since the year 2000 which have applied artificial neural networks for forecasting building energy use and demand, with a particular focus on reviewing the applications, data, forecasting models, and performance metrics used in model evaluations. Based on this review, existing research gaps are identified and presented. Finally, future research directions in the area of artificial neural networks for building energy forecasting are highlighted.
TL;DR: In this paper, a ship power system integrated with renewable energy was built in PSCAD/EMTDC simulation software, and the layout of wind power generation system and photovoltaic power generator system was given for the actual ship, and ship parameters and specific parameters of each simulation module were determined.
Abstract: Renewable energy ship was regarded as one of the ship energy technologies with a good prospect. In order to study the application of solar and wind energy on ships in the marine environment and the impact of ship rolling on the system, the feasibility of applying solar energy and wind energy to ships was analyzed, and the structural composition of ship power system incorporating renewable energy source was studied. The model of the ship power system integrated with renewable energy was built in PSCAD/EMTDC simulation software. The layout of wind power generation system and photovoltaic power generation system was given for the actual ship, and the ship parameters and specific parameters of each simulation module were determined. It can be seen that the rolling of ship will cause fluctuations in the grid-connected power of the photovoltaic power generation system and the wind power generation system from the comparison of the simulation curves. Finally, a simulation experiment is provided to prove the access of the battery can well suppress the grid-connected power fluctuation caused by the rolling of the ship, which has an important impact on the stability of the ship power system with renewable energy.
TL;DR: A review of the current status of algal bio-fuels, important steps of algae biofuel production, and the major commercial production challenges is presented in this article, where the authors discuss the potential of algae as a potential source of renewable energy.
Abstract: The current fossil fuel reserves are not sufficient to meet the increasing demand and very soon will become exhausted. Pollution, global warming, and inflated oil prices have led the quest for renewable energy sources. Algal biofuels represent a potential source of renewable energy. Algae, as the third generation feedstock, are suitable for biodiesel and bioethanol production due to their quick growth, excellent biomass yield, and high lipid and carbohydrate contents. With their huge potential, algae are expected to surpass the first and second generation feedstocks. Only a few thousand algal species have been investigated as possible biofuel sources, and none of them was ideal. This review summarizes the current status of algal biofuels, important steps of algal biofuel production, and the major commercial production challenges.
TL;DR: In this article, an experimental investigation is performed to assess the thermal and electrical performance of a photovoltaic solar panel cooling with multi-walled carbon nanotube-water/ethylene glycol (50:50) nano-suspension (MWCNT/WEG50).
Abstract: In the present work, an experimental investigation is performed to assess the thermal and electrical performance of a photovoltaic solar panel cooling with multi-walled carbon nanotube–water/ethylene glycol (50:50) nano-suspension (MWCNT/WEG50). The prepared nanofluid was stabilized using an ultrasonic homogenizer together with the addition of 0.1vol% of nonylphenol ethoxylates at pH = 8.9. To reduce the heat loss and to improve the heat transfer rate between the coolant and the panel, a cooling jacket was designed and attached to the solar panel. It was also filled with multi-walled carbon nanotube–paraffin phase change material (PCM) and the cooling pipes were passed through the PCM. The MWCNT/WEG50 nanofluid was introduced into the pipes, while the nano-PCM was in the cooling jacket. The electrical and thermal power of the system and equivalent electrical–thermal power of the system was assessed at various local times and at different mass fractions of MWCNTs. Results showed that with an increase in the mass concentration of the coolant, the electricity and power production were promoted, while with an increase in the mass concentration of the nanofluid, the pumping power was augmented resulting in the decrease in the thermal–electrical equivalent power. It was identified that a MWCNT/WEG50 nano-suspension at 0.2wt% can represent the highest thermal and electrical performance of 292.1 W/m2. It was also identified that at 0.2wt%, ~45% of the electricity and 44% of the thermal power can be produced with a photovoltaic (PV) panel between 1:30 pm to 3:30 pm.
TL;DR: In this paper, a technical evaluation of CO2 capture technologies when retrofitted to a cement plant is performed, where the focus of the evaluation is on emission abatement, energy performance, and retrofitability.
Abstract: A technical evaluation of CO2 capture technologies when retrofitted to a cement plant is performed. The investigated technologies are the oxyfuel process, the chilled ammonia process, membrane-assisted CO2 liquefaction, and the calcium looping process with tail-end and integrated configurations. For comparison, absorption with monoethanolamine (MEA) is used as reference technology. The focus of the evaluation is on emission abatement, energy performance, and retrofitability. All the investigated technologies perform better than the reference both in terms of emission abatement and energy consumption. The equivalent CO2 avoided are 73–90%, while it is 64% for MEA, considering the average EU-28 electricity mix. The specific primary energy consumption for CO2 avoided is 1.63–4.07 MJ/kg CO2, compared to 7.08 MJ/kg CO2 for MEA. The calcium looping technologies have the highest emission abatement potential, while the oxyfuel process has the best energy performance. When it comes to retrofitability, the post-combustion technologies show significant advantages compared to the oxyfuel and to the integrated calcium looping technologies. Furthermore, the performance of the individual technologies shows strong dependencies on site-specific and plant-specific factors. Therefore, rather than identifying one single best technology, it is emphasized that CO2 capture in the cement industry should be performed with a portfolio of capture technologies, where the preferred choice for each specific plant depends on local factors.