With the increase in penetration of photovoltaics (PV) into the power system, the correct prediction of return on investment requires accurate prediction of decrease in power output over time. Degradation rates and corresponding degraded energy estimation must be known in order to predict power delivery accurately. Solar radiation plays a key role in long-term solar energy predictions. A combination of auto-encoder and long short-term memory (AE-LSTM) based deep learning approach is adopted for long-term solar radiation forecasting. First, the auto-encoder (AE) is trained for the feature extraction, and then fine-tuning with long short-term memory (LSTM) is done to get the final prediction. The input data consist of clear sky global horizontal irradiance (GHI) and historical solar radiation. After forecasting the solar radiation for three years, the corresponding degradation rate (DR) influenced energy potentials of an a-Si PV system is estimated. The estimated energy is useful economi... [more]

Energy baseline is an important method for measuring the energy-saving benefits of chiller system, and the benefits can be calculated by comparing prediction models and actual results. Currently, machine learning is often adopted as a prediction model for energy baselines. Common models include regression, ensemble learning, and deep learning models. In this study, we first reviewed several machine learning algorithms, which were used to establish prediction models. Then, the concept of clustering to preprocess chiller data was adopted. Data mining, K-means clustering, and gap statistic were used to successfully identify the critical variables to cluster chiller modes. Applying these key variables effectively enhanced the quality of the chiller data, and combining the clustering results and the machine learning model effectively improved the prediction accuracy of the model and the reliability of the energy baselines.

Thermoacoustic refrigerators have huge potential to replace conventional refrigeration systems as an alternative clean refrigeration technology. These devices utilize conversion of acoustic power and heat energy to generate the desired cooling. The stack plays a pivotal role in the performance of Standing Wave Thermoacoustic Refrigerators (SWTARs), as the heat transfer takes place across it. Performance of stacks can be significantly improved by making an arrangement of different materials inside the stack, resulting in anisotropic thermal properties along the length. In the present numerical study, the effect of multi-layered stack on the refrigeration performance of a SWTAR has been evaluated in terms of temperature drop across the stack, acoustic power consumed and device Coefficient of Performance (COP). Two different aspects of multi-layered stack, namely, different material combinations and different lengths of stacked layers, have been investigated. The combinations of four stac... [more]

Due to increasing load and characteristic stagnation and fluctuations of existing generation systems capacity, the reliability assessment of generation systems is crucial to system adequacy. Furthermore, a rapid load increase could amount to a consequent sudden deficit in the generation supply before the next scheduled assessment. Hence, a reliability assessment is conducted at regular and close intervals to ensure adequacy. This study simulates and establishes the relationship between the load growth and generation capacity using the generation and load data of the IEEE reliability test system (IEEE RTS ‘96 standard). The generation capacity states and the risk model were obtained using the sequential Monte Carlo simulation (MCS) method. The load was gradually increased stepwise and is simulated against the constant generation capacity. In each case, the reliability index was recorded in terms of loss-of-load evaluation (LOLE). The recorded reliability index was thereafter fitted with... [more]

Research on applying selective harmonic elimination pulse width modulation (SHE-PWM) to high power converters has drawn tremendous interest, due to the advantages of low switching frequency and high output harmonic performance. In the fields of high power converters such as variable speed traction motor drives and static synchronous compensators (STATCOM), the adoption of high voltage but slow speed semiconductor devices, i.e., IGBT/IGCT, results in a longer dead time of several microseconds, which leads to a motor vibration in the former case or the distortion of grid current in the latter case. This paper analyzes in detail the mechanism of the dead-time effect on 3-level SHE-PWM with different operating conditions considered. For the first time, a general mathematical model describing the relationship between the dead time and harmonic distribution of SHE-PWM wave is established. Based on which an open-loop compensation method by inserting a margin time into the effective switching... [more]

The multiphysics simulation methodology presented in this paper permits extension of computational fluid dynamics (CFD) simulations to account for electric power generation and its effect on the energy transport, the Seebeck voltage, the electrical currents in thermoelectric systems. The energy transport through Fourier, Peltier, Thomson and Joule mechanisms as a function of temperature and electrical current, and the electrical connection between thermoelectric modules, is modeled using subgrid CFD models which make the approach computational efficient and generic. This also provides a solution to the scale separation problem that arise in CFD analysis of thermoelectric heat exchangers and allows the thermoelectric models to be fully coupled with the energy transport in the CFD analysis. Model validation includes measurement of the relevant fluid dynamic properties (pressure and temperature distribution) and electric properties (current and voltage) for a turbulent flow inside a therm... [more]

In recent years, many buildings have been fitted with smart meters, from which high-frequency energy data is available. However, extracting useful information efficiently has been imposed as a problem in utilizing these data. In this study, we analyzed district heating smart meter data from 61 buildings in Copenhagen, Denmark, focused on the peak load quantification in a building cluster and a case study on load shifting. The energy consumption data were clustered into three subsets concerning seasonal variation (winter, transition season, and summer), using the agglomerative hierarchical algorithm. The representative load profile obtained from clustering analysis were categorized by their profile features on the peak. The investigation of peak load shifting potentials was then conducted by quantifying peak load concerning their load profile types, which were indicated by the absolute peak power, the peak duration, and the sharpness of the peak. A numerical model was developed for a re... [more]

According to the basic principle of the hydraulic anti-kink system and flow continuity equation, this paper takes the low-floor tram as the research object and the four vehicles as the research carrier. Based on the correlation parameters between the vehicle subsystem and the hydraulic subsystem, a co-simulation platform of a low-floor tram with hydraulic an anti-kink system is built. The co-simulation results show that the anti-kink system can well maintain the relative yaw angle consistency between the vehicle body and bogie. The anti-kink system restrains the maximum yaw angle and excessive lateral displacement of the vehicle body effectively. The consistency between the experiment results and the simulation results shows the accuracy of the model. The co-simulation model of the low-floor tram with hydraulic anti-kink system can be used to research the dynamic performance when it passes through curve line.

Demand-Side Management (DSM) is an essential tool to ensure power system reliability and stability. In future smart grids, certain portions of a customer’s load usage could be under the automatic control of a cyber-enabled DSM program, which selectively schedules loads as a function of electricity prices to improve power balance and grid stability. In this scenario, the security of DSM cyberinfrastructure will be critical as advanced metering infrastructure and communication systems are susceptible to cyber-attacks. Such attacks, in the form of false data injections, can manipulate customer load profiles and cause metering chaos and energy losses in the grid. The feedback mechanism between load management on the consumer side and dynamic price schemes employed by independent system operators can further exacerbate attacks. To study how this feedback mechanism may worsen attacks in future cyber-enabled DSM programs, we propose a novel mathematical framework for (i) modeling the nonlinea... [more]

Reservoir history matching is a well-known inverse problem for production prediction where enormous uncertain reservoir parameters of a reservoir numerical model are optimized by minimizing the misfit between the simulated and history production data. Gaussian Process (GP) has shown promising performance for assisted history matching due to the efficient nonparametric and nonlinear model with few model parameters to be tuned automatically. Recently introduced Gaussian Processes proxy models and Variogram Analysis of Response Surface-based sensitivity analysis (GP-VARS) uses forward and inverse Gaussian Processes (GP) based proxy models with the VARS-based sensitivity analysis to optimize the high-dimensional reservoir parameters. However, the inverse GP solution (GPIS) in GP-VARS are unsatisfactory especially for enormous reservoir parameters where the mapping from low-dimensional misfits to high-dimensional uncertain reservoir parameters could be poorly modeled by GP. To improve the p... [more]

Reynolds-averaged Navier−Stokes (RANS) three-dimensional (3D) computational fluid dynamics (CFD) simulations of gaseous emissions from combustion engines are very demanding due to the complex geometry, the emissions formation mechanisms, and the transient processes inside the cylinders. The validation of emission simulation is challenging because of modeling simplifications, fundamental differences from reality (e.g., fuel surrogates), and difficulty in the comparison with measured emission values, which depend on the measuring position. In this study, detailed gaseous emission data were acquired for a spark ignition (SI) direct-injection (DI) single-cylinder engine (SCE) fueled with a toluene reference fuel (TRF) surrogate to allow precise comparison with simulations. Multiple devices in different sampling locations were used for the measurement of average emission concentration, as well as hydrocarbon (HC) cycle- and species-resolved values. A RANS 3D-CFD methodology to predict gaseo... [more]

Shallow geothermal energy systems (SGES) may take different forms and have recently taken considerable attention due to energy geo-structures (EGS) resulting from the integration of heat exchange elements in geotechnical structures. Still, there is a lack of systematic design guidelines of SGES. Hence, in order to contribute towards that direction, the current study aims at reviewing the available SGES modeling options along with their various aspects and practices. This is done by first presenting the main analytical and numerical models and methods related to the thermal behavior of SGES. Then, the most important supplementary factors affecting such modeling are discussed. These include: (i) the boundary conditions, in the form of temperature variation or heat flow, that majorly affect the predicted thermal behavior of SGES; (ii) the spatial dimensions that may be crucial when relaxing the infinite length assumption for short heat exchangers such as energy piles (EP); (iii) the deter... [more]

Sunlight is converted into electrical energy due to the photovoltaic effect in photovoltaic cells. Energy yield of photovoltaic systems depends on the solar array location, orientation, tilt, tracking and local weather conditions. In order to determine the amount of energy produced in a photovoltaic system, it is important to analyze the operation of the photovoltaic (PV) arrays in real operating conditions and take into account the impact of external factors such as irradiance, ambient temperature or the speed of blowing wind, which is the natural coolant of PV panels. The analysis was carried out based on mathematical models and actual measurement data, regarding the dependence of the average temperature of PV arrays on variable and difficult to predict ambient conditions. The analysis used standard (nominal operating cell temperature (NOCT)), King, Skoplaki, Faiman and Mattei thermal models and the standard model for flat-plate photovoltaic arrays. Photovoltaic installations PV1, PV... [more]

In-cloud ice mass accretion on wind turbines is a common challenge that is faced by energy companies operating in cold climates. On-shore wind farms in Scandinavia are often located in regions near patches of forest, the heterogeneity length scales of which are often less than the resolution of many numerical weather prediction (NWP) models. The representation of these forests—including the cloud water response to surface roughness and albedo effects that are related to them—must therefore be parameterized in NWP models used as meteorological input in ice prediction systems, resulting in an uncertainty that is poorly understood and, to the present date, not quantified. The sensitivity of ice accretion forecasts to the subgrid representation of forests is examined in this study. A single column version of the HARMONIE-AROME three-dimensional (3D) NWP model is used to determine the sensitivity of the forecast of ice accretion on wind turbines to the subgrid forest fraction. Single column... [more]

The study objective was to adjust the hydrodynamic disintegrator dedicated to sewage sludge pre-treatment (HDS) to work with agricultural substrate. This involved the development and implementation of a mathematical model of flow via the device’s domain. An innovative disintegrator (HAD—hydrodynamic disintegrator for agriculture) was designed, built, and tested based on the obtained results. The main improvements to the HDS include the implementation of shredding knives in order to overcome clogging by crushed substrate, and the application of ribs in the recirculation zone, contributing to the development of an additional structure damage zone. The challenge of this study was also to determine the operating parameters of the HDA that would provide for an increase in methane production with positive energy balance. The testing procedures, for which maize silage was selected, involved batch disintegration tests and biochemical methane potential tests. No clogging of rotor or spontaneous... [more]

A new model of adsorption isotherms Type IV and V is proposed as a basis for theoretical calculations and modelling of adsorption systems such as adsorption heat storage and heat pumps. As the current models have decent yet limited applicability, in this work, we present a new combined model with universal use for micro-mesoporous silica/water adsorption systems. Experimental measurement of adsorption isotherm of water onto seven different samples of micro and mesoporous silica and aluminium-silica were used to fit new adsorption models based on a combination of classical theories and a distribution function related to the pore-size distribution of the selected materials. The fitting was conducted through a repeated non-linear regression using Trust Region Reflective algorithm with weighting factors to compensate for the scalability of the adsorption amount at low relative pressure with optimization of the absolute average deviation fitting parameter. The results display a significant... [more]

Data are essential to urban building energy models and yet, obtaining sufficient and accurate building data at a large-scale is challenging. Previous studies have highlighted that the data impact on urban case studies has not been sufficiently discussed. This paper addresses this gap by providing an analysis of the impact of input data on building energy modelling at an urban scale. The paper proposes a joint review of data impact and data accessibility to identify areas where future survey efforts should be concentrated. Moreover, a Morris sensitivity analysis is carried out on a large-scale residential case study, to rank input parameters by impact on space heating demand. This paper shows that accessible data impact the whole modelling process, from approach selection to model replicability. The sensitivity analysis shows that the setpoint and thermal characteristics were the most impactful for the case study considered. Solutions proposed to overcome availability and accessibility... [more]

Approximately half of the daily oil production from the Canadian oil sands comes from the application of steam assisted gravity drainage (SAGD). Due to the high steam requirements of SAGD, many studies have focused on solvent injection as a means of reducing the steam consumption. One of the multiple variations of the steam-solvent injection process consists on the intermittent co-injection of solvent with steam, also known as a cyclic expanding-solvent (ES)-SAGD process. The current study represents a first attempt to create an analytical model that can describe a cyclic ES-SAGD process. The proposed analytical model uses previous SAGD and ES-SAGD models to describe the steam plus solvent stages of the process. The results obtained from the analytical model were contrasted against numerical simulation results for cases in which the solvent was hexane, pentane, and butane, as well as for cases in which hexane is a solvent and the injection cycle length is variable. In all cases, it was... [more]

Electric vehicle (EV) charging stations have become prominent in electricity grids in the past few years. Their increased penetration introduces both challenges and opportunities; they contribute to increased load, but also offer flexibility potential, e.g., in deferring the load in time. To analyze such scenarios, realistic EV data are required, which are hard to come by. Therefore, in this article we define a synthetic data generator (SDG) for EV charging sessions based on a large real-world dataset. Arrival times of EVs are modeled assuming that the inter-arrival times of EVs follow an exponential distribution. Connection time for EVs is dependent on the arrival time of EV, and can be described using a conditional probability distribution. This distribution is estimated using Gaussian mixture models, and departure times can calculated by sampling connection times for EV arrivals from this distribution. Our SDG is based on a novel method for the temporal modeling of EV sessions, and... [more]

Sliding Rotary Vane Expanders (SVRE) are often employed in Organic Rankine Cycle (ORC)-based power units for Waste Heat Recovery (WHR) in Internal Combustion Engine (ICE) due to their operating flexibility, robustness, and low manufacturing cost. In spite of the interest toward these promising machines, in literature, there is a lack of knowledge referable to the design and the optimization of SVRE: these machines are often rearranged reversing the operational behavior when they operate as compressors, resulting in low efficiencies and difficulty to manage off-design conditions, which are typical in ORC-based power units for WHR in ICE. In this paper, the authors presented a new model of the machine, which, thanks to some specific simplifications, can be used recursively to optimize the design. The model was characterized by a good level of physical representation and also by an acceptable computational time. Despite its simplicity, the model integrated a good capability to reproduce v... [more]

Refracturing technology is one of the key technologies to recover the productivity of horizontal wells in tight oil reservoirs, and the selection of best candidate wells from target blocks is the basis of this technology. Based on the refracturing production database, this paper analyzes the direct relationship between geological data, initial fracturing completion data, and dynamic production data, and the stimulation effect of refracturing. Considering the interaction among multiple factors, the factors affecting the stimulation effect of refracturing are classified and integrated, and a comprehensive index including geology, engineering, and production is constructed, making this index meaningful both for physical and engineering properties. The XGBoost decision tree model is established to analyze the weight of influence for the comprehensive index of geology, engineering, and production in predicting the stimulation effect of refracturing. A comprehensive decision index of refract... [more]

This paper mainly focuses on increasing the conversion efficiency of GaAs solar cells by reducing the light reflection losses. The design of nano-structured gratings and their light trapping performance are modelled and optimised by using the finite-difference time-domain (FDTD) method. The sunlight directly impinges on the solar panel or cells, then a portion of the incident sunlight reflects back to the air from the surface of the panel, thus leading to a reduction in the light absorption capacity of the solar cells. In order to proliferate the light absorption capacity of solar cells nano-grating structures are employed, as they are highly capable of capturing the incident sunlight compared to a conventional (or flat type) solar cell, which results in generating more electrical energy. In this study, we design three different types of nano-grating structures, optimise their parameters and their performance in light capturing capacity. From the simulation results, we confirm that tha... [more]

In the present paper, global horizontal irradiance (GHI) is modelled and forecasted at time horizons ranging from 30 min to 48 h, thus covering intrahour, intraday and intraweek cases, using online Gaussian process regression (OGPR) and online sparse Gaussian process regression (OSGPR). The covariance function, also known as the kernel, is a key element that deeply influences forecasting accuracy. As a consequence, a comparative study of OGPR and OSGPR models based on simple kernels or combined kernels defined as sums or products of simple kernels has been carried out. The classic persistence model is included in the comparative study. Thanks to two datasets composed of GHI measurements (45 days), we have been able to show that OGPR models based on quasiperiodic kernels outperform the persistence model as well as OGPR models based on simple kernels, including the squared exponential kernel, which is widely used for GHI forecasting. Indeed, although all OGPR models give good results whe... [more]

The most used fire effect models on evacuees are only focused on the physical capacity of the evacuees. However, some of the evacuees in a fire situation continuously move through the familiar route, although the familiar route is smoke-filled and they know that they are moving towards the fire source. Thus, the additional evacuation models are required for considering the behavioral changes due to the psychological pressure when the evacuees are moving through the smoke or towards the fire source. In this study, the inner smoke region force is modified to improve the accuracy and practicality of the BR-smoke model by varying the walking speed according to the smoke density. Additionally, the BR-smoke model is applied to FDS+Evac to compare the simulation results of the modified BR-smoke model with those of existing models. Based on the results, the evacuation characteristics inside the smoke region can be improved by using the modified BR-smoke model because the evacuees are continuou... [more]

This work aims at the economic evaluation of a semi-underground pumped hydro storage power plant erected in an abandoned open-pit mine. For the exploratory model-based analysis, we develop and apply both a simple deterministic and a stochastic net present value (NPV) approach, the latter of which uses a Monte Carlo simulation to account for revenue uncertainty from electricity price fluctuations. The analytical framework developed is applied to two promising sites in the Rheinland region in Germany, Hambach and Inden, making reasonable parameter value assumptions and considering and ignoring the lengthy duration of lower reservoir flooding. The investor’s value-at-risk is computed for alternative performance indicators (NPV, net cash recovery, profit-to-investment ratio, and specific production costs) to compare the different outcomes in terms of the project’s financial risk distribution. Calculations show that a semi-underground pumped hydro storage power plant in an abandoned open-pi... [more]