Throughout the course of human history, owing to innovations that shape the future of mankind, many technologies have been innovated and used towards making people’s lives easier. Such technologies have made us who we are today and are involved with every domain that is vital for human survival such as agriculture, healthcare, and transportation. The Internet of Things (IoT) is one such technology that revolutionizes almost every aspect of our lives, found early in the 21st century with the advancement of Internet and Information Communication (ICT) Technologies. As of now, the IoT is served in almost every domain, as we mentioned above, allowing the connectivity of digital objects around us to the Internet, thus allowing the remote monitoring, control, and execution of actions based on underlying conditions, making such objects smarter. Over time, the IoT has progressively evolved and paved the way towards the Internet of Nano-Things (IoNT) which is the use of nano-size miniature IoT devices. The IoNT is a relatively new technology that has lately begun to establish a name for itself, and many are not aware of it, even in academia or research. The use of the IoT always comes at a cost, owing to the connectivity to the Internet and the inherently vulnerable nature of IoT, wherein it paves the way for hackers to compromise security and privacy. This is also applicable to the IoNT, which is the advanced and miniature version of IoT, and brings disastrous consequences if such security and privacy violations were to occur as no one can notice such issues pertaining to the IoNT, due to their miniaturized nature and novelty in the field. The lack of research in the IoNT domain has motivated us to synthesize this research, highlighting architectural elements in the IoNT ecosystem and security and privacy challenges pertaining to the IoNT. In this regard, in the study, we provide a comprehensive overview of the IoNT ecosystem and security and privacy pertaining to the IoNT as a reference to future research.

Maintaining our standard of living and keeping the economy running smoothly is heavily reliant on a consistent supply of energy. Renewable energy systems create abundant energy by utilizing resources such as the sun, wind, earth, and plants. The demand for renewable energy is increasing, despite power scarcity, pollution, and climate change posing challenges to long-term development in the Association of Southeast Asian Nations (ASEAN), which has seen significant social and economic growth in recent years. To achieve its 23% renewable energy (RE) target, ASEAN can develop solar photovoltaic (PV) electricity. Members of the ASEAN have established regulations and incentives to encourage individuals and businesses to use renewable energy in the future. This paper explores Southeast Asian countries’ comprehensive fossil-free energy options, the region’s renewable energy potential, current capacity, goals, and energy needs. Through the ASEAN Plan of Action for Energy Cooperation (APAEC) 2016–2025 and the ASEAN Declaration on Renewable Energy, ASEAN is committed to reducing its greenhouse gas emissions and promoting sustainable development aligning with the Paris Agreement’s aim to limit global warming to well below 2 degrees Celsius above pre-industrial levels. Results show that decarbonizing the region’s energy system is possible, but current policies and actions must be altered to reach that target level. Further research is necessary to optimize the ASEAN region’s renewable resource technical potential and commercial viability with available technology.

Fossil fuel is considered to be the primary power generation source. As this source is not that eco- and environmentally friendly, researchers are constantly searching for an alternative source for power generation. Renewable energy has drawn much attention in this regard in recent times. For solving economic load dispatch issues, numerous operational constraints must be considered. Due to the restructuring of the power sector, there is competition between different power systems organizations. Increasing fossil fuel costs drive power-producing utilities to adopt a cost-effective technique for dispatching actual power output. Due to the presence of nonlinearity and non-convexity in the fuel of cost function of generators, the economic load dispatch is often considered a complex optimization problem. Many researchers have been optimizing fuel costs to solve the economic power dispatch problem. This paper offers a critical analysis of ELD that takes into account both traditional and non-traditional energy sources. The review covers a variety of algorithms, including hybrid algorithms for integrating renewable energy sources (RES). The paper also focuses on several restricted optimization techniques and contemporary algorithms including PSO, Jaya, GWO, SMO, TLBO, Rao, MRao-2, and MFO to reduce the fuel cost of generation units using large-scale solar PV. Moreover, this paper provides a comprehensive overview of the current state of economic load dispatch and provides valuable insights for electricity researchers and practitioners. It also discusses future technologies and next steps in the field of ELD, emphasizing the need for more environmentally friendly and cost-effective power generation and distribution solutions. Overall, the paper demonstrates the benefits of renewable energy sources as well as optimization techniques for creating a more sustainable and efficient power generation system.

Various algorithms have been created in the past to take economic load dispatch (ELD) into account. These algorithms, however, concentrate on multiple tuning parameters, necessitating hyperparameter adjustment. A unique parameterless hybrid is presented to explicitly evaluate ELD for test systems and real‐world power plant systems matching the operational limitations. In addition, earlier algorithms could only offer estimates of the final cost of fuel based on the hyperparameter choices. This may prevent the global minimum values from being met. To find comprehensive solutions to the ELD problem in power systems, this paper suggests a new method called the hybrid Jaya optimization algorithm, which uses the merits of the Jaya and teaching–learning‐based optimization (TLBO) algorithms. This enhancement is proposed to improve the population variety, the balance between local and global search, and the early convergence of the original Jaya optimization method. A metaheuristic optimization technique called TLBO simulates the teaching–learning process in a classroom to optimize problems. The TLBO algorithm uses an exploration phase in which possible solutions are generated at random to discover the best solution. The algorithm then uses the exploitation phase to refine the search space‐based parameter adjustments to enhance the quality of the best solution identified. On the other hand, the Jaya algorithm is a metaheuristic optimization algorithm motivated by the idea of social behavior in nature. Candidate solutions are improved repeatedly through cooperation and competition using a population‐based approach, and each solution adjusts its position based on the best and worst answers in the population. By combining the advantages of both algorithms, hybrid Jaya (Jaya–TLBO) outperforms each method alone and minimizes the cost of power generation, improving convergence solution quality. To test its efficacy, the hybrid Jaya–TLBO algorithm is tested on four different test cases, such as an Institute of Electrical and Electronics Engineers (IEEE) 6‐unit, 13‐unit, 20‐unit, 40‐unit ELD system and an Indonesian 10‐unit one. Simulation results show that the proposed algorithm is superior in cost minimization to other well‐known algorithms that have been used recently. As a result, power system planners can utilize this technique to find the most economical load dispatch.

Fossil fuel is one of the major sources of the world’s energy generation. Greenhouse gases are increasing in the atmosphere due to the excessive use of fossil fuels. To tackle global warming and the shortage of natural gases, researchers are always looking for alternative sources of clean energy. Solar energy is becoming popular due to its inexhaustible nature. To get the most of out of a solar system and to generate maximum power, it is important to operate the panel at maximum power point. In other words, it can be said that MPP techniques are used to maximize the output power. There is a wide range of MPPT algorithm available to calculate MPP. This paper gives a broad overview of the existing MPPT techniques used in practice.

A smart grid is a cutting-edge energy system designed to take over old-fashioned energy infrastructure in the twenty-first century. With comprehensive communication and computation capabilities, its primary objective is to increase energy distribution's dependability and efficiency while minimizing unfavorable effects. A number of approaches are needed for effective analysis and well-informed decision-making due to the massive infrastructure and integrated network of communications of the smart grid. In this study, we examine the architectural elements of the smart grid as well as the uses and methods using machine learning (ML) and deep learning (DL) with regard to the smart grid. We also clarify present research limitations and propose future directions for machine learning-driven data analytics. In order to improve the stability, reliability, security, efficiency, and responsiveness of the smart grid, this paper examines the implementation of several machine learning methodologies. This paper also covers some of the difficulties in putting machine learning solutions for smart grids into practice.