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Deploying emergency vehicles has become a key guarantee for power supply in post-disaster distribution networks on account of their flexibility, maneuverability, safety, and reliability. However, due to limitations in configuration, the continuous power supply capacity of existing electrical vehicles (EVs) is insufficient, making it difficult to meet the needs of energy transfer and flow regulation in post-disaster distribution networks. Therefore, in this study, we comprehensively considered the energy time-shift characteristics of EVs and the flexible control function of soft open points (SOPs), integrated their advantages, and designed an emergency vehicle with SOP (EV-SOP) and its management strategy for distribution network line faults. Firstly, we present the EV-SOP architecture and its mathematical model. Then, aiming to minimize the economic losses caused by power loss during line faults, an EV-SOP emergency management strategy based on data collection, scheduling judgment, and optimal modeling techniques is proposed. Finally, by taking the case study of an IEEE33-node distribution network with contact switches as an example, we validate the effectiveness and superiority of the EV-SOP and its emergency management strategy compared with traditional energy storage emergency vehicles. [ABSTRACT FROM AUTHOR]

Modern electrical power systems are characterized by a high rate of transient processes, the use of digital monitoring and control systems, and the accumulation of a large amount of technological information. The active integration of renewable energy sources contributes to reducing the inertia of power systems and changing the nature of transient processes. As a result, the effectiveness of emergency control systems decreases. Traditional emergency control systems operate based on the numerical analysis of power system dynamic models. This allows for finding the optimal set of preventive commands (solutions) in the form of disconnections of generating units, consumers, transmission lines, and other primary grid equipment. Thus, the steady-state or transient stability of a power system is provided. After the active integration of renewable sources into power systems, traditional emergency control algorithms became ineffective due to the time delay in finding the optimal set of control actions. Currently, machine learning algorithms are being developed that provide high performance and adaptability. This paper contains a meta-analysis of modern emergency control algorithms for power systems based on machine learning and synchronized phasor measurement data. It describes algorithms for determining disturbances in the power system, selecting control actions to maintain transient and steady-state stability, stability in voltage level, and limiting frequency. This study examines 53 studies piled on the development of a methodology for analyzing the stability of power systems based on ML algorithms. The analysis of the research is carried out in terms of accuracy, computational latency, and data used in training and testing. The most frequently used textual mathematical models of power systems are determined, and the most suitable ML algorithms for use in the operational control circuit of power systems in real time are determined. This paper also provides an analysis of the advantages and disadvantages of existing algorithms, as well as identifies areas for further research. [ABSTRACT FROM AUTHOR]

The widespread application of phasor measurement units has improved grid operational reliability. However, this has increased the risk of cyber threats such as false data injection attack that mislead time-critical measurements, which may lead to incorrect operator actions. While a single incorrect operator action might not result in a cascading failure, a series of actions impacting critical lines and transformers, combined with pre-existing faults or scheduled maintenance, might lead to widespread outages. To prevent cascading failures, controlled islanding strategies are traditionally implemented. However, islanding is effective only when the received data are trustworthy. This paper investigates two multi-objective controlled islanding strategies to accommodate data uncertainties under scenarios of lack of or partial knowledge of false data injection attacks. When attack information is not available, the optimization problem maximizes island observability using a minimum number of phasor measurement units for a more accurate state estimation. When partial attack information is available, vulnerable phasor measurement units are isolated to a smaller island to minimize the impacts of attacks. Additional objectives ensure steady-state and transient-state stability of the islands. Simulations are performed on 200-bus, 500-bus, and 2000-bus systems. [ABSTRACT FROM AUTHOR]

Line overload is one of the important causal factors of cascading failures and blackouts in power systems. An optimization method for protection and control measures to mitigate line overloads is proposed in this study. The method consists of two main parts, i.e., the modeling process and the solving process. In the modeling process, an optimization model including overload protection and emergency control measures is developed using PFT (Power Flow Tracing). In the solving process, a multi-stage optimization method using IBSO (Improved Brain Storm Optimization algorithm) is proposed to obtain the final result. The aim of this study is to form a coordinated protection and control strategy that reduces the power on the overloaded line within the safety limits and minimizes the load loss of the power system. The simulation results show the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

Small-scale power generation based on renewable energy sources is gaining popularity in distribution grids, creating new challenges for power system control. At the same time, remote consumers with their own small-scale generation still have low reliability of power supply and poor power quality, due to the lack of proper technology for grid control when the main power supply is lost. Today, there is a global trend in the transition from a power supply with centralized control to a decentralized one, which has led to the Microgrid concept. A microgrid is an intelligent automated system that can reconfigure by itself, maintain the power balance, and distribute power flows. The main purpose of this paper is to study the method of control using reclosers in the Lahsh district of the Rasht grid in Tajikistan with distributed small generation. Based on modified reclosers, a method of decentralized synchronization and restoration of the grid normal operation after the loss of the main power source was proposed. In order to assess the stable operation of small hydropower plants under disturbances, the transients caused by proactive automatic islanding (PAI) and restoration of the interconnection between the microgrid and the main grid are shown. Rustab software, as one of the multifunctional software applications in the field of power systems transients study, was used for simulation purposes. Based on the simulation results, it can be concluded that under disturbances, the proposed method had a positive effect on the stability of small hydropower plants, which are owned and dispatched by the Rasht grid. Moreover, the proposed method sufficiently ensures the quality of the supplied power and improves the reliability of power supply in the Lahsh district of Tajikistan. [ABSTRACT FROM AUTHOR]