With the increasing integration of renewable energy resources and other forms of dispersed generation, more and more variances and uncertainties are brought to modern power systems. The dynamic security assessment (DSA) of modern power systems is facing challenges in ensuring its accuracy for unpredictable operating conditions (OC). This paper proposes a novel approach that uses random forest (RF) for online DSA. Hourly scenarios are generated for the database according to the forecast errors of renewable energy resources, which are calculated from historical data. Fed with online measurement data, it is able to not only predict the security states of current OC with high accuracy, but also indicate the confidence level of the security states one minute ahead of the real time by an outlier identification method. The results of RF together with outlier identification show high accuracy in the presence of variances and uncertainties due to wind power generation. The performance of this approach is verified on the operational model of western Danish power system with around 200 transmission lines and 400 buses. Full article

Fluid flow characteristics and particle motion behavior of an impinging stream reactor with dynamic inlet flow (both inlet velocity patterns exhibit step variation) are investigated and discussed with the computational fluid dynamics–discrete element method (CFD–DEM). The effect of T (variation period of the dynamic inlet flow) and ∆u (inlet velocity difference) on the motion characteristics of single and multiple particles, as well as the mean particle residence time, are studied and discussed. The research results indicate that, compared with the traditional impinging stream reactor (both inlet velocities are equal and constant) with equal mean inlet velocity (u_m) within one period, the impinging surface is instantaneously moving and the flow regime is varied with time in the impinging stream reactor with dynamic inlet flow. The impinging stream reactor with dynamic inlet flow provides higher cost performance over the traditional impinging stream reactor, under equal u_m, in terms of single-particle residence time. Moreover, three new particle motion modes exist in multi-particle motions of the impinging stream reactor with dynamic inlet flow; particles are accelerated by the original or reverse fluid and perform oscillatory motion at least once after an interparticle collision. Whether it is a single particle or multi-particles, the mean particle residence time reaches a maximum value when T/2 is approximately equal to the first particle acceleration time, since the maximum axial kinetic energy increases in every oscillatory motion compared with traditional impinging stream, and the number of oscillatory motions is increasing. The mean residence time of a particle in the impinging stream reactor with a dynamic inlet flow increases with increasing ∆u, since the dynamic inlet conditions and increasing ∆u can continuously supply more energy to particles and thus cause more particles to enter one of the three new modes of particle motion. Full article

Capacitive wireless power transfer can be realized by mutually coupled capacitors operating at a common resonant frequency. An optimal load exists that maximizes either the efficiency or the power transfer to the load. In this work, we utilize the frequency bifurcation effect to propose a frequency agile mode that allows for a nearly coupling-independent regime. We analytically determine the operating conditions of the coupling-independent mode based on the different system gains. In this way, we obtain a solution that achieves nearly constant efficiency and power transfer, even at varying coupling. We compare our results to inductive wireless power transfer where a perfect coupling-independent mode is achievable. Full article

The uncertainty that dominates in the functioning of the electricity market is of great significance and arises, generally, because of the time imbalance in electricity consumption rates and power plants’ production capacity, as well as the influence of many other factors (weather conditions, fuel costs, power plant operating costs, regulations, etc.). In this paper we try to incorporate this uncertainty in the electricity price forecasting model by applying interval numbers to express the price of electricity, with no intention of exploring influencing factors. This paper represents a hybrid model based on fuzzy C-mean clustering and the interval-valued autoregressive process for forecasting the short-term electricity price. A fuzzy C-mean algorithm was used to create interval time series to be forecasted by the interval autoregressive process. In this way, the efficiency of forecasting is improved because we predict the interval, not the crisp value where the price will be. This approach increases the flexibility of the forecasting model. Full article

The potential for using O-methylene phosphonic κ-carrageenan (OMPk) as a filler in the chitosan-based polymer electrolyte N-methylene phosphonic chitosan (NMPC) was investigated. OMPk, a derivative of κ-carrageenan, was synthesized via phosphorylation and characterized using infrared spectroscopy (IR) and nuclear magnetic resonance (NMR). Both the IR and NMR results confirmed the phosphorylation of the parent carrageenan. The solid polymer electrolyte (SPE)-based NMPC was prepared by solution casting with different weight percentages of OMPk ranging from 2 to 8 wt %. The tensile strength of the polymer membrane increased from 18.02 to 38.95 MPa as the amount of OMPk increased to 6 wt %. However, the increase in the ionic conductivity did not match the increase in the tensile strength. The highest ionic conductivity was achieved with 4 wt % OMPk, which resulted in 1.43 × 10⁻⁵ Scm⁻¹. The κ-carrageenan-based OMPk filler strengthened the SPE while maintaining an acceptable level of ionic conductivity. Full article

While previous research has shown the use of attachable air-caps on windows to efficiently reduce a building’s energy consumption, the air-caps considered had to be attached to the entire window’s surface, thus limiting the occupants’ view and creating the inconvenience of needing to detach and attach the air-caps. In this study, a window-mounted air-cap roller module using Velcro tape that may be easily attached, detached, and rolled up or down was developed and performance tested in a full-scale test bed. It was found that as the area of the air-caps attached on a window increased, the required indoor lighting energy increased. However, the window insulation improved, thus reducing the cooling and heating energy needed. Attaching the air-caps to the entire window surface effectively reduced the building’s energy consumption, but views through the window may be disturbed. Thus, the developed window-mounted air-caps enable an occupant to reduce the building energy consumption and maintain their view according to their need. The findings of this study may contribute to a reduction in building energy consumption without sacrificing a pleasant indoor environment. Further studies may be needed to verify their efficacy under varying indoor and outdoor conditions. Full article

Nowadays there are several transmission lines projected to be operating in high-altitude regions. It is well known that the installation altitude has an impact on the dielectric behavior of air-insulated systems. As a result, atmospheric and voltage correction factors must be applied in air-insulated transmission systems operating in high-altitude conditions. This paper performs an exhaustive literature review, including state-of-the-art research papers and International Standards of the available correction factors to limit corona activity and ensure proper performance when planning air-insulated transmission lines intended for high-altitude areas. It has been found that there are substantial differences among the various correction methods, differences that are more evident at higher altitudes. Most high-voltage standards were not conceived to test samples to be installed in high-altitude regions and, therefore, most high-voltage laboratories are not ready to face this issue, since more detailed information is required. It is proposed to conduct more research on this topic so that the atmospheric corrections and altitude correction factors found in the current International Standards can be updated and/or modified so that high-voltage components to be installed in high-altitude regions can be tested with more accuracy, taking into account their insulation structure. Full article

Accurately predicting the carbon price sequence is important and necessary for promoting the development of China’s national carbon trading market. In this paper, a multiscale ensemble forecasting model that is based on ensemble empirical mode decomposition (EEMD-ADD) is proposed to predict the carbon price sequence. First, the ensemble empirical mode decomposition (EEMD) is applied to decompose a carbon price sequence, SZA2013, into several intrinsic mode functions (IMFs) and one residual. Second, the IMFs and the residual are restructured via a fine-to-coarse reconstruction algorithm to generate three stationary and regular frequency components that high frequency component, low frequency component, and trend component. The fluctuation of each component can effectively reveal the factors that influence market operation. Third, extreme learning machine (ELM) is applied to forecast the trend component, support vector machine (SVM) is applied to forecast the low frequency component and the high frequency component is predicted via PSO-ELM, which means extreme learning machine whose input weights and bias threshold were optimized by particle swarm optimization. Then, the predicted values are combined to form a final predicted value. Finally, using the relevant error-type and trend-type performance indexes, the proposed multiscale ensemble forecasting model is shown to be more robust and accurate than the single format models. Three additional emission allowances from the Shenzhen Emissions Exchange are used to validate the model. The empirical results indicate that the established model is effective, efficient, and practical in terms of its statistical measures and prediction performance. Full article

The increase of distributed energy resources in the smart grid calls for new ways to profitably exploit these resources, which can participate in day-ahead ancillary energy markets by providing flexibility. Higher profits are available for resource owners that are able to anticipate price peaks and hours of low prices or zero prices, as well as to control the resource in such a way that exploits the price fluctuations. Thus, this study presents a solution in which artificial neural networks are exploited to predict the day-ahead ancillary energy market prices. The study employs the frequency containment reserve for the normal operations market as a case study and presents the methodology utilized for the prediction of the case study ancillary market prices. The relevant data sources for predicting the market prices are identified, then the frequency containment reserve market prices are analyzed and compared with the spot market prices. In addition, the methodology describes the choices behind the definition of the model validation method and the performance evaluation coefficient utilized in the study. Moreover, the empirical processes for designing an artificial neural network model are presented. The performance of the artificial neural network model is evaluated in detail by means of several experiments, showing robustness and adaptiveness to the fast-changing price behaviors. Finally, the developed artificial neural network model is shown to have better performance than two state of the art models, support vector regression and ARIMA, respectively. Full article

The strive towards ever increasing automotive engine efficiencies for both diesel and gasoline engines has in recent years been forced by ever-stringent emissions regulations, as well as the introduction of fuel consumption regulations. The untapped availability of waste heat in the internal combustion engine (ICE) exhaust and coolant systems has become a very attractive focus of research attention by industry and academia alike. Even state of the art diesel engines operating at their optimum lose approximately 50% of their fuel energy in the form of heat. As a result, waste heat recovery (WHR) systems have gained popularity as they can deliver a reduction in fuel consumption and associated CO₂ emissions. Of these, the Organic Rankine Cycle (ORC) is a well matured waste heat recovery technology that can be applied in vehicle powertrains, mainly due to the low additional exhaust backpressure on the engine and the potential opportunity to utilize various engine waste heat sources. ORCs have attracted high interest again recently but without commercial exploitation as of today due to the significant on-cost they represent to the engine and vehicle. In ORCs, expansion machines are the interface where useable power production takes place; therefore, selection of the expander technology is directly related to the thermal efficiency of the system. Moreover, the cost of the expander-generator units accounts for the largest proportion of the total cost. Therefore, selection of the most appropriate expander is of great importance at the early stage of any automotive powertrain project. This study aims to review the relevant research studies for expansion machines in ORC-ICE applications, analyzing the effects of specific speed on expander selection, exploring the operational characteristics of each expander to further assist in the selection of the most appropriate expander, and comparing the costs of various expanders based on publically available data and correlations. Full article

Ocean energy holds out great potential for supplying remote maritime areas with their energy requirements, where the grid size is often small and unconnected to a continental grid. Thanks to their high maturity and competitive price, solar and wind energies are currently the most used to provide electrical energy. However, their intermittency and variability limit the power supply reliability. To solve this drawback, storage systems and Diesel generators are often used. Otherwise, among all marine renewable energies, tidal and wave energies are reaching an interesting technical level of maturity. The better predictability of these sources makes them more reliable than other alternatives. Thus, combining different renewable energy sources would reduce the intermittency and variability of the total production and so diminish the storage and genset requirements. To foster marine energy integration and new multisource system development, an up-to-date review of projects already carried out in this field is proposed. This article first presents the main characteristics of the different sources which can provide electrical energy in remote maritime areas: solar, wind, tidal, and wave energies. Then, a review of multi-source systems based on marine energies is presented, concerning not only industrial projects but also concepts and research work. Finally, the main advantages and limits are discussed. Full article

This paper investigates the effects of CO${}_2$ price volatility on optimal power system portfolios and on CO${}_2$ emissions assessment. In a stochastic setting in which three sources of uncertainty are considered, namely fossil fuels (gas and coal) and CO${}_2$ prices, we discuss a unifying scheme for quantifying the impact of integrated environmental and renewable energy policies on the power system. We will show that the effects produced by a given environmental policy scheme strongly depend on the configuration of the power system, i.e., on the composition of the generating sources in the power system portfolio. In the empirical analysis performed on U.S. technical and cost data, we found that a non-volatile carbon tax scheme can produce significant effects on the power system portfolio selection problem in the presence of a carbon-free dispatchable source, like nuclear power, but it may have a negligible impact if the (non-renewable) dispatchable part of the power system portfolio is fully composed by fossil fuel, gas and coal, sources. On the other side, generating CO${}_2$ price volatility market-oriented mechanisms can produce relevant effects on both power system configurations. Although the empirical analysis is performed on U.S. data, the proposed methodology is general and can be used as a quantitative support by policy makers in their attempts to reconcile environmental and economic issues. Full article

The Eddy Dissipation Concept (EDC) is common in modeling turbulent combustion. Several model improvements have been proposed in literature; recent modifications aim to extend its validity to Moderate or Intense Low oxygen Dilution (MILD) conditions. In general, the EDC divides a fluid into a reacting and a non-reacting part. The reacting part is modeled as perfectly stirred reactor (PSR) or plug flow reactor (PFR). EDC theory suggests PSR treatment, while PFR treatment provides numerical advantages. Literature lacks a thorough evaluation of the consequences of employing the PFR fine structure treatment. Therefore, these consequences were evaluated by employing tests to isolate the effects of the EDC variations and fine structure treatment and by conducting a Sandia Flame D modeling study. Species concentration as well as EDC species consumption/production rates were evaluated. The isolated tests revealed an influence of the EDC improvements on the EDC rates, which is prominent at low shares of the reacting fluid. In contrast, PSR and PFR differences increase at large fine fraction shares. The modeling study revealed significant differences in the EDC rates of intermediate species. Summarizing, the PFR fine structure treatment might be chosen for schematic investigations, but for detailed investigations a careful evaluation is necessary. Full article

Pillar stability is an important factor for safe working and from an economic standpoint in underground mines. This paper discusses the effect of blast damage on the strength of hard rock pillars using numerical models through a parametric study. The results indicate that blast damage has a significant impact on the strength of pillars with larger width-to-height (W/H) ratios. The blast damage causes softening of the rock at the pillar boundaries leading to the yielding of the pillars in brittle fashion beyond the blast damage zones. The models show that the decrease in pillar strength as a consequence of blasting is inversely correlated with increasing pillar height at a constant W/H ratio. Inclined pillars are less susceptible to blast damage, and the damage on the inclined sides has a greater impact on pillar strength than on the normal sides. A methodology to analyze the blast damage on hard rock pillars using FLAC^3D is presented herein. Full article

Recently, the cooling load forecasting for the short-term has received increasing attention in the field of heating, ventilation and air conditioning (HVAC), which is conducive to the HVAC system operation control. The load forecasting based on weather forecast data is an effective approach. The meteorological parameters are used as the key inputs of the prediction model, of which the accuracy has a great influence on the prediction loads. Obviously, there are errors between the weather forecast data and the actual weather data, but most of the existing studies ignored this issue. In order to deal with the uncertainty of weather forecast data scientifically, this paper proposes an effective approach based on the Monte Carlo Method (MCM) to process weather forecast data by using the 24-h-ahead Support Vector Machine (SVM) model for load prediction as an example. The data-preprocessing method based on MCM makes the forecasting results closer to the actual load than those without process, which reduces the Mean Absolute Percentage Error (MAPE) of load prediction from 11.54% to 10.92%. Furthermore, through sensitivity analysis, it was found that among the selected weather parameters, the factor that had the greatest impact on the prediction results was the 1-h-ahead temperature T(h–1) at the prediction moment. Full article

To improve the accuracy of the day-ahead load forecasting predictions of a single model, a novel modular parallel forecasting model with feature selection was proposed. First, load features were extracted from a historic load with a horizon from the previous 24 h to the previous 168 h considering the calendar feature. Second, a feature selection combined with a predictor process was carried out to select the optimal feature for building a reliable predictor with respect to each hour. The final modular model consisted of 24 predictors with a respective optimal feature subset for day-ahead load forecasting. New England and Singapore load data were used to evaluate the effectiveness of the proposed method. The results indicated that the accuracy of the proposed modular model was higher than that of the traditional method. Furthermore, conducting a feature selection step when building a predictor improved the accuracy of load forecasting. Full article