Probabilistic Forecasting of Electricity Demand Incorporating Mobility Data

Due to extreme weather conditions and anomalous events such as the COVID-19 pandemic, utilities and grid operators worldwide face unprecedented challenges. These unanticipated changes in trends introduce new uncertainties in conventional short-term electricity demand forecasting (EDF) since its result depends on recent usage as an input variable. In order to quantify the uncertainty of EDF effectively, this paper proposes a comprehensive probabilistic EFD method based on Gaussian process regression (GPR) and kernel density estimation (KDE). GPR is a non-parametric method based on Bayesian theory, which can handle the uncertainties in EDF using limited data. Mobility data is incorporated to manage uncertainty and pattern changes and increase forecasting model scalability. This study first performs a correlation study for feature selection that comprises weather, renewable and non-renewable energy, and mobility data. Then, different kernel functions of GPR are compared, and the optimal function is recommended for real applications. Finally, real data are used to validate the effectiveness of the proposed model and are elaborated with three scenarios. Comparison results with other conventional adopted methods show that the proposed method can achieve high forecasting accuracy with a minimum quantity of data while addressing forecasting uncertainty, thus improving decision-making.

Firms and Households during the Pandemic: What Do We Learn from Their Electricity Consumption?

We analyze the impact of the COVID-19 pandemic on electricity consumption patterns. We highlight the importance of decomposing total electricity consumption into consumption by firms and by households to better understand the economic and social impacts of the crisis. While electricity demand by firms has fallen substantially, the demand by households has gone up. In particular, our focus is on Spain where, during the total lockdown, these effects reached –29% and +10% respectively, controlling for temperature and seasonality. While the electricity demand reductions during the second wave were milder, the demand by firms remained 5% below its normal levels. We also document a change in people's daily routines in response to the stringency of the lockdown measures, as reflected in their hourly electricity consumption patterns. [ FROM AUTHOR] Copyright of Energy Journal is the property of International Association for Energy Economics, Inc. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full . (Copyright applies to all s.)

COVID-19 Pandemic Impact on the Electricity Demand of a Middle-Income Non-Industrial Country: Jordanian Case

The outbreak of the infamous Coronavirus disease (COVID-19) has widely affected every aspect and flow of the human daily lives. While this effect extended to the global electrical grid, it impacted every system distinctly owing to the divided global response to the pandemic, the diverse economic development of the countries as well as the different scale and responsiveness of each electrical grid. Jordan, a middle-income economy and one of the countries that were severely impacted by the disease, was known during the start of the pandemic for imposing one of the strictest curfews and nationwide lockdown around the world. These restrictions had significantly affected the electric load demand over the year 2020. With Jordan's high residential load share (exceeding 45%), increasing contribution of generation from renewable energy sources (more than 20% in 2020), alongside its unique pandemic response and its hot summers;the Jordanian electricity demand makes an interesting case to investigate. This paper provides a comparative analysis of the Jordanian load demand during the years 2019 and 2020 and includes insights on the effects of the imposed lockdown and the varying temperature in three different time frames;pre-lockdown, during lockdown and post lockdown. © 2022 IEEE.

A novel deep learning framework with a COVID-19 adjustment for electricity demand forecasting

Electricity demand forecasting is crucial for practical power system management. However, during the COVID-19 pandemic, the electricity demand system deviated from normal system, which has detrimental bias effect in future forecasts. To overcome this problem, we propose a deep learning framework with a COVID-19 adjustment for electricity demand forecasting. More specifically, we first designed COVID-19 related variables and applied a multiple linear regression model. After eliminating the impact of COVID-19, we employed an efficient deep learning algorithm, long short-term memory multiseasonal net deseasonalized approach, to model residuals from the linear model aforementioned. Finally, we demonstrated the merits of the proposed framework using the electricity demand in Taixing, Jiangsu, China, from May 13, 2018 to August 2, 2021. © 2023 The Author(s)

Is it all about supply? Demand-side effects on the Spanish electricity market following Covid-19 lockdown policies

A key characteristic of electricity prices is their sensitivity to changes in supply and demand. In this sense, the Covid-19 lockdown policies modified electricity consumption patterns at both business and household levels, affecting the shape and position of the electricity demand curve and, thus, leading to a direct effect on electricity prices. However, could this demand-side effect be greater than other supply-induced effects? Is it persistent over time? This paper uses a synthetic bidding approach and concludes that the strict lockdown phase had a strong, immediate – but not persistent – effect on the Spanish electricity price. Furthermore, a high share of renewable energy and a reduction in fossil fuel and emission prices have also proven crucial in driving prices down, though lockdown policies had more impact on prices.

IMPACT OF LIFESTYLE CHANGES DURING COVID-19 PANDEMIC ON ELECTRICITY DEMAND IN DETACHED HOUSES (PART 2) : ANALYSIS BY HOUSEHOLD IN THE WINTER OF 2021 AND CONSIDERATION OF RENEWABLE ENERGY SELF-CONSUMPTION

This paper focused on the impact of lifestyle changes in response to the novel coronavirus infection (COVID-19) on the electricity demand of 1339 detached houses from October 2020 to March 2021. Analyzing with the lifestyle questionnaire survey, twelve months after the first state of emergency for COVID-19 at April 2020, “working from home” was the only factor that increased household power consumption for 11% and the other factors were gone. Space heating power consumption in this period did not increase significantly. Lifestyle changes have affected household timely electricity demand and increased self-consumption of renewable energy of photovoltaic power generation systems.

Impact of coronavirus disease 2019 on electricity demand and the unit commitment problem: a long–short-term memory-based machine learning approach

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.

The impact of COVID-19 measures on intraday electricity load curves in the European Union: A panel approach

This paper examines the impact of the intensity of government measures introduced to reduce the spread of COVID-19 on intraday electricity load curves in 23 European countries. The econometric panel model used covers the entire period from the virus outbreak in Europe up to the release of several vaccines;therefore, the estimation considers the introduction, partial lifting, and reintroduction of the interventions. Based on the results, the impacts of the different stringency measures were similar in the 23 analysed EU member states. More stringent interventions had different effects at different times of day: the morning and evening peaks were significantly affected, as was every hour of the day. The impacts were nonlinear, meaning that different measures mutually amplified each other’s impact and led to more substantial changes in electricity consumption and citizens’ lives. The morning and evening peaks are also found to have decreased, causing a flattening of the load curves. In line with this result, the partial effect of an increase in the stringency index depends on the type of day (weekday or weekend), hour of the day, and initial stringency level. Overall, the lockdown measures led to a decrease in hourly electricity consumption of between 1% and 9% on weekdays and between 1% and 13% on weekends. Total daily consumption decreased by up to 9%. Understanding how hourly electricity demand reacts to different stringency measures provides valuable information in operation scheduling and capacity planning. More accurate demand forecasts can support trading decisions and help prevent extreme market mismatches.

A Cyber-Physical-Social Perspective on Future Smart Distribution Systems

An increasing number of distributed energy resources (DERs), such as rooftop photovoltaic (PV), electric vehicles (EVs), and distributed energy storage, are being integrated into the distribution systems. The rise of DERs has come hand-in-hand with large amounts of data generated and explosive growth in data collection, communication, and control devices. In addition, a massive number of consumers are involved in the interaction with the power grid to provide flexibility. Electricity consumers, power networks, and communication networks are three main parts of the distribution systems, which are deeply coupled. In this sense, smart distribution systems can be essentially viewed as cyber-physical-social systems. So far, extensive works have been conducted on the intersection of cyber, physical, and social aspects in distribution systems. These works involve two or three of the cyber, physical, and social aspects. Having a better understanding of how the three aspects are coupled can help to better model, monitor, control, and operate future smart distribution systems. In this regard, this article provides a comprehensive review of the coupling relationships among the cyber, physical, and social aspects of distribution systems. Remarkably, several emerging topics that challenge future cyber-physical-social distribution systems, including applications of 5G communication, the impact of COVID-19, and data privacy issues, are discussed. This article also envisions several future research directions or challenges regarding cyber-physical-social distribution systems.

Assessment of Algeria?s electricity power demands during COVID-19 pandemic and wildfires incidents

The COVID-19 pandemic has had enormous negative impacts on several activity sectors such as health, energy, and the economy. Aiming to limit its spread, extraordinary containment measures have been taken by the Algerian government by proclaiming the confinement on March 22, 2020, while maintaining the security of electricity supply to hospitals, critical infrastructures and the households. This manuscript presents a deep analysis of the impacts of the COVID-19 pandemic on the electricity power demands in northern and southwestern Algeria. The analysis covers the period from January 1, 2019, to December 31, 2021, and considers sudden and large changes (increase or decrease) in electricity power demand that may put power system reliability and stability at risk. One promising solution, which can avoid such situations, is to detect the power ramp rate;it is proposed as a power fluctuation indicator. In addition, the analysis also includes the impacts of wildfires that menace most of northern Algeria during the summer of 2021.

Electricity Demand and CO Emissions during the COVID-19 Pandemic: The Case of India

The present study examines the impact of electricity demand on CO emissions in the Indian economy using daily real-time data during the Covid-19 period. The subject was hardly addressed explicitly and quantitatively in environmental studies. Our study applied recently developed non-linear (asymmetric) autoregressive distributed lag (ARDL) and the Quantile ARDL techniques for analysis. The empirical findings confirm the existence of an asymmetric long-run relationship between electricity demand and CO emissions during the Covid-19 pandemic. Furthermore, the results reveal that the decrease (increase) in electric demand leads to a reduction (increase) in CO emissions in the long run. Besides, the results show that the increase in electricity demand generates more CO emissions in the short run. Our study will be helpful for policy-makers and regulators associated with energy and climate change amid the ongoing pandemic crisis and provide directions to the expected waves of pandemic scenarios. © 2022, Econjournals. All rights reserved.

How good are TSO load and renewable generation forecasts: Learning curves, challenges, and the road ahead

Transmission system operators (TSOs) forecast load and renewable energy generation to maintain smooth functioning of the grid by contracting sufficient generation and reserve capacity. These forecasts are also utilized by third parties, such as energy generators and demand aggregators, in their own forecasting and decision-making pipelines e.g. to determine suitable trading strategies. Inaccurate forecasts by the TSOs can therefore lead to increased balancing needs as well as elevated societal and market costs. The situation is further exacerbated by the challenges arising due to rapidly increasing renewable generation and the effects of the post-Covid era. In this paper, we analyse five years of TSO forecasts for load, wind and solar generation for 16 European countries. More concretely, using a comprehensive set of metrics, we explore relevant questions such as whether there are TSO specific differences in forecast accuracy, and how forecast errors have changed over time and if they can be reduced further. Our results show that while errors tend to increase linearly with demand or renewable generation, most TSOs still have considerable room for improvement in terms of accuracy. The paper concludes with a set of recommendations for TSOs to improve their forecasts, as well as the ENTSO-E transparency platform where we obtained the data used in this study.

Analysis and Modeling of the Impact of Covid-19 Response Measures on Power Demand

Different countries and regions across the world have adopted various responses and containment measures since the early part of 2020 to control the spread of the coronavirus causing Covid-19. These measures have caused significant changes in peoples' mobility, utilization of commercial and industrial facilities, deployment of government resources to provide necessary financial sustenance and support public health. In this study, we have analyzed the impact of these response measures on aggregate electric load in 4 different regions in the US and tried to identify how the load forecasting models may need to be changed post-pandemic. The key findings from our research include (1) the confirmation of a paradigm change in the nature of power demand before and after the pandemic, (2) the emergence of Covid-19 response related factors as predictors of load, and, (3) the reduction in relative importance of traditional load-influencing factors, including temperature, during the pandemic.

Impact analysis of COVID-19 pandemic on the electricity demand, frequency control and electromechanical oscillation modes of the Brazilian Interconnected Power System using low voltage WAMS data

COVID-19 pandemic presented unique features among the range of threats encountered over the last century. Its impact echoes throughout the world affecting societies and their patterns of behavior, hence affecting the usage of electricity and the operation of power systems. This paper provides an analysis of the impact of COVID-19 Pandemic on the electricity demand, frequency control and electromechanical oscillation modes of the Brazilian Interconnected Power System (BIPS), taking into account public data disclosed by the Brazilian Independent System Operator (BISO) and data acquired through the MedFasee Project, the Brazilian low voltage wide area monitoring system (WAMS) leaded by the Federal University of Santa Catarina. Main results indicate that the BISO has been successful on controlling the system frequency and the main electromechanical interarea modes, despite the occurrence of a significant demand reduction in the BIPS in a certain period of time due to the COVID-19 pandemic. The total time of operation in underfrequency or overfrequency registered during the months with most demand reduction is at most 20% lower than the maximum time registered in the other months studied. The damping of the modes observed in the months with demand reduction has not reached values lower than 10% and the frequencies of oscillation have varied in a range of 0.05Hz, in agreement to what has been observed in other months.

Machine learning model to predict electricity demand and thermal generation during the pandemic

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

Impact of COVID-19 on Ontario’s electricity market: Load, generation, emissions

The COVID-19 outbreak not only threatened global health, it has also –affected the energy markets around the world. This paper studies the impact of the pandemic on Ontario’s electricity market assessing the demand and supply balance over three distinct periods: pre-pandemic, start of the pandemic and during the period 2020–2021. The paper also evaluates the contribution of work-from-home and other mandates in reducing GHG emission. Furthermore, the impact of such rare events is studied on load forecasting. Our analysis shows that although demand dropped by 12% during the beginning of pandemic, it started rising to levels higher than the previous years. Consequently, due to the changes in the daily load profile, primarily due to the changes in consumers’ behavior, the emissions declined significantly during the lockdown and increased afterwards. Finally, this paper provides a short-term Feed Forward Neural Network (FFNN) model to predict future demand. The model performance was evaluated during the three distinct periods and showed high accuracy even in the initial stages of the pandemic: MAPE of 3.21% pre-pandemic, 13.86% beginning of pandemic and 4.23% during pandemic.

Day-Ahead Electricity Demand Forecasting Competition: Post-COVID Paradigm

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

Optimization of a microgrid for the new post-covid-19 pandemic energy demand using PV-wind-biogas in Brazil

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

Analysing the impact of lockdown due to the COVID-19 pandemic on the Indian electricity sector

This paper aims to understand the responsiveness of the power sector during the lockdown due to the COVID-19 pandemic beginning from March25, 2020 and analyses its impact on the demand, operation and supply of electricity in the Indian power system. The role of the C&I load share in reducing the electricity demand of the different states in India has been examined using multi-sectoral regression analysis. The impact of the pan India lights-off event on the short-term operational flexibility response of the power system has also been analysed using high-temporal resolution data. The results indicate that there has been a reduction of nearly 70 TWh of electricity demand during the lockdown period, an 11% reduction compared to 2019. The top three states recording the highest reduction were Maharashtra, Gujarat and Tamil Nadu at 11TWh, 10.6TWh and 8.4 TWh, respectively. Regression analysis revealed that out of the total drop in load demand — 73% is owed to the industrial sector, while the remaining 27% is to the commercial sector. This demand drop also impacted the upstream electricity supply mix where 96% of the reduction in supply was borne by the coal generating units, recording its lowest national plant load factor at 35% with a 16% reduction in overall CO2 emission compared to the same period in 2019. In conclusion, a case study of Maharashtra has been used to analyse the impact of this reduction in electricity demand on the supply mix, hourly load profile and cost of supply under the different lockdown phases.

Forecasting Hourly Electricity Demand Under COVID-19 Restrictions

The rapid spread of the COVID-19 pandemic has severely impacted many sectors including the electricity sector. The restrictions such as lockdowns, remote-working, and-schooling significantly altered the consumers’ behaviors and demand structure especially due to a large number of people working at home. Accurate demand forecasts and detailed production plans are crucial for cost-efficient generation and transmission of electricity. In this research, the restrictions and their corresponding timing are classified and mapped with the Turkish electricity demand data to analyze the impact of the restrictions on total demand using a multiple linear regression model. In addition, the model is utilized to forecast the electricity demand in pandemic conditions and to analyze how different types of restrictions impact the total electricity demand. It is found that among three levels of COVID-19 restrictions, age-specific restrictions and the complete lockdown have different effects on the electricity demand on weekends and weekdays. In general, new scheduling approaches for daily and weekly loads are required to avoid supply-demand mismatches as COVID-19 significantly changed the consumer behavior, which appears as altered daily and weekly load profiles of the country. Long-term policy implications for the energy transition and lessons learned from the COVID-19 experience are also discussed. © 2022, Econjournals. All rights reserved.