Journal of Energy Engineering

To evaluate the effect of turbulence and inherent instabilities of flame on flame structural characteristics, experiments were conducted with a ${70\%\text{-}\mathrm{H}}_2/30\%\text{-}\mathrm{CO}$ premixed flame at various equivalence ratios and turbulence intensities at atmospheric pressure and temperature. The convex and concave regions of the large-scale wrinkle in the flame front were extracted and quantitatively evaluated; wavelet transform was used to decompose disturbances in the flame front. The results show that with the development of the flame, the flame became unstable, the fluctuation range of the pulsating radius increased, the area and area ratio of the convex regions increased, and the average area and uniform degree of the convex and concave regions increased. As the turbulence intensity increased, the area and area ratio of the convex regions increased, the average area and uniform degree increased, and the disturbance energy at the same scale increased. Large-scale disturbances were the main factor affecting the flame’s structural characteristics. With the increase in equivalence ratio, the effect of thermal-diffusive instability and turbulence on flame structural characteristics was reduced, decreasing the fluctuation range of the pulsation radius. The interaction between disturbances of different scales in the flame front resulted in the uneven distribution of the pulsation radius.

Heavy fuel oil (HFO) is the predominant marine fuel. In the present study, a detailed model of HFO thermophysical properties, recently developed by the authors, is utilized for extensive computational fluid dynamics (CFD) studies of HFO spray dynamics in a large spray combustion chamber (SCC) and in a large marine engine; results are compared against existing and new experiments, for two HFO qualities. Here, spray modeling is based on a proper adaptation of the cascade atomization and drop break-up (CAB) model, whereas a new kinetic model accounting for HFO aromaticity is used for ignition modeling. Computational results are reported for nonreactive and reactive spray flow in the SCC, and are in good agreement with experimental data. The effects of HFO preheating on spray development are demonstrated. Finally, simulation results in a large marine engine are in good agreement with experiments in terms of pressure, heat release rate, and pollutant emissions. Overall, the present modeling is shown to be appropriate for detailed CFD studies of HFO spray flow and combustion in marine engines.

In a smart grid, the main target is to control both the energy sources and flexible loads to keep the energy balance. In this paper, a demand response (DR) program is optimally applied to keep this balance. Because plug-in electric vehicles (PEVs) can contribute as distributed energy storage and supply in a power grid, DR is applied to PEVs aggregated in parking stations. This paper also includes thermostatically controlled loads (TCLs) such as electric water heaters (EWHs) to be scheduled simultaneously with the PEVs. Part of the Alexandria, Egypt, distribution network is considered for this study. The network contains a number of distributed generation units including energy hubs (EHs), photovoltaic (PV) units, and diesel units. A multilayer individual-based optimization algorithm is proposed to solve the energy management problem of such a system by applying demand-side management (DSM), flexible load schedules, and dispatch of energy sources simultaneously and dependently.

The rise of the future grid (FG) largely depends on the efficient integration of Internet of Things (IoT) and Cloud computing technologies. By utilizing information and control flows, FG can deliver power more effectively and be capable to handle events occurring anywhere in the grid network. However, maintaining such functions consumes a great deal of computational resource which brings an enormous operational cost to the grid owner. In this paper, we propose an integrated task scheduling and resource provisioning model for dynamically operating an IoT-Cloud system to reduce the overall operational cost. Our proposed approach uses a bipartite graph to model the communication pattern between sensor groups and decentralized cloud data centers and a Pareto distribution-based method to estimate the required resources considering capacity limitation and failure of the system in each data center. We formulate the integrated model as a constraint optimization problem over all sensor groups and data centers. We solve the problem with genetic algorithms due to problem complexity, and our extensive computer simulations and comparisons demonstrate the correctness and effectiveness of the proposed model in minimizing operational cost while satisfying system performance requirements.

Power microgrids are developing as complex cyber-physical systems. In this paper, a novel two-layer power control scheme is proposed to coordinate distributed energy resources (DERs), e.g., photovoltaics (PVs), energy storage system (ESSs), and loads, for both the short-term and long-term power imbalance in islanded alternating current (AC) microgrids. The first-layer control is based on local measurement and realized by model-predictive control, which provides fast dynamic response and small steady-state error for power inverters. The master–slave control scheme is adopted to achieve autonomous power balancing of the microgrid. The second-layer control is performed by a consensus-based distributed control for coordination among DERs via cyber networks. The second-layer control operates for power management by charging/discharging ESSs, PV curtailment, and load shedding, when the local first-layer control cannot balance the power in the microgrid. With the proposed control scheme, the power balancing of islanded microgrids can be achieved autonomously by adjusting the power output of all DERs. The two-layer control scheme is implemented in an islanded cyber-physical microgrid and the simulation results validate the effectiveness of the proposed approach.

The influence of $n$-butanol additive and spark timing on the combustion process was studied in a gasoline direct injection (GDI) engine, as well as fuel efficiency and relevant performance indicators. Three concentrations of $n$-butanol blended fuels (i.e., Bu15, Bu30, and Bu50) were used as tested fuels and compared to regular gasoline at low and high loads. The results indicate that increasing the $n$-butanol concentration or advanced spark timing can increase the combustion pressure and temperature, improve the burning rate, and reduce combustion duration. Moreover, brake thermal efficiency (BTE) increases with rising $n$-butanol concentration, but its change with spark timing is affected by $n$-butanol concentration. At the low load, the highest BTE with regular gasoline and Bu15 occurs between varying spark timing with advanced spark timing, while BTE reduces persistently for Bu30 and Bu50. At the high load, furthermore, BTE increases for all the tested fuels with advanced spark timing. However, increasing $n$-butanol concentration or advanced spark timing may increase knock tendency. To summarize, varying spark timing or $n$-butanol concentration can affect the combustion process and fuel efficiency distinctly depending on fuel properties and engine loads. Spark timing should be adjusted to achieve better fuel efficiency for high concentration of $n$-butanol blended fuels.

Intelligent systems (IS) have gained popularity in facilitating very fast dynamic security assessment (DSA). However, conventional IS methods are limited in their ability to be updated with current system operation conditions online due to the excessive training time and complex parameters tuning required for updates. In this paper, an online sequential extreme learning machine (ELM) based method is proposed to enable efficient real-time DSA and online model updating. To enhance the performance of ELMs, feature selection using single-feature estimation is conducted and the results are utilized to design generation shifting as a preventive control. The proposed methods are examined based on the New England 39-bus test system and compared with popular IS methods. The simulation results show that the ELM-based DSA method possesses significant superior computation speed while high, competitive accuracy is maintained. The derived generation shifting is also valid to restore system security.

Wind energy is a clean and renewable energy source and thus has promising future prospects. To increase the utilization rate and power generation of wind turbines and reduce their maintenance costs, it is necessary to monitor the operating conditions of wind turbines. This paper introduces a monitoring method based on state curves and includes a study that analyzes five types of state curves, namely, wind speed–power, wind speed–rotor speed, wind speed–pitch angle, rotor speed–power, and rotor speed–pitch angle. The results indicate that due to the external environment (e.g., atmospheric temperature, atmospheric pressure, wind turbulence, wind direction, topography), the wind turbine internal hardware performance, and the control strategy, the first three curves did not allow the wind turbine to distinguish the normal operation status from a fault status. However, the rotor speed–power and rotor speed–pitch angle curves were able to accurately monitor abnormal conditions of the wind turbine. This study aims to establish theoretical curves of a wind turbine and correct the state curves to calculate the distance from the actual operating point to the state curves. During operation, the wind turbine will be under several conditions, ranging from maximum power point tracking to constant speed to constant power conditions. Using the time window and a confusion matrix to determine the best deviation of the wind turbine under different operating conditions is more effective in reducing the false alarm rate. Based on the optimal offset distances and the relationship among rotor speed, power, and pitch angle during start-up and shut down, a corresponding evaluation system is established. Taking the operational data from a wind farm as an example, the research reveals that when a wind turbine is operating normally, the deviations in the state curves under different operating conditions fall within an appropriate range; once an abnormal condition occurs, if the number of abnormalities exceeds a specified value during a specified time window, then an alarm signal will sound. Compared with the supervisory control and data acquisition (SCADA) system, in this system the alarm time is advanced.