Degradation of energy capacity and energy efficiency of lithium-ion battery energy storage systems depend on their operation conditions and are key factors in their business case feasibility. Using traditional energy reservoir models with energy throughput methods to quantify degradation leads to less accurate revenue estimates and unfeasible dispatch scenarios. This paper proposes a new neural…
In this paper, a new deep learning-based two-stage dataset-clustering/temporal-clustering method is proposed for time aggregation in renewable energy-integrated power systems. In this way, for the first time, the representative period, including one or more representative days, is obtained using a GAN-based model in which the LSTM Network is embedded in both generator and discriminator models.…
The integration of wind energy into the power grid has been on the rise, becoming an increasingly important source of electricity. However, the intermittency of wind energy output, particularly the wind power ramps (the rate at which wind energy output increases or decreases) can pose challenges for the grid and its operators. The first step…
This paper proposes a hybrid deep actor-critic framework for the optimal operation of a phase-changing soft open point (PCSOP) in an unbalanced distribution network. The framework combines algorithmic features of off-policy reinforcement learning and imitation learning. The proposed method comprises a policy-guiding module based on the PCSOP physics and an adaptive dynamic experience replay buffer.…