Analysis of Pre- and during COVID-19 Mixed Load Models on Unbalanced Radial Distribution System Using a New Metaphor-Less Rao Optimization

An unbalanced electrical distribution system (DS) with radial construction and passive nature suffers from significant power loss. The unstable load demand and poor voltage profile resulted from insufficient reactive power in the DS. This research implements a unique Rao algorithm without metaphors for the optimal allocation of multiple distributed generation (DG) and distribution static compensators (DSTATCOM). For the appropriate sizing and placement of the device, the active power loss, reactive power loss, minimum value of voltage, and voltage stability index are evaluated as a multiobjective optimization to assess the device's impact on the 25-bus unbalanced radial distribution system. Various load models, including residential, commercial, industrial, battery charging, and other dispersed loads, were integrated to develop a mixed load model for examining electrical distribution systems. The impact of unpredictable loading conditions resulting from the COVID-19 pandemic lockdown on DS is examined. The investigation studied the role of DG and DSTATCOM (DGDST) penetration in the electrical distribution system for variations in different load types and demand oscillations under the critical emergency conditions of COVID-19. The simulation results produced for the mixed load model during the COVID-19 scenario demonstrate the proposed method's efficacy with distinct cases of DG and DSTATCOM allocation by lowering power loss with an enhanced voltage profile to create a robust and flexible distribution network. Copyright © 2023 Jitendra Singh Bhadoriya et al.

An evaluation of the impact of COVID-19 lockdowns on electricity demand

The COVID-19 pandemic has given rise to significant changes in electricity demand around the world. Although these changes differ from region to region, countries that have implemented stringent lockdown measures to curtail the spread of the virus have experienced the greatest alterations in demand. Within Australia, the state of Victoria has been subject to the largest number of days in hard lockdown during the COVID-19 pandemic. We conduct an exploratory data analysis to identify predictors of demand, and have built a time series forecasting model to predict the half-hourly electrical demand in Victoria. Our model distinguishes between lockdown periods and non-restrictive periods, and aims to identify a variety of patterns that we show to be influential on electricity demand. The model thereby provides a nuanced prediction of electrical demand that captures the shifting demand profile of intermittent lockdowns.

A novel transfer deep learning model with reinforcement-learning-based hyperparameter optimization for short-term load forecasting during the COVID-19 pandemic

The coronavirus disease 2019 pandemic has significantly impacted the world. The sudden decline in electricity load demand caused by strict social distancing restrictions has made it difficult for traditional models to forecast the load demand during the pandemic. Therefore, in this study, a novel transfer deep learning model with reinforcement-learning-based hyperparameter optimization is proposed for short-term load forecasting during the pandemic. First, a knowledge base containing mobility data is constructed, which can reflect the changes in visitor volume in different regions and buildings based on mobile services. Therefore, the sudden decline in load can be analyzed according to the socioeconomic behavior changes during the pandemic. Furthermore, a new transfer deep learning model is proposed to address the problem of limited mobility data associated with the pandemic. Moreover, reinforcement learning is employed to optimize the hyperparameters of the proposed model automatically, which avoids the manual adjustment of the hyperparameters, thereby maximizing the forecasting accuracy. To enhance the hyperparameter optimization efficiency of the reinforcement-learning agents, a new advance forecasting method is proposed to forecast the state-action values of the state space that have not been traversed. The experimental results on 12 real-world datasets covering different countries and cities demonstrate that the proposed model achieves high forecasting accuracy during the coronavirus disease 2019 pandemic. © 2022 - IOS Press. All rights reserved.

Impact of COVID-19 on Power System Load: The case from Oman

Due to the COVID-19 pandemic, the governments around the world were compelled to reduce business activity and took measures in response to minimize the impact of coronavirus. Under this condition the people lifestyle has been changed due to lockdown restrictions and other measures. Hence the electricity sector significantly affected under circumstance of COVID-19. The system demand and total energy consumptions in network is impacted by COVID-19. The main aim this paper is to review, analyze the weak links in the system during the COVID-19 time by comparing the load profiles at different load schedules with respect to different costumers. The analysis on the system load is as considered for the year 2019 of April and May months and compare with the same period of year 2020. However, there could be a difference in ambient weather conditions, which also will reflect on system load, the long period load and analyze the overall effect to minimize such weather-related variations effect. Therefore, this study reviews the impact of COVID-19 on one of the power systems loads in Oman. The results show that the peak load reduction, effective utilization of the operation and control on power system.

COVID-19: Impact analysis and recommendations for power sector operation.

The demand of electricity has been reduced significantly due to the recent COVID-19 pandemic. Governments around the world were compelled to reduce the business activity in response to minimize the threat of coronavirus. This on-going situation due to COVID-19 has changed the lifestyle globally as people are mostly staying home and working from home if possible. Hence, there is a significant increase in residential load demand while there is a substantial decrease in commercial and industrial loads. This devastating situation creates new challenges in the technical and financial activities of the power sector and hence most of the utilities around the world initiated a disaster management plan to tackle this ongoing challenges/threats. Therefore, this study aims to investigate the global scenarios of power systems during COVID-19 along with the socio-economic and technical issues faced by the utilities. Then, this study further scrutinized the Indian power system as a case study and explored scenarios, issues and challenges currently being faced to manage the consumer load demand, including the actions taken by the utilities/power sector for the smooth operation of the power system. Finally, a set of recommendations are presented to support the government/policymakers/utilities around the world not only to overcome the current crisis but also to overcome future unforeseeable pandemic alike scenario.