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As demand for electricity increases on an already strained electrical supply due to urbanization, population growth, and climate change, the likelihood of power outages will also increase. While researchers understand that the number of electrical grid disturbances is increasing, we do not adequately understand how increased power outages will affect a society that has become increasingly dependent on a reliable electric supply. This systematic review aims to understand how power outages have affected society, primarily through health impacts, and identify populations most vulnerable to power outages based on the conclusions from prior studies. Based on search parameters, 762 articles were initially identified, of which only 50 discussed the social impacts of power outages in North America. According to this literature, racial and ethnic minorities, especially Blacks or African Americans, those of lower socioeconomic status, children, older adults, and those living in rural areas experienced more significant impacts from previous power outages. Additionally, criminal activity increased during prolonged power outages with both pro-social and anti-social behaviors observed. Providing financial assistance or resources to replace spoiled goods can reduce crime. Future research on this topic must consider the financial effects of power outages, how power outage impacts seasonally vary, and the different durations of power outage impacts.

This article deals with the prosumer behavior, specifically on an on-grid electrical network that is connected to a larger synchronous electrical network, as well as an off-grid system. In the Simulink (Matlab) application, two models were constructed for this purpose. The modeling of the operation of the electrical network’s on-grid system takes place in one of the models. The simulation of the operation of the electrical network’s off-grid system takes place in the other. We examined the model’s behavior in the provided simulated period from the standpoint of transient states and qualitative indicators of electrical energy under various connection configurations in both systems. The simulations resulted in the possibility of incorporating new sources of energy accumulation, such as pumped storage hydropower plants based on energy storage systems (ESSs), and modifying the model to the user’s needs.

Because of the development of smart grid technology, today's power grid infrastructures are increasingly and heavily coupled with communication networks for many new and existing power applications. The interdependent relationship between the two systems, in which power control relies on the communication system to deliver control and monitoring messages and network devices require power supplies from the electrical grid, brings challenges in the effort to build a highly resilient integrated infrastructure. In this work, the authors summarise existing research on power grid resilience enhancement with the consideration of the interdependencies between power systems and communication networks. They categorise these works according to stages of resilience enhancement (i.e. failure analysis, vulnerability analysis, failure mitigation, and failure recovery) and methodologies (i.e. analytical solutions, co‐simulation, and empirical studies). They also identify the limitations of existing works and propose potential research opportunities in this demanding area.

The smart grid is enabling the collection of massive amounts of high-dimensional and multi-type data about the electric power grid operations, by integrating advanced metering infrastructure, control technologies, and communication technologies. However, the traditional modeling, optimization, and control technologies have many limitations in processing the data; thus, the applications of artificial intelligence (AI) techniques in the smart grid are becoming more apparent. This survey presents a structured review of the existing research into some common AI techniques applied to load forecasting, power grid stability assessment, faults detection, and security problems in the smart grid and power systems. It also provides further research challenges for applying AI technologies to realize truly smart grid systems. Finally, this survey presents opportunities of applying AI to smart grid problems. The paper concludes that the applications of AI techniques can enhance and improve the reliability and resilience of smart grid systems.

Learning efficient predictive models in dynamic environments requires taking into account the continuous changing nature of phenomena generating the data streams, known in machine learning as “concept drift”. Such changes may affect models’ effectiveness over time, requiring permanent updates of parameters and structure to maintain performance. Several supervised machine learning methods have been developed to be adapted to learn in dynamic and non-stationary environments. One of the most well-known and efficient learning methods is neural networks. This paper focuses on the different neural networks developed to build learning models able to adapt to concept drifts on streaming data. Their performance will be studied and compared using meaningful criteria. Their limits to address the challenges related to the problem of the improvement of electrical grid flexibility in presence of distributed Wind–PV renewable energy resources within the context of energy transition will be highlighted. Finally, the study provides a self-adaptive scheme based on the use of neural networks to overcome these limitations and tackle these challenges.

The continued growth of distributed generation (DG) in the electrical grid has led to the expansion of microgrids. Microgrids contain distributed power generation units, energy storage devices, and controllable loads with the capability to operate in both grid‐connected and island modes. The economic operation of a microgrid is achieved through an energy management system that optimally schedules DGs and storage devices and continuously balances supply and demand. In this study, a formulation of optimal unit commitment (UC) and dispatch scheduling of DGs in a grid‐connected microgrid system is presented. Mixed‐integer linear programming is used to implement the optimal UC and dispatch scheduling model. The objective is to minimise the overall operating cost of the system by optimally utilising an energy storage device and a combined heat and power (CHP) generation unit using load and renewable energy generation prediction. Operational constraints such as generation limits of DGs, battery charging/discharging limits and state‐of‐charge limits are to be satisfied during all intervals of operation. Simulation results indicate that the operational cost of the system is significantly reduced through optimal scheduling of an energy storage system and a CHP unit using the proposed strategy.

In this study, a robust and dynamic transactive energy system in smart grid (SG) environment is proposed based on the Tsypkin–Polyak theorem. A key factor of the proposed system is to consider the uncertainties in electrical grid parameters. Moreover, oligopolistic behaviours of agents, i.e. demands and generating units, are considered in the modelling. It is proved that selfish agents offer their true cost parameters in the proposed transactive energy system. Therefore, optimal power flow (OPF) of the electrical grid is obtained in the proposed transactive energy system. In addition, real‐time locational marginal prices (LMPs) are also presented for demand‐side management (DSM). Hence, all levels of demands side can interact and communicate with generation side through real‐time LMPs. This characteristic is known as interoperability of transactive energy systems. However, in addition to all benefits of the proposed system, it has adverse impacts on the stability of the electrical grid. Here, to solve this challenge, a hierarchical and robust control system is proposed by using the Tsypkin–Polyak theorem to control stability and OPF simultaneously. Finally, the effectiveness of the proposed system is validated by implementing it on a test electrical grid.

This article addresses a digital twin-based real-time analysis (DTRA) to meditate the power system vulnerability whenever cascading failures and blackouts occur for any reason, and thus, to improve the resiliency. In addition to this, a water-power package is proposed to enhance the vulnerable percentage of the system by promptly syringing energy to the grid under line/generator outage contingencies. To this end, in the first place, we will develop a digital twin model along with a cloud platform derived from the Amazon Cloud Service (ACS) into the Amazon Web in order to scrutinize the online vulnerability data arising from the equivalent physical twin in real-time. Indeed, such a DTRA model can help us check the real grid’s behavior and determine how to meet the needs of the energy hub system to prevent blackouts. Additionally, a modified bat-based optimization algorithm is matched to settle the energy between the hub system and the electrical grid in furtherance of real-time analysis. To raise awareness, we will first compile how the hub system interactions can be effective in declining the vulnerability indices, and afterward, we will map out the ACS-based digital twin model on the studied case.

Electrification of the propulsion system has opened the door to a new paradigm of propulsion system configurations and novel aircraft designs, which was never envisioned before. Despite lofty promises, the concept must overcome the design and sizing challenges to make it realizable. A suitable modeling framework is desired in order to explore the design space at the conceptual level. A greater investment in enabling technologies, and infrastructural developments, is expected to facilitate its successful application in the market. In this review paper, several scholarly articles were surveyed to get an insight into the current landscape of research endeavors and the formulated derivations related to electric aircraft developments. The barriers and the needed future technological development paths are discussed. The paper also includes detailed assessments of the implications and other needs pertaining to future technology, regulation, certification, and infrastructure developments, in order to make the next generation electric aircraft operation commercially worthy.

The disparate nature of data for electric power utilities complicates the emergency recovery and response process. The reduced efficiency of response to natural hazards and disasters can extend the time that electrical service is not available for critical end-use loads, and in extreme events, leave the public without power for extended periods. This article presents a methodology for the development of a semantic data model for power systems and the integration of electrical grid topology, population, and electric distribution line reliability indices into a unified, cloud-based, serverless framework that supports power system operations in response to extreme events. An iterative and pragmatic approach to working with large and disparate datasets of different formats and types resulted in improved application runtime and efficiency, which is important to consider in real time decision-making processes during hurricanes and similar catastrophic events. This technology was developed initially for Puerto Rico, following extreme hurricane and earthquake events in 2017 and 2020, but is applicable to utilities around the world. Given the highly abstract and modular design approach, this technology is equally applicable to any geographic region and similar natural hazard events. In addition to a review of the requirements, development, and deployment of this framework, technical aspects related to application performance and response time are highlighted.