The number of used batteries is increasing in quantity as time passes by, and this amount is to expand drastically, as electric vehicles are getting increasingly popular. Proper disposal of the spent batteries has always been a concern, but it has also been discovered that these batteries often retain enough energy perfectly suited for other uses, which can extend the batteries' operational lifetime into a second one. Such use of batteries has been termed as the ''second-life,'' and it is high time to adopt such usage in large scale to properly exploit the energy and economics that went into battery production and reduce the environmental impacts of battery waste ending up in landfills. This paper aids in that quest by providing a complete picture of the current state of the second-life battery (SLB) technology by reviewing all the prominent work done in this field previously. The second-life background, manufacturing process of energy storage systems using the SLBs, applications, and impacts of this technology, required business strategies and policies, and current barriers of this technology along with potential solutions are discussed in detail in this paper to act as a major stepping stone for future research in this ever-expanding field. INDEX TERMS Second life battery, battery energy storage system, electric vehicle, battery management system, disposal, battery aging, economic and environmental values, recycling and waste management. NOMENCLATURE A. ABBREVIATIONS

Load forecasting is a pivotal part of the power utility companies. To provide load-shedding free and uninterrupted power to the consumer, decision-makers in the utility sector must forecast the future demand for electricity with a minimum amount of error percentage. Load prediction with less percentage of error can save millions of dollars to the utility companies. There are numerous Machine Learning (ML) techniques to amicably forecast the demand of electricity among which the hybrid models show the best result. Two or more than two predictive models are amalgamated to design a hybrid model, each of which provides improved performances by the merit of individual algorithms. This paper reviews the current stateof-the-art of electric load forecasting technologies and presents recent works pertaining to the combination of different ML algorithms into two or more methods for the construction of hybrid models. A comprehensive study of each single and multiple load forecasting model is performed with an in-depth analysis of their advantages, disadvantages, and functions. A comparison between their performance in terms of Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), and Mean Absolute Percentage Error (MAPE) values are developed with pertinent literature of several models to aid the researchers with the selection of suitable models for load prediction.

Driven by global concerns about the climate and the environment, the world is opting for renewable energy sources (RESs), such as wind and solar. However, RESs suffer from the discredit of intermittency, for which energy storage systems (ESSs) are gaining popularity worldwide. Surplus energy obtained from RESs can be stored in several ways, and later utilized during periods of intermittencies or shortages. The idea of storing excess energy is not new, and numerous researches have been conducted to adorn this idea with innovations and improvements. This review is a humble attempt to assemble all the available knowledge on ESSs to benefit novice researchers in this field. This paper covers all core concepts of ESSs, including its evolution, elaborate classification, their comparison, the current scenario, applications, business models, environmental impacts, policies, barriers and probable solutions, and future prospects. This elaborate discussion on energy storage systems will act as a reliable reference and a framework for future developments in this field. Any future progress regarding ESSs will find this paper a helpful document wherein all necessary information has been assembled.

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The ambitious target of net-zero emission by 2050 has been aggressively driving the renewable energy sector in many countries. Leading the race of renewable energy sources is solar energy, the fastest growing energy source at present. The solar industry has witnessed more growth in the last decade than it has in the past 40 years, owing to its technological advancements, plummeting costs, and lucrative incentives. The United States is one of the largest producers of solar power in the world and has been a pioneer in solar adoption, with major projects across different technologies, mainly photovoltaic, concentrated solar power, and solar heating and cooling, but is expanding towards floating PV, solar combined with storage, and hybrid power plants. Although the United States has tremendous potential for exploiting solar resources, there is a scarcity of research that details the U.S. solar energy scenario. This paper provides a comprehensive review of solar energy in the U.S., highlighting the drivers of the solar industry in terms of technology, financial incentives, and strategies to overcome challenges. It also discusses the prospects of the future solar market based on extensive background research and the latest statistics. In addition, the paper categorizes the U.S. states into five tiers based on their solar prospects calculated using analytical hierarchy process and regression analysis. The price of solar technologies in the U.S. is also predicted up to 2031 using Wright’s law, which projected a 77% reduction in the next decade.