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Increasing demand for electricity and the modernization of power systems within competitive markets has induced power systems to operate close to their stability limits. Therefore, the continuous monitoring and control of power systems through voltage stability indices is urgently needed. This is the first-ever effort to examine more than 40 voltage stability indices based on their formulation, application, performance, and assessment measures. These indices are sorted based on a logical and chronological order considering the most recent indices to be applied worldwide. However, the generalizability of these indices in terms of multivariable objectives is limited. Despite its limitation, this study systematically reviews available indices in the literature within the past three decades to compile an integrated knowledge base with an up-to-date exposition. This is followed by a comparative analysis in terms of their similarity, functionality, applicability, formulation, merit, demerit, and overall performance. Also, a broad categorization of voltage stability indices is addressed. This study serves as an exhaustive roadmap of the issue and can be counted as a reference for planning and operation in the context of voltage stability for students, researchers, scholars, and practitioners.

The present time is a seminal decade for the transition of the energy sector through the deployment of green energy and the optimization of efficiencies using the power of automation and artificial intelligence (AI), which demands competitive policies to handle multidimensional endeavors via a single platform. The failure of energy policies can have far-reaching socioeconomic consequences when policies do not meet the energy and climate goals throughout the lifecycle of the policy. Such shortcomings are reported to be due to inadequate incentives and poor decision making that needs to promote fairness, equality, equity, and inclusiveness in energy policies and project decision making. The integration of AI in energy sectors poses various challenges that this study aims to analyze through a comprehensive examination of energy policy processes. The study focuses on (1) the decision-making process during the development stage, (2) the implementation management process for the execution stage, (3) the integration of data science, machine learning, and deep learning in energy systems, and (4) the requirements of energy systems in the context of substantiality. Synergistically, an emerging blueprint of policy, data science and AI, engineering practices, management process, business models, and social approaches that provides a multilateral design and implementation reference is propounded. Finally, a novel framework is developed to develop and implement modern energy policies that minimize risks, promote successful implementation, and advance society’s journey towards net zero and carbon neutral objectives.

This study presents an innovative framework for leveraging the potential of AI in energy systems through a multidimensional approach. Despite the increasing importance of sustainable energy systems in addressing global climate change, comprehensive frameworks for effectively integrating artificial intelligence (AI) and machine learning (ML) techniques into these systems are lacking. The challenge is to develop an innovative, multidimensional approach that evaluates the feasibility of integrating AI and ML into the energy landscape, to identify the most promising AI and ML techniques for energy systems, and to provide actionable insights for performance enhancements while remaining accessible to a varied audience across disciplines. This study also covers the domains where AI can augment contemporary and future energy systems. It also offers a novel framework without echoing established literature by employing a flexible and multicriteria methodology to rank energy systems based on their AI integration prospects. The research also delineates AI integration processes and technique categorizations for energy systems. The findings provide insight into attainable performance enhancements through AI integration and underscore the most promising AI and ML techniques for energy systems via a pioneering framework. This interdisciplinary research connects AI applications in energy and addresses a varied audience through an accessible methodology.

This study evaluates the inconveniences raised by the installation of Shunt Capacitor Banks (SCB) along the North East Power System (NEPS) in Afghanistan. Besides the numerous advantages, a capacitor bank usually has some drawbacks in terms of transient currents which affect the quality of power supply and exceed the withstand capability of associated equipment. In this study, transient outrush current injects by installed SCB into the nearby faulted point at Pule Khumri and Chimtala substations is investigated. Outrush transient is produced by SCB when the breaker is operating to disconnect the faulted circuit. By applying different methods can mitigate outrush transient and protect the system which Current Limiting Inductance (CLI) is preferred in this study. Integrating CLI in series with SCB is the most relevant method which can limit the amplitude, frequency, and the rate of rise of the outrush transient. The use of inductance could otherwise create some excessive voltage which might exceeds the withstand capability of circuit breakers. Hence sensitivity analysis based on Transient Recovery Voltage (TRV) to confirm the robustness of the proposed approach is carried out. The evaluation is accomplished based on the result derived from the Electromagnetic Transients Program (EMTP), ATP package.

Along with industrialization and rapid urbanization, environmental remediation is globally a perpetual concept to deliver a sustainable environment. Various organic and inorganic wastes from industries and domestic homes are released into water systems. These wastes carry contaminants with detrimental effects on the environment. Consequently, there is an urgent need for an appropriate wastewater treatment technology for the effective decontamination of our water systems. One promising approach is employing nanoparticles of metal oxides as photocatalysts for the degradation of these water pollutants. Transition metal oxides and their composites exhibit excellent photocatalytic activities and along show favorable characteristics like non-toxicity and stability that also make them useful in a wide range of applications. This study discusses some characteristics of metal oxides and briefly outlined their various applications. It focuses on the metal oxides TiO2, ZnO, WO3, CuO, and Cu2O, which are the most common and recognized to be cost-effective, stable, efficient, and most of all, environmentally friendly for a sustainable approach for environmental remediation. Meanwhile, this study highlights the photocatalytic activities of these metal oxides, recent developments, challenges, and modifications made on these metal oxides to overcome their limitations and maximize their performance in the photodegradation of pollutants.

Energy is the fundamental requirement of all physical, chemical, and biological processes which are utilized for better living standards. The toll that the process of development takes on the environment and economic activity is evident from the arising concerns about sustaining the industrialization that has happened in the last centuries. The increase in carbon footprint and the large-scale pollution caused by industrialization has led researchers to think of new ways to sustain the developmental activities, whilst simultaneously minimizing the harming effects on the enviroment. Therefore, decarbonization strategies have become an important factor in industrial expansion, along with the invention of new catalytic methods for carrying out non-thermal reactions, energy storage methods and environmental remediation through the removal or breakdown of harmful chemicals released during manufacturing processes. The present article discusses the structural features and photocatalytic applications of a variety of metal oxide-based materials. Moreover, the practical applicability of these materials is also discussed, as well as the transition of production to an industrial scale. Consequently, this study deals with a concise framework to link metal oxide application options within energy, environmental and economic sustainability, exploring the footprint analysis as well.

Besides many sectors, artificial intelligence (AI) will drive energy sector transformation, offering new approaches to optimize energy systems’ operation and reliability, ensuring techno-economic advantages. However, integrating AI into the energy sector is associated with unforeseen obstacles that might change optimistic approaches to dealing with AI integration. From a multidimensional perspective, these challenges are identified, categorized based on common dependency attributes, and finally, evaluated to align with the viable recommendations. A multidisciplinary approach is employed through the exhaustive literature to assess the main challenges facing the integration of AI into the energy sector. This study also provides insights and recommendations on overcoming these obstacles and highlights the potential benefits of successful integration. The findings suggest the need for a coordinated approach to overcome unforeseen obstacles and can serve as a valuable resource for policymakers, energy practitioners, and researchers looking to unlock the potential of AI in the energy sector.

In recent years, owing to the effect of fossil fuels on global warming, the exhaustion of oil fields, and the lucrative impacts of renewable energy resources (RESs), the penetration of RESs has been increasing significantly in power systems. An effective way to benefit from all RESs advantages is by applying them in microgrid systems (MGS). Furthermore, MGS can ease the way for utilizing a large amount of RESs, if its economic-environmental-technical aspects of it are taken into account. In this regard, this paper proposes an optimal solution for the energy management of a microgrid by considering a comprehensive study. In the proposed methodology, different distributed energy resources such as wind turbines generator (WTG), energy storage (ES), combined heat and power (CHP), rubbish burning agent (RBA), and diesel generators (DG) are modeled. In addition, electric vehicles (EVs) are considered a load with uncertainty. The objective function of the proposed method is to minimize the microgrid’s total cost by considering the microgrid’s emission cost and technical constraints. In this study, the microgrid’s technical, environmental, and economic aspects are investigated. In addition, the optimization problem is converted into a mixed-integer linear programming method by using the proper linearization method. In this paper, the increasing effect of wind energy penetration rate on the total price also has been studied. The simulation results show that by increasing the wind energy penetration rate by up to 30% of total power, the total cost will decrease by up to 30.9%.

The main goal of this study is to evaluate the impact of restrictive measures introduced in connection with COVID-19 on consumption in renewable energy markets. The study will be based on the hypothesis that similar changes in human behavior can be expected in the future with the further spread of COVID-19 and/or the introduction of additional quarantine measures around the world. The analysis also yielded additional results. The strongest reductions in energy generation occurred in countries with a high percentage (more than 80%) of urban population (Brazil, USA, the United Kingdom and Germany). This study uses two models created with the Keras Long Short-Term Memory (Keras LSTM) Model, and 76 and 10 parameters are involved. This article suggests that various restrictive strategies reduced the sustainable demand for renewable energy and led to a drop in economic growth, slowing the growth of COVID-19 infections in 2020. It is unknown to what extent the observed slowdown in the spread from March 2020 to September 2020 due to the policy’s impact and not the interaction between the virus and the external environment. All renewable energy producers decreased the volume of renewable energy market supply in 2020 (except China).

The main focus of the proposed framework is to examine the importance of electricity interconnections with a high share of intermittent Renewable Energy (RE) sources and attempts to link the gap between planning model and operation with considering realistic operating details. Therefore optimal Unit Commitment (UC) is considered to analyze how operational aspects are appropriately done over the planning period. More specifically, a Mixed Integer Linear Programming (MILP) model is developed to address the specific challenges of the underlying UC problem. For modelling purposes, demand forecast, applicable RE potentials and the cost of RE technologies are estimated. To reduce the expenses and improve system stability, energy storage systems (pump storage hydro and thermal energy storage) are considered as well. For optimal UC, a typical day (24 h) is employed to determine the capacity expansion and daily operational planning. Each selected day expresses a part of the year (e.g., a season). Incorporation of short-term decisions into the long-term planning framework can strengthen the accuracy of the decisions and guaranty the stability of power networks. The proposed approach can provide valuable insights into the appropriate energy strategies followed by the investors and policymakers at a national and regional level.