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Every year, electric power systems (EPSs) experience accidents resulting in static and dynamic instability, as well as power supply disruptions. Accidents evolve along various trajectories and sometimes can exhibit a cascading effect. In this case, the sequential tripping of generating and/or electric network equipment occurs due to overloads or voltage drops at various nodes of the electric network. This leads to significant losses for industrial and commercial consumers, while also escalating social tensions within the population. This study aims to develop an algorithm for revealing the possibility of cascading failure processes in EPSs and their development trajectories. The use of the algorithm in planning and managing power flows in EPSs facilitates the identification of the boundary between the regions of admissible and inadmissible post-contingency power flows. The algorithm also enables the assessment of the impact of various topology solutions and operational measures on the development of cascading failure processes. This paper presents the results of steady-state calculation for the test schemes of an EPS incorporating 25, 36, and 40 nodes with voltage levels of 6, 35, 110, and 500 kV to illustrate the influence of topology and the non-homogeneity of network parameters on the occurrence and development of cascading failure processes. The deployment of distributed generation facilities of different capacities and FACTS devices, alongside the redistribution of power flows in the network by changing the load of power plants with different electricity generation costs, are considered topology and operational measures that enhance the survivability of the EPS. The performance of the developed algorithm was illustrated through an analysis of the process of the development of a real cascading systemic accident that occurred in the EPS. The proposed algorithm, when utilized in planning and managing power flows in an EPS, facilitates the identification of possibilities for the cascading failure processes and their development pathways to subsequently design and implement the operational measures and topological adjustments to prevent them.

Modern gas-turbine units (GTUs) and gas-reciprocating units (GRUs) have found a wide use at power plants, including distributed generation facilities, running on gaseous fuel. The design features of these generating units have a considerable effect on the nature and parameters of transient processes due to emergency disturbances in the adjacent network. The study shows that single-shaft gas-turbine and gas-reciprocating units do not allow even short-term considerable frequency drops. These schemes and operating conditions arise due to emergency active power shortages when the connection between the power plant and the power system weakens due to repair conditions or islanded operation. The paper presents the results of transient process calculations for operating power plants (distributed generation facilities), which make it possible to identify the unfavorable properties of GTUs and GRUs. The results show that two-shaft (three-shaft) GTUs and GRUs can switch to out-of-step conditions even when short-circuits in the adjacent network are cleared with high-speed relay protection devices. The features of out-of-step conditions and the admissibility of their short-term duration for the spontaneous restoration of generators’ synchronization are considered. The findings suggest that considering the fundamental design features of generating units provides informed technical decisions on equipping power plants (distributed generation (DG) facilities) and the adjacent network with efficient emergency control systems.

Rural Africa's low level of electrification is a topic of much discussion. One widely cited estimate is that only fourteen percent of rural households in Sub- Saharan Africa have access to electricity (2012). As a first step to improving access, most governments in the region have developed national electrification strategies. Virtually every one of those strategies recommends a two-track approach to providing greater access to grid-based electrification. Although there is widespread agreement on the need for a two-track approach, most national electrification strategies contain few, if any, details on how the two tracks should be implemented. This guide focuses on the regulatory and policy decisions that African electricity regulators and policy makers must make to create a sustainable decentralized track and how the decentralized track can complement the traditional centralized track.

Due to technological advancements in recent years, distributed energy resources (DER) applications have become more prevalent in households and businesses, including various renewable energy applications. While the virtual power plant (VPP) can integrate energy storage, flexible loads and DER, etc., it can support the power grid operating stability and security. Therefore, more and more researchers give their attention to VPP and advise on their optimization. This paper states the VPP concept from other researchers’ studies and provides a detailed explanation. Meanwhile, some typical VPP projects worldwide are also presented. In addition, some potential challenges and future development advice in the VPP studies are also presented.

This study investigates the problem of fault protection in a microgrid containing inverter-based distributed generators (IBDGs). Owing to the low magnitude of short circuit currents generated by IBDGs, traditional protection techniques which relay on current (fuses and overcurrent relays) may fail to protect such networks. This study addresses the problem of finding suitable features derived from local electrical measurements that can be used by statistical classifiers to better discriminate fault events from normal network events. Given a series of simple electrical features, a study of feature selection and data mining techniques is conducted in the context of fault detection in isolated microgrids with IBDGs. Two statistical classifiers are compared and implemented in this framework: Naive Bayes and decision trees. The proposed approach is tested on a facility scale microgrid consisting of three IBDGs.

The recent development of distributed generation technologies is changing the focus of the production of electricity from large centralised power plants to local energy systems scattered over the territory. Under the distributed generation paradigm, the present research scenario emphasises more and more the role of solutions aimed at improving the energy generation efficiency and thus the sustainability of the overall energy sector. In particular, coupling local cogeneration systems to various typologies of chillers and heat pumps allows setting up distributed multi-generation systems for combined production of different energy vectors such as electricity, heat (at different enthalpy levels), cooling power, and so forth. The generation of the final demand energy outputs close to the users enables reducing the losses occurring in the energy chain conversion and distribution, as well as enhancing the overall generation efficiency. This book presents a comprehensive introduction to energy planning and performance assessment of energy systems within the so-called Distributed Multi-Generation (DMG) framework. Typical plant schemes and components are illustrated and modelled, with special focus on applications for trigeneration of electricity, heat and cooling power. A general approach to characterisation and planning of multi-generation systems is formulated in terms of the so-called lambda analysis, which extends the classical models related to the heat-to-power cogeneration ratio analysis in cogeneration plants. A unified theoretical framework leading to synthesise different performance assessment techniques is described in details. In particular, different indicators are presented for evaluating the potential energy benefits of distributed multi-generation systems with respect to classical case of separate production and centralised energy systems. Several case study applications are illustrated to exemplify the models presented and to point out some numerical aspects relevant to equipment available on the market. In particular, schemes with different cogeneration prime mover typologies, as well as electric, absorption and engine-driven chillers and heat pumps, are discussed and evaluated. A number of openings towards modelling and evaluation of environmental and economic issues are also provided. The aspects analysed highlight the prominent role of DMG systems towards the development of more sustainable energy scenarios.

In conventional low-voltage grids, energy-storage devices are mainly driven by final consumers to correct peak consumption or to protect against sources of short-term breaks. With the advent of microgrids and the development of energy-storage systems, the use of this equipment has steadily increased. Distributed generations (DGs), including wind-power plants as a renewable energy source, produces vacillator power due to the nature of variable wind. Microgrids have output power fluctuations, which can cause devastating effects such as frequency fluctuations. Storage can be used to fix this problem. In this paper, a grid-connected wind turbine and a photovoltaic system are investigated considering the atmospheric conditions and wind-speed variations, and a control method is proposed. The main purpose of this paper is to optimize the capacity of energy-storage devices to eliminate power fluctuations in the microgrid. Finally, the conclusion shows that, in microgrids with supercapacitors, the optimal capacity of microgrid supercapacitors is determined. This method of control, utilizing the combined energy-storage system of the battery supercapacitor, in addition to reducing the active power volatility of the wind turbine and photovoltaic generation systems, also considers the level of battery protection and reduction in reactive-power fluctuations. In the proposed control system, the DC link in the energy-storage systems is separate from most of the work conducted, which can increase the reliability of the whole system. The simulations of the studied system are performed in a MATLAB software environment.

In this paper, the optimal allocation problem of the Electric Vehicles Parking Lots and DG in micro‐grid is studied as a novel optimization problem. In the proposed problem, the different factors including the technical and the economic issues are considered for achieving a realistic solution. In addition, FACTS devices have been added to the network by considering the costs of installation, repair and maintenance, and their effecton the technical and economic parameters of the network has been investigated. Also, how to optimally charge and discharge storage devices and how to optimally inject active or reactive power by charging stations to improve energy management in the network has been studied. In order to evaluate the impact and reduce economic costs, the optimal number of distributed renewable energy, storage and FACTS equipment along with the optimal number of parking lots with Existence of different number of electric vehicles in the survey and microgrid network has been designed using a two‐stage optimization method. The method is simulated on the standard IEEE 33 bus. The results show that the optimal power factor of parking lots can be determined during the unloading of vehicles in such a way that the technical parameter of the network is improved. In the proposed problem, the different factors including the technical and the economic issues are considered for achieving a realistic solution. In terms of technical issues, minimizing network losses, and minimizing voltage drop in feeders, as well as supplying all network demand are considered.

During the operation of solar and wind power plants, it is necessary to solve issues related to the guaranteed capacity of these plants, as well as the frequency stabilisation in the power system where they operate, and maintain an operating mode of self-sufficiency conditions. One of the solutions to these problems is the use of energy storage systems. This article proposes a mathematical model for the study of frequency and power regulation processes in power systems with distributed generation, which includes renewable energy resources and energy storage systems. The novelty of the model lies in the possibility of determining energy cost indicators based on instantaneous energy power data. The model allows us to estimate the conditions under which distributed generation becomes self-sufficient. The results of the model calculations of two variants of power system operation, which includes wind generators with a capacity of 1500 MW, demonstrate the ability of the proposed model to accurately reproduce the dynamics of the frequency stabilisation process. The calculation results of the energy-economic indicators of a real power system combined with a powerful subsystem of wind generation and a battery-type energy storage system prove the competitiveness of self-sufficient renewable energy power plants.

In the medical sector, diagnostic technology-related progress is often hindered by data isolation and stringent privacy laws, posing obstacles for institutions that lack extensive disease data. This scarcity impedes the development of precise diagnostic models and reliable auxiliary tools. To address these challenges, we introduce the horizontal federated data augmentation model for medical assistance (HFDAM-MA), a novel approach designed to address the complexities of data scarcity. Our model addresses the limitations of traditional generative adversarial networks (GANs), which often rely on the independent and identically distributed (IID) assumption during training (a condition that is rarely satisfied in real-world medical data scenarios) and face computational challenges in healthcare settings. The HFDAM-MA leverages federated learning (FL) principles to enable non-IID collaborative training across multiple medical institutions. This approach alleviates the data collection pressure at individual sites and ensures the privacy of sensitive medical information. A central node orchestrates the distribution of a unified GAN model to local sites, where it operates in conjunction with two convolutional neural networks (CNNs) to generate synthetic medical images and corresponding labels. Extensive experimental results underscore the effectiveness of our model. As participation increases, we observe a substantial improvement in the diagnostic accuracy of the global model. Moreover, the performance of the local models is bolstered, and the diversity of the generated data is expanded, offering a robust solution to the challenges of data privacy, imbalanced data, and insufficient labeling that are prevalent in the medical sector.