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1.
Sustainable Energy Technologies and Assessments ; : 102027, 2022.
Article in English | ScienceDirect | ID: covidwho-1655154

ABSTRACT

The Covid-19 pandemic is exerting a significant influence on global energy markets, and continuing to hinder the growth of core technology for the implementation of renewable forms of energy. With an unprecedented effect, the new coronavirus, known as SARS-CoV2, has succeeded to seize the control of most cities of the world and led to their closure. The newly-emerged virus has also resulted in environmental changes. The present study was conducted to show the indirect positive effects of COVID-19 on the reduction of air pollution, particularly in countries such as Italy, France, and India. Our research proved the existence of meaningful relationships between probable actions, air quality improvement, and increased energy generation by photovoltaic systems (PVs). Newly-obtained data from the Copernicus Sentinel-5P satellite illustrate that some cities have experienced a 45 to 50% reduction in nitrogen dioxide (NO2) concentration compared to the same period in the past year. This reduction has provided two important and unexpected benefits, namely the reduction in environmental pollution (specifically air pollution) and, as a consequence, an increase in the amount of energy generated by PVs.

2.
Expert Syst Appl ; 187: 115914, 2022 Jan.
Article in English | MEDLINE | ID: covidwho-1415395

ABSTRACT

Reconfiguration of the distribution network to determine its optimal configuration is a technical and low-cost method that can improve different characteristics of the network based on multi-criteria optimization. In this paper reconfiguration of unbalanced distribution networks is presented with the objective of power loss minimization, voltage unbalance minimization, voltage sag improvement, and minimizing energy not supplied by the customers based on fuzzy multi-criteria approach (FMCA) using new improved corona-virus herd immunity optimizer algorithm (ICHIOA). The voltage unbalances and voltage sag is power quality criteria and also the ENS refers to the reliability index. Conventional CHIOA is inspired based on herd immunity against COVID-19 disease via social distancing and is improved using nonlinearly decreasing inertia weight strategy for global and local exploration improvement. The methodology is implemented as single and multi-objective optimization on 33 and 69 bus IEEE standard networks. Moreover, the performance of the ICHIOA in problem-solving is compared with some well-known algorithms such as particle swarm optimization (PSO), grey wolf optimizer (GWO), moth flame optimizer (MFO), ant lion optimizer (ALO), bat algorithm (BA) and also conventional CHIOA. The simulation results based on the FMCA showed that all criteria are improved with reconfiguration due to compromising between them while in single-objective optimization, some criteria may be weakened. Also, the obtained results confirmed the superiority of the ICHIOA in comparison with the other algorithms in achieving better criteria with lower convergence tolerance and more convergence accuracy. Moreover, the results cleared that the ICHIOA based on FMCA is capable to determine the best network configuration optimally to improve the power loss, voltage sag, voltage unbalance, and ENS in different loading conditions.

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