A multi-objective fuzzy robust optimization approach for designing sustainable and reliable power systems under uncertainty

Effect of the COVID-19 Pandemic on Renewable Energy Firm’s Profitability and Capitalization

The COVID-19 pandemic has led many governments to impose restrictive measures that have contributed to a decline in the demand for goods and services, leading to an economic crisis. This study proves a novelty that implies a rise in the capitalization of renewable energy companies during the coronavirus pandemic. The study is based on the hypothesis that, at a time of economic crisis, the prospect of investing in clean energy has increased, through the need to protect the environment and ensure clean air. The analysis provided additional results that there is an inverse relationship between two economic indicators of firms, namely, the percentage change in profitability and capitalization of firms between 2020 and 2021. Analysis of data from companies included in TRBC Industry Name Renewable Fuels provided numerical results that show an average increase in firms’ capitalization of 86%. The study uses analysis techniques such as covariance and correlation. The results show an increase in capitalization of renewable energy companies by 150%, while there is a decrease in income by 2%. However, the capitalization of fossil fuel companies has increased, with an average growth rate of 35%. This situation in the fossil energy market is that company revenues fell by 32% while capitalization increased by 35%. It proves a bubble in the non-renewable energy market. This paper suggests that the period of coronavirus infection has seen a slowdown in economic growth in many countries around the world, but a switch to renewable energy will help improve the quality of life of the population and ensure economic growth.

Renewable Energy Deployment and COVID-19 Measures for Sustainable Development

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).

Sustainable Cyber-Physical Production Systems in Big Data-Driven Smart Urban Economy: A Systematic Literature Review

In this article, we cumulate previous research findings indicating that cyber-physical production systems bring about operations shaping social sustainability performance technologically. We contribute to the literature on sustainable cyber-physical production systems by showing that the technological and operations management features of cyber-physical systems constitute the components of data-driven sustainable smart manufacturing. Throughout September 2020, we performed a quantitative literature review of the Web of Science, Scopus, and ProQuest databases, with search terms including “sustainable industrial value creation”, “cyber-physical production systems”, “sustainable smart manufacturing”, “smart economy”, “industrial big data analytics”, “sustainable Internet of Things”, and “sustainable Industry 4.0”. As we inspected research published only in 2019 and 2020, only 323 articles satisfied the eligibility criteria. By eliminating controversial findings, outcomes unsubstantiated by replication, too imprecise material, or having similar titles, we decided upon 119, generally empirical, sources. Future research should investigate whether Industry 4.0-based manufacturing technologies can ensure the sustainability of big data-driven production systems by use of Internet of Things sensing networks and deep learning-assisted smart process planning.

A risk-sharing-based resilient renewable energy supply network model under the COVID-19 pandemic

Over the past few months, the COVID-19 pandemic has postponed many renewable energy projects because of disruptions in the technology and finance supply. Additionally, the existing power plants are inefficient because of a record drop in demand for goods and services caused by lockdowns in cities. This situation poses huge challenges to the resilience of renewable energy supply networks in the face of deeply hazardous events, such as the COVID-19 pandemic. Therefore, the purpose of this study was to design a resilient renewable energy supply network considering supply, demand, and payment risks caused by COVID-19. The objective of the proposed model was to determine the optimal amount of electric power generated and stored to meet the demands and the risk-sharing effort index to maximize the total resilient profit of the power plant and determine the optimal price adjustment index to minimize the cost to consumers. A government subsidy-based risk-sharing model was developed to enhance the resilience of the concerned renewable energy supply network under the pandemic. To overcome uncertainties in both random and risk events, a robust fuzzy-stochastic programming model was proposed to solve these research problems. Computational experiments were conducted on the test supply network in Vietnam. The results showed that the resilient energy supply network with the risk-sharing model tended to stabilize the total profit with the different impact levels of COVID-19 compared to the network without risk-sharing. The proposed model efficiently tackled both uncertainties in random and hazardous events and had a higher profit and shorter CPU time compared to the robust optimization mode.