Motivated by the growing demand for distributed energy resources (DERs), peer-to-peer (P2P) electricity markets have been explored worldwide. However, such P2P markets must be balanced in much smaller regions with a lot fewer participants than centralized wholesale electricity markets; hence, the market has inherent problems of low liquidity and price instability. In this study, we propose applying a market maker system to the P2P electricity market and developing an efficient market strategy to increase liquidity and mitigate extreme price fluctuations. To this end, we construct an artificial market simulator for P2P electricity trading and design a market agent and general agents (photovoltaic (PV) generators, consumers, and prosumers) to perform power bidding and contract processing. Moreover, we introduce market-maker agents in this study who follow the regulations set by a market administrator and simultaneously place both sell and buy orders in the same market. We implement two t... [more]

Smart buildings that utilize innovative technologies such as artificial intelligence (AI), the internet of things (IoT), and cloud computing to improve comfort and reduce energy waste are gaining popularity. Smart buildings comprise a range of sensors to measure real-time indoor environment variables essential for the heating, ventilation, and air conditioning (HVAC) system control strategies. For accuracy and smooth operation, current HVAC system control strategies require multiple sensors to capture the indoor environment variables. However, using too many sensors creates an extensive network that is costly and complex to maintain. Our proposed research solves the mentioned problem by implementing a machine-learning algorithm to estimate unmeasured variables utilizing a limited number of sensors. Using a six-month data set collected from a three-story smart building in Japan, several extreme gradient boosting (XGBoost) models were designed and trained to estimate unmeasured room temp... [more]

Energy management and utilization for commercial users is becoming increasingly intelligent and refined, fostering a closer and growing connection with the electricity market. In this paper, a novel energy management optimization theoretical framework for commercial users is proposed based on the hybrid simulation of electricity market bidding. The hybrid simulation model based on Multi-Agent Simulation (MAS) with reinforcement learning and System Dynamic Simulation (SDS) is established to solve the problem using a single simulation method: it cannot adjust the clearing price when considering the whole market; considering the uncertainty of Electric Vehicles (EVs) travel and Lighting Loads (LLs), the multi-objective optimization model of energy management for commercial users is constructed to minimize the total energy cost of commercial users, as well as maximize the lighting comfort of indoor office staff, which compensates for the lack of the single-objective optimization of the pow... [more]

In the present work, uncertainty quantification of a venturi tube simulation with the cavitating flow is conducted based on Bayesian inference and point-collocation nonintrusive polynomial chaos (PC-NIPC). A Zwart−Gerber−Belamri (ZGB) cavitation model and RNG k-ε turbulence model are adopted to simulate the cavitating flow in the venturi tube using ANSYS Fluent, and the simulation results, with void fractions and velocity profiles, are validated with experimental data. A grid convergence index (GCI) based on the SLS-GCI method is investigated for the cavitation area, and the uncertainty error (UG) is estimated as 1.12 × 10−5. First, for uncertainty quantification of the venturi flow simulation, the ZGB cavitation model coefficients are calibrated with an experimental void fraction as observation data, and posterior distributions of the four model coefficients are obtained using MCMC. Second, based on the calibrated model coefficients, the forward problem with two random inputs, an inle... [more]

Accurate and rapid forecasting of short-term loads facilitates demand-side management by electricity retailers. The complexity of customer demand makes traditional forecasting methods incapable of meeting the accuracy requirements, so a self-attention based short-term load forecasting (STLF) considering demand-side management is proposed. In the data preprocessing stage, non-parametric kernel density estimation is used to construct customer electricity consumption feature curves, and then historical load data are used to delineate the feasible domain range for outlier detection. In the feature selection stage, the feature data are selected using variational modal decomposition and a maximum information coefficient to enhance the model prediction accuracy. In the model prediction stage, the decomposed intrinsic mode function components are independently predicted and reconstructed using an Informer based on improved self-attention. Additionally, the novel AdaBlief optimizer is used to o... [more]

This paper proposes the full simulation model for the electrical analysis of all-electric ship (AES) based on a medium voltage DC power system. The AES has become popular both in the commercial and the military areas due to a low emission, a high fuel consumption efficiency, and a wide applicability. In spite of many advantages, it is complex and difficult to construct the whole system with many mechanical and electrical components onboard. Full electrical analysis is essentially required to simplify the design of the AES, a control and optimization of a ship electric system. The proposed full simulation model of the AES includes the mechanical and the electrical elements by using the MATLAB/Simulink. The mechanical elements are comprised of a steam turbine and a hydrodynamic model of a ship which is adopted by an average value model that is based on the characteristic equation of the mechanical system. The electrical elements are developed by full detailed models which consist of gene... [more]

The conversion of biomass to olefin by employing gasification has recently gained the attention of the petrochemical sector, and syngas composition is a keystone during the evaluation of process design. Process simulation software is a preferred evaluation tool that employs stoichiometric and kinetic approaches. Despite the available literature, the estimation errors of these simulation methods have scarcely been contrasted. This study compares the errors of stoichiometric and kinetic models by simulating a downdraft steam gasifier in PRO/II. The quantitative examination identifies the model that best predicts the composition of products for the gasification of Japanese wood waste. The simulation adopts reaction mechanisms, flowsheet topology, reactions parameters, and component properties reported in the literature. The results of previous studies are used to validate the models in a comparison of the syngas composition and yield of products. The models are used to reproduce gasificat... [more]

In the course of the switch to renewable energy sources, there is a shift from a few large energy sources (power plants) to a large number of small, distributed energy sources (e.g., photovoltaic systems) and energy storage devices (e.g., electric vehicles). This results in the need to know and identify these energy sources and sinks as soon as new devices are installed, in order to ensure grid stability. This paper presents an approach to identify energy sources and energy storage in smart meter data, using photovoltaic systems and electric vehicles as examples. For this purpose, the Pecan Street dataset is used, which has been extended by charging processes from the ACN dataset. The presented approach comprises a combination of a Convolutional Neural Network and a Multilayer Perceptron, which decides separately, on the basis of the smart meter data of a household, whether an electric vehicle and a photovoltaic system are present. It is shown that the combination of both classifiers a... [more]

In this paper, heat transfer and airflow in the gap between the panes of a central part of a double-glazed window were investigated using mathematical modeling. It has been shown that the cyclical airflow regime, in the form of ascending and descending boundary layers, loses stability and changes to a vortex regime under certain conditions depending on the gap width, transverse temperature gradient, inclination angle and window height, as in Rayleigh−Bernard convection cells. The study made it possible to determine the critical values of the Rayleigh number (Ra) at which the air flow regime in the gap between the panes of a window changes (in the range of values 6.07 × 103 < Ra < 6.7 × 103). As a result of the modeling, the values of the thermal resistance of a central part of double-glazed window were determined as a function of the width of the gap between the panes, the angle of inclination and the transverse temperature gradient.

Given the spatial structures and functional requirements, there are a number of different types of obstacles in long and narrow confined spaces that will cause a premixed gas explosion to produce greater overpressure and influence the flame behavior for different obstacles. Because the volume fraction of unburned gas changes with the changing height of the U-type obstacles, we can further study the influence on the volume fraction of the unburned premixed gas for the characteristics of the overpressure and the flame behaviors in the closed tube with the obstacles. The results show that after the premixed gas is successfully ignited in the pipe, the overpressure in the pipe greatly increases as the unburned premixed gas burns between the adjacent plates. Moreover, the increase of the overpressure in the closed duct becomes faster when the decrease of unburned gas becomes faster. The high-pressure areas between the plates move inversely compared with the direction of flame propagation wh... [more]

With the development of power modules for high voltage, high temperature, and high power density, their size is becoming smaller, and the packaging insulation experiences higher electrical, thermal, and mechanical stress. Packaging insulation needs to meet the requirement that internal electric field, temperature, and mechanical stress should be as low as possible. Focusing on the coupling principles and optimization design among electrical, thermal, and mechanical stresses in the power module packaging insulation, a multi-objective optimization design method based on Spice circuit, finite element field numerical calculation, and multi-objective gray wolf optimizer (MOGWO) is proposed. The packaging insulation optimal design of a 1.2 kV SiC MOSFET half-bridge power module is presented. First, the high field conductivity characteristics of the substrate ceramic and encapsulation silicone of the packaging insulation material were tested at different temperatures and external field streng... [more]

Supervisory Control and Data Acquisition (SCADA) systems are essential for reliable communication and control of smart grids. However, in the cyber-physical realm, it becomes highly vulnerable to cyber-attacks like False Data Injection (FDI) into the measurement signal which can circumvent the conventional detection methods and interfere with the normal operation of grids, which in turn could potentially lead to huge financial losses and can have a large impact on public safety. It is imperative to have an accurate state estimation of power consumption for further operational decision-making.This work presents novel forecasting-aided anomaly detection using an CNN-LSTM based auto-encoder sequence to sequence architecture to combat against false data injection attacks. We further present an adaptive optimal threshold based on the consumption patterns to identify abnormal behaviour. Evaluation is performed on real-time energy demand consumption data collected from the Australian Energy M... [more]

In this paper, we explore the improvement of the aerodynamic characteristics of wind turbine blades under stall conditions using passive flow control with slots. The National Renewable Energy Laboratory (NREL) Phase II rotor, for which detailed simulations and experimental data are available, served as a baseline for assessing the flow control system effects. The position and configuration of the slot used as a flow control system were determined using CFD analysis. The 3D-RANS equations are solved with ANSYS FLUENT using the k-ω SST turbulence closure model. The pressure coefficient for different wind speeds for the baseline configuration is compared to the available experimental data. The comparison shows that CFD results were better for the attached flow. The current work consists of a 3-D CFD modeling of a rotating blade equipped with different flow control systems: single-slot (S-S) and two-slots (T-S). The computation provides a better understanding of the influence of these flow... [more]

The energy use intensity (EUI) of convenience stores was substantially higher than that of office buildings and hotels, due to a compact footprint but a high density of equipment yielded a higher EUI. As a result, it is critical to assess and maintain the state of the convenience store in order to obtain a lower EUI and reduce energy consumption. This study utilizes a convenience store to evaluate energy consumption and perform a CFD simulation to see how the environment influences by cold storage showcase (CSS) equipment. On the basis of field testing and on-site web-based monitoring data, a survey of baseline information through data collecting and energy benchmarking data has been provided and extensively examined. According to energy monitoring, the convenience store’s highest electricity use is 23,055 kWh in June, and the lowest power consumption is 15,216 kWh in February. The CFD simulation results revealed that the temperature near the CSS can be 3−5 °C lower than in other regio... [more]

The thermophysical properties of a working-fluid play an important role in the process of stirring-heating. The heating process of stirring is accompanied by two processes: the friction between the solid mechanism and the working-fluid and the viscous dissipation of the working liquid. Traditionally, the sensible heat of water-based working-fluids is low, while that of oil-based working-fluids is higher, but the load capacity is relatively low. In order to find a balance between the two, an optimal stirring working-fluid should be selected. In this study, an experimental method was used to study the heating process of 30 kinds of working-fluids. The numerical evaluation model of the effects of thermophysical properties on the comprehensive evaluation index of heat (CEIH) was established by multiple linear regression methods, and a computational fluid dynamics (CFD) tool was used to analyze the heat generation and flow field of different working-fluids in the stirring-heating device. Th... [more]

The generation of synthetic vibration signals enables the testing of novel machine diagnostic methods without the costly introduction of real failures. One of major goals of vibration-based condition monitoring is the early detection of bearing faults. This paper presents a novel modeling technique based on the combination of the known mechanical properties of a modeled object (phenomenological part) and observation of a real object (behavioral part). The model uses the real pulse response of bearing housing, along with the external instantaneous machine speed profile. The presented method is object-oriented, so it is applicable to a large group of machinery.

The transition toward high shares of non-programmable renewable energy sources in the power grid requires an increase in the grid flexibility to guarantee grid reliability and stability. This work, developed within the EU project Grasshopper, identifies hydrogen Fuel Cell (FC) power plants, based on low temperature PEM cells, as a source of flexibility for the power grid. A dynamic numerical model of the flexible FC system is developed and tested against experimental data from a 100-kW pilot plant, built within the Grasshopper project. The model is then applied to assess the flexible performance of a 1 MW system in order to optimize the scale-up of the pilot plant to the MW-size. Simulations of load-following operation show the flexibility of the plant, which can ramp up and down with a ramp rate depending only on an externally imposed limit. Warm-up simulations allow proposing solutions to limit the warm-up time. Of main importance are the minimization of the water inventory in the sy... [more]

The efficient use of renewable energy is receiving more and more attention in the context of “carbon neutrality” and “carbon peaking”. For a long time, biomass has been used less efficiently as a renewable energy source, but with the development of fluidized biomass gasification technology, it can play an increasing role in industrial production. A fluidized bed biomass gasifier has a strong nonstationary process due to its complex energy−mass exchange, and analysis of its complex reaction process and products has relied on experiments for a long time. This paper uses a Euler−Euler two-fluid model to establish a three-dimensional CFD model of the fluidized bed biomass gasifier, on which factors affecting syngas generation are analyzed. The simulation shows that increasing the initial bed temperature can effectively improve syngas production, while increasing the air equivalent is not beneficial for syngas production.

The majority of marine hydrates are buried in unconsolidated or poorly consolidated marine sediments with limited cementation and strength. As a result, hydrate decomposition during production may cause significant subsidence of the formation, necessitating a halt in production. The numerical model of unconsolidated hydrate formation, based on geomechanics, was established in order to elucidate the depressurization production process. The sensitive factors of unconsolidated hydrate production were determined by analyzing the influence of formation parameters and production parameters on gas production. Then, a safety formation subsidence was proposed in this paper, and the appropriate well type and parameters for the safe and efficient production of hydrates in unconsolidated formations of various saturations were determined. The sensitivity of gas production to the formation parameters was in the order of formation porosity, hydrate saturation, and buried depth, while the effects of t... [more]

The modelling of surges within PV (photovoltaic) installations has been the subject of much research in recent years. However, accurate simulations cannot be performed unless each and every component within a PV installation is modelled in sufficient detail. The bypass diodes within a PV module are frequently omitted from such simulations. When included, they are often represented by oversimplified models. This article addresses this need by presenting SPICE (Simulation Program with Integrated Circuit Emphasis) models for three Schottky diodes, chosen due to their suitability for use as bypass diodes. These models are the combination of DC (direct current) large-signal and AC (alternating current) small-signal sub-models, which are integrated such that the resulting full circuital models allow for accurate simulations involving large-signal transient stimuli. Two types of experimental setups, one incorporating a DC current−voltage curve sweep, and the other involving VNA-based (vector... [more]

In view of the flow instability problem caused by the strong shock wave and secondary flow in the channel of the high-load counter-rotating compressor, this paper adopts the design method of coupling aerodynamic optimization technology and CFD and establishes a three-dimensional aerodynamic optimization design platform for the blade channel based on an artificial neural network and genetic algorithm. The aerodynamic optimization design and internal flow-field diagnosis of a high-load counter-rotating compressor with a 1/2 + 1 aerodynamic configuration are carried out. The research indicates that the optimized blade channel can drive and adjust the flow better, and the expected supercharging purpose and efficient energy conversion process are achieved by controlling the intensity of the shock wave and secondary flow in the channel. The total pressure ratio at the design point of the compressor exceeds 2.9, the adiabatic efficiency reaches 87%, and the aerodynamic performance is excellen... [more]

This study aims to develop a predictive and reliable data-driven model for forecasting the fluid production (oil, gas, and water) of existing wells and future infill wells for CO2-enhanced oil recovery (EOR) and CO2 storage projects. Several models were investigated, such as auto-regressive (AR), multilayer perceptron (MLP), and long short-term memory (LSTM) networks. The models were trained based on static and dynamic parameters and daily fluid production while considering the inverse distance of neighboring wells. The developed models were evaluated using walk-forward validation and compared based on the quality metrics, span, and variation in the forecasting horizon. The AR model demonstrates a convincing generalization performance across various time series datasets with a long but varied forecasting horizon across eight wells. The LSTM model has a shorter forecasting horizon but strong generalizability and robustness in forecasting horizon consistency. MLP has the shortest and mos... [more]

As a new energy source, gas hydrates have attracted worldwide attention, but their exploration and development face enormous challenges. Thus, it has become increasingly crucial to identify hydrate distribution accurately. Electrical resistivity tomography (ERT) can be used to detect the distribution of hydrate deposits. An ERT inversion network (ERTInvNet) based on a deep neural network (DNN) is proposed, with strong learning and memory capabilities to solve the ERT nonlinear inversion problem. 160,000 samples about hydrate distribution are generated by numerical simulation, of which 10% are used for testing. The impact of different deep learning parameters (such as loss function, activation function, and optimizer) on the performance of ERT inversion is investigated to obtain a more accurate hydrate distribution. When the Logcosh loss function is enabled in ERTInvNet, the average correlation coefficient (CC) and relative error (RE) of all samples in the test sets are 0.9511 and 0.109... [more]

For assessment of the insulation performance of high-voltage (HV) equipment installed in extra-high-voltage (EHV) systems, switching impulse voltage tests are performed in an HV testing laboratory. The waveform parameters of the switching impulse voltages are defined by peak voltage (Up), time to crest (Tp), and time to half (T2) according to IEC 60060-1. In this paper, a new, simplified, and accurate approach used for determination of the waveform parameters of the switching impulse voltages is presented. The formula used in the evaluation of Tp was derived from analytically simulated two-exponential waveforms, where Tp and T2 are in the ranges of 20 μs to 300 μs and 1000 μs to 4000 μs, respectively. The accuracy of the proposed approach was validated by the waveforms collected from the test waveform data generator (TDG) provided by IEC 61083-2, simulations, and experiments. It is found that the accuracy of the proposed approach is relatively higher than the expressions provided by IE... [more]

This paper proposes a novel network anomaly detection framework based on data balance and feature selection. Different from the previous binary classification of network intrusion, the network anomaly detection strategy proposed in this paper solves the problem of multiple classification of network intrusion. Regarding the common data imbalance of a network intrusion detection set, a resampling strategy generated by random sampling and Borderline SMOTE data is developed for data balance. According to the features of the intrusion detection dataset, feature selection is carried out based on information gain rate. Experiments are carried out on three basic machine learning algorithms (K-nearest neighbor algorithm (KNN), decision tree (DT), random forest (RF)), and the optimal feature selection scheme is obtained.