Impacts of COVID-19 on Ontario's Electricity Market
Notice bibliographique
Résumé
The COVID-19 outbreak has not only threatened global health but has also significantly affected the energy sector. Most countries around the world have faced sudden changes in the electricity load as a result of the strict measures that have been taken by mid-March 2020 to limit the spread of the disease. In order to investigate the patterns of changes in the electricity sector and to predict future load, machine learning (ML) techniques, such as descriptive data analytics, clustering, and forecasting methods, have been used widely in practice. This research, in particular, studies the impacts of the pandemic on Ontario’s electricity market by investigating changes in the electricity demand and prices. It further provides insights into incorporating ML methods for electricity load forecast and prescribes enhanced solutions for the pricing of electricity by assessing Ontario’s Market Renewal pricing system during COVID-19. \n \nThe analysis of demand and price changes due to the pandemic is presented through a comprehensive study of Ontario’s hourly electricity demand and hourly electricity prices (HOEP) considering annual, monthly, and daily granularity. Furthermore, the impact of the pandemic on load forecasting is investigated using a short-term Feed Forward Neural Network (FFNN) model, as in such rare events, load forecasting becomes more challenging and less accurate, causing high risks in the electricity system operation. Finally, the potential efficiency of Ontario’s Market Renewal during COVID-19 is assessed through a comparative analysis between Ontario’s current electricity market and New York’s electricity market, which has a comparable electricity system with respect to load and supply of electricity. In order to conduct this study, Ontario’s hourly electricity demand and price data, as well as the hourly weather data are used. \n \nOur data-driven analysis shows that although the electricity demand dropped by 12% during the beginning of the pandemic in March, it started unexpectedly rising by the end of May 2020 to levels that exceeded the electricity demand in 2019. A similar pattern is observed for Ontario’s HOEP. The load forecast model performance is evaluated using the mean absolute percentage error (MAPE) during three distinct periods: pre-pandemic, beginning of the pandemic, and during the pandemic to illustrate how the sudden changes in the early stage of COVID-19 have affected the load forecast compared to other periods. The results of the forecast model show an overall MAPE of: 3.21%, 13.86%, and 4.23%, respective to the periods identified. Expectedly, the performance of the model during the pandemic is significantly affected. However, the model is still considered plausible, as a MAPE index between 10% and 20% is classified as good forecast accuracy. Finally, through the comparative analysis between the current Ontario’s uniformed price market and New York’s locational marginal price (LMP) based market, it is observed that Ontario’s current pricing system is less efficient and that consumers’ welfare could increase with an LMP pricing system, which will be part of the proposed Ontario’s Market Renewal.
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Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,002 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,001 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,002 | 0,001 |
| Science ouverte | 0,001 | 0,001 |
| Intégrité de la recherche | 0,001 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,003 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».