The 50 most-cited articles in Energies, 2016

Wind farm power production is known to be strongly affected by turbine wake effects. The purpose of this study is to develop and test a new analytical model for the prediction of wind turbine wakes and the associated power losses in wind farms. The new model is an extension of the one recently proposed by Bastankhah and Porté-Agel for the wake of stand-alone wind turbines. It satisfies the conservation of mass and momentum and assumes a self-similar Gaussian shape of the velocity deficit. The local wake growth rate is estimated based on the local streamwise turbulence intensity. Superposition of velocity deficits is used to model the interaction of the multiple wakes. Furthermore, the power production from the wind turbines is calculated using the power curve. The performance of the new analytical wind farm model is validated against power measurements and large-eddy simulation (LES) data from the Horns Rev wind farm for a wide range of wind directions, corresponding to a variety of full-wake and partial-wake conditions. A reasonable agreement is found between the proposed…

In the smart grid, one of the most important research areas is load forecasting; it spans from traditional time series analyses to recent machine learning approaches and mostly focuses on forecasting aggregated electricity consumption. However, the importance of demand side energy management, including individual load forecasting, is becoming critical. In this paper, we propose deep neural network (DNN)-based load forecasting models and apply them to a demand side empirical load database. DNNs are trained in two different ways: a pre-training restricted Boltzmann machine and using the rectified linear unit without pre-training. DNN forecasting models are trained by individual customer’s electricity consumption data and regional meteorological elements. To verify the performance of DNNs, forecasting results are compared with a shallow neural network (SNN), a double seasonal Holt–Winters (DSHW) model and the autoregressive integrated moving average (ARIMA). The mean absolute percentage error (MAPE) and relative root mean square error (RRMSE) are used for verification. Our results show…

Two on step ahead wind speed forecasting models were compared. A univariate model was developed using a linear autoregressive integrated moving average (ARIMA). This method’s performance is well studied for a large number of prediction problems. The other is a multivariate model developed using a nonlinear autoregressive exogenous artificial neural network (NARX). This uses the variables: barometric pressure, air temperature, wind direction and solar radiation or relative humidity, as well as delayed wind speed. Both models were developed from two databases from two sites: an hourly average measurements database from La Mata, Oaxaca, Mexico, and a ten minute average measurements database from Metepec, Hidalgo, Mexico. The main objective was to compare the impact of the various meteorological variables on the performance of the multivariate model of wind speed prediction with respect to the high performance univariate linear model. The NARX model gave better results with improvements on the ARIMA model of between 5.5% and 10. 6% for the hourly database and of between 2.3% and 12.8%…

In the actual era of smart homes and smart grids, advanced technological systems that allow the automation of domestic tasks are developing rapidly. There are numerous technologies and applications that can be installed in smart homes today. They enable communication between home appliances and users, and enhance home appliances’ automation, monitoring and remote control capabilities. This review article, by introducing the concept of the smart home and the advent of the smart grid, investigates technologies for smart homes. The technical descriptions of the systems are presented and point out advantages and disadvantages of each technology and product today available on the market. Barriers, challenges, benefits and future trends regarding the technologies and the role of users have also been discussed.

This paper presents an application and extension of multiple-criteria decision-making (MCDM) methods to account for stochastic input variables. More in particular, a comparative study is carried out among well-known and widely-applied methods in MCDM, when applied to the reference problem of the selection of wind turbine support structures for a given deployment location. Along with data from industrial experts, six deterministic MCDM methods are studied, so as to determine the best alternative among the available options, assessed against selected criteria with a view toward assigning confidence levels to each option. Following an overview of the literature around MCDM problems, the best practice implementation of each method is presented aiming to assist stakeholders and decision-makers to support decisions in real-world applications, where many and often conflicting criteria are present within uncertain environments. The outcomes of this research highlight that more sophisticated methods, such as technique for the order of preference by similarity to the ideal solution (TOPSIS)…

Considering the geographical location and climatic conditions of Iran, solar energy can provide a considerable portion of the energy demand for the country. This study develops a two-step framework. In the first step, the map of unsuitable regions is extracted based on the defined constraints. In the next step, in order to identify the suitability of different regions, 11 defined criteria, including solar radiation, average annual temperatures, distance from power transmission lines, distance from major roads, distance from residential area, elevation, slope, land use, average annual cloudy days, average annual humidity and average annual dusty days, are identified. The relative weights of defined criteria and sub-criteria are also determined applying fuzzy analytical hierarchy process (FAHP) technique. Next, by overlaying these criteria layers, the final map of prioritization of different regions of Iran for exploiting solar photovoltaic (PV) plants is developed. Based on Iran’s political divisions, investigation and analysis of the results have been presented for a total of 1057…

The large dv/dt and di/dt outputs of power devices in DC-fed motor drive systems in electric vehicles (EVs) always introduce conducted electromagnetic interference (EMI) emissions and may lead to motor drive system energy transmission losses. The effect of distributed parameters on conducted EMI from the DC-fed high voltage motor drive systems in EVs is studied. A complete test for conducted EMI from the direct current fed(DC-fed) alternating current (AC) motor drive system in an electric vehicle (EV) under load conditions is set up to measure the conducted EMI of high voltage DC cables and the EMI noise peaks due to resonances in a frequency range of 150 kHz–108 MHz. The distributed parameters of the motor can induce bearing currents under low frequency sine wave operation. However the impedance of the distributed parameters of the motor is very high at resonance frequencies of 500 kHz and 30 MHz, and the effect of the bearing current can be ignored, so the research mainly focuses on the distributed parameters in inverters and cables at 500 kHz and 30 MHz, not the effect of…

Water, energy, and food are lifelines for modern societies. The continuously rising world population, growing desires for higher living standards, and inextricable links among the three sectors make the water-energy-food (WEF) nexus a vibrant research pursuit. For the integrated delivery of WEF systems, quantifying WEF connections helps understand synergies and trade-offs across the water, energy, and food sectors, and thus is a critical initial step toward integrated WEF nexus modeling and management. However, current WEF interconnection quantifications encounter methodological hurdles. Also, existing calculation results are scattered across a wide collection of studies in multiple disciplines, which increases data collection and interpretation difficulties. To advance robust WEF nexus quantifications and further contribute to integrated WEF systems modeling and management, this study: (i) summarizes the estimate results to date on WEF interconnections; (ii) analyzes methodological and practical challenges associated with WEF interconnection calculations; and (iii) points out…

This paper addresses the problem of energy consumption prediction using neural networks over a set of public buildings. Since energy consumption in the public sector comprises a substantial share of overall consumption, the prediction of such consumption represents a decisive issue in the achievement of energy savings. In our experiments, we use the data provided by an energy consumption monitoring system in a compound of faculties and research centers at the University of Granada, and provide a methodology to predict future energy consumption using nonlinear autoregressive (NAR) and the nonlinear autoregressive neural network with exogenous inputs (NARX), respectively. Results reveal that NAR and NARX neural networks are both suitable for performing energy consumption prediction, but also that exogenous data may help to improve the accuracy of predictions.

A state-of-charge (SOC) versus open-circuit-voltage (OCV) model developed for batteries should preferably be simple, especially for real-time SOC estimation. It should also be capable of representing different types of lithium-ion batteries (LIBs), regardless of temperature change and battery degradation. It must therefore be generic, robust and adaptive, in addition to being accurate. These challenges have now been addressed by proposing a generalized SOC-OCV model for representing a few most widely used LIBs. The model is developed from analyzing electrochemical processes of the LIBs, before arriving at the sum of a logarithmic, a linear and an exponential function with six parameters. Values for these parameters are determined by a nonlinear estimation algorithm, which progressively shows that only four parameters need to be updated in real time. The remaining two parameters can be kept constant, regardless of temperature change and aging. Fitting errors demonstrated with different types of LIBs have been found to be within 0.5%. The proposed model is thus accurate, and can be…

Current logistics and transportation (L&T) systems include heterogeneous fleets consisting of common internal combustion engine vehicles as well as other types of vehicles using “green” technologies, e.g., plug-in hybrid electric vehicles and electric vehicles (EVs). However, the incorporation of EVs in L&T activities also raise some additional challenges from the strategic, planning, and operational perspectives. For instance, smart cities are required to provide recharge stations for electric-based vehicles, meaning that investment decisions need to be made about the number, location, and capacity of these stations. Similarly, the limited driving-range capabilities of EVs, which are restricted by the amount of electricity stored in their batteries, impose non-trivial additional constraints when designing efficient distribution routes. Accordingly, this paper identifies and reviews several open research challenges related to the introduction of EVs in L&T activities, including: (a) environmental-related issues; and (b) strategic, planning and operational issues associated with…

This paper reports on a comprehensive study of the CO2-EOR (Enhanced oil recovery) process, a detailed literature review and a numerical modelling study. According to past studies, CO2 injection can recover additional oil from reservoirs by reservoir pressure increment, oil swelling, the reduction of oil viscosity and density and the vaporization of oil hydrocarbons. Therefore, CO2-EOR can be used to enhance the two major oil recovery mechanisms in the field: miscible and immiscible oil recovery, which can be further increased by increasing the amount of CO2 injected, applying innovative flood design and well placement, improving the mobility ratio, extending miscibility, and controlling reservoir depth and temperature. A 3-D numerical model was developed using the CO2-Prophet simulator to examine the effective factors in the CO2-EOR process. According to that, in pure CO2 injection, oil production generally exhibits increasing trends with increasing CO2 injection rate and volume (in HCPV (Hydrocarbon pore volume)) and reservoir temperature. In the WAG (Water alternating gas)…

A power transformer outage has a dramatic financial consequence not only for electric power systems utilities but also for interconnected customers. The service reliability of this important asset largely depends upon the condition of the oil-paper insulation. Therefore, by keeping the qualities of oil-paper insulation system in pristine condition, the maintenance planners can reduce the decline rate of internal faults. Accurate diagnostic methods for analyzing the condition of transformers are therefore essential. Currently, there are various electrical and physicochemical diagnostic techniques available for insulation condition monitoring of power transformers. This paper is aimed at the description, analysis and interpretation of modern physicochemical diagnostics techniques for assessing insulation condition in aged transformers. Since fields and laboratory experiences have shown that transformer oil contains about 70% of diagnostic information, the physicochemical analyses of oil samples can therefore be extremely useful in monitoring the condition of power transformers.

This paper presents application of a new effective metaheuristic optimization method namely, the Jaya algorithm to deal with different optimum power flow (OPF) problems. Unlike other population-based optimization methods, no algorithm-particular controlling parameters are required for this algorithm. In this work, three goal functions are considered for the OPF solution: generation cost minimization, real power loss reduction, and voltage stability improvement. In addition, the effect of distributed generation (DG) is incorporated into the OPF problem using a modified formulation. For best allocation of DG unit(s), a sensitivity-based procedure is introduced. Simulations are carried out on the modified IEEE 30-bus and IEEE 118-bus networks to determine the effectiveness of the Jaya algorithm. The single objective optimization cases are performed both with and without DG. For all considered cases, results demonstrate that Jaya algorithm can produce an optimum solution with rapid convergence. Statistical analysis is also carried out to check the reliability of the Jaya algorithm. The…

The prediction accuracy of short-term load forecast (STLF) depends on prediction model choice and feature selection result. In this paper, a novel random forest (RF)-based feature selection method for STLF is proposed. First, 243 related features were extracted from historical load data and the time information of prediction points to form the original feature set. Subsequently, the original feature set was used to train an RF as the original model. After the training process, the prediction error of the original model on the test set was recorded and the permutation importance (PI) value of each feature was obtained. Then, an improved sequential backward search method was used to select the optimal forecasting feature subset based on the PI value of each feature. Finally, the optimal forecasting feature subset was used to train a new RF model as the final prediction model. Experiments showed that the prediction accuracy of RF trained by the optimal forecasting feature subset was higher than that of the original model and comparative models based on support vector regression and…

With the increasing age of the primary equipment of the electrical grids there exists also an increasing need to know its internal condition. For this purpose, off- and online diagnostic methods and systems for power transformers have been developed in recent years. Online monitoring is used continuously during operation and offers possibilities to record the relevant stresses which can affect the lifetime. The evaluation of these data offers the possibility of detecting oncoming faults early. In comparison to this, offline methods require disconnecting the transformer from the electrical grid and are used during planned inspections or when the transformer is already failure suspicious. This contribution presents the status and current trends of different diagnostic techniques of power transformers. It provides significant tutorial elements, backed up by case studies, results and some analysis. The broadness and improvements of the presented diagnostic techniques show that the power transformer is not anymore a black box that does not allow a view into its internal condition.…

This article provides a review of different methods for managing waste tyres. Around 1.5 billion scrap tyres make their way into the environmental cycle each year, so there is an extreme demand to manage and mitigate the environmental impact which occurs from landfilling and burning. Numerous approaches are targeted to recycle and reuse the tyre rubber in various applications. Among them, one of the most important methods for sustainable environmental stewardship is converting tyre rubber components into bio-oil. In this study, scrap tyre management techniques including landfill, retreading, recycling, combustion, and conversion to liquid fuels was reviewed (including gasification, hydrothermal liquefaction, and pyrolysis). The effects of parameters such as reactor types, pyrolysis temperature, and catalyst on the oil, gas and solid products in pyrolysis process were investigated.

Microalgae have been shown to be a source of multiple bio-based products ranging from high value molecules to commodities. Along with their potential to produce a large variety of products, microalgae can also be used for the depollution of wastewaters of different origins (urban, industrial, and agricultural). This paper is focused on the importance of harnessing the bioremediation capacity of microalgae to treat wastewaters in order to develop the microalgae industry (especially the microalgae biofuel industry) and to find other alternatives to the classic wastewater treatment processes. The current research on the potential of microalgae to treat a specific wastewater or a targeted pollutant is reviewed and discussed. Then, both strategies of selecting the best microalgae strain to treat a specific wastewater or pollutant and using a natural or an artificial consortium to perform the treatment will be detailed. The process options for treating wastewaters using microalgae will be discussed up to the final valorization of the biomass. The last part is dedicated to the challenges…

Organic–inorganic hybrid perovskite solar cells (PSCs) have emerged as a new class of optoelectronic semiconductors that revolutionized the photovoltaic research in the recent years. The perovskite solar cells present numerous advantages include unique electronic structure, bandgap tunability, superior charge transport properties, facile processing, and low cost. Perovskite solar cells have demonstrated unprecedented progress in efficiency and its architecture evolved over the period of the last 5–6 years, achieving a high power conversion efficiency of about 22% in 2016, serving as a promising candidate with the potential to replace the existing commercial PV technologies. This review discusses the progress of perovskite solar cells focusing on aspects such as superior electronic properties and unique features of halide perovskite materials compared to that of conventional light absorbing semiconductors. The review also presents a brief overview of device architectures, fabrication methods, and interface engineering of perovskite solar cells. The last part of the review elaborates…

The potential of organic Rankine cycle (ORC) systems is acknowledged by both considerable research and development efforts and an increasing number of applications. Most research aims at improving ORC systems through technical performance optimization of various cycle architectures and working fluids. The assessment and optimization of technical feasibility is at the core of ORC development. Nonetheless, economic feasibility is often decisive when it comes down to considering practical instalments, and therefore an increasing number of publications include an estimate of the costs of the designed ORC system. Various methods are used to estimate ORC costs but the resulting values are rarely discussed with respect to accuracy and validity. The aim of this paper is to provide insight into the methods used to estimate these costs and open the discussion about the interpretation of these results. A review of cost engineering practices shows there has been a long tradition of industrial cost estimation. Several techniques have been developed, but the expected accuracy range of the best…

Control of wave energy converters (WECs) has been very often limited to hydrodynamic control to absorb the maximum energy possible from ocean waves. This generally ignores or significantly simplifies the performance of real power take-off (PTO) systems. However, including all the required dynamics and constraints in the control problem may considerably vary the control strategy and the power output. Therefore, this paper considers the incorporation into the model of all the conversion stages from ocean waves to the electricity network, referred to as wave-to-wire (W2W) models, and identifies the necessary components and their dynamics and constraints, including grid constraints. In addition, the paper identifies different control inputs for the different components of the PTO system and how these inputs are articulated to the dynamics of the system. Examples of pneumatic, hydraulic, mechanical or magnetic transmission systems driving a rotary electrical generator, and linear electric generators are provided.

The objective of the study was to analyze chemical structure of date palm (Phoenix dactylifera L.) by employing ultimate, proximate and thermo-gravimetric techniques. Samples from different anatomical parts of date palm, namely trunk, frond base, frond midrib, leaflets, coir, fruit stem, date stone, and fruit empty bunches were considered for the experiments. Based on the findings in this work palm leaflet samples gave the highest amount of extractives content (32.9%), followed by date palm stone specimens with 31.5%. Cellulose content values of 32.8% and 47.5% were obtained for date palm stone and palm coir samples, respectively. Overall the hemicellulose contents of all samples were relatively similar to those of typical wood or non-wood lignocellulosic materials with the two exceptions of palm coir and palm leaflets. Both palm coir and palm leaflet specimens had 12.6% and 16.1% hemicellulose content. Volatile matter values of 74.3% and 87.5% were determined for leaflets and fruit empty bunch samples. The ash content of the samples ranged from 1.4% for date stone to 15.2% for palm…

Optimal siting of electric vehicle charging stations (EVCSs) is crucial to the sustainable development of electric vehicle systems. Considering the defects of previous heuristic optimization models in tackling subjective factors, this paper employs a multi-criteria decision-making (MCDM) framework to address the issue of EVCS siting. The initial criteria for optimal EVCS siting are selected from extended sustainability theory, and the vital sub-criteria are further determined by using a fuzzy Delphi method (FDM), which consists of four pillars: economy, society, environment and technology perspectives. To tolerate vagueness and ambiguity of subjective factors and human judgment, a fuzzy Grey relation analysis (GRA)-VIKOR method is employed to determine the optimal EVCS site, which also improves the conventional aggregating function of fuzzy Vlsekriterijumska Optimizacijia I Kompromisno Resenje (VIKOR). Moreover, to integrate the subjective opinions as well as objective information, experts’ ratings and Shannon entropy method are employed to determine combination weights. Then, the…

This paper presents a framework to systematically measure and assess power grids’ resilience with a focus on performance as perceived by customers at the power distribution level. The proposed framework considers an analogous measure of availability as a basic metric for resilience and defines other key resilience-related concepts and metrics, such as resistance and brittleness. This framework also provides a measurement for the degree of functional dependency of loads on power grids and demonstrates how the concepts of resilience and dependency are inherently related. It also discusses the implications of considering human-centered processes as fundamental constituting components of infrastructure systems. Thanks to its quantitative nature, the proposed resilience framework enables the creation of tools to evaluate power grids’ performance as a lifeline and to assess the effects of plans for optimal electrical power infrastructure deployment and operation. The discussion is supported by practical examples and empirical records from field damage assessments conducted after recent…

We evaluate and compare two common methods, artificial neural networks (ANN) and support vector regression (SVR), for predicting energy productions from a solar photovoltaic (PV) system in Florida 15 min, 1 h and 24 h ahead of time. A hierarchical approach is proposed based on the machine learning algorithms tested. The production data used in this work corresponds to 15 min averaged power measurements collected from 2014. The accuracy of the model is determined using computing error statistics such as mean bias error (MBE), mean absolute error (MAE), root mean square error (RMSE), relative MBE (rMBE), mean percentage error (MPE) and relative RMSE (rRMSE). This work provides findings on how forecasts from individual inverters will improve the total solar power generation forecast of the PV system.

Given photovoltaics’ (PVs) constant improvements in terms of material usage and energy efficiency, this paper provides a timely update on their life-cycle energy and environmental performance. Single-crystalline Si (sc-Si), multi-crystalline Si (mc-Si), cadmium telluride (CdTe) and copper indium gallium diselenide (CIGS) systems are analysed, considering the actual country of production and adapting the input electricity mix accordingly. Energy pay-back time (EPBT) results for fixed-tilt ground mounted installations range from 0.5 years for CdTe PV at high-irradiation (2300 kWh/(m2·yr)) to 2.8 years for sc-Si PV at low-irradiation (1000 kWh/(m2·yr)), with corresponding quality-adjusted energy return on investment (EROIPE-eq) values ranging from over 60 to ~10. Global warming potential (GWP) per kWhel averages out at ~30 g(CO2-eq), with lower values (down to ~10 g) for CdTe PV at high irradiation, and up to ~80 g for Chinese sc-Si PV at low irradiation. In general, results point to CdTe PV as the best performing technology from an environmental life-cycle perspective, also showing a…

A techno-economic model was developed to investigate the influence of components on the system costs of redox flow batteries. Sensitivity analyses were carried out based on a example of a 10 kW/120 kWh vanadium redox flow battery system and the costs of the individual components were analyzed. Particular consideration was given to the influence of material costs and resistances of bipolar plates and energy storage media as well as voltages and electric currents. Based on the developed model it was possible to formulate statements about the targeted optimization of existing battery systems and general aspects for future developments of redox flow batteries.

This paper presents an optimal dispatch model of an ice storage air-conditioning system for participants to quickly and accurately perform energy saving and demand response, and to avoid the over contact with electricity price peak. The schedule planning for an ice storage air-conditioning system of demand response is mainly to transfer energy consumption from the peak load to the partial-peak or off-peak load. Least Squares Regression (LSR) is used to obtain the polynomial function for the cooling capacity and the cost of power consumption with a real ice storage air-conditioning system. Based on the dynamic electricity pricing, the requirements of cooling loads, and all technical constraints, the dispatch model of the ice-storage air-conditioning system is formulated to minimize the operation cost. The Improved Ripple Bee Swarm Optimization (IRBSO) algorithm is proposed to solve the dispatch model of the ice storage air-conditioning system in a daily schedule on summer. Simulation results indicate that reasonable solutions provide a practical and flexible framework allowing the…

In selecting a mathematical model for simulating physical behaviours, it is important to reach an acceptable compromise between analytical complexity and achievable precision. With the aim of helping researchers and designers working in the area of photovoltaic systems to make a choice among the numerous diode-based models, a criterion for rating both the usability and accuracy of one-diode models is proposed in this paper. A three-level rating scale, which considers the ease of finding the data used by the analytical procedure, the simplicity of the mathematical tools needed to perform calculations and the accuracy achieved in calculating the current and power, is used. The proposed criterion is tested on some one-diode equivalent circuits whose analytical procedures, hypotheses and equations are minutely reviewed along with the operative steps to calculate the model parameters. To assess the achievable accuracy, the current-voltage (I-V) curves at constant solar irradiance and/or cell temperature obtained from the analysed models are compared to the characteristics issued by…

Recently, the mining industry has introduced renewable energy technologies to resolve power supply problems at mines operating in polar regions or other remote areas, and to foster substitute industries, able to benefit from abandoned sites of exhausted mines. However, little attention has been paid to the potential placement of floating photovoltaic (PV) systems operated on mine pit lakes because it was assumed that the topographic characteristics of open-pit mines are unsuitable for installing any type of PV systems. This study analyzed the potential of floating PV systems on a mine pit lake in Korea to break this misconception. Using a fish-eye lens camera and digital elevation models, a shading analysis was performed to identify the area suitable for installing a floating PV system. The layout of the floating PV system was designed in consideration of the optimal tilt angle and array spacing of the PV panels. The System Advisor Model (SAM) by National Renewable Energy Laboratory, USA, was used to conduct energy simulations based on weather data and the system design. The results…

The main function of an airport is to provide access to air transport both for passengers and cargo. The number of air operations over the past 20 years has increased rapidly, and this has led to a rise in the energy needs of airports to satisfy this demand. As a consequence, the cost of energy supply for airport managers has escalated. At the same time, global energy consumption has soared due to the needs of emerging countries like China and India, with the consequent environmental impact. This complex scenario of environmental and economic factors has made airport managers become aware of the need to reduce energy consumption as well as a more efficient use of it. The aim of this article is to analyze the main behaviors and energy trends at airports in more recent research, starting with the description of the main energy sources and consumers, the application of energy conservation and energy efficiency measures, the establishment of energy indicators and benchmarking between airports, as well as energy modeling and simulation.

The combustion of fossil fuels in the transport sector leads to an aggravation of the air quality along city roads and highways. Urban air quality is a serious problem nowadays as the number of vehicles increases on a yearly basis. With stricter Euro emission regulations, vehicle manufacturers are not meeting the imposed limits and are also disregarding the non-exhaust emissions. This paper highlights the relevance of non-exhaust emissions of passenger vehicles, both conventional (diesel and petrol) or electric vehicles (EV), on air quality levels in an urban environment in Belgium. An environmental life cycle assessment was carried out based on a real-world emission model for passenger cars and fuel refinery data. A cut-off was applied to the models to highlight what emissions, both from the refinery to the exhaust and electricity production for EV, do actually occur within Belgium’s borders. Results show that not much progress has been made from Euro 4 to 6 for conventional vehicles. Electric vehicles pose the best alternative solution as a more environmentally friendly means of…

Experiments were performed with a large laboratory-scale high solidity cross-flow turbine to investigate Reynolds number effects on performance and wake characteristics and to establish scale thresholds for physical and numerical modeling of individual devices and arrays. It was demonstrated that the performance of the cross-flow turbine becomes essentially R e -independent at a Reynolds number based on the rotor diameter R eD ≈ 106 or an approximate average Reynolds number based on the blade chord length R ec ≈ 2 × 105 . A simple model that calculates the peak torque coefficient from static foil data and cross-flow turbine kinematics was shown to be a reasonable predictor for Reynolds number dependence of an actual cross-flow turbine operating under dynamic conditions. Mean velocity and turbulence measurements in the near-wake showed subtle differences over the range of R e investigated. However, when transport terms for the streamwise momentum and mean kinetic energy were calculated, a similar R e threshold was revealed. These results imply that physical model studies of…

Accurate forecasting of carbon price is important and fundamental for anticipating the changing trends of the energy market, and, thus, to provide a valid reference for establishing power industry policy. However, carbon price forecasting is complicated owing to the nonlinear and non-stationary characteristics of carbon prices. In this paper, a combined forecasting model based on variational mode decomposition (VMD) and spiking neural networks (SNNs) is proposed. An original carbon price series is firstly decomposed into a series of relatively stable components through VMD to simplify the interference and coupling across characteristic information of different scales in the data. Then, a SNN forecasting model is built for each component, and the partial autocorrelation function (PACF) is used to determine the input variables for each SNN model. The final forecasting result for the original carbon price can be obtained by aggregating the forecasting results of all the components. Actual InterContinental Exchange (ICE) carbon price data is used for simulation, and comprehensive…

The task of site selection for electric vehicle charging stations (EVCS) is hugely important from the perspective of harmonious and sustainable development. However, flaws and inadequacies in the currently used multi-criteria decision making methods could result in inaccurate and irrational decision results. First of all, the uncertainty of the information cannot be described integrally in the evaluation of the EVCS site selection. Secondly, rigorous consideration of the mutual influence between the various criteria is lacking, which is mainly evidenced in two aspects: one is ignoring the correlation, and the other is the unconscionable measurements. Last but not least, the ranking method adopted in previous studies is not very appropriate for evaluating the EVCS site selection problem. As a result of the above analysis, a Preference Ranking Organization Method for Enrichment Evaluations (PROMETHEE) method-based decision system combined with the cloud model is proposed in this paper for EVCS site selection. Firstly, the use of the PROMETHEE method can bolster the confidence and…

Classification problems and their corresponding solving approaches constitute one of the fields of machine learning. The application of classification schemes in Renewable Energy (RE) has gained significant attention in the last few years, contributing to the deployment, management and optimization of RE systems. The main objective of this paper is to review the most important classification algorithms applied to RE problems, including both classical and novel algorithms. The paper also provides a comprehensive literature review and discussion on different classification techniques in specific RE problems, including wind speed/power prediction, fault diagnosis in RE systems, power quality disturbance classification and other applications in alternative RE systems. In this way, the paper describes classification techniques and metrics applied to RE problems, thus being useful both for researchers dealing with this kind of problem and for practitioners of the field.

This paper presents an initial investment cost analysis of public transportation systems operating with wireless charging electric vehicles (EVs). There are three different types of wireless charging systems, namely, stationary wireless charging (SWC), in which charging happens only when the vehicle is parked or idle, quasi-dynamic wireless charging (QWC), in which power is transferred when a vehicle is moving slowly or in stop-and-go mode, and dynamic wireless charging (DWC), in which power can be supplied even when the vehicle is in motion. This analysis compares the initial investment costs for these three types of charging systems for a wireless charging-based public transportation system. In particular, this analysis is focused on the energy logistics cost in transportation, which is defined as the cost of transferring and storing the energy needed to operate the transportation system. Performing this initial investment analysis is complicated, because it involves considerable tradeoffs between the costs of batteries in the EV fleet and different kinds of battery-charging…

In day-ahead electricity price forecasting (EPF) variable selection is a crucial issue. Conducting an empirical study involving state-of-the-art parsimonious expert models as benchmarks, datasets from three major power markets and five classes of automated selection and shrinkage procedures (single-step elimination, stepwise regression, ridge regression, lasso and elastic nets), we show that using the latter two classes can bring significant accuracy gains compared to commonly-used EPF models. In particular, one of the elastic nets, a class that has not been considered in EPF before, stands out as the best performing model overall.

This article presents a summary of the main findings from a collaborative research project between Aalto University in Finland and partner universities. A comparative process synthesis, modelling and thermal assessment was conducted for the production of Bio-synthetic natural gas (SNG) and hydrogen from supercritical water refining of a lipid extracted algae feedstock integrated with onsite heat and power generation. The developed reactor models for product gas composition, yield and thermal demand were validated and showed conformity with reported experimental results, and the balance of plant units were designed based on established technologies or state-of-the-art pilot operations. The poly-generative cases illustrated the thermo-chemical constraints and design trade-offs presented by key process parameters such as plant organic throughput, supercritical water refining temperature, nature of desirable coproducts, downstream indirect production and heat recovery scenarios. The evaluated cases favoring hydrogen production at 5 wt. % solid content and 600 °C conversion temperature…

A distributed power system with renewable energy sources is very popular in recent years due to the rapid depletion of conventional sources of energy. Reasonable sizing for such power systems could improve the power supply reliability and reduce the annual system cost. The goal of this work is to optimize the size of a stand-alone hybrid photovoltaic (PV)/wind turbine (WT)/battery (B)/hydrogen system (a hybrid system based on battery and hydrogen (HS-BH)) for reliable and economic supply. Two objectives that take the minimum annual system cost and maximum system reliability described as the loss of power supply probability (LPSP) have been addressed for sizing HS-BH from a more comprehensive perspective, considering the basic demand of load, the profit from hydrogen, which is produced by HS-BH, and an effective energy storage strategy. An improved ant colony optimization (ACO) algorithm has been presented to solve the sizing problem of HS-BH. Finally, a simulation experiment has been done to demonstrate the developed results, in which some comparisons have been done to emphasize the…

Energy consumption has been increasing steadily due to globalization and industrialization. Studies have shown that buildings are responsible for the biggest proportion of energy consumption; for example in European Union countries, energy consumption in buildings represents around 40% of the total energy consumption. In order to control energy consumption in buildings, different policies have been proposed, from utilizing bioclimatic architectures to the use of predictive models within control approaches. There are mainly three groups of predictive models including engineering, statistical and artificial intelligence models. Nowadays, artificial intelligence models such as neural networks and support vector machines have also been proposed because of their high potential capabilities of performing accurate nonlinear mappings between inputs and outputs in real environments which are not free of noise. The main objective of this paper is to compare a neural network model which was designed utilizing statistical and analytical methods, with a group of neural network models designed…

Redox mediators based on cobalt complexes allowed dye-sensitized solar cells (DSCs) to achieve efficiencies exceeding 14%, thus challenging the emerging class of perovskite solar cells. Unfortunately, cobalt-based electrolytes demonstrate much lower long-term stability trends if compared to the traditional iodide/triiodide redox couple. In view of the large-scale commercialization of cobalt-based DSCs, the scientific community has recently proposed various approaches and materials to increase the stability of these devices, which comprise gelling agents, crosslinked polymeric matrices and mixtures of solvents (including water). This review summarizes the most significant advances recently focused towards this direction, also suggesting some intriguing way to fabricate third-generation cobalt-based photoelectrochemical devices stable over time.

The condition of the internal cellulosic paper and oil insulation are of concern for the performance of power transformers. Over the years, a number of methods have been developed to diagnose and monitor the degradation/aging of the transformer internal insulation system. Some of this degradation/aging can be assessed from electrical responses. Currently there are a variety of electrical-based diagnostic techniques available for insulation condition monitoring of power transformers. In most cases, the electrical signals being monitored are due to mechanical or electric changes caused by physical changes in resistivity, inductance or capacitance, moisture, contamination or aging by-products in the insulation. This paper presents a description of commonly used and modern electrical-based diagnostic techniques along with their interpretation schemes.

This paper reviews the most recent and relevant research into the variability characteristics of wind and solar power resources in Europe. The background for this study is that wind and solar resources will probably constitute major components of the future European power system. Such resources are variable, and EU plans to balance the variability with more grids and demand response. Thus, planning for the future power system requires an in-depth understanding of the variability. Resource variability is a multi-faceted concept best described using a range of distinct characteristics, and this review is structured on the basis of seven of these: Distribution Long-Term (hours to years), Distribution Short-Term (less than one hour), Step Changes, Autocorrelation, Spatial Correlation, Cross Correlation and Predictable Patterns. The review presents simulations and empirical results related to resource variability for each of these characteristics. Results to date reveal that the variability characteristics of the future power system is limited understood. This study recommends the…

Increasing attention is being paid to the energy efficiency in metro systems to reduce the operational cost and to advocate the sustainability of railway systems. Classical research has studied the energy-efficient operational strategy and the energy-efficient system design separately to reduce the traction energy consumption. This paper aims to combine the operational strategies and the system design by analyzing how the infrastructure and vehicle parameters of metro systems influence the operational traction energy consumption. Firstly, a solution approach to the optimal train control model is introduced, which is used to design the Optimal Train Control Simulator(OTCS). Then, based on the OTCS, the performance of some important energy-efficient system design strategies is investigated to reduce the trains’ traction energy consumption, including reduction of the train mass, improvement of the kinematic resistance, the design of the energy-saving gradient, increasing the maximum traction and braking forces, introducing regenerative braking and timetable optimization. As for these…

This paper provides a study of the smart grid projects realised in Europe and presents their technological solutions with a focus on smart metering Low Voltage (LV) applications. Special attention is given to the telecommunications technologies used. For this purpose, we present the telecommunication technologies chosen by several European utilities for the accomplishment of their smart meter national roll-outs. Further on, a study is performed based on the European Smart Grid Projects, highlighting their technological options. The range of the projects analysed covers the ones including smart metering implementation as well as those in which smart metering applications play a significant role in the overall project success. The survey reveals that various topics are directly or indirectly linked to smart metering applications, like smart home/building, energy management, grid monitoring and integration of Renewable Energy Sources (RES). Therefore, the technological options that lie behind such projects are pointed out. For reasons of completeness, we also present the main…

This paper proposes a new electric load forecasting model by hybridizing the fuzzy time series (FTS) and global harmony search algorithm (GHSA) with least squares support vector machines (LSSVM), namely GHSA-FTS-LSSVM model. Firstly, the fuzzy c-means clustering (FCS) algorithm is used to calculate the clustering center of each cluster. Secondly, the LSSVM is applied to model the resultant series, which is optimized by GHSA. Finally, a real-world example is adopted to test the performance of the proposed model. In this investigation, the proposed model is verified using experimental datasets from the Guangdong Province Industrial Development Database, and results are compared against autoregressive integrated moving average (ARIMA) model and other algorithms hybridized with LSSVM including genetic algorithm (GA), particle swarm optimization (PSO), harmony search, and so on. The forecasting results indicate that the proposed GHSA-FTS-LSSVM model effectively generates more accurate predictive results.

In this work, we propose a Realistic Scheduling Mechanism (RSM) to reduce user frustration and enhance appliance utility by classifying appliances with respective constraints and their time of use effectively. Algorithms are proposed regarding functioning of home appliances. A 24 hour time slot is divided into four logical sub-time slots, each composed of 360 min or 6 h. In these sub-time slots, only desired appliances (with respect to appliance classification) are scheduled to raise appliance utility, restricting power consumption by a dynamically modelled power usage limiter that does not only take the electricity consumer into account but also the electricity supplier. Once appliance, time and power usage limiter modelling is done, we use a nature-inspired heuristic algorithm, Binary Particle Swarm Optimization (BPSO), optimally to form schedules with given constraints representing each sub-time slot. These schedules tend to achieve an equilibrium amongst appliance utility and cost effectiveness. For validation of the proposed RSM, we provide a comparative analysis amongst…

The renewable energy industry is undergoing continuous improvement and development worldwide, wind energy being one of the most relevant renewable energies. This industry requires high levels of reliability, availability, maintainability and safety (RAMS) for wind turbines. The blades are critical components in wind turbines. The objective of this research work is focused on the fault detection and diagnosis (FDD) of the wind turbine blades. The FDD approach is composed of a robust condition monitoring system (CMS) and a novel signal processing method. CMS collects and analyses the data from different non-destructive tests based on acoustic emission. The acoustic emission signals are collected applying macro-fiber composite (MFC) sensors to detect and locate cracks on the surface of the blades. Three MFC sensors are set in a section of a wind turbine blade. The acoustic emission signals are generated by breaking a pencil lead in the blade surface. This method is used to simulate the acoustic emission due to a breakdown of the composite fibers. The breakdown generates a set of…

Global energy and material consumption are expected to rise in exponential proportions during the next few decades, generating huge demands for deep earth energy (oil/gas) recovery and mineral processing. Under such circumstances, the continuation of existing methods in rock fragmentation in such applications is questionable due to the proven adverse environmental impacts associated with them. In this regard; the possibility of using more environmentally friendly options as Soundless Chemical Demolition Agents (SCDAs) play a vital role in replacing harmful conventional rock fragmentation techniques for gas; oil and mineral recovery. This study reviews up to date research on soundless cracking demolition agent (SCDA) application on rock fracturing including its limitations and strengths, possible applications in the petroleum industry and the possibility of using existing rock fragmentation models for SCDA-based rock fragmentation; also known as fracking. Though the expansive properties of SCDAs are currently used in some demolition works, the poor usage guidelines available reflect…