The COVID-19 crisis has disrupted electricity systems worldwide. This article disentangles the effects of the demand reductions, fuel price devaluation, and increased forecast errors on New York’s day-ahead and real-time markets by combining machine learning and structural econometrics. From March 2020 to February 2021, statewide demand has decreased by 4.6 TWh (-3%) including 4 TWh (-8%) for New York City alone, and the day-ahead market has depreciated by $250 million (-6%). The real-time market has, however, appreciated by $15 million (+23%) because of abnormally large forecast errors which significantly undermined system efficiency.
AbrellJ.KoschM.RauschS. (2019). “How effective was the U.K. carbon tax? A machine learning approach to policy evaluation.” CER-ETH Working Paper, 19: 317. https://doi.org/10.2139/ssrn.3372388.
2.
AESO (2020). “Impacts of the COVID-19 pandemic and low oil prices on Alberta’s power system.” Technical Report: https://www.aeso.ca/assets/Uploads/Pandemic-Low-Oil-Analysis-Summary-April-20-Final.pdf, accessed May2020.
3.
BartikA.W.CullenZ.B.GlaeserE.L.LucaM.StantonC.T. (2020). “What jobs are being done at home during the COVID-19 crisis? Evidence from firm-Level surveys.” NBER Working Paper. https://doi.org/10.3386/w27422.
4.
BenatiaD. (2018). “Functional econometrics of multi-unit auctions: an application to the New York electricity market.” Working Paper.
5.
BenatiaD. (2020). “Electricity markets under lockdown: Insights from New York.” Energy Forum.
6.
BenatiaD. (2022) “Ring the alarm! Electricity markets, renewables, and the pandemic.” Energy Economics106: 105755. https://doi.org/10.1016/j.eneco.2021.105755.
7.
BenatiaD. and É. Billette de Villemeur (2019). “Strategic reneging in sequential imperfect markets.” Center for Research in Economics and Statistics Working Paper.
8.
BirdL.MilliganM.LewD. (2013). “Integrating variable renewable energy: challenges and solutions.” Technical Report: National Renewable Energy Lab (NREL). https://doi.org/10.2172/1097911.
9.
BrewerD. (2020). “The economics costs of forecasting errors in the PJM interconnection due to the COVID-19 quarantine.” Energy Forum.
10.
BurligF.KnittelC.RapsonD.ReguantM.WolframC. (2020). “Machine learning from schools about energy efficiency.” Journal of the Association of Environmental and Resource Economists 7(6): 1181–1217. https://doi.org/10.1086/710606.
11.
ChenY.YangW.ZhangB. (2020). “Using Mobility for Electrical load forecasting during the COVID-19 pandemic.” arXiv Working Paper 2006.08826.
12.
CicalaS. (2020) “Early economic impacts of Covid-19 in Europe: A view from the grid.” NBER Working Paper. “Powering work from home.” NBER Working Paper.
13.
CramtonP.StoftS.. 2006. “The convergence of market designs for adequate generating capacity.” Working Paper.
14.
DavisL.HausmanC. (2016). “Market impacts of a nuclear power plant closure.” American Economic Journal: Applied Economics 8(2): 92–122. https://doi.org/10.1257/app.20140473.
15.
DavisL.W. (2017). “Evidence of a decline in electricity use by US households.” Economics Bulletin 37(2): 1098–1105.
16.
DebiaS.BenatiaD.PineauP-O.. (2018). “Evaluating an interconnection project: Do strategic interactions matter?” The Energy Journal 39(6). https://doi.org/10.5547/01956574.39.6.sdeb.
17.
EIA (2019). “Electric power monthly June 2019.” Technical Report: Energy Information Agency.
18.
EIA (2020). “Daily electricity demand in New York falls about 13% after COVID-19 mitigation efforts.” Technical Report: Energy Information Agency. https://www.eia.gov/todayinenergy/detail.php?id=43635, accessed May2020.
19.
EIA (2021). “Electric power monthly March 2020- May 2021.” Technical Report: Energy Information Agency.
FabraN. (2018). “A primer on capacity mechanisms.” Energy Economics75: 323–335. https://doi.org/10.1016/j.eneco.2018.08.003.
22.
FabraN.LacuestaA.SouzaM. (2022). “The implicit cost of carbon abatement during the COVID-19 pandemic” European Economic Review Forthcoming. https://doi.org/10.1016/j.euroecorev.2022.104165.
23.
FabraN.ReguantM. (2014). “Pass-through of emissions costs in electricity markets.” American Economic Review 104(9): 2872–99. https://doi.org/10.1257/aer.104.9.2872.
24.
ForeseeF.D.HaganM.T. (1997). “Gauss-Newton approximation to Bayesian learning.” Proceedings of International Conference on Neural Networks (ICNN’97) 3: 1930–1935.
25.
FransesP.H. (2016). “A note on the mean absolute scaled error.” International Journal of Forecasting 32(1): 20–22. https://doi.org/10.1016/j.ijforecast.2015.03.008.
26.
FtitiZ.HachmiB.A.LouhichiW. (2020). “New outlook for oil market in the new post-coronavirus world.” Energy Forum.
27.
GillinghamK.T.KnittelC.R.LiJ.OvaereM.ReguantM. (2020). “The short-run and long-run effects of Covid-19 on energy and the environment.” Joule 4(7): 1337–1341. https://doi.org/10.1016/j.joule.2020.06.010.
28.
GrafC.QuagliaF.WolakF.A. (2020). “(Machine) learning from the COVID-19 lockdown about electricity market performance with a large share of renewables.” Journal of Environmental Economics and Management 102398. https://doi.org/10.2139/ssrn.3732916.
29.
HahnH.Meyer-NiebergS.PicklS. (2009). “Electric load forecasting methods: Tools for decision making.” European journal of operational research 199(3): 902–907. https://doi.org/10.1016/j.ejor.2009.01.062.
30.
HausmanJ.A. (1996). “Valuation of new goods under perfect and imperfect competition.” The Economics of New Goods. 207–248. University of Chicago Press.
31.
Hodge, B.-M., D. Lew, and M. Milligan (2013). “Short-term load forecast error distributions and implications for renewable integration studies.” 2013 IEEE Green Technologies Conference (GreenTech) 435–442. https://doi.org/10.1109/Green-Tech.2013.73.
32.
HoganW. (2005). “On an “energy-only” electricity market design for resource adequacy.” Working Paper.
33.
Hortaçsu, Ali and S. Puller (2008). “Understanding strategic bidding in multi-unit auctions: A case study of the Texas electricity spot market.” The RAND Journal of Economics 39(1): 86–114. https://doi.org/10.1111/j.0741-6261.2008.00005.x.
34.
IMF (2020). “World economic outlook, April2020: The Great Lockdown.” International Monetary Fund World Economic and Financial Surveys.
35.
ISONE (2020). “NEPOOL Participants Committee report.” Technical Report: ISO New England. https://www.iso-ne.com/static-assets/documents/2020/05/may-2020-coo-report.pdf, accessed May2020.
36.
ItoK.ReguantM. (2016). “Sequential markets, market power, and arbitrage.” American Economic Review 106(7): 1921–57. https://doi.org/10.1257/aer.20141529.
37.
JacobD. (2020). “Cross-fitting and averaging for machine learning estimation of heterogeneous treatment effects.” arXiv 2007.02852 Working Paper.
38.
JoskowP.L. (2008). “Capacity payments in imperfect electricity markets: Need and design.” Utilities Policy 16(3): 159 – 170. https://doi.org/10.1016/j.jup.2007.10.003.
39.
KaushikR.ShikharJ.SiddhantJ.DashT. (2021). “Performance evaluation of deep neural networks for forecasting time-series with multiple structural breaks and high volatility.” CAAI Transactions on Intelligence Technology. https://doi.org/10.1049/cit2.12002.
40.
KisslerS.M.TedijantoC.GoldsteinE.GradY. H.LipsitchM.. (2020). “Projecting the transmission dynamics of SARS-CoV-2 through the postpandemic period.” Science 368(6493): 860–868. https://doi.org/10.1126/science.abb5793.
41.
KyriakidesE.PolycarpouM. (2007). “Short-term electric load forecasting: A tutorial.” Trends in Neural Computation 391–418. https://doi.org/10.1007/978-3-540-36122-0_16.
42.
MarcosR.A.BunnD.W.BelloA.RenesesJ. (2020). “Short-term electricity price forecasting with recurrent regimes and structural breaks.” Energies13(20): 5452. https://doi.org/10.3390/en13205452.
43.
MooreK.LipsitchM.JohnB.OsterholmM. (2020). “Part 1: The future of the COVID-19 pandemic: Lessons learned from pandemic influenza.” COVID-19: The CIDRAP Viewpoint.
44.
NarajewskiM.ZielF. (2020). “Changes in electricity demand pattern in Europe due to COVID-19 shutdowns.” Energy Forum.
45.
NewberyD.PollittM.G.RitzR.A.StrielkowskiWadim (2018). “Market design for a high-renewables European electricity system.” Renewable and Sustainable Energy Reviews91: 695–707. https://doi.org/10.1016/j.rser.2018.04.025.
46.
NYISO (2019). “Day-ahead scheduling manual.” Technical Report: New York Independent System Operator.
47.
NYISO (2020). “COVID-19 and the electric grid: load shifts as New Yorkers respond to crisis.” Technical Report: NYISO https://www.nyiso.com/-/covid-19-and-the-electric-grid-load-shifts-as-new-yorkers-respond-to-crisis, accessed May2020.
48.
OECD (2020) “Productivity gains from teleworking in the post COVID-19 era: How can public policies make it happen?” OECD, Paris, France.
49.
Ortega-VazquezM. A.KirschenD. S. (2006). “Economic impact assessment of load forecast errors considering the cost of interruptions.” IEEE Power Engineering Society General Meeting 8–pp. https://doi.org/10.1109/PES.2006.1709231.
50.
PercyS.MountainB. (2020). “Covid-19 and social distancing: does it show up in the demand for electricity?” Energy Forum.
Potomac Economics (2020). “2019 State of the market report for the New York ISO markets.” Technical Report: New York Independent System Operator.
53.
ReguantMar. 2014. “Complementary bidding mechanisms and startup costs in electricity markets.” The Review of Economic Studies 81(4): 1708–1742. https://doi.org/10.1093/restud/rdu022.
54.
Rodríguez-SarastyJ.A.DebiaS.PineauP-O. (2021). “Deep decarbonization in northeastern North America: The value of electricity market integration and Hydropower.” Energy Policy152: 112210. https://doi.org/10.1016/j.enpol.2021.112210.
55.
RTE (2020). “L’impact de la crise sanitaire sur le fonctionnement du système electrique.” Technical Report: Réseau Transport Électricité https://www.rte-france.com/sites/default/files/impacts_de_la_crise_sanitaire_covid-19_sur_le_systeme_electrique.pdf, accessed May2020.
56.
TsagkariM. (2020). “Impact of coronavirus on distributed energy generation with the application of demand-side management.” Energy Forum.
57.
WolakF.A. (2003). “Identification and estimation of cost functions using observed bid data: An application to electricity markets.” Advances in Economics and Econometrics - Theory and Applications 115–149.
58.
WolframC.D. (1999). “Measuring duopoly power in the British electricity spot market.” https://doi.org/10.1257/aer.89.4.805.
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