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The COVID-19 pandemic affects all the aspects of modern society worldwide, especially in the power sector. Measures of flexibility enhancement are regarded as solutions to guarantee reliable and flexible electricity supply in such an emergency. This study aims at investigating the impact of flexibility enhancement measures (electricity storage and flexible demand) in different situations of the preliminary COVID-19 pandemic. Case studies in different regions (Denmark, the Netherlands, and the Sichuan province of China) are conducted and assessed using the hourly simulation tool EnergyPLAN. These regions own different electricity supply mix and level of renewable electricity. It is found that the flexible demand measure within one day or one week can hardly eliminate the electricity imbalance caused by either the pandemic or the increasing renewable electricity. The monthly flexible demand is effective for balancing, but its potential in these regions is not enough. However, electricity storage measure enhances the electricity balance even during the most extreme situation of the pandemic. From the economic perspective, electricity storage measure leads to an increase of up to 15% in total system costs, while flexible demand measure has a negligible effect on costs. This study serves as the first step to understand the performance of flexibility enhancement measures in the power sector under the shock of a pandemic. • It analyzes the preliminary impact of the COVID-19 pandemic on power sector. • It explores the role of flexibility enhancement measures in the context of COVID-19. • The methods and results facilitate decision-making in a similar urgent crisis. [ABSTRACT FROM AUTHOR]

• A stochastic model for the revenue of WPP considering pruchasing reserve is built. • A DRL based bidding approach is proposed. • The impact of reserve price on the revenue of WPP is evaluated. Wind power producers (WPP) are punished when take part in the short-term electricity market due to the inaccuracy of wind power prediction. The profit loss can be partially offset by strategic reserve purchasing. Due to the uncertainty of real-time wind power production, the reserve capacity price and the highly dynamic price regulation, it is difficult to determine the quantity of reserve to purchase, which might have a great impact on the profit of WPP. This paper investigates the possible influence on the revenue of WPP if they take part in both the energy and reserve market. This problem is first formulated as a Markov decision process (MDP). After that, the asynchronous advantage actor-critic (A3C) method is used to take care of this problem. Several agents are applied to explore the action space simultaneously. Neural networks are applied to fit the policy function and value function, which are trained against each other to learn the optimal dynamic bidding strategy from historical data by constantly trial and error. Simulation results of a wind farm in Denmark validate that the profit loss of the WPP can be significantly reduced when the A3C algorithm is used for bidding strategy formulation when WPP participate in both the energy and reserve market. [ABSTRACT FROM AUTHOR]