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The pandemic caused by the coronavirus disease 2019 (COVID-19) has produced a global health calamity that has a profound impact on the way of perceiving the world and everyday lives. This has appeared as the greatest threat of the time for the entire world in terms of its impact on human mortality rate and many other societal fronts or driving forces whose estimations are yet to be known. Therefore, this study focuses on the most crucial sectors that are severely impacted due to the COVID-19 pandemic, in particular reference to India. Considered based on their direct link to a country’s overall economy, these sectors include economic and financial, educational, healthcare, industrial, power and energy, oil market, employment, and environment. Based on available data about the pandemic and the above-mentioned sectors, as well as forecasted data about COVID-19 spreading, four inclusive mathematical models, namely—exponential smoothing, linear regression, Holt, and Winters, are used to analyse the gravity of the impacts due to this COVID-19 outbreak which is also graphically visualized. All the models are tested using data such as COVID-19 infection rate, number of daily cases and deaths, GDP of India, and unemployment. Comparing the obtained results, the best prediction model is presented. This study aims to evaluate the impact of this pandemic on country-driven sectors and recommends some strategies to lessen these impacts on a country’s economy.

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With the emergence of various technologies in renewable energy sources (RESs), consumers nowadays have led to multiple techniques for integrating various resources. An optimum energy flow management delivers an effective, reliable integrated renewable energy system (IRES) with higher efficiency. In this paper, an optimum model for the IRES is designed through the optimal sizing of system components for an estimated demand. Using integer linear programming, an energy management scheme has been developed based upon scheduling of energy utilization technique of deferrable loads. The considered approach is modeled to reduce the peak energy utilization (hourly) of the area of study through the consumption scheduling of machines. Furthermore, the MHP‐solar‐wind‐battery‐based IRES with energy Management strategy has been optimized in MATLAB. Size optimization of the system has been done without and with the energy management strategy using improved harmony search algorithm. The results show a significant improvement in the cost and savings using the strategy. Considering the 5% LPSP, the optimum size for the integrated system offers a total annualized cost of INR 3.21 million with a levelized cost of energy of 4.5 INR/kWh. The comparison of proposed method with PSO shows the better convergence rate of the system. Comparing systems with and without energy management, it has been noted that with a strategy, systems size and expenditures are reduced significantly.