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The recent COVID-19 pandemic has precipitated drastic changes in economic and lifestyle conditions, significantly altering residual electricity demand behavior. This alteration has expanded the demand gap between actual and forecasted electricity usage based on pre-pandemic data, highlighting a critical global issue. Many studies in the pandemic have explored the features of this widening gap, which is impacted by major social events like fast virus spread and lockdowns. However, the influence of factors like economic shifts and lifestyle changes on this demand remains largely unexplored, primarily due to the pandemic's significant effects in these areas. Understanding the essential factors affecting the demand gap is crucial for stakeholders in the electricity sector to develop effective strategies. This study examines the hourly electricity consumption and related factors during the specified period. We present a method combining time-series forecasting and sparse modeling. This helps identify critical factors affecting the electricity demand gap during the pandemic, highlighting the most crucial variables. Utilizing this method, we identify the variables that have undergone significant changes during the pandemic and evaluate their effects on the electricity demand gap. The effectiveness is proven by applying it to the dataset collected in German.

This paper presents a methodology for optimizing the short-term operation of electric vehicle (EV) charging and discharging while considering the potential curtailment of active power due to volt-var control (VVC) prioritizing reactive power output. The proposed approach involves exchanging information between the EV aggregator and the distribution system operator (DSO). This approach allows the EV aggregator to optimize EV charge/discharge schedules while considering voltage-related constraints in the distribution system (DS). Initially, the aggregator shares the optimized schedule with the DSO to estimate the anticipated active power reduction through power flow analysis. Subsequently, the aggregator revises the constraint on active power output to avoid its expected curtailment and performs a second optimization for EV charging and discharging operation. Numerical simulations conducted on a realistic DS model in Japan validate the effectiveness of the proposed method in enhancing profitability in the day-ahead market while ensuring the quality of DS voltage. The results demonstrate an increase in profit by shifting the time of EV charging and discharging based on shared information from the DSO.