Multi-scenario renewable energy absorption capacity assessment method based on the attention-enhanced time convolutional network

As the penetration rate of renewable energy in modern power grids continues to increase, the assessment of renewable energy absorption capacity plays an increasingly important role in the planning and operation of power and energy systems. However, traditional methods for assessing renewable energy absorption capacity rely on complex mathematical modeling, resulting in low assessment efficiency. Assessment in a single scenario determined by the source-load curve is difficult because it fails to reflect the random fluctuation characteristics of the source-load, resulting in inaccurate assessment results. To address and solve the above challenges, this paper proposes a multi-scenario renewable energy absorption capacity assessment method based on an attention-enhanced time convolutional network (ATCN). First, a source-load scene set is generated based on a generative adversarial network (GAN) to accurately characterize the uncertainty on both sides of the source and load. Then, the dependence of historical time series information in multiple scenarios is fully mined using the attention mechanism and temporal convolution network (TCN). Finally, simulation and experimental verification are carried out using a provincial power grid located in southwest China. The results show that the method proposed in this article has higher evaluation accuracy and speed than the traditional model

Investigation on Water Levels for Cascaded Hydropower Reservoirs to Drawdown at the End of Dry Seasons

Operators often have a dilemma in deciding what water levels the over-year hydropower reservoirs should drawdown at the end of dry seasons, either too high to achieve a large firm hydropower output during the dry seasons in the current year and minor spillage in coming flood seasons, or too low to refill to the full storage capacity at the end of the flood seasons and a greater firm hydropower output in the coming year. This work formulates a third-monthly (in an interval of about ten days) hydropower scheduling model, which is linearized by linearly concaving the nonlinear functions and presents a rolling strategy to simulate many years of reservoir operations to investigate how the water level at the end of dry seasons will impact the performances, including the energy production, firm hydropower output, full-refilling rate, etc. Applied to 11 cascaded hydropower reservoirs in a river in southwest China, the simulation reveals that targeting a drawdown water level between 1185–1214 m for one of its major over-year reservoirs and 774–791 m for another is the most favorable option for generating more hydropower and yielding larger firm hydropower output