() Alternative fuels are an important aspect of transportation and energy production. The study of Alternative fuels are an important aspect of transportation and energy production. The study of bioalcohols combustion and pollutant formation in spark ignition power units is essential, especially for direct injection engines. Within this context, and with particle emissions becoming an ever-pressing matter, optical techniques provide substantial insight into local phenomena and constitute a solid background for developing optimized control strategies.

Waste management and energy systems are often interlinked, either directly by waste-to-energy technologies, or indirectly as processes for recovery of resources—such as materials, oils, manure, or sludge—use energy in their processes or substitute conventional production of the commodities for which the recycling processes provide raw materials. A special issue in Energies on the topic of “Energy and Waste Management” attained a lot of attention from the scientific community. In particular, papers contributing to improved understanding of the combined management of waste and energy were invited.

Methanol Production and Use.pdf - Ebook download as PDF File (.pdf), Text File (.txt) or read book online. Nuclear Reactor Physics. Numerical Solutions for Multigroup Diffusion Theory. Nuclear Reactor Dynamics. Delayed Fission Neutrons. Neutrons Emitted in. Fertile-to-Fissile Conversion and Breeding. Availability of Neutrons. Conversion and Breeding Ratios.

In all, 9 papers were published out of 24 unique submissions. The papers cover technical topics such as leaching of heavy metals, pyrolysis, and production of synthetic natural gas in addition to different systems assessments of horse manure, incineration, and complex future scenarios at a national level.

All papers except one focused on energy recovery from waste. That particular paper focused on waste management of infrastructure in an energy system (wind turbines).

Published papers illustrate research in the field of energy and waste management on both a current detailed process level as well as on a future system level. Knowledge gained on both types is necessary to be able to make progress towards a circular economy.

In this research, the development of a diesel engine thermal overload monitoring system is presented with applications and test results. The designed diesel engine thermal overload monitoring system consists of two set of sensors, i.e., a lambda sensor to measure the oxygen concentration and a fast response thermocouple to measure the temperature of the gas leaving the cylinder.

A medium speed Ruston diesel engine is instrumented to measure the required engine process parameters, measurements are taken at constant load and variable fuel delivery i.e., normal and excessive injection. It is indicated that with excessive injection, the test engine is of high risk to be operated at thermal overload condition. Further tests were carried out on a Sulzer 7RTA84T engine to explore the influence of engine operating at thermal overload condition on exhaust gas temperature and oxygen concentration in the blow down gas. It is established that a lower oxygen concentration in the blow down gas corresponds to a higher exhaust gas temperature.

The piston crown wear rate will then be much higher due to the high rate of heat transfer from a voluminous flame. Various crops can be considered as potential bioenergy and biofuel production feedstocks. The selection of the crops to be cultivated for that purpose is based on several factors. For an objective comparison between different crops, a common framework is required to assess their economic or energetic performance. In this paper, a computational tool for the energy cost evaluation of multiple-crop production systems is presented.

All the in-field and transport operations are considered, providing a detailed analysis of the energy requirements of the components that contribute to the overall energy consumption. A demonstration scenario is also described. The scenario is based on three selected energy crops, namely Miscanthus, Arundo donax and Switchgrass. The tool can be used as a decision support system for the evaluation of different agronomical practices (such as fertilization and agrochemicals application), machinery systems, and management practices that can be applied in each one of the individual crops within the production system. Within the context of ever wider expansion of direct injection in spark ignition engines, this investigation was aimed at improved understanding of the correlation between fuel injection strategy and emission of nanoparticles.

Measurements performed on a wall guided engine allowed identifying the mechanisms involved in the formation of carbonaceous structures during combustion and their evolution in the exhaust line. In-cylinder pressure was recorded in combination with cycle-resolved flame imaging, gaseous emissions and particle size distribution.

This complete characterization was performed at three injection phasing settings, with butanol and commercial gasoline. Optical accessibility from below the combustion chamber allowed visualization of diffusive flames induced by fuel deposits; these localized phenomena were correlated to observed changes in engine performance and pollutant species. With gasoline fueling, minor modifications were observed with respect to combustion parameters, when varying the start of injection. The alcohol, on the other hand, featured marked sensitivity to the fuel delivery strategy. Even though the start of injection was varied in a relatively narrow crank angle range during the intake stroke, significant differences were recorded, especially in the values of particle emissions.

This was correlated to the fuel jet-wall interactions; the analysis of diffusive flames, their location and size confirmed the importance of liquid film formation in direct injection engines, especially at medium and high load. In the last few years, several investigations have been carried out in the field of optimal sizing of energy storage systems (ESSs) at both the transmission and distribution levels. Nevertheless, most of these works make important assumptions about key factors affecting ESS profitability such as efficiency and life cycles and especially about the specific costs of the ESS, without considering the uncertainty involved. In this context, this work aims to answer the question: what should be the costs of different ESS technologies in order to make a profit when considering peak shaving applications? The paper presents a comprehensive sensitivity analysis of the interaction between the profitability of an ESS project and some key parameters influencing the project performance. The proposed approach determines the break-even points for different ESSs considering a wide range of life cycles, efficiencies, energy prices, and power prices. To do this, an optimization algorithm for the sizing of ESSs is proposed from a distribution company perspective.

From the results, it is possible to conclude that, depending on the values of round trip efficiency, life cycles, and power price, there are four battery energy storage systems (BESS) technologies that are already profitable when only peak shaving applications are considered: lead acid, NaS, ZnBr, and vanadium redox. The paper presents the concept of a hybrid power system with additional energy storage to support electric vehicles (EVs) charging stations. The aim is to verify the possibilities of mutual cooperation of individual elements of the system from the point of view of energy balances and to show possibilities of utilization of accumulation for these purposes using mathematical modeling.

The description of the technical solution of the concept is described by a mathematical model in the Matlab Simulink programming environment. Individual elements of the assembled model are described in detail, together with the algorithm of the control logic of charging the supporting storage system. The resulting model was validated via an actual small-scale hybrid system (HS). Within the outputs of the mathematical model, two simulation scenarios are presented, with the aid of which the benefits of the concept presented were verified.

Battery energy storage systems (BESS) coupled with rooftop-mounted residential photovoltaic (PV) generation, designated as PV-BESS, draw increasing attention and market penetration as more and more such systems become available. The manifold BESS deployed to date rely on a variety of different battery technologies, show a great variation of battery size, and power electronics dimensioning. However, given today’s high investment costs of BESS, a well-matched design and adequate sizing of the storage systems are prerequisites to allow profitability for the end-user. The economic viability of a PV-BESS depends also on the battery operation, storage technology, and aging of the system. In this paper, a general method for comprehensive PV-BESS techno-economic analysis and optimization is presented and applied to the state-of-art PV-BESS to determine its optimal parameters. Using a linear optimization method, a cost-optimal sizing of the battery and power electronics is derived based on solar energy availability and local demand. At the same time, the power flow optimization reveals the best storage operation patterns considering a trade-off between energy purchase, feed-in remuneration, and battery aging.

Using up to date technology-specific aging information and the investment cost of battery and inverter systems, three mature battery chemistries are compared; a lead-acid (PbA) system and two lithium-ion systems, one with lithium-iron-phosphate (LFP) and another with lithium-nickel-manganese-cobalt (NMC) cathode. The results show that different storage technology and component sizing provide the best economic performances, depending on the scenario of load demand and PV generation. In this work, a practical methodology is proposed to analyze, before undertaking a large investment, an outdoor lighting installation renewal with light-emitting diode (LED) luminaires. The main problems found in many of the luminaires tested are associated with inrush peak currents in cold start (which may cause ignition problems with random shutdowns), the harmonic distortions caused by their AC/DC associated electronic nature driver, and their working and efficiency dependency on the ambient temperature.

All these issues have been tested in the context of a large metal halide (MH) to LED luminaires lighting point renewal where six commercial LED projectors have been analyzed with the above considerations. This research has isolated a single-phase circuit powered with constant stabilized 230 V AC voltage source in a real public lighting installation. All of them have been sequentially installed and their main electrical and power-quality parameters measured and recorded. The results indicate that each luminaire option will influence the expected long-term reliability (>50.000 h or more as expressed by the U.S. Department of Energy) of the lighting installation (in the case poor power quality is generated on the grid).

The economic analysis made to estimate the profitability of the investment may be severely affected by the difference between the declared and the real consumption values in which they perform in our specific installation. In this work, a neuro-fuzzy (NF) simulation study was conducted in order to screen candidate reservoirs for enhanced oil recovery (EOR) projects in Angolan oilfields. First, a knowledge pattern is extracted by combining both the searching potential of fuzzy-logic (FL) and the learning capability of neural network (NN) to make a priori decisions. The extracted knowledge pattern is validated against rock and fluid data trained from successful EOR projects around the world. Then, data from Block K offshore Angolan oilfields are then mined and analysed using box-plot technique for the investigation of the degree of suitability for EOR projects. The trained and validated model is then tested on the Angolan field data (Block K) where EOR application is yet to be fully established.

The results from the NF simulation technique applied in this investigation show that polymer, hydrocarbon gas, and combustion are the suitable EOR techniques. Current commercial battery management systems (BMSs) do not provide adequate information in real time to mitigate issues of battery cells such as thermal runway. This paper explores and evaluates the integration of fiber optic Bragg grating (FBG) sensors inside lithium-ion battery (LiB) coin cells.

Strain and internal and external temperatures were recorded using FBG sensors, and the battery cells were evaluated at a cycling C/20 rate. The preliminary results present scanning electron microscope (SEM) images of electrode degradation upon sensor integration and the systematic process of sensor integration to eliminate degradation in electrodes during cell charge/discharge cycles. Recommendation for successful FBG sensor integration is given, and the strain and temperature data is presented. The FBG sensor was placed on the inside of the coin cell between the electrodes and the separator layers towards the most electrochemically active area.

On the outside, the temperature of the coin cell casing as well as the ambient temperature was recorded. Results show stable strain behavior within the cell and about 10 °C difference between the inside of the coin cell and the ambient environment over time during charging/discharging cycles.

This study is intended to contribute to the safe integration of FBG sensors inside hermetically sealed batteries and to detection of real-time temperature and strain gradient inside a cell, ultimately improving reliability of current BMSs. This paper evaluates the performance of a fuel cell/battery vehicle with an on-board autothermal reformer, fed by different liquid and gaseous hydrocarbon fuels. A sensitivity analysis is performed to investigate the system behavior under the variation of the steam to carbon and oxygen to carbon ratios. This is done in order to identify the most suitable operating conditions for a direct on-board production of hydrogen to be used in a high temperature polymer electrolyte membrane fuel cell. The same system should be able to process different fuels, to allow the end-user to freely decide which one to use to refuel the vehicle. Hence, the obtained operating conditions result in a trade-off between system flexibility as the feeding fuel changes, CO poisoning effect on the fuel cell and overall efficiency. The system is thus coupled to a high temperature fuel cell, modeled by means of a self-made tool, able to reproduce the polarization curve as the input syngas composition varies, and the overall system is afterwards tested on a plug-in fuel cell/battery vehicle simulator, in order to provide a thorough feasibility analysis, focusing on the entire system efficiency.

Results show that a proper energy management strategy can mitigate the effect of the fuel variation on the reformer efficiency, allowing for good overall powertrain performance. In recent years, several tools and models have been developed and used for the design and analysis of future national energy systems.

Many of these models focus on the integration of various renewable energy resources and the transformation of existing fossil-based energy systems into future sustainable energy systems. The models are diverse and often end up with different results and recommendations. This paper analyses this diversity of models and their implicit or explicit theoretical backgrounds. In particular, two archetypes are defined and compared. On the one hand, the prescriptive investment optimisation or optimal solutions approach.

On the other hand the analytical simulation or alternatives assessment approach. Awareness of the dissimilar theoretical assumption behind the models clarifies differences between the models, explains dissimilarities in results, and provides a theoretical and methodological foundation for understanding and interpreting results from the two archetypes. In this study, the environmental impacts of monolithic silicon heterojunction organometallic perovskite tandem cells (SHJ-PSC) and single junction organometallic perovskite solar cells (PSC) are compared with the impacts of crystalline silicon based solar cells using a prospective life cycle assessment with a time horizon of 2025. This approach provides a result range depending on key parameters like efficiency, wafer thickness, kerf loss, lifetime, and degradation, which are appropriate for the comparison of these different solar cell types with different maturity levels.

The life cycle environmental impacts of SHJ-PSC and PSC solar cells are similar or lower compared to conventional crystalline silicon solar cells, given comparable lifetimes, with the exception of mineral and fossil resource depletion. A PSC single-junction cell with 20% efficiency has to exceed a lifetime of 24 years with less than 3% degradation per year in order to be competitive with the crystalline silicon single-junction cells.

If the installed PV capacity has to be maximised with only limited surface area available, the SHJ-PSC tandem is preferable to the PSC single-junction because their environmental impacts are similar, but the surface area requirement of SHJ-PSC tandems is only 70% or lower compared to PSC single-junction cells. The SHJ-PSC and PSC cells have to be embedded in proper encapsulation to maximise the stability of the PSC layer as well as handled and disposed of correctly to minimise the potential toxicity impacts of the heavy metals used in the PSC layer. The injection of CO 2 as part of the water-alternating-gas (WAG) process has been widely employed in many mature oil fields for effectively enhancing oil production and sequestrating carbon permanently inside the reservoirs. In addition to simulations, the use of intelligent tools is of particular interest for evaluating the uncertainties in the WAG process and predicting technical or economic performance.

This study proposed the comprehensive evaluations of a water-alternating-CO 2 process utilizing the artificial neural network (ANN) models that were initially generated from a qualified numerical data set. Totally two uncertain reservoir parameters and three installed surface operating factors were designed as input variables in each of the three-layer ANN models to predicting a series of WAG production performances after 5, 15, 25, and 35 injection cycles. In terms of the technical view point, the relationships among parameters and important outputs, including oil recovery, CO 2 production, and net CO 2 storage were accurately reflected by integrating the generated network models. More importantly, since the networks could simulate a series of injection processes, the sequent variations of those technical issues were well presented, indicating the distinct application of ANN in this study compared to previous works. The economic terms were also briefly introduced for a given fiscal condition which included sufficient concerns for a general CO 2 flooding project, in a range of possible oil prices. Using the ANN models, the net present value (NPV) optimization results for several specific cases apparently expressed the profitability of the present enhanced oil recovery (EOR) project according to the unstable oil prices, and most importantly provided the most relevant injection schedules corresponding with each different scenario.

Obviously, the methodology of applying traditional ANN as shown in this study can be adaptively adjusted for any other EOR project, and in particular, since the models have demonstrated their flexible capacity for economic analyses, the method can be promisingly developed to engage with other economic tools on comprehensively assessing the project. Multi-terminal high voltage direct current transmission based on voltage source converter (VSC-HVDC) grids can connect non-synchronous alternating current (AC) grids to a hybrid alternating current and direct current (AC/DC) power system, which is one of the key technologies in the construction of smart grids. However, it is still a problem to control the converter to achieve the function of each AC system sharing the reserve capacity of the entire network. This paper proposes an improved control strategy based on the slope control of the DC voltage and AC frequency (V–f slope control), in which the virtual inertia is introduced. This method can ensure that each AC sub-system shares the primary frequency control function. Additionally, with the new control method, it is easy to apply the secondary frequency control method of traditional AC systems to AC/DC hybrid systems to achieve the steady control of the DC voltage and AC frequency of the whole system.

Most importantly, the new control method is better than the traditional control method in terms of dynamic performance. In this paper, a new control method is proposed, and the simulation model has been established in Matlab/Simulink to verify the effectiveness of the proposed control method.

Many previous contributions to methods of forecasting the performance of polymer flooding using artificial neural networks (ANNs) have been made by numerous researchers previously. In most of those forecasting cases, only a single polymer slug was employed to meet the objective of the study. The intent of this manuscript is to propose an efficient recovery factor prediction tool at different injection stages of two polymer slugs during polymer flooding using an ANN. In this regard, a back-propagation algorithm was coupled with six input parameters to predict three output parameters via a hidden layer composed of 10 neurons. Evaluation of the ANN model performance was made with multiple linear regression. With an acceptable correlation coefficient, the proposed ANN tool was able to predict the recovery factor with errors of. Wind towers or wind catchers, as passive cooling systems, can provide natural ventilation in buildings located in hot, arid regions.

These natural cooling systems can provide thermal comfort for the building inhabitants throughout the warm months. In this paper, a modular design of a wind tower is introduced. The design, called a modular wind tower with wetted surfaces, was investigated experimentally and analytically. To determine the performance of the wind tower, air temperature, relative humidity (RH) and air velocity were measured at different points. Measurements were carried out when the wind speed was zero.

The experimental results were compared with the analytical ones. The results illustrated that the modular wind tower can decrease the air temperature significantly and increase the relative humidity of airflow into the building. The average differences for air temperature and air relative humidity between ambient air and air exiting from the wind tower were approximately 10 °C and 40%, respectively.

The main advantage of the proposed wind tower is that it is a modular design that can reduce the cost of wind tower construction. When medium- or high-voltage power conversion is preferred for renewable energy sources, multilevel power converters have received much of the interest in this area as methods for enhancing the conversion efficiency and cost effectiveness. In such cases, multilevel, multi-input multi-output (MIMO) configurations of DC-DC converters come to the scenario for integrating several sources together, especially considering the stringent regulatory needs and the requirement of multistage power conversion systems. Considering the above facts, a three-level dual input dual output (DIDO) buck-boost converter, as the simplest form of MIMO converter, is proposed in this paper for DC-link voltage regulation. The capability of this converter for cross regulating the DC-link voltage is analyzed in detail to support a three-level neutral point clamped inverter-based grid connection in the future. The cross-regulation capability is examined under a new type of pulse delay control (PDC) strategy and later compared with a three-level boost converter (TLBC). Compared to conventional boost converters, the high-voltage three-level buck boost converter (TLBBC) with PDC exhibits a wide controllability range and cross regulation capability.

These enhanced features are extremely important for better regulating variable output renewable energy sources such as solar, wind, wave, marine current, etc. The simulation and experimental results are provided to validate the claim. Modern economies run on the backbone of electricity as one of major factors behind industrial development.

India is endowed with plenty of natural resources and the majority of electricity within the country is generated from thermal and hydro-electric plants. A few nuclear plants assist in meeting the national requirements for electricity but still many rural areas remain uncovered. As India is primarily a rural agrarian economy, providing electricity to the remote, undeveloped regions of the country remains a top priority of the government. A vital, untapped source is livestock generated biomass which to some extent has been utilized to generate electricity in small scale biogas based plants under the government's thrust on rural development. This study is a preliminary attempt to correlate developments in this arena in the Asian region, as well as the developed world, to explore the possibilities of harnessing this resource in a better manner. The current potential of 2600 million tons of livestock dung generated per year, capable of yielding 263,702 million m 3 of biogas is exploited. Our estimates suggest that if this resource is utilized judiciously, it possesses the potential of generating 477 TWh (Terawatt hour) of electrical energy per annum.

The objective of this work was to optimize and to evaluate a solar-driven trigeneration system which operates with nanofluid-based parabolic trough collectors. The trigeneration system includes an organic Rankine cycle (ORC) and an absorption heat pump operating with LiBr-H 2O which is powered by the rejected heat of the ORC. Toluene, n-octane, Octamethyltrisiloxane (MDM) and cyclohexane are the examined working fluids in the ORC. The use of CuO and Al 2O 3 nanoparticles in the Syltherm 800 (base fluid) is investigated in the solar field loop. The analysis is performed with Engineering Equation Solver (EES) under steady state conditions in order to give the emphasis in the exergetic optimization of the system. Except for the different working fluid investigation, the system is optimized by examining three basic operating parameters in all the cases.

The pressure in the turbine inlet, the temperature in the ORC condenser and the nanofluid concentration are the optimization variables. According to the final results, the combination of toluene in the ORC with the CuO nanofluid is the optimum choice.

The global maximum exergetic efficiency is 24.66% with pressure ratio is equal to 0.7605, heat rejection temperature 113.7 °C and CuO concentration 4.35%. Solar desiccant cooling is widely considered as an attractive replacement for conventional vapor compression air conditioning systems because of its environmental friendliness and energy efficiency advantages. The system performance of solar desiccant cooling strongly depends on the input parameters associated with the system components, such as the solar collector, storage tank and backup heater, etc. In order to understand the implications of different design parameters on the system performance, this study has conducted a parametric analysis on the solar collector area, storage tank volume, and backup heater capacity of a solid solar desiccant cooling system for an office building in Brisbane, Australia climate.

In addition, a parametric analysis on the outdoor air humidity ratio control set-point which triggers the operation of the desiccant wheel has also been investigated. The simulation results have shown that either increasing the storage tank volume or increasing solar collector area would result in both increased solar fraction ( SF) and system coefficient of performance ( COP), while at the same time reduce the backup heater energy consumption. However, the storage tank volume is more sensitive to the system performance than the collector area. From the economic aspect, a storage capacity of 30 m 3/576 m 2 has the lowest life cycle cost ( LCC) of $405,954 for the solar subsystem. In addition, 100 kW backup heater capacity is preferable for the satisfaction of the design regeneration heating coil hot water inlet temperature set-point with relatively low backup heater energy consumption. Moreover, an outdoor air humidity ratio control set-point of 0.008 kgWater/kgDryAir is more reasonable, as it could both guarantee the indoor design conditions and achieve low backup heater energy consumption. In this study, the physical properties of briquettes produced from two different biomass feedstocks (sawdust and date palm trunk) and different plastic wastes, without using any external binding agent, were investigated.

The biomass feedstocks were blended with different ratios of two waste from electrical and electronic equipment (WEEE) plastics (halogen-free wire and printed circuit boards (PCBs)) and automotive shredder residues (ASR). The briquettes production is studied at different waste proportions (10–30%), pressures (22–67 MPa) and temperatures (room–130 °C). Physical properties as density and durability rating were measured, usually increasing with temperature. Palm trunk gave better results than sawdust in most cases, due to its moisture content and the extremely fine particles that are easily obtained. This paper discusses a data-driven, cooperative control strategy to maximize wind farm power production. Conventionally, every wind turbine in a wind farm is operated to maximize its own power production without taking into account the interactions between the wind turbines in a wind farm. Because of wake interference, such greedy control strategy can significantly lower the power production of the downstream wind turbines and, thus, reduce the overall wind farm power production.

As an alternative to the greedy control strategy, we study a cooperative wind farm control strategy that determines and executes the optimum coordinated control actions for maximizing the total wind farm power production. To determine the optimum coordinated control actions of the wind turbines, we employ a data-driven optimization method that seeks to find the optimum control actions using only the power measurement data collected from the wind turbines in a wind farm.

In particular, we employ the Bayesian Ascent (BA) algorithm, a probabilistic optimization method constructed based on Gaussian Process regression and the trust region concept. Wind tunnel experiments using 6 scaled wind turbine models are conducted to assess (1) the effectiveness of the cooperative control strategy in improving the power production; and (2) the efficiency of the BA algorithm in determining the optimum control actions of the wind turbines using only the input control actions and the output power measurement data.

The focus on alternative energy sources has increased significantly throughout the last few decades, leading to a considerable development in the wave energy sector. In spite of this, the sector cannot yet be considered commercialized, and many challenges still exist, in which mooring of floating wave energy converters is included. Different methods for assessment and design of mooring systems have been described by now, covering simple quasi-static analysis and more advanced and sophisticated dynamic analysis. Design standards for mooring systems already exist, and new ones are being developed specifically forwave energy converter moorings, which results in other requirements to the chosen tools, since these often have been aimed at other offshore sectors. The present analysis assesses a number of relevant commercial software packages for full dynamic mooring analysis in order to highlight the advantages and drawbacks.

The focus of the assessment is to ensure that the software packages are capable of fulfilling the requirements of modeling, as defined in design standards and thereby ensuring that the analysis can be used to get a certified mooring system. Based on the initial assessment, the two software packages DeepC and OrcaFlex are found to best suit the requirements.

They are therefore used in a case study in order to evaluate motion and mooring load response, and the results are compared in order to provide guidelines for which software package to choose. In the present study, the OrcaFlex code was found to satisfy all requirements. Understanding mechanical behavior and permeability of coal at ambient and high temperature is key in optimizing high-temperature in-situ processes such as underground coal gasification. The main objectives of this study were to characterize thermal deformation, stress-strain behavior, and gas permeability of coal samples acquired from the Genesee coal mine in Central Alberta, Canada under various temperatures and confining stresses.

These measurements were conducted in a high-pressure high-temperature triaxial apparatus. Initial thermal expansion of the coal was followed by contraction in both axial and lateral directions at about 140 °C. This temperature corresponds to occurrence of pyrolysis in the coal. All specimens showed brittle behavior during shear while forming complex shear planes.

The specimens exhibited compressional volumetric strain responses at all temperatures. Deformation localization initiated at various stage during shearing. Specimens sheared at 200 °C showed higher peak stresses and larger axial strains compared to those tested at room temperature (24 °C). Fluctuations of permeability were observed with confining stress and temperature.

Permeability dropped at 80 °C due to thermal expansion of coal and closure of initial fractures; however, it increased at 140 and 200 °C due to a combined response of thermal expansion and pyrolysis. Small axial strain during shear was observed to reduce permeability.

The utilization of the captured CO 2 as a carbon source for the production of energy storage media offers a technological solution for overcoming crucial issues in current energy systems. Solar energy production generally does not match with energy demand because of its intermittent and non-programmable nature, entailing the adoption of storage technologies. Hydrogen constitutes a chemical storage for renewable electricity if it is produced by water electrolysis and is also the key reactant for CO 2 methanation (Sabatier reaction). The utilization of CO 2 as a feedstock for producing methane contributes to alleviate global climate changes and sequestration related problems. The produced methane is a carbon neutral gas that fits into existing infrastructure and allows issues related to the aforementioned intermittency and non-programmability of solar energy to be overcome. In this paper, an experimental apparatus, composed of an electrolyzer and a tubular fixed bed reactor, is built and used to produce methane via Sabatier reaction.

The objective of the experimental campaign is the evaluation of the process performance and a comparison with other CO 2 valorization paths such as methanol production. The investigated pressure range was 2–20 bar, obtaining a methane volume fraction in outlet gaseous mixture of 64.75% at 8 bar and 97.24% at 20 bar, with conversion efficiencies of, respectively, 84.64% and 99.06%. The methanol and methane processes were compared on the basis of an energy parameter defined as the spent energy/stored energy. It is higher for the methanol process (0.45), with respect to the methane production process (0.41–0.43), which has a higher energy storage capability.

The latest technological developments are challenging for finding new solutions to mitigate the massive integration of renewable-based electricity generation in the electrical networks and to support new and dynamic energy and ancillary services markets. Smart meters have become ubiquitous equipment in the low voltage grid, enabled by the decision made in many countries to support massive deployments. The smart meter is the only equipment mandatory to be mounted when supplying a grid connected user, as it primarily has the function to measure delivered and/or produced energy on its common coupling point with the network, as technical and legal support for billing. Active distribution networks need new functionalities, to cope with the bidirectional energy flow behaviour of the grid, and many smart grid requirements need to be implemented in the near future. However there is no real coupling between smart metering systems and smart grids, as there is not yet a synergy using the opportunity of the high deployment level in smart metering. The paper presents a new approach for managing the smart metering and smart grid orchestration by presenting a new general design based on an unbundled smart meter (USM) concept, labelled as next generation open real-time smart meters (NORM), for integrating the smart meter, phasor measurement unit (PMU) and cyber-security through an enhanced smart metering gateway (SMG).

NORM is intended to be deployed everywhere at the prosumer’s interface to the grid, as it is usually now done with the standard meter. Furthermore, rich data acquired from NORM is used to demonstrate the potential of assessing grid data inconsistencies at a higher level, as function to be deployed in distribution security monitoring centers, to address the higher level cyber-security threats, such as false data injections and to allow secure grid operations and complex market activities at the same time. The measures are considering only non-sensitive data from a privacy perspective, and is therefore able to be applied everywhere in the grid, down to the end-customer level, where a citizen’s personal data protection is an important aspect.

Smart grid (SG) will be one of the major application domains that will present severe pressures on future communication networks due to the expected huge number of devices that will be connected to it and that will impose stringent quality transmission requirements. To address this challenge, there is a need for a joint management of both monitoring and communication systems, so as to achieve a flexible and adaptive management of the SG services. This is the issue addressed in this paper, which provides the following major contributions. We define a new strategy to optimize the accuracy of the state estimation (SE) of the electric grid based on available network bandwidth resources and the sensing intelligent electronic devices (IEDs) installed in the field.

In particular, we focus on phasor measurement units (PMUs) as measurement devices. We propose the use of the software defined networks (SDN) technologies to manage the available network bandwidth, which is then assigned by the controller to the forwarding devices to allow for the flowing of the data streams generated by the PMUs, by considering an optimization routine to maximize the accuracy of the resulting SE. Additionally, the use of SDN allows for adding and removing PMUs from the monitoring architecture without any manual intervention. We also provide the details of our implementation of the SDN solution, which is used to make simulations with an IEEE 14-bus test network in order to show performance in terms of bandwidth management and estimation accuracy.

A post-fracturing evaluation is essential to optimize a fracturing design for a multi-stage fractured well located in unconventional reservoirs. To accomplish this task, a production logging tool (PLT) can be utilized to provide the oil production rate of each fracturing stage. In this research, a practical method is proposed to integrate PLT and surface production data into a reservoir model. It applies the ensemble smoother for history-matching to integrate various kinds of dynamic data. To investigate the validity of the proposed method, three cases are designed according to the frequency of PLT surveys.

Each fracture half-length calibrated by PLT data is similar to the true value, and the dynamic behavior also has the same trend as true production behavior. Integration with PLT data can reduce error ratios for fracture half-length down to 48%.

In addition, it presents the applicability of reserve prediction and uncertainty assessment. It has been proven that the more frequently PLTs are surveyed, the more accurate the results. By sensitivity analysis of PLT frequency—a cost-effective strategy—a combination of only one PLT survey and continuous surface production data is employed to demonstrate this proposed concept. Industrial hydrogen production via alkaline water electrolysis (AEL) is a mature hydrogen production method.

One argument in favor of AEL when supplied with renewable energy is its environmental superiority against conventional fossil-based hydrogen production. However, today electricity from the national grid is widely utilized for industrial applications of AEL. Also, the ban on asbestos membranes led to a change in performance patterns, making a detailed assessment necessary. This study presents a comparative Life Cycle Assessment (LCA) using the GaBi software (version 6.115, thinkstep, Leinfelden-Echterdingen, Germany), revealing inventory data and environmental impacts for industrial hydrogen production by latest AELs (6 MW, Zirfon membranes) in three different countries (Austria, Germany and Spain) with corresponding grid mixes. The results confirm the dependence of most environmental effects from the operation phase and specifically the site-dependent electricity mix.

Construction of system components and the replacement of cell stacks make a minor contribution. At present, considering the three countries, AEL can be operated in the most environmentally friendly fashion in Austria.

Concerning the construction of AEL plants the materials nickel and polytetrafluoroethylene in particular, used for cell manufacturing, revealed significant contributions to the environmental burden. Many future electricity scenarios, including those from the International Energy Agency, use natural gas to bridge the transition to renewables, in particular as a means of balancing intermittent generation from new renewables. Given that such strategies may be inconsistent with strategies to limit climate change to below 2 °C, we address the question of whether such use of gas is necessary or cost effective. We conduct a techno-economic case study of Switzerland, using a cost optimization model. We explore a range of electricity costs, comparing scenarios in which gas is used as a source of base-load power, a source of balancing capacity, and not used at all. Costs at the high end of the range show that a complete decarbonization increases system-wide costs by 3% compared to a gas bridging scenario, and 13–46% compared to a carbon-intensive scenario, depending on the relative shares of solar and wind.

Costs at the low end of the range show that system-wide costs are equal or lower for both completely decarbonized and gas bridging scenarios. In conclusion, gas delivers little to no cost savings as a bridging fuel in a system that switches to wind and solar. The latest forecasts on the upcoming effects of climate change are leading to a change in the worldwide power production model, with governments promoting clean and renewable energies, as is the case of tidal energy. Nevertheless, it is still necessary to improve the efficiency and lower the costs of the involved processes in order to achieve a Levelized Cost of Energy (LCoE) that allows these devices to be commercially competitive. In this context, this paper presents a novel complementary control strategy aimed to maximize the output power of a Tidal Stream Turbine (TST) composed of a hydrodynamic turbine, a Doubly-Fed Induction Generator (DFIG) and a back-to-back power converter. In particular, a global control scheme that supervises the switching between the two operation modes is developed and implemented.

When the tidal speed is low enough, the plant operates in variable speed mode, where the system is regulated so that the turbo-generator module works in maximum power extraction mode for each given tidal velocity. For this purpose, the proposed back-to-back converter makes use of the field-oriented control in both the rotor side and grid side converters, so that a maximum power point tracking-based rotational speed control is applied in the Rotor Side Converter (RSC) to obtain the maximum power output. Analogously, when the system operates in power limitation mode, a pitch angle control is used to limit the power captured in the case of high tidal speeds. Both control schemes are then coordinated within a novel complementary control strategy. The results show an excellent performance of the system, affording maximum power extraction regardless of the tidal stream input. This work was devoted to study experimentally and numerically the oxygen carrier (NiO/NiAl 2O 4) performances for Chemical-Looping Combustion applications.

Various kinetic models including Shrinking Core, Nucleation Growth and Modified Volumetric models were investigated in a one-dimensional approach to simulate the reactive mass transfer in a fixed bed reactor. The preliminary numerical results indicated that these models are unable to fit well the fuel breakthrough curves. Therefore, the oxygen carrier was characterized after several operations using Scanning Electronic Microscopy (SEM) coupled with equipped with an energy dispersive X-ray spectrometer (EDX). These analyses showed a layer rich in nickel on particle surface.

Below this layer, to a depth of about 10 µm, the material was low in nickel, being the consequence of nickel migration. Illinois Drivers License Signs Test. From these observations, two reactive sites were proposed relative to the layer rich in nickel (particle surface) and the bulk material, respectively. Then, a numerical model, taking into account of both reactive sites, was able to fit well fuel breakthrough curves for all the studied operating conditions.

The extracted kinetic parameters showed that the fuel oxidation was fully controlled by the reaction and the effect of temperature was not significant in the tested operating conditions range. The search for new energy resources is a crucial task nowadays. Research on the use of solar energy is growing every year. The aim is the design of devices that can produce a considerable amount of energy using the Sun’s radiation. The modeling of solar cells (SCs) is based on the estimation of the intrinsic parameters of electrical circuits that simulate their behavior based on the current vs. Voltage characteristics. The problem of SC design is defined by highly nonlinear and multimodal objective functions.

Most of the algorithms proposed to find the best solutions become trapped into local solutions. This paper introduces the Chaotic Improved Artificial Bee Colony (CIABC) algorithm for the estimation of SC parameters. It combines the use of chaotic maps instead random variables with the search capabilities of the Artificial Bee Colony approach. CIABC has also been modified to avoid the generation of new random solutions, preserving the information of previous iterations. In comparison with similar optimization methods, CIABC is able to find the global solution of complex and multimodal objective functions. Experimental results and comparisons prove that the proposed technique can design SCs, even with the presence of noise.

The fault diagnosis of wind farms has been proven to be a challenging task, and motivates the research activities carried out through this work. Therefore, this paper deals with the fault diagnosis of a wind park benchmark model, and it considers viable solutions to the problem of earlier fault detection and isolation. The design of the fault indicator involves data-driven approaches, as they can represent effective tools for coping with poor analytical knowledge of the system dynamics, noise, uncertainty, and disturbances.

In particular, the proposed data-driven solutions rely on fuzzy models and neural networks that are used to describe the strongly nonlinear relationships between measurement and faults. The chosen architectures rely on nonlinear autoregressive with exogenous input models, as they can represent the dynamic evolution of the system over time.

The developed fault diagnosis schemes are tested by means of a high-fidelity benchmark model that simulates the normal and the faulty behaviour of a wind farm installation. The achieved performances are also compared with those of a model-based approach relying on nonlinear differential geometry tools. Finally, a Monte-Carlo analysis validates the robustness and reliability of the proposed solutions against typical parameter uncertainties and disturbances. The current generation of nuclear reactors are evolutionary in design, mostly based on the technology originally designed to power submarines, and dominated by light water reactors. The aims of the Generation IV consortium are driven by sustainability, safety and reliability, economics, and proliferation resistance. The aims are extended here to encompass the ultimate and universal vision for strategic development of energy production, the “perpetuum mobile”—at least as close as possible. We propose to rethink nuclear reactor design with the mission to develop an innovative system which uses no fresh resources and produces no fresh waste during operation as well as generates power safe and reliably in economic way.

The results of the innovative simulations presented here demonstrate that, from a theoretical perspective, it is feasible to fulfil the mission through the direct reuse of spent nuclear fuel from currently operating reactors as the fuel for a proposed new reactor. The produced waste is less burdensome than current spent nuclear fuel which is used as feed to the system. However, safety, reliability and operational economics will need to be demonstrated to create the basis for the long term success of nuclear reactors as a major carbon free, sustainable, and applied highly reliable energy source. There is a need for energy storage to improve the efficiency and effectiveness of energy distribution with the increasing penetration of renewable energy sources. Among the various energy storage technologies being developed, ‘power-to-gas’ is one such concept which has gained interest due to its ability to provide long term energy storage and recover the energy stored through different energy recovery pathways. Incorporation of such systems within the energy infrastructure requires analysis of the key factors influencing the operation of electrolyzers and hydrogen storage.

This study focusses on assessing the benefits power-to-gas energy storage while accounting for uncertainty in the following three key parameters that could influence the operation of the energy system: (1) hourly electricity price; (2) the number of fuel cell vehicles serviced; and (3) the amount of hydrogen refueled. An hourly time index is adopted to analyze how the energy hub should operate under uncertainty. The results show that there is a potential economic benefit for the power-to-gas system if it is modeled using the two-stage stochastic programming approach in comparison to a deterministic optimization study. The power-to-gas system also offers environmental benefits both from the perspective of the producer and end user of hydrogen.

Screw-type expanders offer excellent prospects for energy conversion in lower and medium power ranges, for instance as expansion engines in Rankine cycles with regard to either waste or geothermal heat recovery. With the aim of identifying the potential in organic Rankine cycle (ORC) power systems, an oil-flooded twin-screw expander without timing gears was designed and experimentally investigated in an ORC with R245fa as working fluid. Here, the scope for the experimental determination of the expander characteristic map was limited by the test rig specifications.

Based on the experimental results, a multi-chamber model of the test twin-screw expander was calibrated and theoretical approaches according to mechanical and hydraulic loss calculation were applied. Consequently, the expander’s complete characteristic map could be calculated. Furthermore, relevant mechanisms influencing the operational behaviour of oil-flooded twin-screw expanders were identified and analysed in-depth. This paper presents a study to assess how wind turbines could increase their energy yield when their grid connection point is not strong enough for the rated power.

It is state of the art that in such situations grid operators impose feed-in management on the affected wind turbines, i.e., the maximum power is limited. Playbill Template Illustrator Cs6. For this study a 5 MW wind turbine is introduced in a small grid that has only limited power transfer capabilities to the upstream power system. Simulations of one particular day are conducted with the electric load, the temperature, and the wind speed as measured on that day. This simulation is conducted twice: once with the 5 MW wind turbine controlled with conventional feed-in management, and a second time when its power is controlled flexibly, i.e., with continuous feed-in management. The results of these two simulations are compared in terms of grid performance, and in terms of mechanical stress on the 5 MW wind turbine.

Finally, the conclusion can be drawn that continuous feed-in management is clearly superior to conventional feed-in management. It exhibits much better performance in the grid in terms of energy yield and also in terms of constancy of voltage and temperature of grid equipment. Although it causes somewhat more frequent stress for the wind turbine, the maximum stress level is not increased. A DC-DC converter that can be applied for battery chargers with the power-capacity of over 7-kW for electric vehicles (EVs) is presented in this paper. Due to a new architecture, the proposed converter achieves a reduction of conduction losses at the primary side by as much as 50% and has many benefits such as much smaller circulating current, less duty-cycle loss, and lower secondary-voltage stress. In addition, its power handing capacity can be upsized easily with the use of two full-bridge inverters and two transformers. Besides, all the switches in the converter achieve zero-voltage switching (ZVS) during whole battery charging process, and the size of output filter can be significantly reduced.

The circuit configuration, operation, and relevant analysis are presented, followed by the experiment on a prototype realized with a 7-kW charger. The experimental results validate the theoretical analysis and show the effectiveness of the proposed converter as battery charger. Turbo machinery is an essential part in the power generation cycle. However, it is the main source of noise that annoys workers and users, and contributes to environmental problems. Thus, it is important to reduce this noise when operating the power generation cycle. This noise is created by a flow instability on the trailing edge of the rotor blade—an airfoil that becomes a section of the rotor blade of the rotating machine—manufactured as a blunt trailing edge (T.E.), with a round or flatback shape, rather than the ideal sharp T.E. Shape, for the purposes of production and durability.

This increases the tonal noise and flow-induced vibrations at a low frequency, owing to vortex shedding behind T.E. When compared with a sharp T.E. In order to overcome this problem, the present study investigates the oblique T.E. Shape using numerical simulations.

In order to do so, flow was simulated using large eddy simulation (LES) and the noise was analyzed by acoustic analogy coupled with the LES result. Once the simulation results were verified using the flatback airfoil measurements of the Sandia National Laboratories, numerical prediction was performed to analyze the flow and the noise characteristics for the airfoils, which were modified to have oblique trailing edge angles of 60°, 45°, and 30°. From the simulation results of the oblique T.E. Airfoil, it could be seen that the vortex shedding frequency moves in accordance with the oblique angle and that the vortex shedding noise characteristics change according to the angle, when compared to the flatback T.E. Therefore, it is considered that modifying the flatback T.E.

Airfoil with an appropriate oblique angle can reduce noise and change the tonal frequency to a bandwidth that is suitable for mechanical systems. Multi-terminal Direct Current Transmission (MTDC) is an emerging and promising technology for the transmission of electricity and the main initiator of the development of MTDC grids is offshore wind generation.

However, prior to their construction, a thorough investigation of different aspects of their implementation and operation is required. In this research, an MTDC grid with voltage margin control consisting of voltage source converters (VSCs) and a high frequency cable model was implemented in Matlab/SIMULINK (R2015b, The MathWorks, Inc., Natick, MA, USA). Small-signal stability analysis was carried out to investigate the sensitivity of the grid’s interaction modes to the operating point, the structure of the grid, and the selection of the voltage controlling converter. Based on the findings of these analyses, a strategy for droop control method is proposed and demonstrated. Accurate and reliable forecasting on energy-related carbon dioxide (CO 2) emissions is of great significance for climate policy decision making and energy planning.

Due to the complicated nonlinear relationships of CO 2 emissions with its driving forces, the accurate forecasting for CO 2 emissions is a tedious work, which is an important issue worth studying. In this study, a novel CO 2 emissions prediction method is proposed which employs the latest nature-enlightened optimization method, named the Whale optimization algorithm (WOA), to search the optimized values of two parameters of LSSVM (least squares support vector machine), namely the WOA-LSSVM model. Meanwhile, the driving forces of CO 2 emissions including GDP (gross domestic product), energy consumption and population are chosen to be the import variables of the proposed WOA-LSSVM method. Taking China’s CO 2 emissions as an instance, the effectiveness of WOA-LSSVM-based CO 2 emissions forecasting is verified. The comparative analysis results indicate that the WOA-LSSVM model is significantly superior to other selected models, namely FOA (fruit fly optimization algorithm)-LSSVM, LSSVM, and OLS (ordinary least square) models in terms of CO 2 emissions forecasting. The proposed WOA-LSSVM model has the potential to effectively improve the accuracy of CO 2 emissions forecasting. Meanwhile, as a new nature-enlightened heuristic optimization algorithm, the WOA has the prospect for wide application.

The influence of recycling on double-pass solar air collectors with welding of the V-corrugated absorber has been studied experimentally and theoretically. Welding the V-corrugated absorber and the recycle-effect concept to the solar air collector was proposed to strengthen the convective heat-transfer coefficient due to turbulence promotion. Both the recycle effect and the V-corrugated absorber can effectively enhance the heat transfer efficiency compared to various designs such as single-pass, flat-plate double-pass, and double-pass wire mesh packed devices. Recycling operations and welding the V-corrugated absorber could enhance the collector efficiency by increasing the recycle ratio, incident solar radiations, and air mass flow rates. The most efficient and economical operating conditions were found at R ≈ 0.5, with relatively small hydraulic dissipated energy compensation.

It was found that the turbulence intensity increase from welding the V-corrugated absorber into the solar air collector channel could compensate for the power consumption increase, when considering economic feasibility. Inaccurate forecasting of photovoltaic (PV) power generation is a great concern in the planning and operation of stable and reliable electric grid systems as well as in promoting large-scale PV deployment.

The paper proposes a generalized PV power forecasting model based on support vector regression, historical PV power output, and corresponding meteorological data. Weather conditions are broadly classified into two categories, namely, normal condition (clear sky) and abnormal condition (rainy or cloudy day). A generalized day-ahead forecasting model is developed to forecast PV power generation at any weather condition in a particular region. The proposed model is applied and experimentally validated by three different types of PV stations in the same location at different weather conditions. Furthermore, a conventional artificial neural network (ANN)-based forecasting model is utilized, using the same experimental data-sets of the proposed model. The analytical results showed that the proposed model achieved better forecasting accuracy with less computational complexity when compared with other models, including the conventional ANN model.

The proposed model is also effective and practical in forecasting existing grid-connected PV power generation. The use of non-edible, second-generation feedstocks for the production of biodiesel has been an active area of research, due to its potential in replacing fossil diesel as well as its environmentally friendly qualities. Despite this, more needs to be done to remove the technical barriers associated with biodiesel production and usage, to increase its quality as well as to widen the choice of available feedstocks; so as to avoid over-dependence on limited sources.

This paper assesses the feasibility of using a local plant, Reutealis trisperma, whose seeds contain a high percentage of oil of up to 51%, as one of the possible feedstocks. The techno-economic and sensitivity analysis of biodiesel production from Reutealis trisperma oil as well as implementation aspects and environmental effects of the biodiesel plant are discussed. Analysis indicates that the 50 kt Reutealis trisperma biodiesel production plant has a life cycle cost of approximately $710 million, yielding a payback period of 4.34 years.

The unit cost of the biodiesel is calculated to be $0.69/L with the feedstock cost accounting for the bulk of the cost. The most important finding from this study is that the biodiesel from Reutealis trisperma oil can compete with fossil diesel, provided that appropriate policies of tax exemptions and subsidies can be put in place. To conclude, further studies on biodiesel production and its limitations are necessary before the use of biodiesel from Reutealis trisperma oil may be used as a fuel source to replace fossil diesel. Equivalent salt deposit density (ESDD) and non-soluble deposit density (NSDD) measurements are a basic requirement of power systems. In order to predict the site pollution severity (SPS) of insulators, a new method based on random forests (RFs) is proposed. Using mutual information (MI) theory and RFs, the weights of factors related to the SPS of insulators are analyzed. The samples of contaminated insulators are extracted from the transmission lines of high voltage alternating current (HVAC) and high voltage direct current transmission (HVDC).

The regression models of RFs and support vector machines (SVM) are constructed and compared, which helps to support the lack of information in predicting NSDD in previous works. The results are as follows: according to the mean decrease accuracy (MDA), mean decrease Gini, (MDG), and MI, the types of the insulators (including surface area, surface orientation, and total length) as well as the hydrophobicity are the main factors affecting both ESDD and NSDD. Compared with NSDD, the electrical parameters have a significant effect on ESDD. For the influence factors of ESDD, the weights of the insulator type, hydrophobicity, and meteorological factors are 52.94%, 6.35%, and 21.88%, respectively. For the influence factors of NSDD, the weights of the insulator type, hydrophobicity, and meteorological factors are 55.37%, 11.04%, and 14.26%, respectively.

The influence voltage level ( vl), voltage type ( vt), polarity/phases ( pp) exerted on ESDD are 1.5 times, 3 times, and 4.5 times of NSDD, respectively. The influence that distance from the coastline ( d), wind velocity ( wv), and rainfall ( rf) exert on NSDD are 1.5 times, 2 times, and 2.5 times that of ESDD, respectively. Compared with the natural contamination test and the SVM regression model, the RFs regression model can effectively predict the contamination degree of insulators, and the relative error of the predicted ESDD and NSDD is 8.31% and 9.62%, respectively. Since the demand response (DR) market was introduced in Korea, load aggregators have also been allowed to participate in the electricity market. However, a risk-management-based method for the efficient operation of demand response resources (DRRs) has not been studied from the load aggregators’ perspective. In this paper, a systematic DRR allocation method is proposed for load aggregators to operate DRRs using mean-variance portfolio theory.

The proposed method is designed to determine the lowest-risk DRR portfolio for a given level of expected return using mean-variance portfolio theory from the perspective of load aggregators. The numerical results show that the proposed method can be used to reduce the risk compared to that obtained by the baseline method, in which all individual DRRs are allocated in a DRR group by maximum curtailment capability. The major challenges for the integration of solar collecting devices into a building envelope are related to the poor aesthetic view of the appearance of buildings in addition to the low efficiency in collection, transportation, and utilization of the solar thermal and electrical energy. To tackle these challenges, a novel design for the integration of solar collecting elements into the building envelope was proposed and discussed. This involves the dedicated modular and multiple-layer combination of the building shielding, insulation, and solar collecting elements. On the basis of the proposed modular structure, the energy performance of the solar envelope was investigated by using the Energy-Plus software. It was found that the solar thermal efficiency of the modular envelope is in the range of 41.78–59.47%, while its electrical efficiency is around 3.51% higher than the envelopes having photovoltaic (PV) alone.

The modular solar envelope can increase thermal efficiency by around 8.49% and the electrical efficiency by around 0.31%, compared to the traditional solar photovoltaic/thermal (PV/T) envelopes. Thus, we have created a new envelope solution with enhanced solar efficiency and an improved aesthetic view of the entire building. This paper presents a single-phase bidirectional current-source AC/DC converter for vehicle to grid (V2G) applications. The presented converter consists of a line frequency commutated unfolding bridge and an interleaved buck-boost stage.

The low semiconductor losses of the line frequency commutated unfolding bridge contribute to the comparatively good efficiency of this converter. The buck and boost operating modes of the interleaved buck-boost stage provide operation over a wide battery voltage range. The interleaved structure of the interleaved buck-boost stage results in lower battery current ripple. In addition, sinusoidal input current, bidirectional power flow and reactive power compensation capability are also guaranteed. This paper presents the topology and operating principles of the presented converter.

The feasibility of the converter is validated using MATLAB simulations, as well as experimental results. Integration of demand response (DR) programs and battery energy storage system (BESS) in microgrids are beneficial for both microgrid owners and consumers. The intensity of DR programs and BESS size can alter the operation of microgrids. Meanwhile, the optimal size for BESS units is linked with the uncertainties associated with renewable energy sources and load variations.

Similarly, the participation of enrolled customers in DR programs is also uncertain and, among various other factors, uncertainty in market prices is a major cause. Therefore, in this paper, the impact of DR program intensity and BESS size on the operation of networked microgrids is analyzed while considering the prevailing uncertainties. The uncertainties associated with forecast load values, output of renewable generators, and market price are realized via the robust optimization method.

Robust optimization has the capability to provide immunity against the worst-case scenario, provided the uncertainties lie within the specified bounds. The worst-case scenario of the prevailing uncertainties is considered for evaluating the feasibility of the proposed method.

The two representative categories of DR programs, i.e., price-based and incentive-based DR programs are considered. The impact of change in DR intensity and BESS size on operation cost of the microgrid network, external power trading, internal power transfer, load profile of the network, and state-of-charge (SOC) of battery energy storage system (BESS) units is analyzed. Simulation results are analyzed to determine the integration of favorable DR program and/or BESS units for different microgrid networks with diverse objectives.

The deregulation of the electricity sector has culminated in the introduction of competitive markets. In addition, the emergence of new forms of electric energy production, namely the production of renewable energy, has brought additional changes in electricity market operation. Renewable energy has significant advantages, but at the cost of an intermittent character. The generation variability adds new challenges for negotiating players, as they have to deal with a new level of uncertainty. In order to assist players in their decisions, decision support tools enabling assisting players in their negotiations are crucial.

Artificial intelligence techniques play an important role in this decision support, as they can provide valuable results in rather small execution times, namely regarding the problem of optimizing the electricity markets participation portfolio. This paper proposes a heuristic method that provides an initial solution that allows metaheuristic techniques to improve their results through a good initialization of the optimization process. Results show that by using the proposed heuristic, multiple metaheuristic optimization methods are able to improve their solutions in a faster execution time, thus providing a valuable contribution for players support in energy markets negotiations. Electricity load forecasting, optimal power system operation and energy management play key roles that can bring significant operational advantages to microgrids.

This paper studies how methods based on time series and neural networks can be used to predict energy demand and production, allowing them to be combined with model predictive control. Comparisons of different prediction methods and different optimum energy distribution scenarios are provided, permitting us to determine when short-term energy prediction models should be used.

The proposed prediction models in addition to the model predictive control strategy appear as a promising solution to energy management in microgrids. The controller has the task of performing the management of electricity purchase and sale to the power grid, maximizing the use of renewable energy sources and managing the use of the energy storage system. Simulations were performed with different weather conditions of solar irradiation. The obtained results are encouraging for future practical implementation. Accurate modeling and forecasting monthly electricity consumption are the keys to optimizing energy management and planning. This paper examines the seasonal characteristics of electricity consumption in Hong Kong—a subtropical city with 7 million people.

Using the data from January 1970 to December 2014, two novel nonlinear seasonal models for electricity consumption in the residential and commercial sectors were obtained. The models show that the city’s monthly residential and commercial electricity consumption patterns have different seasonal variations.

Specifically, monthly residential electricity consumption (mainly for appliances and cooling in summer) has a quadratic relationship with monthly mean air temperature, while monthly commercial electricity consumption has a linear relationship with monthly mean air temperature. The nonlinear seasonal models were used to predict residential and commercial electricity consumption for the period January 2015–December 2016. The correlations between the predicted and actual values were 0.976 for residential electricity consumption and 0.962 for commercial electricity consumption, respectively.

The root mean square percentage errors for the predicted monthly residential and commercial electricity consumption were 7.0% and 6.5%, respectively. The new nonlinear seasonal models can be applied to other subtropical urban areas, and recommendations on the reduction of commercial electricity consumption are given.

Fe 3O 4 nanoparticles were prepared by a simple solid-state method under ambient conditions. The obtained nanoparticles, with small size and large surface area, were used as a catalyst for direct coal liquefaction (DCL).

The results display that high conversion and oil yield were achieved with the nanocatalysts in direct liquefaction of two kinds of coals, i.e., Heishan coal and Dahuangshan coal. The effects of the temperature, initial H 2 pressure, and holding time on conversion and product distribution have been investigated in the catalytic hydrogenation of Dahuangshan coal. The optimal reaction condition for DCL in which conversion and oil yield are 96.6 and 60.4 wt% was determined with Fe 3O 4 nanocatalysts. This facile solid-state route is beneficial for scale-up synthesis of iron-based catalysts with good performance for DCL. In this paper, an effective low-speed oscillating wave power generator and its energy storage system have been proposed. A vertical flux-switching permanent magnet (PM) machine is designed as the generator while supercapacitors and batteries are used to store the energy. First, the overall power generation system is established and principles of the machine are introduced.

Second, three modes are proposed for the energy storage system and sliding mode control (SMC) is employed to regulate the voltage of the direct current (DC) bus, observe the mechanical input, and feedback the status of the storage system. Finally, experiments with load and sinusoidal mechanical inputs are carried out to validate the effectiveness and stability of power generation for wave energy. The results show that the proposed power generation system can be employed in low-speed environment around 1 m/s to absorb random wave power, achieving over 60% power efficiency. The power generation approach can be used to capture wave energy in the future.

The dual power flow wind energy conversion system (DPF-WECS) is a novel system which is based on the electrical variable transmission (EVT) machine. The proposed sensorless control for the DPF-WECS is based on the model reference adaptive system (MRAS) observer by combining the sliding mode (SM) theory. The SM-MRAS observer is on account of the calculations without the requirement of the proportional-integral (PI) loop which exists in the classical MRAS observer. Firstly, the sensorless algorithm is applied in the maximum power point tracking (MPPT) control considering the torque loss for the outer rotor of the EVT. Secondly, the sensorless control is adopted for the inner rotor control of the EVT machine. The proposed sensorless control method based on the SM-MRAS for the DPF-WECS is verified by the simulation and experimental results. Herein, the nitridophosphate Na 3V(PO 3) 3N is synthesized by solid state method.

X-ray diffraction (XRD) and Rietveld refinement confirm the cubic symmetry with P2 13 space group. The material exhibits very good thermal stability and high operating voltage of 4.0 V vs. Na/Na + due to V 3+/V 4+ redox couple. In situ X-ray diffraction studies confirm the two-phase (de-)sodiation process to occur with very low volume changes. The refinement of the sodium occupancies reveal the low accessibility of sodium cations in the Na2 and Na3 sites as the main origin for the lower experimental capacity (0.38 eq. Na +, 28 mAh g −1) versus the theoretical one (1.0 eq. Na +, 74 mAh g −1).

These observations provide valuable information for the further optimization of this materials class in order to access their theoretical electrochemical performance as a potentially interesting zero-strain and safe high-voltage cathode material for sodium-ion batteries. The greenhouse gases and air pollution generated by extensive energy use have exacerbated climate change. Electric-bus (e-bus) transportation systems help reduce pollution and carbon emissions. This study analyzed the minimization of construction costs for an all battery-swapping public e-bus transportation system.

A simulation was conducted according to existing timetables and routes. Daytime charging was incorporated during the hours of operation; the two parameters of the daytime charging scheme were the residual battery capacity and battery-charging energy during various intervals of daytime peak electricity hours. The parameters were optimized using three algorithms: particle swarm optimization (PSO), a genetic algorithm (GA), and a PSO–GA. This study observed the effects of optimization on cost changes (e.g., number of e-buses, on-board battery capacity, number of extra batteries, charging facilities, and energy consumption) and compared the plug-in and battery-swapping e-bus systems. The results revealed that daytime charging can reduce the construction costs of both systems. In contrast to the other two algorithms, the PSO–GA yielded the most favorable optimization results for the charging scheme.

Finally, according to the cases investigated and the parameters of this study, the construction cost of the plug-in e-bus system was shown to be lower than that of the battery-swapping e-bus system. 6.5 kV level IGBT (Insulated Gate Bipolar Transistor) modules are widely applied in megawatt locomotive (MCUs) traction converters, to achieve an upper 3.5 kV DC link, which is beneficial for decreasing power losses and increasing the power density. Reverse Conducting IGBT (RC-IGBT) constructs the conventional IGBT function and freewheel diode function in a single chip, which has a greater flow ability in the same package volume. In the same cooling conditions, RC-IGBT allows for a higher operating temperature. In this paper, a mathematic model is developed, referring to the datasheets and measurement data, to study the 6.5 kV/1000 A RC-IGBT switching features.

The relationship among the gate desaturated pulse, conducting losses, and recovery losses is discussed. Simulations and tests were carried out to consider the influence of total losses on the different amplitudes and durations of the desaturated pulse. The RC-IGBT traction converter system with gate pulse desaturated control is built, and the simulation and measurements show that the total losses of RC-IGBT with desaturated control decreased comparing to the RC-IGBT without desaturated control or conventional IGBT. Finally, a proportional small power platform is developed, and the test results prove the correction of the theory analysis. The strong coupling between electric power and heat supply highly restricts the electric power generation range of combined heat and power (CHP) units during heating seasons. This makes the system operational flexibility very low, which leads to heavy wind power curtailment, especially in the region with a high percentage of CHP units and abundant wind power energy such as northeastern China. The heat storage capacity of pipelines and buildings of the district heating system (DHS), which already exist in the urban infrastructures, can be exploited to realize the power and heat decoupling without any additional investment.

We formulate a combined heat and power dispatch model considering both the pipelines’ dynamic thermal performance (PDTP) and the buildings’ thermal inertia (BTI), abbreviated as the CPB-CHPD model, emphasizing the coordinating operation between the electric power and district heating systems to break the strong coupling without impacting end users’ heat supply quality. Simulation results demonstrate that the proposed CPB-CHPD model has much better synergic benefits than the model considering only PDTP or BTI on wind power integration and total operation cost savings. High-voltage direct current (HVDC) grids are emerging, and their reliability has been an increasing concern for the utilities. HVDC grids are different from typical two-terminal HVDC transmission systems due to the loops in their topology, which makes it difficult to evaluate the reliability by conventional analytical methods. This paper proposes an innovative hybrid method to evaluate the reliability of meshed HVDC grids.

First, steady-state models and reliability models are established for the components in HVDC grids, especially for converters and power flow controllers. In the models, virtual buses are introduced to represent the external AC connections to the HVDC grid. Then a hybrid reliability evaluation method is proposed based on an analytical approach and Monte Carlo simulation. One innovation of the paper is the application of an analytical analysis method to accelerate state evaluation in Monte Carlo simulation by skipping unnecessary optimization.

The proposed models and methods are verified on two HVDC grids. Test results show that HVDC grids under most failure states (approximately 70%) tend to shed no load except on buses connected to faulted converters, and the application of the analytical method could promote evaluation efficiency significantly. This paper presents a single-degree-of-freedom energy optimization strategy to solve the energy management problem existing in power-split hybrid electric vehicles (HEVs). The proposed strategy is based on a quadratic performance index, which is innovatively designed to simultaneously restrict the fluctuation of battery state of charge (SOC) and reduce fuel consumption. An extended quadratic optimal control problem is formulated by approximating the fuel consumption rate as a quadratic polynomial of engine power. The approximated optimal control law is obtained by utilizing the solution properties of the Riccati equation and adjoint equation.

It is easy to implement in real-time and the engineering significance is explained in details. In order to validate the effectiveness of the proposed strategy, the forward-facing vehicle simulation model is established based on the ADVISOR software (Version 2002, National Renewable Energy Laboratory, Golden, CO, USA).

The simulation results show that there is only a little fuel consumption difference between the proposed strategy and the Pontryagin’s minimum principle (PMP)-based global optimal strategy, and the proposed strategy also exhibits good adaptability under different initial battery SOC, cargo mass and road slope conditions. Energy shortage and atmospheric pollution problems are getting more serious in China, and transportation is the main source of energy consumption, pollutants, and carbon emissions. This study combined the activity-based analysis method with emission models, and investigated the influence mechanism of people’s activity travel scheduling on transportation energy consumption and emissions on holidays. Based on the holiday travel behavior survey data, the multinomial logistic regression model was first applied to explore the decision mechanisms of individual travel-mode choices in holidays. Next, the emission model was integrated with an activity-based travel demand model to calculate and compare transportation energy consumption and emissions under different policy scenarios. The results showed that socio-demographic characteristics had significant effects on holiday activity–travel patterns, and combined mode chains had a larger number of activity points than single mode chains.

With an increase in the trip time of cars, and decrease of travel distance and the number of activity points, transportation energy consumption and emissions could be reduced greatly with an adjustment of holiday activity–travel patterns. The reduced portion is mainly attracted by slow traffic and public transport. However, the effects of a single policy strategy are very limited, thus portfolio policies need to be considered by policy makers. The construction of large-scale wind farms results in a dramatic increase of wind turbine (WT) faults. The failure mode is also becoming increasingly complex.

This study proposes a new model for early warning and diagnosis of WT faults to solve the problem of Supervisory Control And Data Acquisition (SCADA) systems, given that the traditional threshold method cannot provide timely warning. First, the characteristic quantity of fault early warning and diagnosis analyzed by clustering analysis can obtain in advance abnormal data in the normal threshold range by considering the effects of wind speed.

Based on domain knowledge, Adaptive Neuro-fuzzy Inference System (ANFIS) is then modified to establish the fault early warning and diagnosis model. This approach improves the accuracy of the model under the condition of absent and sparse training data. Case analysis shows that the effect of the early warning and diagnosis model in this study is better than that of the tra.