To encourage and prepare a sustainable pipeline of highly trained minority students to enter into National Security Enterprise (NSE) workforce by excelling in STEM field by carrying out transformative research with practical experience and training through applicable research in the area of Electrical Engineering, with emphasis on Electric Power and Cyber-Physical Energy Systems Engineering. Background: The major goal of the proposed Consortium is to encourage and prepare a sustainable pipeline of highly trained minority students to enter into National Security Enterprise (NSE) workforce by excelling in STEM field by carrying out transformative research with practical experience and training through applicable research in the area of Electrical Engineering, with emphasis on Electric Power and Energy Systems Engineering, Renewable Energy Engineering, and related Cyber-Security issues. This further involves educating the next generation of diverse Energy Systems workforce by delivering strong analytical skills and increased interest in advanced research relevant to the National Nuclear Security Administration (NNSA). In order to accomplish the goal, the proposed Consortium brings together investigators from three Minority Serving Institutions (MSIs) including one Historically Black Serving University (HBSU): Florida International University (FIU), the University of Texas at El Paso (UTEP), and Alabama Agricultural and Mechanical University (AAMU); two NNSA National Labs: Lawrence Livermore National Laboratory (LLNL) and Sandia National Labs, and focuses on research, training, and mentoring of undergraduate (UG) students, and graduate students, at MSIs in the core area of NNSA. Tasks/Scientific Goals: (1) undergraduate research and education in the broader area of Electric Power and Energy Systems, AI/Machine Learning, Electric Grid Resiliency, , Smart Grid and Cybersecurity and (2) Generation of advanced STEM workforce with specialization in Smart Grid operations,, Real-time Simulation of Cyber-Physical Systems, Predictive Modeling and Data Analytics, and AI/ML applications to cyber-physical power systems