ABSTRACT
The residential sector in Thailand has been a fast-growing energy consumption sector since 1995 at a rate of 6% per year. This sector makes a significant contribution to Thailand's rising electricity demand especially during the COVID-19 pandemic. This study projects Thailand's residential electricity consumption characteristics and the factors affecting the growth of electricity consumption using a system dynamics (SD) modeling approach to forecast long-term electricity consumption in Thailand. Furthermore, the COVID-19 pandemic and the lockdown can be seen as a forced social experiment, with the findings demonstrating how to use resources under particular circumstances. Four key factors affecting the electricity demand used in the SD model development include (1) work and study from home, (2) socio-demographic, (3) temperature changing, and (4) rise of GDP. Secondary and primary data, through questionnaire survey method, were used as data input for the model. The simulation results reveal that changing behavior on higher-wattage appliances has huge impacts on overall electricity consumption. The pressure to work and study at home contributes to rises of electricity consumption in the residential sector during and after COVID-19 pandemic. The government and related agencies may use the study results to plan for the electricity supply in the long term. © 2023 World Scientific Publishing Co.
ABSTRACT
During the COVID-19 pandemic, the U.S. power sector witnessed remarkable electricity demand changes in many geographical regions. These changes were evident in population-dense cities. This paper incorporates a techno-economic analysis of energy storage systems (ESSs) to investigate the pandemic's influence on ESS development. In particular, we employ a linear program-based revenue maximization model to capture the revenues of ESS from participating in the electricity market, by performing arbitrage on the energy trading, and regulation market, by providing regulation services to stabilize the grid's frequency. We consider five dominant energy storage technologies in the U.S., namely, Lithium-ion, Advanced Lead Acid, Flywheel, Vanadium Redox Flow, and Lithium-Iron Phosphate storage technologies. Extensive numerical results conducted on the case of New York City (NYC) allow us to highlight the negative impact that COVID-19 had on the NYC power sector. © 2023 IEEE.
ABSTRACT
In this paper, the short-, medium-, and long-term effects of the COVID-19 pandemic on the Italian power system, particularly electricity consumption behavior and electricity market prices, are investigated by defining various metrics. The investigation reveals that COVID-19 lockdown caused a drop in load consumption and, consequently, a decrement in day-ahead market prices and an increase in ancillary service prices. © 2015 CSEE.
ABSTRACT
Extreme disruptive scenarios such as pandemic lockdown force people to alter regular daily routines, impacting their energy consumption pattern. The implication of such a disruptive scenario for a more extended period on energy consumption is uncertain. This study aimed to investigate the impact of COVID-19 lockdown on residential electricity consumption in 100 houses from the southwestern UK. For the study, we analysed highly granular (1-minutely) electricity consumption data for April-September 2020 compared to the same months in 2019 for the same houses. Our study showed statistically significant differences during the lockdown period (the analysed six months) in energy demand. The minutely average electricity demand was 1.4-10% lower during April-September 2020 than in 2019. Our analysis showed that not all houses had similar type of changes during the lockdown. Some houses demonstrated a 38% increase in electricity demand, whereas some houses showed a 54% reduction during the lockdown period compared to 2019. Some houses showed significantly higher electricity use during the morning and afternoon than in 2019, which might be due to working and schooling from homes during the lockdown. © International Building Performance Simulation Association, 2022
ABSTRACT
This study attempts to study the impact of social and economic constraints, identification of new diseases, wind and solar energy consumption during the 2019 crisis on daily electricity demand by constructing multivariate correlation regression. The aim of the study is to determine the impact of the COVID-19 pandemic on the structure of electricity consumption by building regression models to analyse how various variables (detection of new diseases, wind and solar energy consumption) and social behaviour affect electricity demand. Tasks: to identify the main dates from the chronologies of COVID-19 in Russia, compare the electricity indicators by years, compare the data with the pre-pandemic period, study the share of generated electricity in the balance, conduct a correlation-regression analysis in order to identify the relationship between the detection of new cases of COVID-19 disease in the period from 03/30/2020 to 10/27/2021 and energy consumption, to study the impact of social activity on the level of consumption of renewable energy sources. This study identified links between new cases of coronavirus disease and energy consumption;wind energy consumption and general indicator;consumption of wind energy and solar with an indicator of morbidity. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.
ABSTRACT
Owing to the global lockdown caused by the pandemic of COVID-19, the electricity demand is greatly affected, and the electricity market is also constantly fluctuating. During the pandemic period, the prediction of electricity demand is crucial to the economy and power dispatching. In this study, we combine the pandemic data and government anti-pandemic policies data to predict the electricity demand of the Contiguous United States by using the artificial neural network and recurrent neural network. In addition, the linear regression method is used to forecast the thermal generation with total generation data. Some experiments have developed to verify the effectiveness of the model. Then the model is used to forecast electricity demand and thermal generation under different policies and pandemic development, and the result were analyzed. © 2021 IEEE
ABSTRACT
The COVID-19 related shutdowns have made significant impacts on the electric grid operation worldwide. The global electrical demand plummeted around the planet in 2020 continuing into 2021. Moreover, demand shape has been profoundly altered as a result of industry shutdowns, business closures, and people working from home. In view of such massive electric demand changes, energy forecasting systems struggle to provide an accurate demand prediction, exposing operators to technical and financial risks, and further reinforcing the adverse economic impacts of the pandemic. In this context, the “IEEE DataPort Day-Ahead Electricity Demand Forecasting Competition: Post-COVID Paradigm" was organized to support the development and dissemination state-of-the-art load forecasting techniques that can mitigate the adverse impact of pandemic-related demand uncertainties. This paper presents the findings of this competition from the technical and organizational perspectives. The competition structure and participation statistics are provided, and the winning methods are summarized. Furthermore, the competition dataset and problem formulation is discussed in detail. Finally, the dataset is published along with this paper for reproducibility and further research. Author
ABSTRACT
Optimizing the configuration of a microgrid allows to reduce energy losses and costs. Due to the new demand profile that emerged with the covid-19 pandemic with remote work, it is important to study its impact on the dimensioning of a microgrid, as well as the way in which renewable resources are distributed and dimensioned, in order to promote its use. Thus, this paper aims to propose the optimal configuration of distributed energy resources that meet two scenarios, pre-pandemic and post-pandemic, of electricity demand in a household in Salvador, Brazil. Therefore, an optimization problem was formulated in GAMS using environmental data and electricity demand, as well as the costs involved in the implementation and operation of the system, considering the resources of solar, wind and biogas energy. There was a change in the post-pandemic scenario, with the tendency to increase the use of solar energy, due to the demand being distributed throughout the day. It was observed for both scenarios that biogas energy had the greatest participation in domestic energy generation, followed by solar and wind energy. Therefore, the use of biogas in combination with other renewable resources can minimize costs and, at the same time, meeting the energy demand of a residence. In addition, this contributes to the environmental and economic sustainability of the region, as consumers begin to produce their own energy using renewable resources. © 2021 IEEE
ABSTRACT
Electricity demand have dropped sharply in general as governments around the world executed the lockdown restrictions. The load compositions and the daily load profiles have also changed. Utilities are basically engaged in providing various power requirements accordingly for different types of tariffs. The various tariffs are applicable to consumers like Domestic, Industry, Agriculture, Government sources, Commercial, Public lighting etc. The COVID-19 Pandemic have affected the energy consumption of various tariff categories in different ways. Since the COVID-19 Pandemic deprived most of the Industrial and Commercial establishments of their functioning, their energy consumption drastically came down whereas the domestic energy consumption increased. This paper attempts to compare assess the energy consumption data of various categories during the pandemic period and how it has affected the Utilities sale of KSEB Power Utility Grid. © 2021 IEEE.
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Distribution network outages have significant socioeconomic impacts, and potentially pose a threat to life when critical infrastructures are affected. Shocks and stresses, such as climate change, extreme weather or intentional attacks, in combination with an expected rise in electricity demand, pose an increasing risk to power network reliability. Detailed data on distribution network outages can be used for further research in prevention and mitigation of such outages. This paper presents and describes a unique and comprehensive dataset of UK distribution network outages. The dataset for 2020 is analyzed to identify correlations with shocks and stresses, such as demand, extreme weather, and COVID-19 lockdown. The results justify further research in prevention and mitigation of distribution network outages, support the industry in future planning of distribution networks, and can feed into models for operational planning in face of upcoming shocks and stresses. © 2021 IEEE.
ABSTRACT
The pandemics outbreak of Covid-19 in the world has made society and industrial activities very dynamic. The operating power plant must prepare to fulfil the fluctuating electricity demand from the load dispatcher. Hence, predicting the electrical power output is important to give the accuracy to maximize the profit and minimize losses. This paper discusses and predicts the half-hourly electrical output of Paiton Coal-Fired Power Station Unit 1 by develops many predictive models using five different machine learning regression methods. The five parameters that affect the electrical power output are used in the dataset, such as main steam flow, total coal flow, primary airflow, secondary airflow, and vacuum condenser pressure. These input and target variables as the dataset were collected over one year. The dataset is sorted and observed. Then, the best prediction model is sought for predicting electrical power output. Thus, the best performance of the best subset, which contains a complete set of input variables, has been analyzed using the most accurate machine learning algorithm, which is the random forest, with R-squared of 0.996. © 2021 IEEE.
ABSTRACT
This paper presents a proposal methodology to study the temperature dependence of the Italian electricity demand. Indeed, weather temperature has a significant influence on the electricity consumption. From a Transmission System Operator (TSO) perspective, an accurate estimation of this effect is crucial to interpret and predict demand fluctuations. Several dispatching applications consider these phenomena, as for example adequacy analysis, demand forecasting tools, and real-time operational procedures. Based on the geographical features of Italy, it was possible to identify various sensitivity behaviors at regional scale. The purpose of this study is to develop a temperature sensitivity model to be applied on electricity demand profile with different time granularity (e.g., daily, hourly). A clustering analysis on the historical input data is performed. Furthermore, a thorough investigation to identify the optimal best-fitting method for this application is described. In order to test the methodology, some relevant business cases are simulated considering also extreme scenarios. Results on COVID-19 scenario is also described. Finally, an outlook on the planned future developments of the method is provided. © 2021 AEIT.