The worldwide spread of the COVID-19 pandemic in 2020 forced most countries to intervene with policies and actions—including lockdowns, social-distancing and smart working measures—aimed at mitigating the health system and socio-economic disruption risks. The electricity sector was impacted as well, with performance largely reflecting the changes in the industrial and commercial sectors operations and in the social behavior patterns. The most immediate consequences concerned the power demand profiles, the generation mix composition and the electricity price trends. As a matter of fact, the electricity sectors experienced a foretaste of the future, with higher renewable energy penetration and concerns for security of supply. This paper presents a systemic approach toward assessing the impacts of the COVID-19 pandemic on the power sector. This is aimed at supporting decision making—particularly for policy makers, regulators, and system operators—by quantifying shorter term effects and identifying longer term impacts of the pandemic waves on the power system. Various metrics are defined in different areas—system operation, security, and electricity markets—to quantify those impacts. The methodology is finally applied to the European power system to produce a comparative assessment of the effects of the lockdown in the European context.

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Wind energy and wind power forecast errors have a direct impact on operational decision problems involved in the integration of this form of energy into the electricity system. As the relationship between wind and the generated power is highly nonlinear and time-varying, and given the increasing number of available forecasting techniques, it is possible to use alternative models to obtain more than one prediction for the same hour and forecast horizon. To increase forecast accuracy, it is possible to combine the different predictions to obtain a better one or to dynamically select the best one in each time period. Hybrid alternatives based on combining a few selected forecasts can be considered when the number of models is large. One of the most popular ways to combine forecasts is to estimate the coefficients of each prediction model based on its past forecast errors. As an alternative, we propose using multivariate reduction techniques and Markov chain models to combine forecasts. The combination is thus not directly based on the forecast errors. We show that the proposed combination strategies based on dimension reduction techniques provide competitive forecasting results in terms of the Mean Square Error.

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