ABSTRACT
Coronavirus disease 2019 (COVID-19) has affected many behaviours and aspects of society. Electricity consumption has been considerably affected by the pandemic, with significant effects on the electricity load demand profile. In this article, the impact of COVID-19 on electricity demand in the state of Florida is investigated through a novel machine learning technique. The LSTM technique shows good accuracy in forecasting the load profiles for all days studied (weekdays and weekends) and also before and during the pandemic. The UC problem is solved considering the load profiles, and the impact of COVID-19 on power plant scheduling is evaluated. The simulation results show an increase in residential demand for electricity at weekends, while both residential and commercial demand are reduced during weekdays. Therefore, the operating cost of a weekday in 2020 was lower than that in 2019, while the operating cost of a weekend was higher in 2020 than in 2019.
ABSTRACT
During this decade, many countries have experienced natural and accidental disasters, such as typhoons, floods, earthquakes, and nuclear plant accidents, causing catastrophic damage to infrastructures. Since the end of 2019, all countries of the world are struggling with the COVID-19 and pursuing countermeasures, including inoculation of vaccine, and changes in our lifestyle and social structures. All these experiences have made the residents in the affected regions keenly aware of the need for new infrastructures that are resilient and autonomous, so that vital lifelines are secured during calamities. A paradigm shift has been taking place toward reorganizing the energy social service management in many countries, including Japan, by effective use of sustainable energy and new supply schemes. However, such new power sources and supply schemes would affect the power grid through intermittency of power output and the deterioration of power quality and service. Therefore, new social infrastructures and novel management systems to supply energy and social service will be required. In this paper, user-friendly design, operation and control assist tools for resilient microgrids and autonomous communities are proposed and applied to the standard microgrid to verify its effectiveness and performance. © 2022
ABSTRACT
With diminishing fossil fuel resources and increasing environmental concerns, large-scale deployment of Renewable Energy Sources (RES) has accelerated the transition towards clean energy systems, leading to significant RES generation share in power systems worldwide. Among different RES, solar PV is receiving major focus as it is most abundant in nature compared to others, complimented by falling prices of PV technology. However, variable, intermittent and non-synchronous nature of PV power generation technology introduces several technical challenges, ranging from short-term issues, such as low inertia, frequency stability, voltage stability and small signal stability, to long-term issues, such as unit commitment and scheduling issues. Therefore, such technical issues often limit the amount of non-synchronous instantaneous power that can be securely accommodated by a grid. In this backdrop, this research work proposes a tool to estimate maximum PV penetration level that a given power system can securely accommodate for a given unit commitment interval. The proposed tool will consider voltage and frequency while estimating maximum PV power penetration of a system. The tool will be useful to a system operator in assessing grid stability and security under a given generation mix, network topology and PV penetration level. Besides estimating maximum PV penetration, the proposed tool provides useful inputs to the system operator which will allow the operator to take necessary actions to handle high PV penetration in a secure and stable manner.