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An integrated energy system (IES) shows great potential in reducing the terminal energy supply cost and improving energy efficiency, but the operation scheduling of an IES, especially integrated with inter-connected multiple energy stations, is rather complex since it is affected by various factors. Toward a comprehensive operation scheduling of multiple energy stations, in this paper, a day-ahead and intra-day collaborative operation model is proposed. The targeted IES consists of electricity, gas, and thermal systems. First, the energy flow and equipment composition of the IES are analyzed, and a detailed operation model of combined equipment and networks is established. Then, with the objective of minimizing the total expected operation cost, a robust optimization of day-ahead and intra-day scheduling for energy stations is constructed subject to equipment operation constraints, network constraints, and so on. The day-ahead operation provides start-up and shut-down scheduling of units, and in the operating day, the intra-day rolling operation optimizes the power output of equipment and demand response with newly evolved forecasting information. The photovoltaic (PV) uncertainty and electric load demand response are also incorporated into the optimization model. Eventually, with the piecewise linearization method, the formulated optimization model is converted to a mixed-integer linear programming model, which can be solved using off-the-shelf solvers. A case study on an IES with five energy stations verifies the effectiveness of the proposed day-ahead and intra-day collaborative robust operation strategy.

PV windows can potentially produce electricity, reduce the air-conditioning load, and provide natural light. Cross-seasonal experimental tests for long period could eliminate the gap between real outdoor test and simulation estimation. In this research, the lighting-thermal-electricity performance of high-efficiency c-silicon PV windows was tested, and the improvement has been put forward according to the conditions. The long-term experiment was conducted cross different seasons, including summer, autumn, and winter seasons. The highest average power generation, 50W/m2, could be found in autumn. The average outside surface temperature of the PV window would reach 48 °C in sunny days in the autumn, which was higher than other seasons. Although the c-Si PV window maybe block the partial daylighting, the daylighting requirement still could be satisfied with the most days. Furthermore, the average Useful Daylight Illuminance was the highest in summer up to 0.79, and the average illumination uniform could be achieved at a high level in all seasons. For the improvement suggestions, some measures could be taken to reduce the indoor cooling load in summer. During winter, appropriate inner shading measures might be taken to prevent excessive illumination in the building, and allow the electricity and thermal performance of PV window.

There is great interest in data-driven modelling for the forecasting of building energy consumption while using machine learning (ML) modelling. However, little research considers classification-based ML models. This paper compares the regression and classification ML models for daily electricity and thermal load modelling in a large, mixed-use, university building. The independent feature variables of the model include outdoor temperature, historical energy consumption data sets, and several types of ‘agent schedules’ that provide proxy information that is based on broad classes of activity undertaken by the building’s inhabitants. The case study compares four different ML models testing three different feature sets with a genetic algorithm (GA) used to optimize the feature sets for those ML models without an embedded feature selection process. The results show that the regression models perform significantly better than classification models for the prediction of electricity demand and slightly better for the prediction of heat demand. The GA feature selection improves the performance of all models and demonstrates that historical heat demand, temperature, and the ‘agent schedules’, which derive from large occupancy fluctuations in the building, are the main factors influencing the heat demand prediction. For electricity demand prediction, feature selection picks almost all ‘agent schedule’ features that are available and the historical electricity demand. Historical heat demand is not picked as a feature for electricity demand prediction by the GA feature selection and vice versa. However, the exclusion of historical heat/electricity demand from the selected features significantly reduces the performance of the demand prediction.

The classification of drilling conditions is a crucial task in the drilling process, playing a vital role in improving drilling efficiency and reducing costs. In this study, we propose an improved stacking ensemble learning algorithm with the objective of enhancing the performance of drilling conditions classification. Additionally, this algorithm aims to have a positive impact on automated drilling time estimation and the continuous improvement of efficiency. In our experimental setup, we employed various base learners, such as random forests, support vector machine, and the K-nearest neighbors algorithm, as initial models for the task of drilling conditions classification. To improve the model’s expressive power and feature relevance specifically for this task, we enhanced the meta-model component of the stacking algorithm by incorporating feature engineering techniques. The experimental results show that the improved ensemble learning algorithm achieves an accuracy and recall rate of 97% and 98%, respectively. Through continuous improvement in drilling operations, the average sliding time is reduced by 21.1%, and the average Rate of Penetration (ROP) is increased by 15.65%. This research holds significant importance for engineering practice in the drilling industry, providing robust support for optimizing and enhancing the drilling process.

The near-wall flame structure and pollutant emissions of the laminar premixed biogas-hydrogen impinging flame were simulated with a detailed chemical mechanism. The spatial distributions of the temperature, critical species, and pollutant emissions near the wall of the laminar premixed biogas–hydrogen impinging flame were obtained and investigated quantitatively. The results show that the cold wall can influence the premixed combustion process in the flame front, which is close to the wall but does not touch the wall, and results in the obviously declined concentrations of OH, H, and O radicals in the premixed combustion zone. After flame quenching, a high CO concentration can be observed near the wall at equivalence ratios (φ) of both 0.8 and 1.2. Compared with that at φ = 1.0, more unburned fuel is allowed to pass through the quenching zone and generate CO after flame quenching near the wall thanks to the suppressed fuel consumption rate near the wall and the excess fuel in the unburned gases at φ = 0.8 and 1.2, respectively. By isolating the formation routes of NO production, it is found that the fast-rising trend of NO concentration near the wall in the post flame region at φ = 0.8 is attributed to the NO transportation from the NNH route primarily, while the prompt NO production accounts for more than 90% of NO generation in the wall vicinity at φ = 1.2. It is thus known that, thanks to the effectively increased surface-to-volume ratio, the premixed combustion process in the downsized chamber will be affected more easily by the amplified cooling effects of the cold wall, which will contribute to the declined combustion efficiency, increased CO emission, and improved prompt NO production.

Experiments were carried out to investigate the heat transfer and pollutants emission characteristics of a slot LPG premixed flame array impinging normally onto a flat plate. The effects of jet-to-jet spacing (S/de), nozzle-to-plate distance (H/de), and jet Reynolds number (Re) on the heat flux and emission index of CO, CO2, and NOx/NO2 were examined. In addition, the thermal and emission characteristics between slot jets and circular jets were compared under identical experimental conditions. The results show that the more uniform heat flux distribution and higher total heat flux can be obtained at moderate jet-to-jet spacing, large jet-to-plate distance, and higher Reynolds number. EICO emissions can be influenced by the combined effects of jet-to-jet spacing, jet-to-plate distance, and higher Reynolds number. For the sake of the better combustion efficiency and lower EICO emission, the moderate jet-to-jet spacing (S/de = 2.5), larger jet-to-plate distance (H/de = 4), and relatively higher Reynolds number (Re = 1500) are preferred for the slot jet flame array. Furthermore, it is found that there exists a trade-off between the EICO and EINOx of the slot LPG flame array. Compared with multiple circular flame jets, multiple slot flames jets have the higher area-averaged heat flux due to the larger heating area and more uniform heat flux distribution, while the higher EICO emission and lower EINOx emission are due to the greater jet interaction suppressing the air entrainment. Thus, it is known that the slot flame array has a better heating performance but relatively higher pollutant emissions than the circular flame array.

The combustion characteristics of laminar biogas premixed and diffusion flames in the presence of upward decreasing magnetic fields have been investigated in this study. The mechanism of magnet–flame interaction in the literature, in which magnetic fields change the behaviors of laminar flames due to the paramagnetic and diamagnetic properties of the constituent gases, is examined and the results are as follows. The magnetic field has no noticeable effect on premixed flames due to low oxygen concentration of the mixed gas at the injection and the relatively high flow momentum. However, due to the diffusion nature of diffusion flames and paramagnetic property of oxygen in ambient air, oxygen distributions are subjected to the gradient of magnetic flux, thus shortening the height of diffusion flames. Results also show that the flame volume is more strongly varied than flame height. Altered oxygen distributions result in improved combustion and higher flame temperature. In the case of current magnet–flame interaction, the magnetic driving force is combined with gravitational force, and a modified gravity g* as well as gravity modification factor G are derived to characterize the paramagnetism theory of oxygen.

The doubly-fed induction generator (DFIG) wind turbine system, which is composed of the wind turbine, generator, rotor-side converter, grid-side converter, and so on, is a typical multi-time scale system. The dynamic processes at different time scales do not exist in isolation. Furthermore, neglecting the coupling of parameters of different time scales to reduce the order of the model will lead to deviation between the simulation results and the actual results, which may not be suitable for power system transient analysis. This paper proposes an electromechanical transient model and an electromagnetic transient model of the DFIG wind turbine system that consider the interaction of multiple time-scale dynamic processes. Firstly, the paper applies the modal analysis method to explain the multi-time scale characteristics of the DFIG wind turbine system. Secondly, the variation in the eigenvalues of the DFIG wind turbine system before and after the order reduction and the coupling between variables and the system, as well as the coupling between variables of different time scales, are analyzed to obtain the preliminary 21-order simplified model. Thirdly, considering the weak coupling characteristics between the mechanical part and the electromagnetic part of the DFIG wind turbine system, the 21-order simplified model is decomposed into a 15-order electromagnetic transient model and a six-order electromechanical transient model on the basis of their time scales. Then, according to the balance between simulation time and simulation accuracy, the 14-order electromagnetic transient model and the 10 or 12-order electromechanical transient model are finally obtained. Finally, the rationality of the simplified models is verified by simulations under two large disturbance conditions, namely wind speed abrupt change and voltage sag. The obtained simplified models have reference significance for improving the simulation speed of a wind power grid-connected system and analyzing the internal mechanism of the DFIG wind turbine system’s stability.

Due to the huge data of large-scale photovoltaic (PV) power plants, the establishment of its equivalent model is more practical than a detailed model. In connection with the current research status, this paper reviews the steady-state equivalent model and transient equivalent model of PV power plants. The steady-state equivalent model is used for power flow calculation and static stability analysis. Transient equivalent models contain the single-machine equivalent model and the multi-machine equivalent models, which are used for the simulation analysis of large disturbances. The calculation of equivalent parameters and the equivalence of power collection system are briefly introduced. The conclusion and problems to be solved are put forward at the end. The establishment of the equivalent model simplifies the detailed model, which is convenient for the planning and simulation analysis of the PV power plant, and can also accurately characterize the operating characteristics of the PV power plant, which is of great significance.