The area of a Microgrid (μG) is a very fast-growing and promising system for overcoming power barriers. This paper examines the impacts of a microgrid system considering Electric Vehicle Grid Integration (EVGI) based on stochastic metaheuristic methods. One of the biggest challenges to slowing down global climate change is the transition to sustainable mobility. Renewable Energy Sources (RESs) integrated with Evs are considered a solution for the power and environmental issues needed to achieve Sustainable Development Goal Seven (SDG7) and Climate Action Goal 13 (CAG13). The aforementioned goals can be achieved by coupling Evs with the utility grid and other RESs using Vehicle-to-Grid (V2G) technology to form a hybrid system. Overloading is a challenge due to the unknown number of loads (unknown number of Evs). Thus, this study helps to establish the system impact of the uncertainties (arrival and departure Evs) by proposing Stochastic Monte Carlo Method (SMCM) to be addressed. The main objective of this research is to size the system configurations using a metaheuristic algorithm and analyze the impact of an uncertain number of Evs on the distribution of residential power in Tripoli-Libya to gain a cost-effective, reliable, and renewable system. The Improved Antlion Optimization (IALO) algorithm is an optimization technique used for determining the optimal number of configurations of the hybrid system considering multiple sources, while the Rule-Based Energy Management Strategy (RB-EMS) controlling algorithm is used to control the flow of power in the electric power system. The sensitivity analysis of the effect parameters has been taken into account to assess the expected impact in the future. The results obtained from the sizing, controlling, and sensitivity analyses are discussed.

Libya is primarily concerned with the green economy as it relies on a single source of national income and a single source of energy. It loses its national income source as industrial countries abandon fossil fuels and replace them with environmentally friendly alternative energy sources. Therefore, Libya must begin the battle for green economy transformation, and policymakers need to develop strategies for a rapid and safe transition to sustainable green development. This research outlines the basic framework for green economy transition strategies by reviewing other countries' experiences and theoretical studies. It proposes a temporal and financial transformation plan that includes three paths to achieve the green economy transition. The first path involves reducing emissions by using natural gas instead of oil fuel and introducing carbon capture systems in polluting systems. The second path suggests increasing the contribution of zero-emission technologies in the energy mix, such as solar energy, wind energy, and hydrogen. The third path involves enhancing negative emission environmental systems like reforestation and algae cultivation, localizing renewable and environmentally friendly energy industries, and facilitating investment in zero and negative emission projects. This path also aims to achieve sustainable development by utilizing oil in petrochemical industries, which will generate more revenue and jobs than burning it for energy production. To implement this strategic plan, $8.3 billion needs to be allocated for installing carbon capture systems on all electric power plants, plus approximately $7 million in annual payments for maintenance and operation. Additionally, around $39.5 billion is required to establish concentrated solar power plants and use them as a sustainable clean fuel instead of fossil fuels, with annual payments for operation and maintenance estimated at approximately $735 million. Furthermore, about $2.1 million is needed for investment in negative emission environmental systems such as forests, and around $2.5 million in annual payments for managing approximately 20,400 hectares of forests, which will achieve net-zero carbon for the current electric power industry.

Artificial intelligence (AI) has moved past its primitive stages and is now poised to revolutionize various fields, making it a disruptive technology. This technology is expected to completely transform traditional engineering in design, electrical, communication, and renewable energy approaches that have been human-centred. Despite being in its early stages, AI-powered engineering applications can work with vague design parameters and resolve intricate engineering problems that cannot be tackled using traditional design, electrical, communication, and renewable energy methods. This article aims to shed light on the current progress and future research trends in AI applications in engineering concepts, focusing on the ramp-up period of the last 5 years. Various methods such as machine learning, genetic algorithm, and fuzzy logic have been carefully evaluated from an engineering standpoint. AI-powered design studies have been reviewed and categorized for different design stages such as inspiration, idea and concept generation, evaluation, optimization, decision-making, and modeling. The review shows that there has been an increased interest in data-based design methods and explainable artificial intelligence in recent years. The use of AI methods in engineering applications has proven to be efficient, fast, accurate, and comprehensive, particularly with the use of deep learning methods and combinations that address situations where human capacity is inadequate. However, it is crucial to choose the appropriate AI method for an engineering problem to achieve successful results.

As populations expand and cities grow, the horizontal development of sustainable initiatives, coupled with the preservation of natural resources and the shift towards agricultural ventures, has led to an increased necessity for road lighting to mitigate traffic accidents. The burgeoning field of photovoltaic (PV) energy is significantly altering the energy paradigm, gaining prominence within regional energy mixes and power systems. This study presents an examination of various off-grid solar PV system designs for the illumination of the Kuwaiti roundabout, highlighting the distinct differences among these approaches. Through mathematical modeling and subsequent validation via PVsyst software, the focus is placed on sophisticated light emitting diode (LED) street lighting systems featuring automatic controls powered by solar energy. LEDs, acclaimed for their energy efficiency and longevity, are progressively supplanting traditional lighting technologies worldwide. This investigation explores multiple system configurations, transitioning from centralized systems employing sodium flashlights to autonomous systems with LED lamps. Key challenges such as power consumption, spatial limitations, and network load considerations are addressed. Innovative solutions including dual-voltage lamps and charge controllers are introduced, pinpointing optimal design strategies for roadway applications, which have implications for sustainable urban lighting paradigms. Additionally, the proposal of a solar-powered searchlight underscores potential cost-effectiveness, reflecting the continuous evolution of solar lighting technologies. Collectively, the findings underscore the crucial role of comprehensive design considerations in achieving efficient and sustainable lighting solutions within urban settings.

The intricacy of the power system configuration, coupled with the contemporary trends in power generation and demand, renders the attainment of adequate supply quality a daunting task for distribution companies. Several regulations govern the power quality (PQ) of the electrical system. In addition, EN-50160 outlines the voltage characteristics supplied by public electricity networks, which directly impact distribution companies. Secondly, the EN-61000 standards series regulates the electromagnetic compatibility of network-connected devices, which affects the loads. Both power companies and device manufacturers are responsible for ensuring and being impacted by the quality of supply. Artificial Intelligence (AI) techniques refer to a variety of methods and algorithms that enable machines to perform tasks that typically require human-like intelligence, such as perception, reasoning, learning, and decision-making. AI techniques include machine learning, natural language processing, computer vision, robotics, expert systems, and other approaches that use algorithms to analyze and understand complex data, recognize patterns, and make predictions or decisions based on that data Notwithstanding the regulations, there are still unresolved aspects of the supply quality, one of the most significant being the location of the origin of disturbances. This article presents an investigation of the main techniques used to identify the cause of disturbances and locate their origin in the power grid.

The electric power industry sector has become increasingly aware of how counterproductive voltage sag affects distribution network systems (DNS). The voltage sag backfires disastrously at the demand load side and affects equipment in DNS. To settle the voltage sag issue, this paper achieved its primary purpose to mitigate the voltage sag based on integrating a hydrogen fuel cell (HFC) with the DNS using a distribution static synchronous compensator (D-STATCOM) system. Besides, this paper discusses the challenges and opportunities of D-STATCOM in DNS. In this paper, using HFC is well-designed, modeled, and simulated to mitigate the voltage sag in DNS with a positive impact on the environment and an immediate response to the issue of the injection of voltage. Furthermore, this modeling and controller are particularly suitable in terms of cost-effectiveness as well as reliability based on the adaptive network fuzzy inference system (ANFIS), fuzzy logic system (FLC), and proportional–integral (P-I). The effectiveness of the MATLAB simulation is confirmed by implementing the system and carrying out a DNS connection, obtaining efficiencies over 94.5% at three-phase fault for values of injection voltage in HFC D-STATCOM using a P-I controller. Moreover, the HFC D-STATCOM using FLC proved capable of supporting the network by 97.00%. The HFC D-STATCOM based ANFIS proved capable of supporting the network by 98.00% in the DNS.