Bayesian Updating of Solar Panel Fragility Curves and Implications of Higher Panel Strength for Solar Generation Resilience

Solar generation can become a major and global source of clean energy by 2050. Nevertheless, few studies have assessed its resilience to extreme events, and none have used empirical data to characterize the fragility of solar panels. This paper develops fragility functions for rooftop and ground-mounted solar panels calibrated with solar panel structural performance data in the Caribbean for Hurricanes Irma and Maria in 2017 and Hurricane Dorian in 2019. After estimating hurricane wind fields, we follow a Bayesian approach to estimate fragility functions for rooftop and ground-mounted panels based on observations supplemented with existing numerical studies on solar panel vulnerability. Next, we apply the developed fragility functions to assess failure rates due to hurricane hazards in Miami-Dade, Florida, highlighting that panels perform below the code requirements, especially rooftop panels. We also illustrate that strength increases can improve the panels' structural performance effectively. However, strength increases by a factor of two still cannot meet the reliability stated in the code. Our results advocate reducing existing panel vulnerabilities to enhance resilience but also acknowledge that other strategies, e.g., using storage or deploying other generation sources, will likely be needed for energy security during storms.

Microgrid Operations Planning Based on Improving the Flying Sparrow Search Algorithm

Microgrid operations planning is crucial for emerging energy microgrids to enhance the share of clean energy power generation and ensure a safe symmetry power grid among distributed natural power sources and stable functioning of the entire power system. This paper suggests a new improved version (namely, ESSA) of the sparrow search algorithm (SSA) based on an elite reverse learning strategy and firefly algorithm (FA) mutation strategy for the power microgrid optimal operations planning. Scheduling cycles of the microgrid with a distributed power source’s optimal output and total operation cost is modeled based on variables, e.g., environmental costs, electricity interaction, investment depreciation, and maintenance system, to establish grid multi-objective economic optimization. Compared with other literature methods, such as Genetic algorithm (GA), Particle swarm optimization (PSO), Firefly algorithm (FA), Bat algorithm (BA), Grey wolf optimization (GWO), and SSA show that the proposed plan offers higher performance and feasibility in solving microgrid operations planning issues.

Clean Water Using Solar and Wind: Outside the Power Grid (Persian Translation)

Solar photo-voltaic (PV) and wind offer to bring both clean energy and clean water to remote regions and peri-urban areas in the world, outside the conventional electric grids. One out of seven people has no electric power available that would bring light to the home, cook the food, pump to access water and purify or re-use it. Off-grid systems are scalable and can be designed to any size, from household to village and community levels. The renewable energy cost development is remarkable and can make electric power affordable also for the poorest. Renewables promise an end to the era where energy security is closely related to geopolitics. The expenditure is up-front capital cost while “fuel” is free. With renewables, there is no geopolitical pressure where one country has deposits of a fossil fuel while another does not. This book aims to show how clean water and clean energy are reachable for all while contributing to both a better climate and a healthier life. ISBN: 9781789062953 (eBook)

An Analysis of the Impact of Clean and Non-clean Energy Consumption on Economic Growth and Carbon Emission: Evidence from PIMC Countries

This study empirically estimates the impact of clean and non-clean energy consumption on economic growth and carbon dioxide emissions within the framework of the environmental Kuznets curve and pollution haven hypothesis in the case of PIMC countries from 1980 to 2019. The results of the panel cointegration test proposed by Westerlund (2007) show a long-term equilibrium relationship among the variables of each designated model. The long-term elasticities of economic growth and carbon emission estimated by AMG, CCEMG and MG estimators indicate that both clean and non-clean energy consumption have a significant impact on economic growth, while carbon emission hinders growth. The results also reveal that economic growth, non-clean energy consumption and interaction between trade openness and non-clean energy consumption have a driving effect on carbon dioxide emission, however, clean energy consumption is found to reduce carbon emission. In addition, the analysis confirms the existence of the inverted U-shaped environmental Kuznets curve and pollution haven hypothesis in the panel of PIMC economies. Finally, there is a one-way causality from non-clean energy consumption to economic growth, but no such causation exists between clean energy consumption and economic growth. The objective of sustained economic growth with a safe environment may be achieved by encouraging clean energy consumption in the PIMC economies.

Exploring the Effects of Economic Complexity and the Transition to a Clean Energy Pattern on Ecological Footprint From the Indian Perspective

The study explores the association between economic complexity, globalization, renewable and non-renewable energy consumption on the ecological footprint in the case of India from 1990–2018. The autoregressive distributed lag (ARDL) is applied to measure the long-run elasticity, while the vector error correction model (VECM) is applied to classify the causal path. The empirical findings demonstrate that economic complexity, globalization process, and renewable energy consumption play a dominant role in minimizing environmental degradation. In contrast, economic growth and non-renewable energy consumption are more responsible for increasing the pollution level in both the short and long run. Furthermore, the VECM outcomes disclose that there is long-run causality between ecological footprint and economic complexity. Moreover, the empirical outcomes are robust to various robustness checks performed for analysis to the consistency of our main results. The Indian government/policymakers should encourage a more environmentally friendly production process and eco-friendly technologies in exports to minimize environmental degradation.

Investigating the potential role of innovation and clean energy in mitigating the ecological footprint in N11 countries

This paper seeks to explore the potential function of technological innovation and clean power in mitigating the ecological footprint in the N-11 nations during the phase 1992–2015 by applying panel cointegration analysis. The outcomes of the panel cointegration test signify the occurrence of a long-run relation among the clean energy (CE) variable, the ecological footprint (EF) variable, the per capita GDP (Y) variable, the financial development (FIN) variable, and technological innovation (TI) variable. The outcomes of the VECM signify a long-run causal relation from the ecological footprint (EF) variable to the clean energy (CE) variable, the GDP per capita (Y) variable, and technological innovation (TI) variable. This implies that the environmental degradation faced by the N-11 countries leads to shifting toward clean energy sources and technological innovation in the long run. Thus, the N-11 countries are in need to design policies that enhance shifting toward environmentally friendly energy sources.