Energies

2152 KiB

Open AccessEditor’s ChoiceArticle

Predictive Control Applied to a Solar Desalination Plant Connected to a Greenhouse with Daily Variation of Irrigation Water Demand

by Lidia Roca, Jorge A. Sánchez, Francisco Rodríguez, Javier Bonilla, Alberto De la Calle and Manuel Berenguel

Energies 2016, 9(3), 194; https://doi.org/10.3390/en9030194 - 14 Mar 2016

Cited by 27 | Viewed by 8794

Abstract

The water deficit in the Mediterranean area is a known matter severely affecting agriculture. One way to avoid the aquifers’ exploitation is to supply water to crops by using thermal desalination processes. Moreover, in order to guarantee long-term sustainability, the required thermal energy [...] Read more.

The water deficit in the Mediterranean area is a known matter severely affecting agriculture. One way to avoid the aquifers’ exploitation is to supply water to crops by using thermal desalination processes. Moreover, in order to guarantee long-term sustainability, the required thermal energy for the desalination process can be provided by solar energy. This paper shows simulations for a case study in which a solar multi-effect distillation plant produces water for irrigation purposes. Detailed models of the involved systems are the base of a predictive controller to operate the desalination plant and fulfil the water demanded by the crops. Full article

(This article belongs to the Special Issue Agriculture and Energy)

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1575 KiB

Open AccessEditor’s ChoiceArticle

Optimal Power Management Strategy for Energy Storage with Stochastic Loads

by Stefano Pietrosanti, William Holderbaum and Victor M. Becerra

Energies 2016, 9(3), 175; https://doi.org/10.3390/en9030175 - 9 Mar 2016

Cited by 22 | Viewed by 8122

Abstract

In this paper, a power management strategy (PMS) has been developed for the control of energy storage in a system subjected to loads of random duration. The PMS minimises the costs associated with the energy consumption of specific systems powered by a primary [...] Read more.

In this paper, a power management strategy (PMS) has been developed for the control of energy storage in a system subjected to loads of random duration. The PMS minimises the costs associated with the energy consumption of specific systems powered by a primary energy source and equipped with energy storage, under the assumption that the statistical distribution of load durations is known. By including the variability of the load in the cost function, it was possible to define the optimality criteria for the power flow of the storage. Numerical calculations have been performed obtaining the control strategies associated with the global minimum in energy costs, for a wide range of initial conditions of the system. The results of the calculations have been tested on a MATLAB/Simulink model of a rubber tyre gantry (RTG) crane equipped with a flywheel energy storage system (FESS) and subjected to a test cycle, which corresponds to the real operation of a crane in the Port of Felixstowe. The results of the model show increased energy savings and reduced peak power demand with respect to existing control strategies, indicating considerable potential savings for port operators in terms of energy and maintenance costs. Full article

(This article belongs to the Special Issue Control of Energy Storage)

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9544 KiB

Open AccessEditor’s ChoiceArticle

Investigation of a High Head Francis Turbine at Runaway Operating Conditions

by Chirag Trivedi, Michel J. Cervantes and B. K. Gandhi

Energies 2016, 9(3), 149; https://doi.org/10.3390/en9030149 - 2 Mar 2016

Cited by 74 | Viewed by 11285

Abstract

Hydraulic turbines exhibit total load rejection during operation because of high fluctuations in the grid parameters. The generator reaches no-load instantly. Consequently, the turbine runner accelerates to high speed, runaway speed, in seconds. Under common conditions, stable runaway is only reached if after [...] Read more.

Hydraulic turbines exhibit total load rejection during operation because of high fluctuations in the grid parameters. The generator reaches no-load instantly. Consequently, the turbine runner accelerates to high speed, runaway speed, in seconds. Under common conditions, stable runaway is only reached if after a load rejection, the control and protection mechanisms both fail and the guide vanes cannot be closed. The runner life is affected by the high amplitude pressure loading at the runaway speed. A model Francis turbine was used to investigate the consequences at the runaway condition. Measurements and simulations were performed at three operating points. The numerical simulations were performed using standard k-ε, k-ω shear stress transport (SST) and scale-adaptive simulation (SAS) models. A total of 12.8 million hexahedral mesh elements were created in the complete turbine, from the spiral casing inlet to the draft tube outlet. The experimental and numerical analysis showed that the runner was subjected to an unsteady pressure loading up to three-times the pressure loading observed at the best efficiency point. Investigates of unsteady pressure pulsations at the vaneless space, runner and draft tube are discussed in the paper. Further, unsteady swirling flow in the blade passages was observed that was rotating at a frequency of 4.8-times the runaway runner angular speed. Apart from the unsteady pressure loading, the development pattern of the swirling flow in the runner is discussed in the paper. Full article

(This article belongs to the Special Issue Hydropower)

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5582 KiB

Open AccessEditor’s ChoiceArticle

Parameter Sensitivity Analysis for Fractional-Order Modeling of Lithium-Ion Batteries

by Daming Zhou, Ke Zhang, Alexandre Ravey, Fei Gao and Abdellatif Miraoui

Energies 2016, 9(3), 123; https://doi.org/10.3390/en9030123 - 24 Feb 2016

Cited by 59 | Viewed by 8473

Abstract

This paper presents a novel-fractional-order lithium-ion battery model that is suitable for use in embedded applications. The proposed model uses fractional calculus with an improved Oustaloup approximation method to describe all the internal battery dynamic behaviors. The fractional-order model parameters, such as equivalent [...] Read more.

This paper presents a novel-fractional-order lithium-ion battery model that is suitable for use in embedded applications. The proposed model uses fractional calculus with an improved Oustaloup approximation method to describe all the internal battery dynamic behaviors. The fractional-order model parameters, such as equivalent circuit component coefficients and fractional-order values, are identified by a genetic algorithm. A modeling parameters sensitivity study using the statistical Multi-Parameter Sensitivity Analysis (MPSA) method is then performed and discussed in detail. Through the analysis, the dynamic effects of parameters on the model output performance are obtained. It has been found out from the analysis that the fractional-order values and their corresponding internal dynamics have different degrees of impact on model outputs. Thus, they are considered as crucial parameters to accurately describe a battery’s dynamic voltage responses. To experimentally verify the accuracy of developed fractional-order model and evaluate its performance, the experimental tests are conducted with a hybrid pulse test and a dynamic stress test (DST) on two different types of lithium-ion batteries. The results demonstrate the accuracy and usefulness of the proposed fractional-order model on battery dynamic behavior prediction. Full article

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3191 KiB

Open AccessFeature PaperEditor’s ChoiceReview

A Review of Modeling Bioelectrochemical Systems: Engineering and Statistical Aspects

by Shuai Luo, Hongyue Sun, Qingyun Ping, Ran Jin and Zhen He

Energies 2016, 9(2), 111; https://doi.org/10.3390/en9020111 - 18 Feb 2016

Cited by 64 | Viewed by 11607

Abstract

Bioelectrochemical systems (BES) are promising technologies to convert organic compounds in wastewater to electrical energy through a series of complex physical-chemical, biological and electrochemical processes. Representative BES such as microbial fuel cells (MFCs) have been studied and advanced for energy recovery. Substantial experimental [...] Read more.

Bioelectrochemical systems (BES) are promising technologies to convert organic compounds in wastewater to electrical energy through a series of complex physical-chemical, biological and electrochemical processes. Representative BES such as microbial fuel cells (MFCs) have been studied and advanced for energy recovery. Substantial experimental and modeling efforts have been made for investigating the processes involved in electricity generation toward the improvement of the BES performance for practical applications. However, there are many parameters that will potentially affect these processes, thereby making the optimization of system performance hard to be achieved. Mathematical models, including engineering models and statistical models, are powerful tools to help understand the interactions among the parameters in BES and perform optimization of BES configuration/operation. This review paper aims to introduce and discuss the recent developments of BES modeling from engineering and statistical aspects, including analysis on the model structure, description of application cases and sensitivity analysis of various parameters. It is expected to serves as a compass for integrating the engineering and statistical modeling strategies to improve model accuracy for BES development. Full article

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921 KiB

Open AccessEditor’s ChoiceArticle

Recovery of Bio-Oil from Industrial Food Waste by Liquefied Dimethyl Ether for Biodiesel Production

by Kiyoshi Sakuragi, Peng Li, Maromu Otaka and Hisao Makino

Energies 2016, 9(2), 106; https://doi.org/10.3390/en9020106 - 17 Feb 2016

Cited by 34 | Viewed by 9486

Abstract

The development of new energy sources has become particularly important from the perspective of energy security and environmental protection. Therefore, the utilization of waste resources such as industrial food wastes (IFWs) in energy production is expected. The central research institute of electric power [...] Read more.

The development of new energy sources has become particularly important from the perspective of energy security and environmental protection. Therefore, the utilization of waste resources such as industrial food wastes (IFWs) in energy production is expected. The central research institute of electric power industry (CRIEPI, Tokyo, Japan) has recently developed an energy-saving oil-extraction technique involving the use of liquefied dimethyl ether (DME), which is an environmentally friendly solvent. In this study, three common IFWs (spent coffee grounds, soybean, and rapeseed cakes) were evaluated with respect to oil yield for biodiesel fuel (BDF) production by the DME extraction method. The coffee grounds were found to contain 16.8% bio-oil, whereas the soybean and rapeseed cakes contained only approximately 0.97% and 2.6% bio-oil, respectively. The recovered oils were qualitatively analysed by gas chromatography-mass spectrometry. The properties of fatty acid methyl esters derived from coffee oil, such as kinematic viscosity, pour point, and higher heating value (HHV), were also determined. Coffee grounds had the highest oil content and could be used as biofuel. In addition, the robust oil extraction capability of DME indicates that it may be a favourable alternative to conventional oil extraction solvents. Full article

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3314 KiB

Open AccessEditor’s ChoiceArticle

Design and Analysis of Electrical Distribution Networks and Balancing Markets in the UK: A New Framework with Applications

by Vijayanarasimha Hindupur Pakka and Richard Mark Rylatt

Energies 2016, 9(2), 101; https://doi.org/10.3390/en9020101 - 9 Feb 2016

Cited by 13 | Viewed by 9478

Abstract

We present a framework for the design and simulation of electrical distribution systems and short term electricity markets specific to the UK. The modelling comprises packages relating to the technical and economic features of the electrical grid. The first package models the medium/low [...] Read more.

We present a framework for the design and simulation of electrical distribution systems and short term electricity markets specific to the UK. The modelling comprises packages relating to the technical and economic features of the electrical grid. The first package models the medium/low distribution networks with elements such as transformers, voltage regulators, distributed generators, composite loads, distribution lines and cables. This model forms the basis for elementary analysis such as load flow and short circuit calculations and also enables the investigation of effects of integrating distributed resources, voltage regulation, resource scheduling and the like. The second part of the modelling exercise relates to the UK short term electricity market with specific features such as balancing mechanism and bid-offer strategies. The framework is used for investigating methods of voltage regulation using multiple control technologies, to demonstrate the effects of high penetration of wind power on balancing prices and finally use these prices towards achieving demand response through aggregated prosumers. Full article

(This article belongs to the Special Issue Multi-Disciplinary Perspectives on Energy and Sustainable Development)

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1593 KiB

Open AccessEditor’s ChoiceArticle

Effects of Reynolds Number on the Energy Conversion and Near-Wake Dynamics of a High Solidity Vertical-Axis Cross-Flow Turbine

by Peter Bachant and Martin Wosnik

Energies 2016, 9(2), 73; https://doi.org/10.3390/en9020073 - 26 Jan 2016

Cited by 118 | Viewed by 13796

Abstract

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 [...] Read more.

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 e_D ≈ 10⁶ or an approximate average Reynolds number based on the blade chord length R e_c ≈ 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 cross-flow turbines should achieve R e_D ∼ 10⁶ to properly approximate both the performance and wake dynamics of full-scale devices and arrays. Full article

(This article belongs to the Special Issue Wind Turbine 2015)

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Graphical abstract

9983 KiB

Open AccessEditor’s ChoiceReview

Experimental and Numerical Studies of a High-Head Francis Turbine: A Review of the Francis-99 Test Case

by Chirag Trivedi, Michel J. Cervantes and Ole G. Dahlhaug

Energies 2016, 9(2), 74; https://doi.org/10.3390/en9020074 - 26 Jan 2016

Cited by 73 | Viewed by 12015

Abstract

Hydraulic turbines are widely used to meet real-time electricity demands. Computational fluid dynamic (CFD) techniques have played an important role in the design and development of such turbines. The simulation of a complete turbine requires substantial computational resources. A specific approach that is [...] Read more.

Hydraulic turbines are widely used to meet real-time electricity demands. Computational fluid dynamic (CFD) techniques have played an important role in the design and development of such turbines. The simulation of a complete turbine requires substantial computational resources. A specific approach that is applied to investigate the flow field of one turbine may not work for another turbine. A series of Francis-99 workshops have been planned to discuss and explore the CFD techniques applied within the field of hydropower with application to high-head Francis turbines. The first workshop was held in December 2014 at the Norwegian University of Science and Technology, Norway. The steady-state measurements were conducted on a model Francis turbine. Three operating points, part load, best efficiency point, and high load, were investigated. The complete geometry, meshing, and experimental data concerning the hydraulic efficiency, pressure, and velocity were provided to the academic and industrial research groups. Various researchers have conducted extensive numerical studies on the high-head Francis turbine, and the obtained results were presented during the workshop. This paper discusses the presented numerical results and the important outcome of the extensive numerical studies on the Francis turbine. The use of a wall function assuming equilibrium between the production and dissipation of turbulence is widely used in the simulation of hydraulic turbines. The boundary layer of hydraulic turbines is not fully developed because of the continuously-changing geometry and large pressure gradients. There is a need to develop wall functions that enable the estimation of viscous losses under boundary development for accurate simulations. Improved simulations and results enable reliable estimation of the blade loading. Numerical investigations on leakage flow through the labyrinth seals were conducted. The volumetric efficiency and losses in the seals were determined. The seal leakage losses formulated through analytical techniques are sufficient. Full article

(This article belongs to the Special Issue Hydropower)

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2572 KiB

Open AccessEditor’s ChoiceArticle

A Comparison of Energy Consumption Prediction Models Based on Neural Networks of a Bioclimatic Building

by Hamid R. Khosravani, María Del Mar Castilla, Manuel Berenguel, Antonio E. Ruano and Pedro M. Ferreira

Energies 2016, 9(1), 57; https://doi.org/10.3390/en9010057 - 20 Jan 2016

Cited by 94 | Viewed by 10012

Abstract

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. [...] Read more.

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 benefiting from a multi objective genetic algorithm. Moreover, the neural network models were compared to a naïve autoregressive baseline model. The models are intended to predict electric power demand at the Solar Energy Research Center (Centro de Investigación en Energía SOLar or CIESOL in Spanish) bioclimatic building located at the University of Almeria, Spain. Experimental results show that the models obtained from the multi objective genetic algorithm (MOGA) perform comparably to the model obtained through a statistical and analytical approach, but they use only 0.8% of data samples and have lower model complexity. Full article

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1387 KiB

Open AccessFeature PaperEditor’s ChoiceArticle

What Do Capacity Deployment Rates Tell Us about the Efficiency of Electricity Generation from Renewable Energy Sources Support Measures in Greece?

by Sotiris Papadelis, Vasssilis Stavrakas and Alexandros Flamos

Energies 2016, 9(1), 38; https://doi.org/10.3390/en9010038 - 13 Jan 2016

Cited by 20 | Viewed by 7634

Abstract

The efficiency of fiscal support for electricity generation from renewable energy sources (RES-E) is a multifaceted notion that cannot be adequately described by a single metric. Efficiency is related to the ability of a policy measure to support deployment without creating negative feedback [...] Read more.

The efficiency of fiscal support for electricity generation from renewable energy sources (RES-E) is a multifaceted notion that cannot be adequately described by a single metric. Efficiency is related to the ability of a policy measure to support deployment without creating negative feedback effects. These negative effects may stem from saturation of the grid’s ability to absorb an increased amount of RES-E power, the inability of regulatory bodies to cope with the larger workload due to the increased number of projects requesting permits or from rent-seeking behavior. Furthermore, the primary rationale for feed-in tariffs (FITs) and other fiscal support schemes is that increased deployment of RES-E technologies will lead to reductions in costs and increases in efficiency. As a result, the efficiency of an RES-E support policy should be also judged by its ability to capitalize on cost reductions. Overall, we present an approach to facilitate ongoing assessments of the efficiency of support measures for RES-E deployment. We demonstrate the proposed approach using the FIT support policy in Greece as a case study. In particular, the RES-E support policy in Greece has been recently revised through tariff cuts and a moratorium on new production licenses. We aim to demonstrate that if publicly available data are appropriately monitored, a policy revision can take place in a timelier and less disruptive manner. Full article

(This article belongs to the Special Issue Applied Energy System Modeling 2015)

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4050 KiB

Open AccessEditor’s ChoiceArticle

Assessing the Environmental Sustainability of Electricity Generation in Turkey on a Life Cycle Basis

by Burcin Atilgan and Adisa Azapagic

Energies 2016, 9(1), 31; https://doi.org/10.3390/en9010031 - 7 Jan 2016

Cited by 45 | Viewed by 7376

Abstract

Turkey’s electricity mix is dominated by fossil fuels, but the country has ambitious future targets for renewable and nuclear energy. At present, environmental impacts of electricity generation in Turkey are unknown so this paper represents a first attempt to fill this knowledge gap. [...] Read more.

Turkey’s electricity mix is dominated by fossil fuels, but the country has ambitious future targets for renewable and nuclear energy. At present, environmental impacts of electricity generation in Turkey are unknown so this paper represents a first attempt to fill this knowledge gap. Taking a life cycle approach, the study considers eleven impacts from electricity generation over the period 1990–2014. All 516 power plants currently operational in Turkey are assessed: lignite, hard coal, natural gas, hydro, onshore wind and geothermal. The results show that the annual impacts from electricity have been going up steadily over the period, increasing by 2–9 times, with the global warming potential being higher by a factor of five. This is due to a four-fold increase in electricity demand and a growing share of fossil fuels. The impact trends per unit of electricity generated differ from those for the annual impacts, with only four impacts being higher today than in 1990, including the global warming potential. Most other impacts are lower from 35% to two times. These findings demonstrate the need for diversifying the electricity mix by increasing the share of domestically-abundant renewable resources, such as geothermal, wind, and solar energy. Full article

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4839 KiB

Open AccessEditor’s ChoiceArticle

Methods for Global Survey of Natural Gas Flaring from Visible Infrared Imaging Radiometer Suite Data

by Christopher D. Elvidge, Mikhail Zhizhin, Kimberly Baugh, Feng-Chi Hsu and Tilottama Ghosh

Energies 2016, 9(1), 14; https://doi.org/10.3390/en9010014 - 25 Dec 2015

Cited by 289 | Viewed by 30749

Abstract

A set of methods are presented for the global survey of natural gas flaring using data collected by the National Aeronautics and Space Administration/National Oceanic and Atmospheric Administration NASA/NOAA Visible Infrared Imaging Radiometer Suite (VIIRS). The accuracy of the flared gas volume estimates [...] Read more.

A set of methods are presented for the global survey of natural gas flaring using data collected by the National Aeronautics and Space Administration/National Oceanic and Atmospheric Administration NASA/NOAA Visible Infrared Imaging Radiometer Suite (VIIRS). The accuracy of the flared gas volume estimates is rated at ±9.5%. VIIRS is particularly well suited for detecting and measuring the radiant emissions from gas flares through the collection of shortwave and near-infrared data at night, recording the peak radiant emissions from flares. In 2012, a total of 7467 individual flare sites were identified. The total flared gas volume is estimated at 143 (±13.6) billion cubic meters (BCM), corresponding to 3.5% of global production. While the USA has the largest number of flares, Russia leads in terms of flared gas volume. Ninety percent of the flared gas volume was found in upstream production areas, 8% at refineries and 2% at liquified natural gas (LNG) terminals. The results confirm that the bulk of natural gas flaring occurs in upstream production areas. VIIRS data can provide site-specific tracking of natural gas flaring for use in evaluating efforts to reduce and eliminate routine flaring. Full article

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Graphical abstract

852 KiB

Open AccessEditor’s ChoiceArticle

Wind Turbine Fault Detection through Principal Component Analysis and Statistical Hypothesis Testing

by Francesc Pozo and Yolanda Vidal

Energies 2016, 9(1), 3; https://doi.org/10.3390/en9010003 - 23 Dec 2015

Cited by 56 | Viewed by 7712

Abstract

This paper addresses the problem of online fault detection of an advanced wind turbine benchmark under actuators (pitch and torque) and sensors (pitch angle measurement) faults of different type: fixed value, gain factor, offset and changed dynamics. The fault detection scheme starts by [...] Read more.

This paper addresses the problem of online fault detection of an advanced wind turbine benchmark under actuators (pitch and torque) and sensors (pitch angle measurement) faults of different type: fixed value, gain factor, offset and changed dynamics. The fault detection scheme starts by computing the baseline principal component analysis (PCA) model from the healthy or undamaged wind turbine. Subsequently, when the structure is inspected or supervised, new measurements are obtained are projected into the baseline PCA model. When both sets of data—the baseline and the data from the current wind turbine—are compared, a statistical hypothesis testing is used to make a decision on whether or not the wind turbine presents some damage, fault or misbehavior. The effectiveness of the proposed fault-detection scheme is illustrated by numerical simulations on a well-known large offshore wind turbine in the presence of wind turbulence and realistic fault scenarios. The obtained results demonstrate that the proposed strategy provides and early fault identification, thereby giving the operators sufficient time to make more informed decisions regarding the maintenance of their machines. Full article

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