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The recommended power quality (PQ) indices in electric power systems are defined based on the discrete Fourier transform (DFT). DFT is mostly evaluated applying fast Fourier transform (FFT) algorithm which requires signals to be periodic in nature. Hence, it evaluates PQ indices accurately for stationary PQ disturbances only. However, in case of nonstationary PQ disturbances signal is aperiodic and FFT results in erroneous assessment due to spectral leakage phenomena. Furthermore, FFT cannot provide time information of the PQ indices as computation is performed in frequency domain only. Which is significant shortcoming of the FFT when dealing with time-varying PQ disturbances to extract dynamic signature of the signal. Thereby, this paper presents a framework of redefining PQ indices for stationary and nonstationary PQ disturbances employing time-frequency distribution (TFD) method. Among all TFDs, reduced interference distribution (RID) represents the most suitable properties for analyzing time-varying PQ disturbances. Hence, in this work, it is employed to reformulate PQ indices typically used in electric power systems. The results of two synthetic examples considering stationary and nonstationary PQ disturbances imply that the RID-based redefined PQ indices are closer to the actual values. Also, they provide more accurate results than the existing transient PQ indices and traditional FFT-based method.

Electric vehicles (EVs) are considered a substitute for fossil-fueled vehicles due to rising fossil fuel prices and accompanying environmental concerns, and their use is predicted to increase dramatically shortly. However, the widespread use of EVs and their large-scale integration into the energy system will present several operational and technological hurdles. In the energy industry, an innovative solution known as the EVs smart parking lot (SPL) is introduced to handle EV charging and discharging electricity and energy supply challenges. This paper investigates social equity access and mobile charging stations (MCSs) for EVs, where the owner of MCSs is the EV parking lot. Accordingly, a new self-scheduling model for SPLs is presented in this paper that incorporates scheduling of the MCSs as temporary charging infrastructures while considering social equity access and optimizes SPL energy generation and storage schedule. The main objectives of this research are to (i) develop MCSs accessibility measures and quantify the equity impacts of MCSs locations by modeling prioritized demand based on several indices; (ii) determine the optimal set-points of SPL components (i.e., combined heat and power (CHP), photovoltaic system, electrical and heat-energy storage, and MCSs) to manage electrical peak demand and to maximize the economic benefits of SPLs. Results indicate that the proposed demand prioritization function model can meet the required EV charging demands for prioritized events, and the self-scheduling model for SPLs satisfies the charging demand of the EVs in the SPL location. Also, the social equity access to the EV charging stations is satisfied by analyzing the operation of MCSs around the prioritized demand of the prioritized events and social equity access indices.

The potential of renewable energy should be fully exploited in the transportation sector to achieve a cleaner production. Therefore, this paper proposes an on-grid hybrid hydrogen refueling and battery swapping station powered by wind energy. This novel concept can promote the development of low-carbon emission vehicles including hydrogen-based vehicle and battery electric vehicle. During the daily operation of the station, the multiple uncertainties may lead to a higher operational cost. To address this problem, a hybrid stochastic/distributionally robust optimization method is proposed to handle different uncertainties for the energy management problem. The first type of uncertainties can be depicted by a certain distribution, i.e. electricity price and wind power, which is processed by a stochastic optimization method. The second type of uncertainties is associated with human behaviors and is difficult to find its probability distribution, i.e. the hydrogen demand of hydrogen-based vehicles, so the second type is processed by a distributionally robust optimization method. The overall objective is to minimize the total operational cost of the station, which also considers the battery swapping station overstock punishment. Because a reasonable battery swapping scheduling can reduce the waiting time of users and operational cost of the station. The results indicate that the proposed method can effectively address the conservatism of solutions as its total operational cost is 4.4% lower than that of the hybrid stochastic/robust optimization method under a high confidence level.

The adoption of solar systems has witnessed a remarkable growth rate in recent years, driven by increasing awareness of renewable energy and declining costs of solar technology. Solar systems offer several advantages, including abundant energy source, reduced carbon emissions, and potential cost savings. However, they also face challenges such as intermittency, limited energy storage capacity, and grid integration issues. By incorporating hydrogen in smart grids, these drawbacks can be addressed as hydrogen can serve as a means of energy storage, allowing excess solar energy to be stored as hydrogen and utilized during periods of low solar generation. Hydrogen-incorporated smart grids thus provide a complementary solution to enhance the reliability, stability, and scalability of solar systems, facilitating their integration into the broader energy landscape. Consequently, this chapter aims to provide a comprehensive review of green hydrogen-integrated sector-coupled smart grids and presents prospects for future advancements. The background and significance of hydrogen integration within smart grid systems are established. The fundamentals of hydrogen integration, including its role as an energy carrier and its integration within smart grid systems, are explored. The concept of sector coupling in smart grids is examined, emphasizing the interconnection of different energy sectors and the importance of achieving energy system integration. Existing green hydrogen-incorporated smart grid projects are reviewed, and experiences gathered from successful implementations are analyzed. Technological advancements, such as emerging green hydrogen production and storage technologies, are discussed along with smart grid control and management systems for efficient green hydrogen utilization. Economic and environmental considerations are evaluated, encompassing cost analysis, evaluation of environmental impacts, and identification of economic incentives. Future prospects and research directions are explored, aiming to identify key challenges, address gaps, and highlight areas for further investigation. Overall, through this comprehensive review and exploration of future prospects, a deeper understanding of hydrogen-integrated sector-coupled smart grids and their potential for advancing sustainable energy systems can be achieved.

The reclosing time of a transmission line has a distinct influence on system stability. A new method of calculating the optimal reclosing time based on the transient energy function (TEF) is presented in this paper. It has been shown that an optimal reclosing time can be used to set the autoreclosing device so that the system stability or power-transfer capability can be significantly improved. Though power-system TEF is based on classical models, the results from simulation studies of a real system (46 generators and 230 buses) with complex models show that the optimal reclosing time calculated using TEF can be used effectively.

Multi-layer microgrids cluster (MLMC) has been formed in some certain areas with microgrids (MGs) merging into smart distributed system. It relies on an energy management system (EMS) to manage and schedule the optimal power flow among MGs and public power system in a stable and balanced way. Compared with EMS for single MG, energy management optimization for MLMC is much more complex that the interactions with other MGs require to be considered. In this paper a multi-time-scales EMS is proposed to minimize the total operating cost of MLMC. It consists of two parts: day-ahead static scheduling and real-time dynamic compensation. The day-ahead scheduling is an economic optimization based on the forecasting data within 24 hours. The real-time dynamic compensation is presented to compensate scheduling signal based on the real-time operating information, which is caused by the predictive error. Furthermore, charge-discharge protections of energy storage system (ESS) for each single MG are taken into account. A general-structure MLMG is studied as an example, and this problem is solved by mixed integer linear programming (MILP). The effectiveness of proposed EMS is verified in the MG Research Laboratory in Aalborg University, Denmark. The hardware-in-loop simulation results show the proposed EMS has a good convergence and performance in grid-connected mode.

Rotor imbalances present a serious problem for wind turbines. In particular, for offshore wind turbines, aerodynamic imbalance can have a severe impact because of the large rotor size. In this study, the impact of the aerodynamic imbalance is investigated. A novel framework for detecting aerodynamic imbalance is proposed. Firstly, a model of a 3MW direct-driven wind turbine was developed. The signals were acquired to test and verify the impact of aerodynamic imbalance. Secondly, a method based on optimized maximum correlated kurtosis deconvolution was proposed for the primary detection. The intrinsic mode functions of nacelle vibration were adopted as the input variable. The weak unbalanced signals could be discerned. Moreover, the azimuth of rotor allows the unbalanced blades to be obtained. Thirdly, a convolutional neural network with a new structure was used to determine the magnitudes of aerodynamic imbalances. The first layer of the convolutional neural network is sufficiently wide for improving feature extraction, it could make nacelle acceleration as the input. This structure exhibits accuracy and robustness satisfactorily. Finally, the framework was demonstrated in a high-fidelity simulation environment. Different scenarios of aerodynamic imbalance were tested, the results demonstrate the satisfactory performance of the proposed framework.