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In this paper, we propose a droop-free distributed frequency control for the hybrid photovoltaic and battery energy storage (PV-BES) based microgrid. A distributed state of charge (SOC) balancing regulator achieves balanced SOC among the distributed generators (DGs) with BES utilizing a distributed average SOC estimator and the power sharing regulator ensures proportional power sharing among the PV-BES based DGs. These regulators generate two frequency correction terms which are then added to the microgrid rated frequency to generate references for the lower level controllers. The performance of the proposed distributed control is validated through real-time simulations in OPAL-RT, which demonstrates the effectiveness of the proposed control in achieving frequency regulation, SOC balancing, and active power sharing in the hybrid PV-BES units under both islanded and grid-connected operation modes. 

We propose GLassoformer, a novel and efficient transformer architecture leveraging group Lasso regularization to reduce the number of queries of the standard self-attention mechanism. Due to the sparsified queries, GLassoformer is more computationally efficient than the standard transformers. On the power grid post-fault voltage prediction task, GLassoformer shows remarkably better prediction than many existing benchmark algorithms in terms of accuracy and stability. 

Transient analysis is vital to the planning and operation of electric power systems. Traditional transient analysis utilizes numerical methods to solve the differential-algebraic equations (DAEs) to compute the trajectories of quantities in the grid. For this, various numerical integration methods have been developed and used for decades. On the other hand, solving the DAEs for a relatively large system such as power grids is computationally intensive and is particularly challenging to perform online. In this paper, a novel machine learning (ML) based approach is proposed and developed to predict post-contingency trajectories of a generator in the time domain. The training data are generated by using an off-line simulation platform considering random disturbance occurrences and clearing times. As a proof-of-concept study, the proposed ML-based approach is applied to a single generator. A Long Short Term Memory (LSTM) network representation of the selected generator is successfully trained to capture the dependencies of its dynamics across a sufficiently long time span. In the online assessment stage, the LSTM network predicts the entire post-contingency transient trajectories given initial conditions of the power system triggered by system changes due to fault scenarios. Numerical experiments in the New York/New England 16-machine 86-bus power system show that the trained LSTM network accurately predicts the generator’s transient trajectories. Compared to existing numerical integration methods, the post-disturbance trajectories of generator’s dynamic states are computed much faster using the trained predictor, offering great promises for significantly accelerating both offline and online transient studies.