Energies, Volume 12, Issue 18 (September-2 2019) – 185 articles

In order to improve the energy efficiency and economic effect of conventional diesel-powered rubber-tired gantry (RTG) cranes in container terminals, various hybrid RTG cranes were studied. However, these current hybrid RTG cranes have several disadvantages, such as high initial investment cost and poor versatility of energy management methods. In this paper, a hybrid RTG crane consisting of a small-sized diesel generator (DG), a ternary material lithium battery, and a supercapacitor (SC) is studied, and a hybrid RTG crane energy management method based on game theory is proposed. The DG, lithium battery, and SC are modeled as three independent agents to participate in the game, and a multi-agent system (MAS) is established. During the RTG crane work process, agents achieve a coordinated and stable working state through the game, i.e., the Nash equilibrium. Three typical crane operation scenarios, the rated load, continuous work, and intermittent work, are simulated and studied. According to the results, combinations of the three devices can meet the power demand and system performance. The power of the DG in the hybrid system is small (only 20 kW), reducing fuel consumption and overall emissions during RTG crane operation. Full article

In recent years, although deep learning algorithms have been widely applied to various fields, ranging from translation to time series forecasting, researchers paid limited attention to modelling parameter optimization and the combination of the fuzzy time series. In this paper, a novel hybrid forecasting system, named CFML (complementary ensemble empirical mode decomposition (CEEMD)-fuzzy time series (FTS)-multi-objective grey wolf optimizer (MOGWO)-long short-term memory (LSTM)), is proposed and tested. This model is based on the LSTM model with parameters optimized by MOGWO, before which a fuzzy time series method involving the LEM2 (learning from examples module version two) algorithm is adopted to generate the final input data of the optimized LSTM model. In addition, the CEEMD algorithm is also used to de-noise and decompose the raw data. The CFML model successfully overcomes the nonstationary and irregular features of wind speed data and electrical power load series. Several experimental results covering four wind speed datasets and two electrical power load datasets indicate that our hybrid forecasting system achieves average improvements of 49% and 70% in wind speed and electrical power load, respectively, under the metric MAPE (mean absolute percentage error). Full article

The conventional microgrid (MG) price-based operation scheme with respect to the hourly market price considers only profit maximization from energy transactions and disregards variability. This causes flexibility burdens on the main grid system operator (SO), which must then utilize its ramping capability to cover the net load variability. As the proportion of renewable energy sources (RESs) involving intermittency in MGs continues to increase owing to global energy policies, net load variability within shorter time intervals has also increased, making proper management guidelines necessary. Thus, this paper proposes an MG-SO variability contract on intra-hour and inter-hour time intervals for regulating variability such that the SO can support and distribute its relevant costs between the MG and the SO. To prove the effectiveness of the proposed contract, an MG variability contract-based scheduling model is also proposed, and the results were compared with those of the price-based model. A case study demonstrates that the introduction of RESs increases the variability in shorter intervals and that the suggested contract is effective in terms of decreasing the variability with increased MG operating costs. A sensitivity analysis between the reduced variability and additional operating costs was also conducted in the case study. Full article

Given the large-scale exploitation and utilization of wind power, the problems caused by the high stochastic and random characteristics of wind speed make researchers develop more reliable and precise wind power forecasting (WPF) models. To obtain better predicting accuracy, this study proposes a novel compound WPF strategy by optimal integration of four base forecasting engines. In the forecasting process, density-based spatial clustering of applications with noise (DBSCAN) is firstly employed to identify meaningful information and discard the abnormal wind power data. To eliminate the adverse influence of the missing data on the forecasting accuracy, Lagrange interpolation method is developed to get the corrected values of the missing points. Then, the two-stage decomposition (TSD) method including ensemble empirical mode decomposition (EEMD) and wavelet transform (WT) is utilized to preprocess the wind power data. In the decomposition process, the empirical wind power data are disassembled into different intrinsic mode functions (IMFs) and one residual (Res) by EEMD, and the highest frequent time series IMF1 is further broken into different components by WT. After determination of the input matrix by a partial autocorrelation function (PACF) and normalization into [0, 1], these decomposed components are used as the input variables of all the base forecasting engines, including least square support vector machine (LSSVM), wavelet neural networks (WNN), extreme learning machine (ELM) and autoregressive integrated moving average (ARIMA), to make the multistep WPF. To avoid local optima and improve the forecasting performance, the parameters in LSSVM, ELM, and WNN are tuned by backtracking search algorithm (BSA). On this basis, BSA algorithm is also employed to optimize the weighted coefficients of the individual forecasting results that produced by the four base forecasting engines to generate an ensemble of the forecasts. In the end, case studies for a certain wind farm in China are carried out to assess the proposed forecasting strategy. Full article

In the energy sector, decisions and technology implementations often necessitate a mid- to long-term perspective. Thus, reliable assessments of future resource availability are needed to support the decision-making process. In Switzerland, similarly to other countries, only a limited part of the available wet biomass feedstock is currently used for anaerobic digestion. Understanding potential future trajectories of the available biomass amount is therefore essential to facilitate its deployment for energetic use and to establish adequate bioenergy strategies. Here, we utilized extensive government data, historical trends, and data from academic literature to identify relevant drivers and their trends. Starting with current biomass potential, the future availability and variation of resources was estimated by taking into account selected drivers and their projected future development. Our results indicated an increase of over 6% in available wet bioenergy resources by 2050 (from 43.4 petajoules (PJ) of primary energy currently to 44.3 PJ in 2035 and 45.4 PJ in 2050), where a Monte Carlo analysis showed that this projection is linked to high uncertainty. Manure remains by far the biomass with the largest additional potential. Possible consequences regarding the country’s pool of biogas facilities and their development are discussed. Full article

This paper describes an analytical-numerical method for the skin and proximity effects in a system of two parallel conductors of circular cross section—a system very frequently encountered in various applications. The magnetic field generated by the current applied on each conductor is expressed by means of vector magnetic potential and expanded into Fourier series. Using the Laplace and Helmholtz equations, as well as the classical boundary conditions, the current density induced due to the proximity and skin effect is determined in each conductor. The resulting current density is expressed as a series of successive reactions. The results obtained are compared with those obtained via finite elements. Although the paper is theoretical, the considered problem has a practical significance, because transmission lines with round conductors are universally used. Besides, the results can be used to estimate errors when only the first reaction is taken into account, which gives relatively simple formulas. Full article

In power system operations, unforeseen energy imbalances commonly occur, resulting in unexpected constraints on the system. This leads to a disturbance in normal operation. In systems with integration of large intermittent wind power resources, additional complications are imposed on the system, especially under heavy winds that require immediate measures to minimize possible impact of abrupt wind power fallout. Effective power system fortifications have to be put in place to address the challenges. Wind varies more on the sub-hourly time scales; therefore, sub-hourly dispatch is bound to address more of these issues than commonly used hourly methods. Hybrid power system operation with wind necessitates the use of fast start-up generation and storage to improve quality of power. In this work, the impact of intermittent wind power curtailment on power system operation is addressed to prevent system instability. A modified wind turbine power curve is used to restrict the onset of the normal cut-off point, thereby allowing sufficient time for effective power switchover with pumped hydro generation. This improves the voltage stability of the power system during curtailment. Singular value decomposition matrix of the power system network is employed to evaluate the performance of the proposed method. Full article

Microgrids are distribution networks consisting of distributed energy sources such as photovoltaic and wind turbines, that have traditionally been one of the most popular sources of energy. Furthermore, microgrids consist of energy storage systems and loads (e.g., industrial and residential) that may operate in grid-connected mode or islanded mode. While microgrids are an efficient source in terms of inexpensive, clean and renewable energy for distributed renewable energy sources that are connected to the existing grid, these renewable energy sources also cause many difficulties to the microgrid due to their characteristics. These difficulties mainly include voltage collapses, voltage and frequency fluctuations and phase difference faults in both islanded mode and in the grid-connected mode operations. Stability of the microgrid structure is necessary for providing transient stability using intelligent optimization methods to eliminate the abovementioned difficulties that affect power quality. This paper presents optimization and control techniques that can be used to provide transient stability in the islanded or grid-connected mode operations of a microgrid comprising renewable energy sources. The results obtained from these techniques were compared, analyzing studies in the literature and finding the advantages and disadvantages of the various methods presented. Thus, a comprehensive review of research on microgrid stability is presented to identify and guide future studies. Full article

With the increasing penetration of wind power generation, the frequency regulation burden on conventional synchronous generators has become heavier, as the rotor speed of doubly-fed induction generator (DFIG) is decoupled with the system frequency. As the frequency regulation capability of wind farms is an urgent appeal, the inertia control of DFIG has been studied by many researchers and the energy storage (ES) system has been installed in wind farms to respond to frequency deviation with doubly-fed induction generators (DFIGs). In view of the high allocation and maintenance cost of the ES system, the capacity allocation scheme of the ES system—especially for fast-frequency response—is proposed in this paper. The capacity allocation principle was to make the wind farm possess the same potential inertial energy as that of synchronous generators set with equal rated power. After the capacity of the ES system was defined, the coordinated control strategy of the DFIG-ES system with consideration of wind speed was proposed in order to improve the frequency nadir during fast-frequency response. The overall power reference of the DFIG-ES system was calculated on the basis of the frequency response characteristic of synchronous generators. In particular, once the power reference of DFIG was determined, a novel virtual inertia control method of DFIG was put forward to release rotational kinetic energy and produce power surge by means of continuously modifying the proportional coefficient of maximum power point tracking (MPPT) control. During the deceleration period, the power reference smoothly decreased with the rotor speed until it reached the MPPT curve, wherein the rotor speed could rapidly recover by virtue of wind power so that the secondary frequency drop could be avoided. Afterwards, a fuzzy logic controller (FLC) was designed to distribute output power between the DFIG and ES system according to the rotor speed of DFIG and $SoC$ of ES; thus the scheme enabled the DFIG-ES system to respond to frequency deviation in most cases while preventing the secondary frequency drop and prolonging the service life of the DFIG-ES system. Finally, the test results, which were based on the simulation system on MATLAB/Simulink software, verified the effectiveness of the proposed control strategy by comparison with other control methods and verified the rationality of the designed fuzzy logic controller and proposed capacity allocation scheme of the ES system. Full article

Due to growing concerns about carbon emissions, Carbon Capture and Storage (CCS) techniques have become an interesting alternative to overcome this problem. CO₂-Argon-Steam-Oxy (CARSOXY)-fuel gas turbines are an innovative example that integrates CCS with gas turbine powergen improvement. Replacing air-fuel combustion by CARSOXY combustion has been theoretically proven to increase gas turbine efficiency. Therefore, this paper provides a novel approach to continuously supply a gas turbine with a CARSOXY blend within required molar fractions. The approach involves H₂ and N₂ production, therefore having the potential of also producing ammonia. Thus, the concept allows CARSOXY cycles to be used to support production of ammonia whilst increasing power efficiency. An ASPEN PLUS model has been developed to demonstrate the approach. The model involves the integrations of an air separation unit (ASU), a steam methane reformer (SMR), water gas shift (WGS) reactors, pressure swing adsorption (PSA) units and heat exchanged gas turbines (HXGT) with a CCS unit. Sensitivity analyses were conducted on the ASU-SMR-WGS-PSA-CCS-HXGT model. The results provide a baseline to calibrate the model in order to produce the required CARSOXY molar fraction. A MATLAB code has also been developed to study CO₂ compression effects on the CARSOXY gas turbine compressor. Thus, this paper provides a detailed flowsheet of the WGS-PSA-CCS-HXGT model. The paper provides the conditions in which the sensitivity analyses have been conducted to determine the best operable regime for CARSOXY production with other high valuable gases (i.e., hydrogen). Under these specifications, the sensitivity analyses on the (SMR) sub-model spots the H₂O mass flow rates, which provides the maximum hydrogen level, the threshold which produces significant CO₂ levels. Moreover, splitting the main CH₄ supply to sub-supply a SMR reactor and a furnace reactor correlates to best practices for CARSOXY. The sensitivity analysis has also been performed on the (ASU) sub-model to characterise its response with respect to the variation of air flow rate, distillation/boiling rates, product/feed stage locations and the number of stages of the distillation columns. The sensitivity analyses have featured the response of the ASU-SMR-WGS-PSA-CCS-HXGT model. In return, the model has been qualified to be calibrated to produce CARSOXY within two operability modes, with hydrogen and nitrogen or with ammonia as by-products. Full article

Existing DC fast-charging stations are experiencing power quality issues such as high harmonics in the line current, poor power factor in the input supply, and overloading of distribution transformers, due to the dynamic behavior of charging patterns when it is connected to the power grid. Most of the recent works involve the usage of renewable energy sources to mitigate the issues on the distribution grid. In order to design a mobile plug and play DC fast charging station, solar energy is the best and viable solution to carry out. In this paper, plug and play solar photovoltaic power plant to charge electric vehicles (EVs) is proposed and modelled using MATLAB/Simulink software. The proposed system can act as a mobile power plant. The controller allows the system to charge the battery, whenever there is abundant solar energy. Incoming EVs will be charged directly from the system battery where the charger acts as a rapid charging system. The proposed system can meet the concept of Solar Photovoltaic Rapid Charging Stations (SPRCS), which shows that 80% of charge can be fed to an EV in 10.25 min. Full article

Electric servo system (ESS) is a servo mechanism in a control system of an aircraft, a ship, etc., which controls efficiency and directly affects the energy consumption and the dynamic characteristics of the system. However, the control performance of the ESS is affected by uncertainties such as friction, clearance, and component aging. In order to improve the control performance of the ESS, a control technology combining particle swarm optimization (PSO) and finite time servo system control (FTSSC) was introduced into ESS. In fact, it is difficult to know the uncertain physical parameters of the real ESS. In this paper, the genetic algorithm (GA) was introduced into PSO and the inertia weight was improved, which increased the parameter optimization precision and convergence speed. A new feedback controller is proposed to improve response speed and reduce errors by using FTSSC theory. The performance of the controller based on PSO identification algorithm was verified by co-simulation experiments based on Automatic Dynamic Analysis of Mechanical Systems (ADAMS) (MSC software, Los Angeles, CA, USA) and matrix laboratory (MATLAB)/Simulink (MathWorks, Natick, MA, USA). Meanwhile, the proposed strategy was validated on the servo test platform in the laboratory. Compared with the existing control strategy, the control error was reduced by 75% and the steady-state accuracy was increased by at least 50%. Full article

Formic acid decomposition to H₂-rich gas was investigated over a CuO-CeO₂/γ-Al₂O₃ catalyst. The catalyst was characterized by XRD, HR TEM and EDX methods. A 100% conversion of formic acid was observed over the copper-ceria catalyst under ambient pressure, at 200–300 °C, N₂:HCOOH = 75:25 vol.% and flow rate 3500–35,000 h⁻¹ with H₂ yield of 98%, wherein outlet CO concentration is below the equilibrium data (<0.5 vol.%). The copper-ceria catalyst proved to be promising for multifuel processor application, and the H₂ generation from dimethoxymethane, methanol, dimethyl ether and formic acid on the same catalyst for fuel cell supply. Full article

The basic and adaptive maximum power point tracking algorithms have been studied for distributed photovoltaic systems to maximize the energy production of a photovoltaic (PV) module. However, the basic maximum power point tracking algorithms using a fixed step size, such as perturb and observe and incremental conductance, suffer from a trade-off between tracking accuracy and tracking speed. Although the adaptive maximum power point tracking algorithms using a variable step size improve the maximum power point tracking efficiency and dynamic response of the basic algorithms, these algorithms still have the oscillations at the maximum power point, because the variable step size is sensitive to external factors. Therefore, this paper proposes an enhanced maximum power point tracking algorithm that can have fast dynamic response, low oscillations, and high maximum power point tracking efficiency. To achieve these advantages, the proposed maximum power point tracking algorithm uses two methods that can apply the optimal step size to each operating range. In the operating range near the maximum power point, a small fixed step size is used to minimize the oscillations at the maximum power point. In contrast, in the operating range far from the maximum power point, a variable step size proportional to the slope of the power-voltage curve of PV module is used to achieve fast tracking speed under dynamic weather conditions. As a result, the proposed algorithm can achieve higher maximum power point tracking efficiency, faster dynamic response, and lower oscillations than the basic and adaptive algorithms. The theoretical analysis and performance of the proposed algorithm were verified by experimental results. In addition, the comparative experimental results of the proposed algorithm with the other maximum power point tracking algorithms show the superiority of the proposed algorithm. Full article

This paper tests the validity of the FUND model’s energy impact functions, and the hypothesis that global warming of 2 °C or more above pre-industrial times would negatively impact the global economy. Empirical data of energy expenditure and average temperatures of the US states and census divisions are compared with projections using the energy impact functions with non-temperature drivers held constant at their 2010 values. The empirical data indicates that energy expenditure decreases as temperatures increase, suggesting that global warming, by itself, may reduce US energy expenditure and thereby have a positive impact on US economic growth. These findings are then compared with FUND energy impact projections for the world at 3 °C of global warming from 2000. The comparisons suggest that warming, by itself, may reduce global energy consumption. If these findings are correct, and if FUND projections for the non-energy impact sectors are valid, 3 °C of global warming from 2000 would increase global economic growth. In this case, the hypothesis is false and policies to reduce global warming are detrimental to the global economy. We recommend the FUND energy impact functions be modified and recalibrated against best available empirical data. Our analysis and conclusions warrant further investigation. Full article

Time-varying pricing is seen as an appropriate means for unlocking the potential flexibility from electric vehicle users. This in turn facilitates the future integration of electric vehicles and renewable energy resources into the power grid. The most complex form of time-varying pricing is dynamic pricing. Its application to electric vehicle charging is receiving growing attention and an increasing number of different approaches can be found in the literature. This work aims at providing an overview and a categorization of the existing work in this growing field of research. Furthermore, user studies and the modeling of user preferences via utility functions are discussed. Full article

This paper aims at minimizing the total energy consumption of multi-train in an urban rail transit (URT) system by optimizing and updating speed profiles considering regenerative braking power losses on the catenary. To make full use of regenerative energy and decrease traction energy consumption simultaneously, energy-efficient control strategies of multi-train and a corresponding solution method are proposed. The running process of multi-train is divided into several sections based on passenger stations. Speed profiles of each train in each section are collaboratively optimized by searching only one transition point from the optimized single-train speed profile, which can be worked out by searching the switching point of coasting mode, and the optimized multi-train speed profiles are updated based on departure orders of trains. Moreover, an electrical network model is established to analyze energy flows, and dynamic losses of recovered regenerative energy on the line can be calculated. Besides, an improved optimization strategy of multi-train, which contains seven motion phases, is presented for steep slope. Simulation results based on Guangzhou Metro Line 8 verify the effectiveness of the proposed methods. Total energy consumption of optimized multi-train can be decreased by 6.95% compared with multi-train adopted single-train optimal control strategy, and the energy-saving rate of 21.08% can be achieved compared with the measured data by drivers under same trip time. In addition, the influence of departure interval on total energy consumption is analyzed and the optimal departure interval can be obtained. Full article

This paper addresses a new reliability model, based on energy dissipation, considering performance degradation behaviors. Different from the two-state reliability model and traditional reliability model based on failure rate statistics, this paper focuses on the component energy loss due to its fault evolution, such as fatigue, aging and wear, and presents a reliability model based on the component’s energy dissipation, as well as establishing a power dissipation constrained reliability model for degradation-based reliability assessment. As a demonstration, the proposed method is applied to model and evaluate the failure behavior of the electro-hydrostatic actuation system. The results indicate that the proposed method is effective in describing its life-cycle degradation in the energy field, and provides a reliability assessment based on energy dissipation. Full article

Effects of salt on anaerobic digestion are dosage-dependent. As salt is a widely used condiment in food processing, effects of salt are bound to be considered when food waste is digested. In this study, salt addition effects (0, 2, 4, 6, 9, 12 g∙L⁻¹) on biogas and methane yields and kinetics of biogas production were researched. Meanwhile, component characteristics (food waste featured in carbohydrate, protein and fat, respectively) and fermentation concentrations (5 and 8 gVS∙L⁻¹) were also taken into consideration. Results showed that 2–4 g∙L⁻¹ salt addition was the optimal addition dosage for AD systems as they not only have the maximum biogas and methane yields, but also the maximum vs. removal in most cases. Also, according to the results of a modified Gompertz model, which is used to predict biogas and methane production rates, suitable salt addition can accelerate biogas production, improving the maximum biogas production rate (R_max). Factorial design (2 × 2) proved that interaction of salt and fermentation concentrations was significant for food waste featured with carbohydrate and with protein (p < 0.05). High salt addition and fermentation concentration can break the AD system when the feeding material was food waste featured with carbohydrate, but for food waste featured with protein, interaction of fermentation concentrations and salt addition can alleviate inhibition degrees. Full article

In this paper, we propose a scheme that implements a Distributed Ledger Technology (DLT) based on Directed Acyclic Graph (DAG) to generate, validate, and confirm the electricity transaction in Smart Grid. The convergence of the Smart Grid and distributed ledger concept has recently been introduced. Since Smart Grids require a distributed network architecture for power distribution and trading, the Distributed Ledger-based Smart Grid design is a spotlighted research domain. However, only the Blockchain-based methods, which are a type of the distributed ledger scheme, are currently either being considered or adopted in the Smart Grid. Due to computation-intensive consensus schemes such as Proof-of-Work and discrete block generation, Blockchain-based distributed ledger systems suffer from efficiency and latency issues. We propose a DAG-based distributed ledger for Smart Grids, called PowerGraph, to resolve this problem. Since a DAG-based distributed ledger does not need to generate blocks for confirmation, each transaction of the PowerGraph undergoes the validation and confirmation process individually. In addition, transactions in PowerGraph are used to keep track of the energy trade and include various types of transactions so that they can fully encompass the events in the Smart Grid network. Finally, to ensure that PowerGraph maintains a high performance, we modeled the PowerGraph performance and proposed a novel consensus algorithm that would result in the rapid confirmation of transactions. We use numerical evaluations to show that PowerGraph can accelerate the transaction processing speed by over 5 times compared to existing DAG-based DLT system. Full article

Due to its variability, solar power generation poses challenges to grid energy management. In order to ensure an economic operation of a national grid, including its stability, it is important to have accurate forecasts of solar power. The current paper discusses probabilistic forecasting of twenty-four hours ahead of global horizontal irradiance (GHI) using data from the Tellerie radiometric station in South Africa for the period August 2009 to April 2010. Variables are selected using a least absolute shrinkage and selection operator (Lasso) via hierarchical interactions and the parameters of the developed models are estimated using the Barrodale and Roberts’s algorithm. Two forecast combination methods are used in this study. The first is a convex forecast combination algorithm where the average loss suffered by the models is based on the pinball loss function. A second forecast combination method, which is quantile regression averaging (QRA), is also used. The best set of forecasts is selected based on the prediction interval coverage probability (PICP), prediction interval normalised average width (PINAW) and prediction interval normalised average deviation (PINAD). The results demonstrate that QRA gives more robust prediction intervals than the other models. A comparative analysis is done with two machine learning methods—stochastic gradient boosting and support vector regression—which are used as benchmark models. Empirical results show that the QRA model yields the most accurate forecasts compared to the machine learning methods based on the probabilistic error measures. Results on combining prediction interval limits show that the PMis the best prediction limits combination method as it gives a hit rate of 0.955 which is very close to the target of 0.95. This modelling approach is expected to help in optimising the integration of solar power in the national grid. Full article

Battery storage has been highlighted as one way to increase the share of renewables in energy systems. The use of local battery storage is also beneficial when reducing power variations in the grid, thereby contributing to more robust and cost-effective energy systems. The purpose of this paper is to investigate barriers, drivers and non-energy benefits (NEB) for investments in battery storage in photovoltaic systems (PV) in the context of farmers with PV systems in Sweden. The study is based on a questionnaire about barriers, driving forces and NEB for investment in battery storage connected to PV. The questionnaire was sent to farmers in Sweden who already have photovoltaics installed and about 100 persons answered, a response rate of 59%. The major barriers found are related to the technical and economic risks of investing in battery storage. One of the main conclusions is that the highest-ranked driver, i.e., to use a larger part of the produced electricity oneself, turns out to be the highest priority for the grid-owner seeking to reduce the need for extensive investments in the grid. The primary NEBs found were the possibility of becoming independent from grid electricity. Full article

This paper presents a coordinated control based on the advection consensus control algorithm to implement power dispatch for each wave-energy converter (WEC) in a WEC array. Under unbalanced conditions, the proposed algorithm is applied in order to control each WEC to output power coordinately, to enable the total output power of the WEC array to satisfy the time-varying load requirements. The purpose of the additional energy storage unit on each WEC is to smooth the power output of each WEC and to obtain more margin. Case studies include the demonstration of some simulations and experiments, and the results show that the WEC array under the proposed control method can accurately respond to the demand for power supply under unbalanced initial conditions. Full article

With the grid-connected operation of large-scale wind farms, the contradiction between supply and demand of power systems is becoming more and more prominent. The introduction of multiple types of flexible resources provides a new technical means for improving the supply–demand matching relationship of system flexibility and promoting wind power consumption. In this paper, multi-type flexible resources made up of deep peak regulation of thermal units, demand response, and energy storage were utilized to alleviate the peak regulation pressure caused by large-scale wind power integration. Based on current thermal plant deep peak regulation technology, a three-phase peak regulation cost model of thermal power generation considering the low load fatigue life loss and oil injection cost of the unit was proposed. Additionally, from the perspective of supply–demand balance of power system flexibility, the flexibility margin index of a power system containing source-load-storage flexible resources was put forward to assess the contribution from each flexibility provider to system flexibility. Moreover, an optimal dispatching model of a multi-energy power system with large-scale wind power and multi-flexible resources was constructed, aimed at the lowest total dispatching cost of the whole scheduling period. Finally, the model proposed in this paper was validated by a modified RTS96 system, and the effects of different flexibility resources and wind power capacity on the optimal scheduling results were discussed. Full article

Maintaining a suitable cabin air temperature distribution is essential for providing an acceptable thermal environment for passengers and crew. However, cabin air may be very cold for the first flight in winter morning. It could be difficult to heat quickly the cabin air and to maintain an acceptable temperature gradient before boarding with the existing environmental control system. This study developed numerical model for predicting the heating process that coupled airflow and heat transfer in a cabin. The model was validated by using the experimental data obtained from an MD-82 airliner. With the validated numerical model, this investigation proposed to use an electric blanket to heat cabin air quickly and to reduce the air temperature gradient. Full article

Southern Taiwan has excellent solar energy resources that remain largely unused. This study incorporated a measure that aids in providing simple and effective power generation efficiency assessments of solar panel brands in the planning stage of installing these panels on roofs. The proposed methodology can be applied to evaluate photovoltaic (PV) power generation panels installed on building rooftops in Southern Taiwan. In the first phase, this study selected panels of the BP3 series, including BP350, BP365, BP380, and BP3125, to assess their PV output efficiency. BP Solar is a manufacturer and installer of photovoltaic solar cells. This study first derived ideal PV power generation and then determined the suitable tilt angle for the PV panels leading to direct sunlight that could be acquired to increase power output by panels installed on building rooftops. The potential annual power outputs for these solar panels were calculated. Climate data of 2016 were used to estimate the annual solar power output of the BP3 series per unit area. The results indicated that BP380 was the most efficient model for power generation (183.5 KWh/m²-y), followed by BP3125 (182.2 KWh/m²-y); by contrast, BP350 was the least efficient (164.2 KWh/m²-y). In the second phase, to simulate meteorological uncertainty during hourly PV power generation, a surface solar radiation prediction model was developed. This study used a deep learning–based deep neural network (DNN) for predicting hourly irradiation. The simulation results of the DNN were compared with those of a backpropagation neural network (BPN) and a linear regression (LR) model. In the final phase, the panel of module BP3125 was used as an example and demonstrated the hourly PV power output prediction at different lead times on a solar panel. The results demonstrated that the proposed method is useful for evaluating the power generation efficiency of the solar panels. Full article

Energy performance certificates (EPCs) were introduced to give property buyers better information about the energy efficiency of dwellings and provide incentives to make energy-efficient investments. Previous studies on the effect of EPCs on property value have yielded divergent results, with some studies finding that energy labels affect property values, but others finding that energy labels have little or no effect. The present paper takes the analysis one step further. Using data on energy prices in combination with transaction data from Oslo, we conclude that not only the energy label, but also the energy performance of dwellings in general, has little to no effect on transaction prices. This result is in line with the inferences of several survey studies, which indicate that when people buy a dwelling, they pay considerably less attention to its energy performance compared with other factors, such as the location, neighborhood, size, garden, and the number of bedrooms. Full article

This paper compares the methods of mechanical mining of rock in terms of their efficiency, energy consumption, and the durability of the tools they involve. It presents the advantages of mechanical heading-driving methods. In the first part, we described the methods used to assess rock workability, and the influence of rock types and parameters on mining efficiency. Furthermore, we discussed the compact-rock mining process in terms of the energy it consumes. We provided the description of the most common mechanical methods, such as milling, static crumpling and undercutting, including the tools involved, and the requirements and limitations for the use of these methods. The paper presents unique machinery solutions designed to reduce the energy consumed by mining processes. In the final part of the paper, we propose a solution to select the mechanical method of rock mining as a function of rock type and parameters. Full article

A proposal of the dynamic thermal rating (DTR) applied and optimized for low-loaded power transformers equipped with on-line hot-spot (HS) measuring systems is presented in the paper. The proposed method concerns the particular population of mid-voltage (MV) to high-voltage (HV) transformers, a case study of the population of over 1500 units with low average load is analyzed. Three representative real-life working units are selected for the method evaluation and verification. Temperatures used for analysis were measured continuously within two years with 1 h steps. Data from 2016 are used to train selected models based on various machine learning (ML) algorithms. Data from 2017 are used to verify the trained models and to validate the method. Accuracy analysis of all applied ML algorithms is discussed and compared to the conventional thermal model. As a result, the best accuracy of the prediction of HS temperatures is yielded by a generalized linear model (GLM) with mean prediction error below 0.71% for winding HS. The proposed method may be implemented as a part of the technical assessment decision support systems and freely adopted for other electrical power apparatus after relevant data are provided for the learning process and as predictors for trained models. Full article

Advanced metering infrastructure (AMI) is spreading to households in some countries, and could be a source for forecasting the residential electric demand. However, load forecasting of a single household is still a fairly challenging topic because of the high volatility and uncertainty of the electric demand of households. Moreover, there is a limitation in the use of historical load data because of a change in house ownership, change in lifestyle, integration of new electric devices, and so on. The paper proposes a novel method to forecast the electricity loads of single residential households. The proposed forecasting method is based on convolution neural networks (CNNs) combined with a data-augmentation technique, which can artificially enlarge the training data. This method can address issues caused by a lack of historical data and improve the accuracy of residential load forecasting. Simulation results illustrate the validation and efficacy of the proposed method. Full article