The 100 most-cited articles in Energies, 2018

Background: With the development of smart grids, accurate electric load forecasting has become increasingly important as it can help power companies in better load scheduling and reduce excessive electricity production. However, developing and selecting accurate time series models is a challenging task as this requires training several different models for selecting the best amongst them along with substantial feature engineering to derive informative features and finding optimal time lags, a commonly used input features for time series models. Methods: Our approach uses machine learning and a long short-term memory (LSTM)-based neural network with various configurations to construct forecasting models for short to medium term aggregate load forecasting. The research solves above mentioned problems by training several linear and non-linear machine learning algorithms and picking the best as baseline, choosing best features using wrapper and embedded feature selection methods and finally using genetic algorithm (GA) to find optimal time lags and number of layers for LSTM model…

In the last few years, the application of Model Predictive Control (MPC) for energy management in buildings has received significant attention from the research community. MPC is becoming more and more viable because of the increase in computational power of building automation systems and the availability of a significant amount of monitored building data. MPC has found successful implementation in building thermal regulation, fully exploiting the potential of building thermal mass. Moreover, MPC has been positively applied to active energy storage systems, as well as to the optimal management of on-site renewable energy sources. MPC also opens up several opportunities for enhancing energy efficiency in the operation of Heating Ventilation and Air Conditioning (HVAC) systems because of its ability to consider constraints, prediction of disturbances and multiple conflicting objectives, such as indoor thermal comfort and building energy demand. Despite the application of MPC algorithms in building control has been thoroughly investigated in various works, a unified framework that…

Recent concerns about environmental pollution and escalating energy consumption accompanied by the advancements in battery technology have initiated the electrification of the transportation sector. With the universal resurgence of Electric Vehicles (EVs) the adverse impact of the EV charging loads on the operating parameters of the power system has been noticed. The detrimental impact of EV charging station loads on the electricity distribution network cannot be neglected. The high charging loads of the fast charging stations results in increased peak load demand, reduced reserve margins, voltage instability, and reliability problems. Further, the penalty paid by the utility for the degrading performance of the power system cannot be neglected. This work aims to investigate the impact of the EV charging station loads on the voltage stability, power losses, reliability indices, as well as economic losses of the distribution network. The entire analysis is performed on the IEEE 33 bus test system representing a standard radial distribution network for six different cases of EV…

The production of biofuels from renewable sources is a major challenge in research. Methanol, ethanol, dimethyl ether (DME), synthetic natural gas (SNG), and hydrogen can be produced from syngas which is the result of the gasification of biomasses. Syngas composition varies according to the gasification technology used (such as fixed bed reactors, fluidized bed reactors, entrained flow reactors), the feedstock characteristics, and the operating parameters. This paper presents a review of the predominant biomass gasification technologies and biofuels obtained from syngas by biomass gasification.

Fossil fuels have been a major contributor to greenhouse gases, the amounts of which could be reduced if biofuels such as bioethanol and biodiesel were used for transportation. One of the most promising biofuels is ethyl alcohol. In 2015, the world production of ethanol was 25.6 billion gallons and the USA, Brazil, China, the European Union, and 28 other countries have set targets for blending ethanol with gasoline. The two major bio-source materials used for ethanol production are corn and sugarcane. For 1st generation biofuels, sugarcane and corn feedstocks are not able to fulfill the current demand for alcohol. Non-edible lignocellulosic biomass is an alternative bio-source for creating 2nd generation biofuels and algae biomass for 3rd and 4th generation biofuels. This review discusses the significance of biomass for the different generations of biofuels, and biochemical and thermochemical processes, and the significance of biorefinery products.

Accurate electrical load forecasting is of great significance to help power companies in better scheduling and efficient management. Since high levels of uncertainties exist in the load time series, it is a challenging task to make accurate short-term load forecast (STLF). In recent years, deep learning approaches provide better performance to predict electrical load in real world cases. The convolutional neural network (CNN) can extract the local trend and capture the same pattern, and the long short-term memory (LSTM) is proposed to learn the relationship in time steps. In this paper, a new deep neural network framework that integrates the hidden feature of the CNN model and the LSTM model is proposed to improve the forecasting accuracy. The proposed model was tested in a real-world case, and detailed experiments were conducted to validate its practicality and stability. The forecasting performance of the proposed model was compared with the LSTM model and the CNN model. The Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE) and Root Mean Square Error (RMSE) were…

Biomass is a promising sustainable and renewable energy source, due to its high diversity of sources, and as it is profusely obtainable everywhere in the world. It is the third most important fuel source used to generate electricity and for thermal applications, as 50% of the global population depends on biomass. The increase in availability and technological developments of recent years allow the use of biomass as a renewable energy source with low levels of emissions and environmental impacts. Biomass energy can be in the forms of biogas, bio-liquid, and bio-solid fuels. It can be used to replace fossil fuels in the power and transportation sectors. This paper critically reviews the facts and prospects of biomass, the pyrolysis process to obtain bio-oil, the impact of different pyrolysis technology (for example, temperature and speed of pyrolysis process), and the impact of various reactors. The paper also discusses different pyrolysis products, their yields, and factors affecting biomass products, including the present status of the pyrolysis process and future challenges. This…

Power systems are the most complex systems that have been created by men in history. To operate such systems in a stable mode, several control loops are needed. Voltage frequency plays a vital role in power systems which need to be properly controlled. To this end, primary and secondary frequency control loops are used to control the frequency of the voltage in power systems. Secondary frequency control, which is called Load Frequency Control (LFC), is responsible for maintaining the frequency in a desirable level after a disturbance. Likewise, the power exchanges between different control areas are controlled by LFC approaches. In recent decades, many control approaches have been suggested for LFC in power systems. This paper presents a comprehensive literature survey on the topic of LFC. In this survey, the used LFC models for diverse configurations of power systems are firstly investigated and classified for both conventional and future smart power systems. Furthermore, the proposed control strategies for LFC are studied and categorized into different control groups. The paper…

In the past five years, there have been numerous cases of Li-ion battery fires and explosions, resulting in property damage and bodily injuries. This paper discusses the thermal runaway mechanism and presents various thermal runaway mitigation approaches, including separators, flame retardants, and safety vents. The paper then overviews measures for extinguishing fires, and concludes with a set of recommendations for future research and development.

Hydrothermal liquefaction (HTL) of biomass is emerging as an effective technology to efficiently valorize different types of (wet) biomass feedstocks, ranging from lignocellulosics to algae and organic wastes. Significant research into HTL has been conducted in batch systems, which has provided a fundamental understanding of the different process conditions and the behavior of different biomass. The next step towards continuous plants, which are prerequisites for an industrial implementation of the process, has been significantly less explored. In order to facilitate a more focused future development, this review—based on the sources available in the open literature—intends to present the state of the art in the field of continuous HTL as well as to suggest means of interpretation of data from such plants. This contributes to a more holistic understanding of causes and effects, aiding next generation designs as well as pinpointing research focus. Additionally, the documented experiences in upgrading by catalytic hydrotreating are reported. The study reveals some interesting features…

Anaerobic digestion (AD) is an effective biological treatment for stabilizing organic compounds in waste/wastewater and in simultaneously producing biogas. However, it is often limited by the slow reaction rates of different microorganisms’ syntrophic biological metabolisms. Stable and fast interspecies electron transfer (IET) between volatile fatty acid-oxidizing bacteria and hydrogenotrophic methanogens is crucial for efficient methanogenesis. In this syntrophic interaction, electrons are exchanged via redox mediators such as hydrogen and formate. Recently, direct IET (DIET) has been revealed as an important IET route for AD. Microorganisms undergoing DIET form interspecies electrical connections via membrane-associated cytochromes and conductive pili; thus, redox mediators are not required for electron exchange. This indicates that DIET is more thermodynamically favorable than indirect IET. Recent studies have shown that conductive materials (e.g., iron oxides, activated carbon, biochar, and carbon fibers) can mediate direct electrical connections for DIET. Microorganisms attach…

The solar photovoltaic (PV) energy has an important place among the renewable energy sources. Therefore, several researchers have been interested by its modelling and its prediction, in order to improve the management of the electrical systems which include PV arrays. Among the existing techniques, artificial neural networks have proved their performance in the prediction of the solar radiation. However, the existing neural network models don’t satisfy the requirements of certain specific situations such as the one analyzed in this paper. The aim of this research work is to supply, with electricity, a race sailboat using exclusively renewable sources. The developed solution predicts the direct solar radiation on a horizontal surface. For that, a Nonlinear Autoregressive Exogenous (NARX) neural network is used. All the specific conditions of the sailboat operation are taken into account. The results show that the best prediction performance is obtained when the training phase of the neural network is performed periodically.

One of the most important research topics in smart grid technology is load forecasting, because accuracy of load forecasting highly influences reliability of the smart grid systems. In the past, load forecasting was obtained by traditional analysis techniques such as time series analysis and linear regression. Since the load forecast focuses on aggregated electricity consumption patterns, researchers have recently integrated deep learning approaches with machine learning techniques. In this study, an accurate deep neural network algorithm for short-term load forecasting (STLF) is introduced. The forecasting performance of proposed algorithm is compared with performances of five artificial intelligence algorithms that are commonly used in load forecasting. The Mean Absolute Percentage Error (MAPE) and Cumulative Variation of Root Mean Square Error (CV-RMSE) are used as accuracy evaluation indexes. The experiment results show that MAPE and CV-RMSE of proposed algorithm are 9.77% and 11.66%, respectively, displaying very high forecasting accuracy.

The increasing volume of sewage sludge from wastewater treatment facilities is becoming a prominent concern globally. The disposal of this sludge is particularly challenging and poses severe environmental hazards due to the high content of organic, toxic and heavy metal pollutants among its constituents. This study presents a simple review of four sewage to energy recovery routes (anaerobic digestion, combustion, pyrolysis and gasification) with emphasis on recent developments in research, as well as benefits and limitations of the technology for ensuring cost and environmentally viable sewage to energy pathway. This study focusses on the review of various commercially viable sludge conversion processes and technologies used for energy recovery from sewage sludge. This was done via in-depth process descriptions gathered from literatures and simplified schematic depiction of such energy recovery processes when utilised for sludge. Specifically, the impact of fuel properties and its effect on the recovery process were discussed to indicate the current challenges and recent scientific…

Sate of charge (SOC) accurate estimation is one of the most important functions in a battery management system for battery packs used in electrical vehicles. This paper focuses on battery SOC estimation and its issues and challenges by exploring different existing estimation methodologies. The key technologies of lithium-ion battery state estimation methodologies of the electrical vehicles categorized under five groups, such as the conventional method, adaptive filter algorithm, learning algorithm, nonlinear observer, and the hybrid method, are explored in an in-depth analysis. Lithium-ion battery characteristic, battery model, estimation algorithm, and cell unbalancing are the most important factors that affect the accuracy and robustness of SOC estimation. Finally, this paper concludes with the challenges of SOC estimation and suggests other directions for possible research efforts.

Wave energy is substantial as a resource, and its potential to significantly contribute to the existing energy mix has been identified. However, the commercial utilization of wave energy is still very low. This paper reviewed the background of wave energy harvesting technology, its evolution, and the present status of the industry. By covering the theoretical formulations, wave resource characterization methods, hydrodynamics of wave interaction with the wave energy converter, and the power take-off and electrical systems, different challenges were identified and discussed. Solutions were suggested while discussing the challenges in order to increase awareness and investment in wave energy industry as a whole.

The sustainability of petroleum-based fuel supply has gained broad attention from the global community due to the increase of usage in various sectors, depletion of petroleum resources, and uncertain around crude oil market prices. Additionally, environmental problems have also arisen from the increasing emissions of harmful pollutants and greenhouse gases. Therefore, the use of clean energy sources including biodiesel is crucial. Biodiesel is mainly produced from unlimited natural resources through a transesterification process. It presents various advantages over petro-diesel; for instance, it is non-toxic, biodegradable, and contains less air pollutant per net energy produced with low sulphur and aromatic content, apart from being safe. Considering the importance of this topic, this paper focuses on the use of palm oil, its by-products, and mill effluent for biodiesel production. Palm oil is known as an excellent raw material because biodiesel has similar properties to the regular petro-diesel. Due to the debate on the usage of palm oil as food versus fuel, extensive studies have…

Accurate electricity price forecasting has become a substantial requirement since the liberalization of the electricity markets. Due to the challenging nature of electricity prices, which includes high volatility, sharp price spikes and seasonality, various types of electricity price forecasting models still compete and cannot outperform each other consistently. Neural Networks have been successfully used in machine learning problems and Recurrent Neural Networks (RNNs) have been proposed to address time-dependent learning problems. In particular, Long Short Term Memory (LSTM) and Gated Recurrent Units (GRU) are tailor-made for time series price estimation. In this paper, we propose to use multi-layer Gated Recurrent Units as a new technique for electricity price forecasting. We have trained a variety of algorithms with three-year rolling window and compared the results with the RNNs. In our experiments, three-layered GRUs outperformed all other neural network structures and state-of-the-art statistical techniques in a statistically significant manner in the Turkish day-ahead market.

The ability to predict short-term electric energy demand would provide several benefits, both at the economic and environmental level. For example, it would allow for an efficient use of resources in order to face the actual demand, reducing the costs associated to the production as well as the emission of CO 2 . To this aim, in this paper we propose a strategy based on ensemble learning in order to tackle the short-term load forecasting problem. In particular, our approach is based on a stacking ensemble learning scheme, where the predictions produced by three base learning methods are used by a top level method in order to produce final predictions. We tested the proposed scheme on a dataset reporting the energy consumption in Spain over more than nine years. The obtained experimental results show that an approach for short-term electricity consumption forecasting based on ensemble learning can help in combining predictions produced by weaker learning methods in order to obtain superior results. In particular, the system produces a lower error with respect to the existing…

Field oriented control (FOC), direct torque control (DTC) and finite set model predictive control (FS-MPC) are different strategies for high performance electrical drive systems. FOC uses linear controllers and pulse width modulation (PWM) to control the fundamental components of the load voltages. On the other hand, DTC and FS-MPC are nonlinear strategies that generate directly the voltage vectors in the absence of a modulator. This paper presents all three methods starting from theoretic operating principles, control structures and implementation. Experimental assessment is performed to discuss their advantages and limitations in detail. As main conclusions of this work, it is affirmed that different strategies have their own merits and all meet the requirements of modern high performance drives.

Photovoltaic power has great volatility and intermittency due to environmental factors. Forecasting photovoltaic power is of great significance to ensure the safe and economical operation of distribution network. This paper proposes a novel approach to forecast short-term photovoltaic power based on a gated recurrent unit (GRU) network. Firstly, the Pearson coefficient is used to extract the main features that affect photovoltaic power output at the next moment, and qualitatively analyze the relationship between the historical photovoltaic power and the future photovoltaic power output. Secondly, the K-means method is utilized to divide training sets into several groups based on the similarities of each feature, and then GRU network training is applied to each group. The output of each GRU network is averaged to obtain the photovoltaic power output at the next moment. The case study shows that the proposed approach can effectively consider the influence of features and historical photovoltaic power on the future photovoltaic power output, and has higher accuracy than the traditional…

At sea, the electrical power system of a ship can be considered as an islanded microgrid. When connected to shore power at berth, the same power system acts as a grid connected microgrid or an extension of the grid. Therefore, ship microgrids show some resemblance to terrestrial microgrids. Nevertheless, due to the presence of large dynamic loads, such as electric propulsion loads, keeping the voltage and frequency within a permissible range and ensuring the continuity of supply are more challenging in ship microgrids. Moreover, with the growing demand for emission reductions and fuel efficiency improvements, alternative energy sources and energy storage technologies are becoming popular in ship microgrids. In this context, the integration of multiple energy sources and storage systems in ship microgrids requires an efficient power management system (PMS). These challenging environments and trends demand advanced control and power management solutions that are customized for ship microgrids. This paper presents a review on recent developments of control technologies and power…

Since it first appeared in literature in the early nineties, the Circular Economy (CE) has grown in significance amongst academic, policymaking, and industry groups. The latest developments in the CE field have included the interrogation of CE as a paradigm, and its relationship with sustainability and other concepts, including iterative definitions. Research has also identified a significant opportunity to apply circular approaches to our rapidly changing industrial system, including manufacturing processes and Industry 4.0 (I4.0) which, with data, is enabling the latest advances in digital technologies (DT). Research which fuses these two areas has not been extensively explored. This is the first paper to provide a synergistic and integrative CE-DT framework which offers directions for policymakers and guidance for future research through a review of the integrated fields of CE and I4.0. To achieve this, a Systematic Literature Review (SLR; n = 174) of the empirical literature related to digital technologies, I4.0, and circular approaches is conducted. The SLR is based on…

Energy sustainability is one of the key questions that drive the debate on cities’ and urban areas development. In parallel, artificial intelligence and cognitive computing have emerged as catalysts in the process aimed at designing and optimizing smart services’ supply and utilization in urban space. The latter are paramount in the domain of energy provision and consumption. This paper offers an insight into pilot systems and prototypes that showcase in which ways artificial intelligence can offer critical support in the process of attaining energy sustainability in smart cities. To this end, this paper examines smart metering and non-intrusive load monitoring (NILM) to make a case for the latter’s value added in context of profiling electric appliances’ electricity consumption. By employing the findings in context of smart cities research, the paper then adds to the debate on energy sustainability in urban space. Existing research tends to be limited by data granularity (not in high frequency) and consideration of about six kinds of appliances. In this paper, a hybrid genetic…

Electricity demand has grown over the past few years and will continue to grow in the future. The increase in electricity demand is mainly due to industrialization and the shift from a conventional to a smart-grid paradigm. The number of microgrids, renewable energy sources, plug-in electric vehicles and energy storage systems have also risen in recent years. As a result, future electricity grids have to be revamped and adapt to increasing load levels. Thus, new complications associated with future electrical power systems and technologies must be considered. Demand-side management (DSM) programs offer promising solutions to these issues and can considerably improve the reliability and financial performances of electrical power systems. This paper presents a review of various initiatives, techniques, impacts and recent developments of the DSM of electrical power systems. The potential benefits derived by implementing DSM in electrical power networks are presented. An extensive literature survey on the impacts of DSM on the reliability of electrical power systems is also provided for…

From last few decades, piezoelectric materials have played a vital role as a mechanism of energy harvesting, as they have the tendency to absorb energy from the environment and transform it to electrical energy that can be used to drive electronic devices directly or indirectly. The power of electronic circuits has been cut down to nano or micro watts, which leads towards the development of self-designed piezoelectric transducers that can overcome power generation problems and can be self-powered. Moreover, piezoelectric energy harvesters (PEHs) can reduce the need for batteries, resulting in optimization of the weight of structures. These mechanisms are of great interest for many researchers, as piezoelectric transducers are capable of generating electric voltage in response to thermal, electrical, mechanical and electromagnetic input. In this review paper, Fluid Structure Interaction-based, human-based, and vibration-based energy harvesting mechanisms were studied. Moreover, qualitative and quantitative analysis of existing PEH mechanisms has been carried out.

With regard to social and environmental sustainability, second-generation biofuel and biogas production from lignocellulosic material provides considerable potential, since lignocellulose represents an inexhaustible, ubiquitous natural resource, and is therefore one important step towards independence from fossil fuel combustion. However, the highly heterogeneous structure and recalcitrant nature of lignocellulose restricts its commercial utilization in biogas plants. Improvements therefore rely on effective pretreatment methods to overcome structural impediments, thus facilitating the accessibility and digestibility of (ligno)cellulosic substrates during anaerobic digestion. While chemical and physical pretreatment strategies exhibit inherent drawbacks including the formation of inhibitory products, biological pretreatment is increasingly being advocated as an environmentally friendly process with low energy input, low disposal costs, and milder operating conditions. Nevertheless, the promising potential of biological pretreatment techniques is not yet fully exploited. Hence, we…

Energy management systems are designed to monitor, optimize, and control the smart grid energy market. Demand-side management, considered as an essential part of the energy management system, can enable utility market operators to make better management decisions for energy trading between consumers and the operator. In this system, a priori knowledge about the energy load pattern can help reshape the load and cut the energy demand curve, thus allowing a better management and distribution of the energy in smart grid energy systems. Designing a computationally intelligent load forecasting (ILF) system is often a primary goal of energy demand management. This study explores the state of the art of computationally intelligent (i.e., machine learning) methods that are applied in load forecasting in terms of their classification and evaluation for sustainable operation of the overall energy management system. More than 50 research papers related to the subject identified in existing literature are classified into two categories: namely the single and the hybrid computational intelligence…

Buildings consume a significant amount of energy, estimated at about one-third of total primary energy resources. Building energy efficiency has turned out to be a major issue in limiting the increasing energy demands of the sector. Literature shows that building user behavior can increase the efficiency of the energy used in the building and different strategies have been tested to address and support this issue. These strategies often combine the quantification of energy savings and qualitative interpretation of occupant behavior in order to foster energy efficiency. Strategies that influence building occupant behaviors include eco-feedback, social interaction, and gamification. This review paper presents a study conducted on the state of the art related to the impact of building user behavior on energy efficiency, in order to provide the research community with a better understanding and up-to-date knowledge of energy, comfort-related practices, and potential research opportunities. Achieving and maintaining energy-efficient behavior without decreasing the comfort of building…

The aim of the conducted research was to obtain information on the main components of ashes from 35 biomass species used in combustion processes to obtain reference data for the development of utility possibilities for these ashes, with particular emphasis on agricultural use. The examined biomass samples were divided into groups depending on origin: woody biomass and energetic woody plants I-WWB, herbaceous and grassy energy plants II-EC, agricultural waste III-AR, forest waste IV-FR and waste from the agri-food industry V-AFIW. The analysis of the studied elements contents was carried out in the designated groups. The chemical composition of ash was dominated by the macroelements Ca, K, P and S, which suggests the possibility of their agricultural use. At the same time, the low content of toxic elements such as As and Pb should not be a limiting feature in their use, with the exception of wood biomass. In addition, ashes obtained from the biomass combustion were enriched with microelements such as Zn, Cu and Mn, which further increases their possibilities of fertilizer use. The…

Open circuit voltage (OCV) is an important characteristic parameter of lithium-ion batteries, which is used to analyze the changes of electronic energy in electrode materials, and to estimate battery state of charge (SOC) and manage the battery pack. Therefore, accurate OCV modeling is a great significance for lithium-ion battery management. In this paper, the characteristics of high-capacity lithium-ion batteries at different temperatures were considered, and the OCV-SOC characteristic curves at different temperatures were studied by modeling, exponential, polynomial, sum of sin functions, and Gaussian model fitting method with pulse test data. The parameters of fitting OCV-SOC curves by exponential model (n = 2), polynomial model (n = 3~7), sum of sin functions model (n = 3), and Gaussian model (n = 4) at temperatures of 45 °C, 25 °C, 0 °C, and −20°C are obtained, and the errors are analyzed. The experimental results show that the operating temperature of the battery influences the OCV-SOC characteristic significantly. Therefore, these factors need to be considered in order to…

This paper presents a general description of local flexibility markets as a market-based management mechanism for aggregators. The high penetration of distributed energy resources introduces new flexibility services like prosumer or community self-balancing, congestion management and time-of-use optimization. This work is focused on the flexibility framework to enable multiple participants to compete for selling or buying flexibility. In this framework, the aggregator acts as a local market operator and supervises flexibility transactions of the local energy community. Local market participation is voluntary. Potential flexibility stakeholders are the distribution system operator, the balance responsible party and end-users themselves. Flexibility is sold by means of loads, generators, storage units and electric vehicles. Finally, this paper presents needed interactions between all local market stakeholders, the corresponding inputs and outputs of local market operation algorithms from participants and a case study to highlight the application of the local flexibility market in…

Particulate Matter (PM) emissions from gasoline direct injection (GDI) engines, particularly Particle Number (PN) emissions, have been studied intensively in both academia and industry because of the adverse effects of ultrafine PM emissions on human health and other environmental concerns. GDI engines are known to emit a higher number of PN emissions (on an engine-out basis) than Port Fuel Injection (PFI) engines, due to the reduced mixture homogeneity in GDI engines. Euro 6 emission standards have been introduced in Europe (and similarly in China) to limit PN emissions from GDI engines. This article summarises the current state of research in GDI PN emissions (engine-out) including a discussion of PN formation, and the characteristics of PN emissions from GDI engines. The effect of key GDI engine operating parameters is analysed, including air-fuel ratio, ignition and injection timing, injection pressure, and EGR; in addition the effect of fuel composition on particulate emissions is explored, including the effect of oxygenate components such as ethanol.

Responsible, efficient and environmentally aware energy consumption behavior is becoming a necessity for the reliable modern electricity grid. In this paper, we present an intelligent data mining model to analyze, forecast and visualize energy time series to uncover various temporal energy consumption patterns. These patterns define the appliance usage in terms of association with time such as hour of the day, period of the day, weekday, week, month and season of the year as well as appliance-appliance associations in a household, which are key factors to infer and analyze the impact of consumers’ energy consumption behavior and energy forecasting trend. This is challenging since it is not trivial to determine the multiple relationships among different appliances usage from concurrent streams of data. Also, it is difficult to derive accurate relationships between interval-based events where multiple appliance usages persist for some duration. To overcome these challenges, we propose unsupervised data clustering and frequent pattern mining analysis on energy time series, and Bayesian…

This paper presents a case study of using hydrogen for large-scale long-term storage application to support the current electricity generation mix of South Australia state in Australia, which primarily includes gas, wind and solar. For this purpose two cases of battery energy storage and hybrid battery-hydrogen storage systems to support solar and wind energy inputs were compared from a techno-economical point of view. Hybrid battery-hydrogen storage system was found to be more cost competitive with unit cost of electricity at $0.626/kWh (US dollar) compared to battery-only energy storage systems with a $2.68/kWh unit cost of electricity. This research also found that the excess stored hydrogen can be further utilised to generate extra electricity. Further utilisation of generated electricity can be incorporated to meet the load demand by either decreasing the base load supply from gas in the present scenario or exporting it to neighbouring states to enhance economic viability of the system. The use of excess stored hydrogen to generate extra electricity further reduced the cost to…

Due to the expansion of distributed renewable energy resources, peer to peer energy trading (P2P DET) is expected to be one of the key elements of next generation power systems. P2P DET can provide various benefits such as creating a competitive energy market, reducing power outages, increasing overall efficiency of power systems and supplementing alternative sources of energy according to user preferences. Because of these promising advantages, P2P DET has attracted the attention of several researchers. Current research related to P2P DET include demand response optimization, power routing, network communication, security and privacy. This paper presents a review of the main research topics revolving around P2P DET. Particularly, we present a comprehensive survey of existing demand response optimization models, power routing devices and power routing algorithms. We also identify some key challenges faced in realizing P2P DET. Furthermore, we discuss state of the art enabling technologies such as Energy Internet, Blockchain and Software Defined Networking (SDN) and we provide…

Mineral oil is the most popular insulating liquid for high voltage transformers due to its function as a cooling liquid and an electrical insulator. Kraft paper has been widely used as transformer solid insulation for a long time already. The degradation process of transformer paper due to thermal aging in mineral oil can change the physical and chemical structure of the cellulose paper. Fourier transform infrared (FTIR) spectroscopy analysis was used to identify changes in the chemical structure of transformer paper aged in mineral oil. FTIR results show that the intensity of the peak absorbance of the O–H functional group decreased with aging but the intensity of the peak absorbance of the C–H and C=O functional groups increased with aging. Changes in the chemical structure of the cellulose paper during thermal aging in mineral oil can be analyzed by an oxidation process of the cellulose paper and the reaction process between the carboxylic acids in the mineral oil and the hydroxyl groups on the cellulose. The correlation between the functional groups and the average number of…

Measurements taken before and after the commissioning of three wind farms reveal that the wind speeds just upstream of each wind farm decrease relative to locations farther away after the turbines are turned on. At a distance of two rotor diameters upstream, the average derived relative slowdown is 3.4%; at seven to ten rotor diameters upstream, the average slowdown is 1.9%. Reynolds-Averaged Navier-Stokes (RANS) simulations point to wind-farm-scale blockage as the primary cause of these slowdowns. Blockage effects also cause front row turbines to produce less energy than they each would operating in isolation. Wind energy prediction procedures in use today ignore this effect, resulting in an overprediction bias that pervades the entire wind farm.

The measurement of sustainability is actively used today as one of the main preventative instruments in order to reduce the decline of the environment. Sustainable decision-making in solving energy issues can be supported and contradictory effects can be evaluated by scientific achievements of multi-criteria decision-making (MCDM) techniques. The main goal of this paper is to overview the application of decision-making methods in dealing with sustainable energy development issues. In this study, 105 published papers from the Web of Science Core Collection (WSCC) database are selected and reviewed, from 2004 to 2017, related to energy sustainability issues and MCDM methods. All the selected papers were categorized into 9 fields by the application area and into 10 fields by the used method. After the categorization of the scientific articles and detailed analysis, SWOT analysis of MCDM approaches in dealing with sustainable energy development issues is provided. The widespread application and use of MCDM methods confirm that MCDM methods can help decision-makers in solving energy…

Smart grids are robust, self-healing networks that allow bidirectional propagation of energy and information within the utility grid. This introduces a new type of energy user who consumes, produces, stores and shares energy with other grid users. Such a user is called a “prosumer.” Prosumers’ participation in the smart grid is critical for the sustainability and long-term efficiency of the energy sharing process. Thus, prosumer management has attracted increasing attention among researchers in recent years. This paper systematically examines the literature on prosumer community based smart grid by reviewing relevant literature published from 2009 to 2018 in reputed energy and technology journals. We specifically focus on two dimensions namely prosumer community groups and prosumer relationships. Based on the evaluated literature, we present eight propositions and thoroughly describe several future research directions.

Active research on biomass hydrothermal carbonization (HTC) continues to demonstrate its advantages over other thermochemical processes, in particular the interesting benefits that are associated with carbonaceous solid products, called hydrochar (HC). The areas of applications of HC range from biofuel to doped porous material for adsorption, energy storage, and catalysis. At the same time, intensive research has been aimed at better elucidating the process mechanisms and kinetics, and how the experimental variables (temperature, time, biomass load, feedstock composition, as well as their interactions) affect the distribution between phases and their composition. This review provides an analysis of the state of the art on HTC, mainly with regard to the effect of variables on the process, the associated kinetics, and the characteristics of the solid phase (HC), as well as some of the more studied applications so far. The focus is on research made over the last five years on these topics.

Rapid reduction in the price of photovoltaic (solar PV) cells and modules has resulted in a rapid increase in solar system deployments to an annual expected capacity of 200 GW by 2020. Achieving high PV cell and module efficiency is necessary for many solar manufacturers to break even. In addition, new innovative installation methods are emerging to complement the drive to lower $/W PV system price. The floating PV (FPV) solar market space has emerged as a method for utilizing the cool ambient environment of the FPV system near the water surface based on successful FPV module (FPVM) reliability studies that showed degradation rates below 0.5% p.a. with new encapsulation material. PV module temperature analysis is another critical area, governing the efficiency performance of solar cells and module. In this paper, data collected over five-minute intervals from a PV system over a year is analyzed. We use MATLAB to derived equation coefficients of predictable environmental variables to derive FPVM’s first module temperature operation models. When comparing the theoretical prediction to…

This paper reviews current trends in wearable device technology, and provides an overview of its prevalent and potential deployments in the mining industry. This review includes the classification of wearable devices with some examples of their utilization in various industrial fields as well as the features of sensors used in wearable devices. Existing applications of wearable device technology to the mining industry are reviewed. In addition, a wearable safety management system for miners and other possible applications are proposed. The findings of this review show that by introducing wearable device technology to mining sites, the safety of mining operations can be enhanced. Therefore, wearable devices should be further used in the mining industry.

Amidst rapid urban development, sustainable transportation solutions are required to meet the increasing demands for mobility whilst mitigating the potentially negative social, economic, and environmental impacts. This study analyses autonomous vehicles (AVs) as a potential transportation solution for smart and sustainable development. We identified privacy and cybersecurity risks of AVs as crucial to the development of smart and sustainable cities and examined the steps taken by governments around the world to address these risks. We highlight the literature that supports why AVs are essential for smart and sustainable development. We then identify the aspects of privacy and cybersecurity in AVs that are important for smart and sustainable development. Lastly, we review the efforts taken by federal governments in the US, the UK, China, Australia, Japan, Singapore, South Korea, Germany, France, and the EU, and by US state governments to address AV-related privacy and cybersecurity risks in-depth. Overall, the actions taken by governments to address privacy risks are mainly in the…

For an AC-stacked photovoltaic (PV) inverter system with N cascaded inverters, existing control methods require at least N communication links to acquire the grid synchronization signal. In this paper, a novel decentralized control is proposed. For N inverters, only one inverter nearest the point of common coupling (PCC) needs a communication link to acquire the grid voltage phase and all other N − 1 inverters use only local measured information to achieved fully decentralized local control. Specifically, one inverter with a communication link utilizes the grid voltage phase and adopts current control mode to achieve a required power factor (PF). All other inverters need only local information without communication links and adopt voltage control mode to achieve maximum power point tracking (MPPT) and self-synchronization with grid voltage. Compared with existing methods, the communication link and complexity is greatly reduced, thus improved reliability and reduced communication costs are achieved. The effectiveness of the proposed control is verified by simulation tests.

Heating, Ventilating, and Air Conditioning (HVAC) systems are the major energy-consuming devices in buildings. Nowadays, due to the high demand for HVAC system installation in buildings, designing an effective controller in order to decrease the energy consumption of the devices while meeting the thermal comfort demands in buildings are the most important goals of control designers. The purpose of this article is to investigate the different control methods for Heating, Ventilating, and Air Conditioning and Refrigeration (HVAC & R) systems. The advantages and disadvantages of each control method are discussed and finally the Fuzzy Cognitive Map (FCM) method is introduced as a new strategy for HVAC systems. The FCM method is an intelligent and advanced control technique to address the nonlinearity, Multiple-Input and Multiple-Output (MIMO), complexity and coupling effect features of the systems. The significance of this method and improvements by this method are compared with other methods.

In wind farms, wakes originating from upstream turbines cause reduced energy extraction and increased loading variability in downstream rows. The prospect of mitigating these detrimental effects through coordinated controllers at the wind-farm level has fueled a multitude of research efforts in wind-farm control. The main strategies in wind-farm control are to influence the velocity deficits in the wake by deviating from locally optimal axial induction setpoints on the one hand, and steering wakes away from downstream rows through yaw misalignment on the other hand. The current work investigates dynamic induction and yaw control of individual turbines for wind-farm power maximization in large-eddy simulations. To this end, receding-horizon optimal control techniques combined with continuous adjoint gradient evaluations are used. We study a 4 × 4 aligned wind farm, and find that for this farm layout yaw control is more effective than induction control, both for uniform and turbulent inflow conditions. Analysis of optimal yaw controls leads to the definition of two simplified yaw…

Hydrothermal liquefaction (HTL) is regarded as a promising technology for the production of biofuels from biomass and wastes. As such, there is a drive towards continuous-flow processing systems to aid process scale-up and eventually commercialization. The current study presents results from a novel pilot-scale HTL reactor with a feed capacity of up to 100 L/h and a process volume of approximately 20 L. The pilot plant employs a heat exchanger for heat recovery and a novel hydraulic oscillation system to increase the turbulence in the tubular reactor. The energy grass Miscanthus and the microalgae Spirulina, both representing advanced dedicated energy crops, as well as sewage sludge as high-potential waste stream were selected to assess the reactor performance. Biomass slurries with up to 16 wt% dry matter content were successfully processed. The heat recovery of the heat exchanger is found to increase with reactor run time, reaching 80% within 5–6 h of operation. The hydraulic oscillation system is shown to improve mixing and enhance heat transfer. Bio-crudes with average yields of…

Due to a rapid increase in the utilization of power converter-based renewable energy sources (RESs), the overall system inertia in an interconnected power system might be significantly reduced, increasing the vulnerability of the interconnected power system to the system instability. To overcome problems caused by the significant reduction in system inertia, this paper proposes a new application of virtual inertia control to improve frequency stability of the interconnected power system due to high penetration level of RESs. The derivative control technique is introduced for higher-level applications of virtual inertia emulation. Thus, the proposed virtual inertia control loop has a second-order characteristic, which provides a simultaneous enabling of damping and inertia emulations into the interconnected power system, enhancing frequency stability and resiliency. System modeling and simulation results are carried out using MATLAB/Simulink® (R2016b, MathWorks, Natick, MA, USA). Trajectory sensitivities are also performed to analyze the dynamic effects of virtual inertia control’s…

Building integrated photovoltaic (BIPV) technology provides an aesthetical, economic, and technical solution for electricity self-sufficiency in buildings. As one of the most promising technologies for solar energy harvesting in urban areas, BIPV technology provides multiple benefits for buildings, including power generation from renewable energy resources, the replacement of traditional wall cladding, daytime lighting, heating/cooling load reduction, etc. This paper systematically reviews the progress of recent research on the electrical, thermal, optical, and overall energy performances of BIPV systems. Furthermore, based on the literature review on the energy payback time and the greenhouse-gas emission of various BIPV technologies, the research progress of the life-cycle assessment of BIPV systems is also discussed. It is anticipated that the review results can provide meaningful reference and support for the research and development of BIPV technology.

Parameter estimation of photovoltaic (PV) models from experimental current versus voltage (I-V) characteristic curves acts a pivotal part in the modeling a PV system and optimizing its performance. Although many methods have been proposed for solving this PV model parameter estimation problem, it is still challenging to determine highly accurate and reliable solutions. In this paper, this problem is firstly transformed into an optimization problem, and an objective function (OF) is formulated to quantify the overall difference between the experimental and simulated current data. And then, to enhance the performance of original cuckoo search algorithm (CSA), a novel improved cuckoo search algorithm (ImCSA) is proposed, by combining three strategies with CSA. In ImCSA, a quasi-opposition based learning (QOBL) scheme is employed in the population initialization step of CSA. Moreover, a dynamic adaptation strategy is developed and introduced for the step size without Lévy flight step in original CSA. A dynamic adjustment mechanism for the fraction probability (Pa) is proposed to achieve…

Electric power consumption short-term forecasting for individual households is an important and challenging topic in the fields of AI-enhanced energy saving, smart grid planning, sustainable energy usage and electricity market bidding system design. Due to the variability of each household’s personalized activity, difficulties exist for traditional methods, such as auto-regressive moving average models, machine learning methods and non-deep neural networks, to provide accurate prediction for single household electric power consumption. Recent works show that the long short term memory (LSTM) neural network outperforms most of those traditional methods for power consumption forecasting problems. Nevertheless, two research gaps remain as unsolved problems in the literature. First, the prediction accuracy is still not reaching the practical level for real-world industrial applications. Second, most existing works only work on the one-step forecasting problem; the forecasting time is too short for practical usage. In this study, a hybrid deep learning neural network framework that…

Ambitious targets to reduce emissions caused by aviation in the light of an expected ongoing rise of the air transport demand in the future drive the research of propulsion systems with lower CO2 emissions. Regional hybrid electric aircraft (HEA) powered by conventional gas turbines and battery powered electric motors are investigated to test hybrid propulsion operation strategies. Especially the role of the battery within environmentally friendly concepts with significantly reduced carbon footprint is analyzed. Thus, a new simulation approach for HEA is introduced. The main findings underline the importance of choosing the right power-to-energy-ratio of a battery according to the flight mission. The gravimetric energy and power density of the electric storages determine the technologically feasibility of hybrid concepts. Cost competitive HEA configurations are found, but do not promise the targeted CO2 emission savings, when the well-to-wheel system is regarded with its actual costs. Sensitivity studies are used to determine external levers that favor the profitability of HEA.

Electric vehicles reduce pollution only if a high percentage of the electricity mix comes from renewable sources and if the battery manufacturing takes place at a site far from the vehicle use region. Industries developed due to increased electric vehicle adoption may also cause additional air pollution. The Indian government has committed to solving New Delhi’s air pollution issues through an ambitious policy of switching 100% of the light duty consumer vehicles to electric vehicles by 2030. This policy is based on vehicle grid interaction and relies on shared mobility through the electric vehicle fleet. There are several human behavioral changes necessary to achieve 100% adoption of electric vehicles. This paper reviews different steps in the lifecycle of an electric vehicle (EV), their impact on environmental emissions, and recommends policies suitable for different socio-economic group that are relevant to the Indian market. To reduce air pollution through adoption of electric vehicles, the Indian government needs to adopt policies that increase sale of electric vehicles,…

In the near future, the challenges to reduce the economic and social dependency on fossil fuels must be faced increasingly. A sustainable and efficient energy supply based on renewable energies enables large-scale applications of electro-fuels for, e.g., the transport sector. The high gravimetric energy density makes liquefied hydrogen a reasonable candidate for energy storage in a light-weight application, such as aviation. Current aircraft structures are designed to accommodate jet fuel and gas turbines allowing a limited retrofitting only. New designs, such as the blended-wing-body, enable a more flexible integration of new storage technologies and energy converters, e.g., cryogenic hydrogen tanks and fuel cells. Against this background, a tank-design model is formulated, which considers geometrical, mechanical and thermal aspects, as well as specific mission profiles while considering a power supply by a fuel cell. This design approach enables the determination of required tank mass and storage density, respectively. A new evaluation value is defined including the vented…

Electricity plays a crucial role in the well-being of humans and is a determining factor of the economic development of a country. Electricity issues have encouraged researchers to focus on improving power availability and quality along with reliability. This pursuit has increasingly raised the intention to integrate renewable energy (RE) into power systems to curb the problem of energy deficiency. However, intermittency in the sources of RE supply coupled with fluctuating changes in demand with respect to time has induced high risk in maintaining system reliability in terms of providing adequate supply to consumers. Whilst an energy storage system (ESS) is not another source of electricity, it is proven to be effective and viable in solving the aforementioned issues. Thus, this paper comprehensively reviews the development of ESS technologies and discusses the benefits and real-life applications of these technologies. The concept of reliability in power systems is also explored to provide an improved understanding of this study. Lastly, notable studies that have addressed the…

The traditional power grid is inadequate to overcome modern day challenges. As the modern era demands the traditional power grid to be more reliable, resilient, and cost-effective, the concept of smart grid evolves and various methods have been developed to overcome these demands which make the smart grid superior over the traditional power grid. One of the essential components of the smart grid, home energy management system (HEMS) enhances the energy efficiency of electricity infrastructure in a residential area. In this aspect, we propose an efficient home energy management controller (EHEMC) based on genetic harmony search algorithm (GHSA) to reduce electricity expense, peak to average ratio (PAR), and maximize user comfort. We consider EHEMC for a single home and multiple homes with real-time electricity pricing (RTEP) and critical peak pricing (CPP) tariffs. In particular, for multiple homes, we classify modes of operation for the appliances according to their energy consumption with varying operation time slots. The constrained optimization problem is solved using heuristic…

A smart grid facilitates more effective energy management of an electrical grid system. Because both energy consumption and associated building operation costs are increasing rapidly around the world, the need for flexible and cost-effective management of the energy used by buildings in a smart grid environment is increasing. In this paper, we consider an energy management system for a smart energy building connected to an external grid (utility) as well as distributed energy resources including a renewable energy source, energy storage system, and vehicle-to-grid station. First, the energy management system is modeled using a Markov decision process that completely describes the state, action, transition probability, and rewards of the system. Subsequently, a reinforcement-learning-based energy management algorithm is proposed to reduce the operation energy costs of the target smart energy building under unknown future information. The results of numerical simulation based on the data measured in real environments show that the proposed energy management algorithm gradually reduces…

This article reviews the state-of-the-art developments in Multi-Agent Systems (MASs) and their application to energy optimization problems. This methodology and related tools have contributed to changes in various paradigms used in energy optimization. Behavior and interactions between agents are key elements that must be understood in order to model energy optimization solutions that are robust, scalable and context-aware. The concept of MAS is introduced in this paper and it is compared with traditional approaches in the development of energy optimization solutions. The different types of agent-based architectures are described, the role played by the environment is analysed and we look at how MAS recognizes the characteristics of the environment to adapt to it. Moreover, it is discussed how MAS can be used as tools that simulate the results of different actions aimed at reducing energy consumption. Then, we look at MAS as a tool that makes it easy to model and simulate certain behaviors. This modeling and simulation is easily extrapolated to the energy field, and can even evolve…

A new analytical wake model for wind turbines, considering ambient turbulence intensity, thrust coefficient and yaw angle effects, is proposed from numerical and analytical studies. First, eight simulations by the Reynolds Stress Model are conducted for different thrust coefficients, yaw angles and ambient turbulence intensities. The wake deflection, mean velocity and turbulence intensity in the wakes are systematically investigated. A new wake deflection model is then proposed to analytically predict the wake center trajectory in the yawed condition. Finally, the effects of yaw angle are incorporated in the Gaussian-based wake model. The wake deflection, velocity deficit and added turbulence intensity in the wake predicted by the proposed model show good agreement with the numerical results. The model parameters are determined as the function of ambient turbulence intensity and thrust coefficient, which enables the model to have good applicability under various conditions.

There is a growing interest in the application of microgrids around the world because of their potential for achieving a flexible, reliable, efficient and smart electrical grid system and supplying energy to off-grid communities, including their economic benefits. Several research studies have examined the application issues of microgrids. However, a lack of in-depth considerations for the enabling planning conditions has been identified as a major reason why microgrids fail in several off-grid communities. This development requires research efforts that consider better strategies and framework for sustainable microgrids in remote communities. This paper first presents a comprehensive review of microgrid technologies and their applications. It then proposes the STEEP model to examine critically the failure factors based on the social, technical, economic, environmental and policy (STEEP) perspectives. The model details the key dimensions and actions necessary for addressing the challenge of microgrid failure in remote communities. The study uses remote communities within Nigeria,…

Integration of high volume (high penetration) of photovoltaic (PV) generation with power grids consequently leads to some technical challenges that are mainly due to the intermittent nature of solar energy, the volume of data involved in the smart grid architecture, and the impact power electronic-based smart inverters. These challenges include reverse power flow, voltage fluctuations, power quality issues, dynamic stability, big data challenges and others. This paper investigates the existing challenges with the current level of PV penetration and looks into the challenges with high PV penetration in future scenarios such as smart cities, transactive energy, proliferation of plug-in hybrid electric vehicles (PHEVs), possible eclipse events, big data issues and environmental impacts. Within the context of these future scenarios, this paper reviewed the existing solutions and provides insights to new and future solutions that could be explored to ultimately address these issues and improve the smart grid’s security, reliability and resiliency.

A photovoltaic system is highly susceptible to partial shading. Based on the functionality of a photovoltaic system that relies on solar irradiance to generate electrical power, it is tacitly assumed that the maximum power of a partially shaded photovoltaic system always decreases as the shading heaviness increases. However, the literature has reported that this might not be the case. The maximum power of a partially shaded photovoltaic system under a fixed configuration and partial shading pattern can be highly insusceptible to shading heaviness when a certain critical point is met. This paper presents an investigation of the impact of partial shading and the critical point that reduce the susceptibility of shading heaviness. Photovoltaic string formed by series-connected photovoltaic modules is used in this research. The investigation of the P-V characteristic curve under different numbers of shaded modules and shading heaviness suggests that the photovoltaic string becomes insusceptible to shading heaviness when the shaded modules irradiance reaches a certain critical point. The…

Condition monitoring and early fault diagnosis for wind turbines have become essential industry practice as they help improve wind farm reliability, overall performance and productivity. If not detected and rectified at early stages, some faults can be catastrophic with significant loss or revenue along with interruption to the business relying mainly on wind energy. The failure of Wind turbine results in system downtime and repairing or replacement expenses that significantly reduce the annual income. Such failures call for more systematized operation and maintenance schemes to ensure the reliability of wind energy conversion systems. Condition monitoring and fault diagnosis systems of wind turbine play an important role in reducing maintenance and operational costs and increase system reliability. This paper is aimed at providing the reader with the overall feature for wind turbine condition monitoring and fault diagnosis which includes various potential fault types and locations along with the signals to be analyzed with different signal processing methods.

In recent years, concerns about severe environmental pollution and fossil fuel consumption has grabbed attention in the transportation industry, particularly in marine vessels. Another key challenge in ships is the fluctuations caused by high dynamic loads. In order to have a higher reliability in shipboard power systems, presently more generators are kept online operating much below their efficient point. Hence, to improve the fuel efficiency of shipboard power systems, the minimum generator operation with N-1 safety can be considered as a simple solution, a tradeoff between fuel economy and reliability. It is based on the fact that the fewer the number of generators that are brought online, the more load is on each generator such that allowing the generators to run on better fuel efficiency region. In all-electric ships, the propulsion and service loads are integrated to a common network in order to attain improved fuel consumption with lesser emissions in contrast to traditional approaches where propulsion and service loads are fed by separate generators. In order to make the…

This paper elaborates a comprehensive overview of a photovoltaic (PV) system model, and compares the attributes of various conventional and improved incremental conductance algorithms, perturbation and observation techniques, and other maximum power point tracking (MPPT) algorithms in normal and partial shading conditions. Performance evaluation techniques are discussed on the basis of the dynamic parameters of the PV system. Following a discussion of the MPPT algorithms in each category, a table is drawn to summarize their key specifications. In the performance evaluation section, the appropriate PV module technologies, atmospheric effects on PV panels, design complexity, and number of sensors and internal parameters of the PV system are outlined. In the last phase, a comparative table presents performance-evaluating parameters of MPPT design criterion. This paper is organized in such a way that future researchers and engineers can select an appropriate MPPT scheme without complication.

The demand for clean energy is strong, and the shift from fossil-fuel-based energy to environmentally friendly sources is the next step to eradicating the world’s greenhouse gas (GHG) emissions. Solar energy technology has been touted as one of the most promising sources for low-carbon, non-fossil fuel energy production. However, the true potential of solar-based technologies is established by augmenting efficiency through satisfactory environmental performance in relation to other renewable energy systems. This paper presents an environmental life-cycle assessment (LCA) of a solar-photovoltaic (PV) system and a solar-thermal system. Single crystalline Si solar cells are considered for the solar PV system and an evacuated glass tube collector is considered for the solar thermal system in this analysis. A life-cycle inventory (LCI) is developed considering all inputs and outputs to assess and compare the environmental impacts of both systems for 16 impact indicators. LCA has been performed by the International Reference Life Cycle Data System (ILCD), Impact 2002+, Cumulative Energy…

Traditional electrification methods, including grid extension and stand-alone diesel generators, have shown limitations to sustainability in the face of rural electrification challenges in sub-Saharan Africa (SSA), where electrification rates remain the lowest in the world. This study aims at performing a techno-economic analysis and optimization of a pumped-hydro energy storage based 100%-renewable off-grid hybrid energy system for the electrification of Djoundé, which is a small village in northern Cameroon. Hybrid Optimization of Multiple Energy Resources (HOMER) software was used as an analysis tool, and the resulting optimal system architecture included an 81.8 kW PV array and a 15 kW biogas generator, with a cost of energy (COE) and total net present cost (NPC) of €0.256/kWh and €370,426, respectively. The system showed promise given the upcoming decrease in installation cost of photovoltaic systems. It will be viable in parts of SSA region but, significant investment subsidies will be needed elsewhere. The originality of this study can be emphasized in three points: (1) the…

Among renewable sources of energy, wind is the most widely used resource due to its commercial acceptance, low cost and ease of operation and maintenance, relatively much less time for its realization from concept till operation, creation of new jobs, and least adverse effect on the environment. The fast technological development in the wind industry and availability of multi megawatt sized horizontal axis wind turbines has further led the promotion of wind power utilization globally. It is a well-known fact that the wind speed increases with height and hence the energy output. However, one cannot go above a certain height due to structural and other issues. Hence other attempts need to be made to increase the efficiency of the wind turbines, maintaining the hub heights to acceptable and controllable limits. The efficiency of the wind turbines or the energy output can be increased by reducing the cut-in-speed and/or the rated-speed by modifying and redesigning the blades. The problem is tackled by identifying the optimization parameters such as annual energy yield, power…

Even though ocean waves around the world are known to contain high and dense amounts of energy, wave energy harvesters are still not as mature as other forms of renewable energy harvesting devices, especially when it comes to commercialization, mass production, and grid integration, but with the recent studies and optimizations, the point absorber wave energy harvester might be a potential candidate to stand out as the best solution to harvest energy from highly energetic locations around the world’s oceans. This paper presents an extensive literature review on point absorber wave energy harvesters and covers their recent theoretical and experimental development. The paper focuses on three main parts: One-body point absorbers, two-body point absorbers, and power take-offs. This review showcases the high amount of work being done to push point absorbers towards technological maturity to eventually kick off commercialization and mass production. It should also provide a good background on the recent status of point absorber development for researchers in the field.

A stable power supply is very important in the management of power infrastructure. One of the critical tasks in accomplishing this is to predict power consumption accurately, which usually requires considering diverse factors, including environmental, social, and spatial-temporal factors. Depending on the prediction scope, building type can also be an important factor since the same types of buildings show similar power consumption patterns. A university campus usually consists of several building types, including a laboratory, administrative office, lecture room, and dormitory. Depending on the temporal and external conditions, they tend to show a wide variation in the electrical load pattern. This paper proposes a hybrid short-term load forecast model for an educational building complex by using random forest and multilayer perceptron. To construct this model, we collect electrical load data of six years from a university campus and split them into training, validation, and test sets. For the training set, we classify the data using a decision tree with input parameters including…

Power systems are becoming more and more complex in nature due to the integration of several power electronic devices. Protection of such systems and augmentation of reliability as well as stability highly depend on limiting the fault currents. Several fault current limiters (FCLs) have been applied in power systems as they provide rapid and efficient fault current limitation. This paper presents a comprehensive literature review of the application of different types of FCLs in power systems. Applications of superconducting and non-superconducting FCLs are categorized as: (1) application in generation, transmission and distribution networks; (2) application in alternating current (AC)/direct current (DC) systems; (3) application in renewable energy resources integration; (4) application in distributed generation (DG); and (5) application for reliability, stability and fault ride through capability enhancement. Modeling, impact and control strategies of several FCLs in power systems are presented with practical implementation cases in different countries. Recommendations are provided…

This paper conducts a structured review on the topic of energy efficiency and environmental sustainability in the supply chain management context to define research trends on the topic and identify research gaps. The review is carried out using the largest databases of peer-reviewed literature (Scopus and Web of Science). A sample of 122 papers focusing on the topic of energy-efficient and sustainable supply chain management was selected and analyzed through descriptive and content analysis. The review highlights that despite there is a growing research trend on the topic, different research gaps remain to be covered. These gaps concern the factors influencing energy efficiency and environmental sustainability initiatives, the classification of energy efficiency and environmental sustainability initiatives, the impact of energy efficiency and environmental sustainability on supply chain performance, the customer perspective in sustainable and energy-efficient supply chain, and the different technologies supporting the energy efficiency and environmental sustainability initiatives.…

The present paper deals with the new opportunities deriving from the interconnections of the European and North African transmission systems. In order to achieve a single international market for electricity exchanges, interconnections between networks in different countries are becoming increasingly important and Sicily, for its geographical position in the middle of the Mediterranean Sea, will undoubtedly play an important role as an electrical bridge between Europe and the North Africa in the near future. The paper, presenting the actual electro-energetic context of Tunisia, reports the new important interconnection already realized in South Italy (in particular in Sicily) and describe the planned interventions of the near future. In the second part of the paper, using the Neplan software package (10.7.4, NEPLAN AG, CH-8700 Küsnacht (ZH), Switzerland) for simulating the grid, some load flows are carried out to check some operating scenarios (2020 and 2025) considering energy flows from north to south, avoiding system violations.

There is need to accurately design pipelines to transport the expected increase of CO2 captured from industrial processes after the signing of the Paris Climate Agreement in 2016. This paper reviews several aspects of CO2 pipeline design with emphasis on pressure drop and models for the calculation of pipeline diameter. Two categories of pipeline equations were identified. The first category is independent of pipeline length and has two different equations. This category is used to specify adequate pipeline diameter for the volume of fluid transported. The optimum economic pipe diameter equation (Eq. 17) with nearly uniform resultant velocity values at different flow rates performed better than the standard velocity flow equation (Eq. 20). The second category has four different equations and is used to calculate pipeline pressure drop or pipeline distance for the installation of booster stations after specifying minimum and maximum pipeline pressures. The hydraulic equation is preferred because it gave better resultant velocity values and the closest diameter value obtained using…

Selection of suitable sites for solar power plants requires spatial evaluation taking technical, economic, and environmental considerations into account. This research has applied a fuzzy logic model to carry out spatial site selection for solar power plants in Markazi Province of Iran. Geographical Information System (GIS) capabilities have been used for spatial analysis and visualization of the research results. The suitable areas for solar power plants installation were identified by employing Boolean logic and defining a range of selection criteria all of which were evaluated by fuzzy functions. The raster layers of Boolean and fuzzy logic have been combined to come up with suitable sites with solar energy potential. The results identified some areas in the vicinity of Mahalat and Zarandineh cities as suitable for solar energy utilization. The research also validated the employed combined method as a suitable site selection approach for solar power plants.

The ongoing industrialization and modernization period has increased the demand for energy in Viet Nam. This has led to over-exploitation and exhausts fossil fuel sources. Nowadays, Viet Nam’s energy mix is primarily based on thermal and hydro power. The Vietnamese government is trying to increase the proportion of renewable energy. The plan will raise the total solar power capacity from nearly 0 to 12,000 MW, equivalent to about 12 nuclear reactors, by 2030. Therefore, the construction of solar power plants is needed in Viet Nam. In this study, the authors present a multi-criteria decision making (MCDM) model by combining three methodologies, including fuzzy analytical hierarchy process (FAHP), data envelopment analysis (DEA), and the technique for order of preference by similarity to ideal solution (TOPSIS) to find the best location for building a solar power plant based on both quantitative and qualitative criteria. Initially, the potential locations from 46 sites in Viet Nam were selected by several DEA models. Then, AHP with fuzzy logic is employed to determine the weight of…

The aim of this study is to characterise the changes in mechanical properties and to provide a comprehensive micro-structural analysis of Harcourt granite over different pre-heating temperatures under two cooling treatments (1) rapid and (2) slow cooling. A series of uniaxial compression tests was conducted to evaluate the mechanical properties of granite specimens subjected to pre-heating to temperatures ranging from 25–1000 °C under both cooling conditions. An acoustic emission (AE) system was incorporated to identify the fracture propagation stress thresholds. Furthermore, the effect of loading and unloading behaviour on the elastic properties of Harcourt granite was evaluated at two locations prior to failure: (1) crack initiation and (2) crack damage. Scanning electron microscopy (SEM) analyses were conducted on heat-treated thin rock slices to observe the crack/fracture patterns and to quantify the extent of micro-cracking during intense heating followed by cooling. The results revealed that the thermal field induced in the Harcourt granite pore structure during heating up to…

Fresh water resources are depleting rapidly as the water demand around the world continues to increase. Fresh water resources are also not equally distributed geographically worldwide. The best way to tackle this situation is to use solar energy for desalination to not only cater for the water needs of humanity, but also to offset some detrimental environmental effects of desalination. A comprehensive review of the latest literature on various desalination technologies utilizing solar energy is presented here. This paper also highlights the environmental impacts of desalination technologies along with an economic analysis and cost comparison of conventional desalination methods with different solar energy based technologies. This review is part of an investigation into integration of solar thermal desalination into existing grid infrastructure in the Australian context.

Providing accurate electric load forecasting results plays a crucial role in daily energy management of the power supply system. Due to superior forecasting performance, the hybridizing support vector regression (SVR) model with evolutionary algorithms has received attention and deserves to continue being explored widely. The cuckoo search (CS) algorithm has the potential to contribute more satisfactory electric load forecasting results. However, the original CS algorithm suffers from its inherent drawbacks, such as parameters that require accurate setting, loss of population diversity, and easy trapping in local optima (i.e., premature convergence). Therefore, proposing some critical improvement mechanisms and employing an improved CS algorithm to determine suitable parameter combinations for an SVR model is essential. This paper proposes the SVR with chaotic cuckoo search (SVRCCS) model based on using a tent chaotic mapping function to enrich the cuckoo search space and diversify the population to avoid trapping in local optima. In addition, to deal with the cyclic nature of…

Energy sustainability and environmental issues pose greater challenges on different primary energy sectors as the global energy demand increases and it is projected to further increase with an upsurge in population. On the other hand, energy sources from conventional fossil-based fuels are depleting, forcing explorations in challenging and difficult locations. As a result, the use of alternative fuels received dramatic consideration to substitute these conventional fuels, of which natural gas took the significant share. However, the share of natural-gas vehicles in the current vehicle market is quite small, and it is estimated to be below 5%. This paper reviews the current resource scenarios including proven and potential reserves, current production, and consumption, along with the fueling infrastructure, distribution, and storage. It also provides summary of the development of fuel-injection technologies aimed to enhance the performance of gas engines. More attention was also given to natural-gas engines and their limitations. Parameters affecting the performance and combustion of…

The impact of conversion process parameters in pyrolysis (maximum temperature, inert gas flow rate) and hydrothermal carbonization (maximum temperature, residence time and post-washing) on biochar and hydrochar properties is investigated. Pine wood (PW) and corn digestate (CD), with low and high inorganic species content respectively, are used as feedstock. CD biochars show lower H/C ratios, thermal recalcitrance and total specific surface area than PW biochars, but higher mesoporosity. CD and PW biochars present higher naphthalene and phenanthrene contents, respectively, which may indicate different reaction pathways. High temperatures (>500 °C) lead to lower PAH (polycyclic aromatic hydrocarbons) content (<12 mg/kg) and higher specific surface area. With increasing process severity the biochars carbon content is also enhanced, as well as the thermal stability. High inert gas flow rates increase the microporosity and wettability of biochars. In hydrochars the high inorganic content favor decarboxylation over dehydration reactions. Hydrochars show mainly mesoporosity, with a higher…

Wind speed prediction with spatio–temporal correlation is among the most challenging tasks in wind speed prediction. In this paper, the problem of predicting wind speed for multiple sites simultaneously is investigated by using spatio–temporal correlation. This paper proposes a model for wind speed prediction with spatio–temporal correlation, i.e., the predictive deep convolutional neural network (PDCNN). The model is a unified framework, integrating convolutional neural networks (CNNs) and a multi-layer perceptron (MLP). Firstly, the spatial features are extracted by CNNs located at the bottom of the model. Then, the temporal dependencies among these extracted spatial features are captured by the MLP. In this way, the spatial and temporal correlations are captured by PDCNN intrinsically. Finally, PDCNN generates the predicted wind speed by using the learnt spatio–temporal correlations. In addition, three error indices are defined to evaluate the prediction accuracy of the model on the wind turbine array. Experiment results on real-world data show that PDCNN can capture the…

The use of lithium batteries for power and energy-hungry applications has risen drastically in recent years. For such applications, it is necessary to connect the batteries in large assemblies of cells in series and parallel. With a large number of cells operating together, it is necessary to understand their intrinsic variabilities, not only at the initial stage but also upon aging. In this study, we studied a batch of commercial cells to address their initial cell-to-cell variations and also the variations induced by cycling. To do so, we not only tracked several metrics associated with cell performance, the maximum capacity, the resistance, and the rate capability but also the degradation mechanism via a non-invasive quantification of the loss of lithium inventory (LLI), the loss of active material (LAM) and the kinetic degradation on both electrodes. We found that, even with small initial cell-to-cell variations, significant variations will be observed upon aging because the cells degrade at a different pace. We also observed that these variations were not correlated with the…

Performance of the current battery management systems is limited by the on-board embedded systems as the number of battery cells increases in the large-scale lithium-ion (Li-ion) battery energy storage systems (BESSs). Moreover, an expensive supervisory control and data acquisition system is still required for maintenance of the large-scale BESSs. This paper proposes a new cloud-based battery condition monitoring and fault diagnosis platform for the large-scale Li-ion BESSs. The proposed cyber-physical platform incorporates the Internet of Things embedded in the battery modules and the cloud battery management platform. Multithreads of a condition monitoring algorithm and an outlier mining-based battery fault diagnosis algorithm are built in the cloud battery management platform (CBMP). The proposed cloud-based condition monitoring and fault diagnosis platform is validated by using a cyber-physical testbed and a computational cost analysis for the CBMP. Therefore, the proposed platform will support the on-board health monitoring and provide an intelligent and cost-effective…

CoO-ZnO-based composites have attracted considerable attention for the development of energy storage devices because of their multifunctional characterization and ease of integration with existing components. This paper reports the synthesis of CoO@ZnO (CZ) nanostructures on Ni foam by the CBD method for facile and eco-friendly supercapacitor applications. The formation of a CoO@ZnO electrode functioned with cobalt, zinc, nickel and oxygen groups was confirmed by X-ray diffraction, X-ray photoelectron spectroscopy, low and high-resolution of scanning electron microscopy, and transmission electron microscopy. The as-synthesized hierarchical nanocorn skeleton-like structure of CoO@ZnO-3h (CZ3h) electrode delivered a higher specific capacitance of 1136 F/g at a current density of 3 A/g with outstanding cycling stability, showing 98.3% capacitance retention over 3000 cycles in an aqueous 2 M KOH electrolyte solution. This retention was significantly better than that of other prepared electrodes, such as CoO (CO), ZnO (ZO), CoO@ZnO-1h (CZ1h), and CoO@ZnO-7h (CZ7h) (274, 383, 240 and 537…

In light of increasing alerts about limited energy sources and environment degradation, it has become essential to search for alternatives to thermal engine-based vehicles which are a major source of air pollution and fossil fuel depletion. Hybrid electric vehicles (HEVs), encompassing multiple energy sources, are a short-term solution that meets the performance requirements and contributes to fuel saving and emission reduction aims. Power management methods such as regulating efficient energy flow to the vehicle propulsion, are core technologies of HEVs. Intelligent power management methods, capable of acquiring optimal power handling, accommodating system inaccuracies, and suiting real-time applications can significantly improve the powertrain efficiency at different operating conditions. Rule-based methods are simply structured and easily implementable in real-time; however, a limited optimality in power handling decisions can be achieved. Optimization-based methods are more capable of achieving this optimality at the price of augmented computational load. In the last few years,…

Smart buildings concept aims at the use of the smart technology to reduce energy consumption as well as improvement of the comfort conditions and users’ satisfaction. It is based on the use of smart sensors to follow both outdoor and indoor conditions as well as software for the control of comfort and security devices. The optimal control of the energy devices requires software for indoor temperature forecasting. This paper presents an ANN – based model for the indoor temperature forecasting. The model is developed using data recoded in an old building of the engineering school Polytech’Lille. Data covered both indoor and outdoor conditions. Analysis of the relevance of the input parameters allowed to develop a simplified forecasting model of the indoor temperature that uses only the outdoor temperature as well as the history of the façade temperature as input parameters. The paper presents successively, data collection, the ANN concept used in the temperature forecasting, and finally the ANN model developed for the façade and indoor forecasting. It shows that an ANN-based model…

Hydrothermal carbonization (HTC) is a useful method to convert wet biomass to value-added products. Fruit waste generated in juice industries is a huge source of moist feedstock for such conversion to produce hydrochar. This paper deals with four types of fruit wastes as feedstocks for HTC; namely, rotten apple (RA), apple chip pomace (ACP), apple juice pomace (AJP), and grape pomace (GP). The operating conditions for HTC processing were 190 °C, 225 °C, and 260 °C for 15 min. For all samples, higher heating value and fixed carbon increased, while volatile matter and oxygen content decreased after HTC. Except for ACP, the ash content of all samples increased after 225 °C. For RA, AJP, and GP, the possible explanation for increased ash content above 225 °C is that the hydrochar increases in porosity after 230 °C. It was observed that an increase in HTC temperature resulted in an increase in the mass yield for RA and GP, which is in contrast with increasing HTC temperature for lignocellulose biomass. Other characterization tests like thermogravimetric analysis (TGA) and scanning…

Greenhouses are high energy-consuming and anti-seasonal production facilities. In some cases, energy consumption in greenhouses accounts for 50% of the cost of greenhouse production. The high energy consumption has become a major factor hindering the development of greenhouses. In order to improve the energy efficiency of the greenhouse, it is important to predict its energy consumption. In this study, the energy consumption mathematical model of a Venlo greenhouse is established based on the principle of energy conservation. Three optimization algorithms are used to identify the parameters which are difficult to determine in the energy consumption model. In order to examine the accuracy of the model, some verifications are made. The goal of achieving high yield, high quality and high efficiency production is a problem in the study of greenhouse environment control. Combining the prediction model of greenhouse energy consumption with the relatively accurate weather forecast data for the next week, the energy consumption of greenhouse under different weather conditions is predicted.…

One of the benefits of the supercritical CO 2 Brayton cycle in Sodium-cooled Fast Reactors is an enhanced plant safety, since potential reactions of CO 2 with liquid sodium have been reported to be less stringent than a sodium-water reaction found in the Rankine cycle. However, moderate chemical interactions between CO 2 and liquid sodium make detecting CO 2 ingress accidents harder. Thus, this paper proposes a new physics-based detection algorithm by comparing the real-time pressure measurements of two identical heat exchangers for the early detection. The CO 2 ingress occurs owing to a crack at the pressure boundary wall, a certain self-recovery of structural damage does not happen over time, and an accident probabilistically starts at only one component of two. The proposed physics-based method with the probabilistic analysis was compared to the pure data-based method. Finally, the damage degradation was developed with a simplified mass and energy transfer model, and the proposed algorithm was verified with experimental data. The results show that a 99.99% detection probability…

To cope with the increasing charging demand of electric vehicle (EV), this paper presents a forecasting method of EV charging load based on random forest algorithm (RF) and the load data of a single charging station. This method is completed by the classification and regression tree (CART) algorithm to realize short-term forecast for the station. At the same time, the prediction algorithm of the daily charging capacity of charging stations with different scales and locations is proposed. By combining the regression and classification algorithms, the effective learning of a large amount of historical charging data is completed. The characteristic data is divided from different aspects, realizing the establishment of RF and the effective prediction of fluctuate charging load. By analyzing the data of each charging station in Shenzhen from the aspect of time and space, the algorithm is put into practice. The application form of current data in the algorithm is determined, and the accuracy of the prediction algorithm is verified to be reliable and practical. It can provide a reference…

The increasing demand for global energy consumption expedites major opportunities for the innovation of green energy technologies. Addressing the issue of sustainable energy is highly crucial for societies in order to maintain secure and balanced future progress in the economy and ecologically. Recently, there has been a growing interest in the development of improved and efficient sustainable energy technologies that are capable of reducing the global environmental footprint. The growing knowledge of hybrid techniques contributes to a decrease in the use of environmental resources while generating energy. However, various factors including the availability of natural resources, and different economic policies restrict the development of sustainable energies. Water and energy are the two major aspects for progressing towards a sustainable future. Recently, membrane-based technologies have begun to play an essential role in the advancement of sustainable energy and water demands. In this review article, the opportunities for membrane technologies dealing with water and energy…

In recent years, the International Maritime Organization agreed on aiming to reduce shipping’s greenhouse gas emissions by 50% with respect to 2009 levels. Meanwhile, cruise ship tourism is growing at a fast pace, making the challenge of achieving this goal even harder. The complexity of the energy system of these ships makes them of particular interest from an energy systems perspective. To illustrate this, we analyzed the energy and exergy flow rates of a cruise ship sailing in the Baltic Sea based on measurements from one year of the ship’s operations. The energy analysis allows identifying propulsion as the main energy user (46% of the total) followed by heat (27%) and electric power (27%) generation; the exergy analysis allowed instead identifying the main inefficiencies of the system: while exergy is primarily destroyed in all processes involving combustion (76% of the total), the other main causes of exergy destruction are the turbochargers, the heat recovery steam generators, the steam heaters, the preheater in the accommodation heating systems, the sea water coolers, and…

In the light of the ever increasing dangers of global warming, the efforts to reduce energy consumption by radiative cooling techniques have been designed, but are inefficient under strong sunlight during the daytime. With the advent of metamaterials and their selective control over optical properties, radiative cooling under direct sunlight is now possible. The key principles of metamaterial-based radiative cooling are: almost perfect reflection in the visible and near-infrared spectrum (0.3–3 µm) and high thermal emission in the infrared atmospheric window region (8–13 µm). Based on these two basic principles, studies have been conducted using various materials and structures to find the most efficient radiative cooling system. In this review, we analyze the materials and structures being used for radiative cooling, and suggest the future perspectives as a substitute in the current cooling industry.

Polymer flooding is an important enhanced oil recovery technology introduced in field projects since the late 1960s. The key to a successful polymer flood project depends upon proper estimation of polymer retention. The aims of this paper are twofold. First, to show the mechanism of polymer flooding and how this mechanism is affected by polymer retention. Based on the literature, the mobility ratio significantly increases as a result of the interactions between the injected polymer molecules and the reservoir rock. Secondly, to provide a better understanding of the polymer retention, we discussed polymer retention types, mechanisms, factors promoting or inhibiting polymer retention, methods and modeling techniques used for estimating polymer retention.

Recent attention to industrial peak shaving applications sparked an increased interest in battery energy storage. Batteries provide a fast and high power capability, making them an ideal solution for this task. This work proposes a general framework for sizing of battery energy storage system (BESS) in peak shaving applications. A cost-optimal sizing of the battery and power electronics is derived using linear programming based on local demand and billing scheme. A case study conducted with real-world industrial profiles shows the applicability of the approach as well as the return on investment dependence on the load profile. At the same time, the power flow optimization reveals the best storage operation patterns considering a trade-off between energy purchase, peak-power tariff, and battery aging. This underlines the need for a general mathematical optimization approach to efficiently tackle the challenge of peak shaving using an energy storage system. The case study also compares the applicability of yearly and monthly billing schemes, where the highest load of the year/month is…

Maximum Power Point Tracking (MPPT) methods are used in photovoltaic (PV) systems to continually maximize the PV array output power, which strongly depends on both solar radiation and cell temperature. The PV power oscillations around the maximum power point (MPP) resulting from the conventional methods and complexity of the non-conventional ones are convincing reasons to look for novel MPPT methods. This paper deals with simple Genetic Algorithms (GAs) based MPPT method in order to improve the convergence, rapidity, and accuracy of the PV system. The proposed method can also efficiently track the global MPP, which is very useful for partial shading. At first, a review of the algorithm is given, followed with many test examples; then, a comparison by means Matlab/Simulink© (R2009b) is conducted between the proposed MPPT and, the popular Perturb and Observe (PO) and Incremental Conductance (IC) techniques. The results show clearly the superiority of the proposed controller. Indeed, with the proposed algorithm, oscillations around the MPP are dramatically minimized, a better stability…

There is a lack of literature reporting the measurement and prediction of biochemical methane potential (BMP) of vegetable crop residues (VCRs) and similarly, the kinetic assessment on the anaerobic digestion process of VCR is rarely investigated. In this paper, the BMP tests of five different vegetable (snap bean, capsicum, cucumber, eggplant, and tomato) crop residues were conducted at feed to inoculum ratio (F/I) of 2.0 under mesophilic (36 ± 1 °C) conditions. A series of single-variable and multiple-variable regression models were built based on organic components (hemicellulose, cellulose, lignin, total fat, total sugar, and crude protein) for BMP prediction. Three kinetic models, including the first-order kinetic model, the Chen and Hashimoto model, and the modified Gompertz model, were used to simulate the methane yield results of VCR and obtain valuable model parameters simultaneously. As a result, the BMPs and volatile solids (VS) degradation degree of different VCRs were respectively in the range of 94.2–146.8 mL g−1 VS and 40.4–49.9%; the regression prediction models with…

In this paper, a simplified model of a Polymer Electrolyte Membrane (PEM) water electrolysis cell is presented and compared with experimental data at 60 °C and 80 °C. The model utilizes the same modelling approach used in previous work where the electrolyzer cell is divided in four subsections: cathode, anode, membrane and voltage. The model of the electrodes includes key electrochemical reactions and gas transport mechanism (i.e., H2, O2 and H2O) whereas the model of the membrane includes physical mechanisms such as water diffusion, electro osmotic drag and hydraulic pressure. Voltage was modelled including main overpotentials (i.e., activation, ohmic, concentration). First and second law efficiencies were defined. Key empirical parameters depending on temperature were identified in the activation and ohmic overpotentials. The electrodes reference exchange current densities and change transfer coefficients were related to activation overpotentials whereas hydrogen ion diffusion to Ohmic overvoltages. These model parameters were empirically fitted so that polarization curve obtained…