Abstract:

Sustainability is a relevant aspect to consider for the survival of the generations. The analysis of improved methods to achieve secure and reliable operation of smart grids represents a relevant contribution in a sustainable scenario. Electrical microgrids are networks present as part of the smart grid and as a consequence the cybersecure and optimal operation of these systems makes a great contribution to sustainability. The United States National Science Foundation (NSF) is currently supporting research associated with Cybersecurity, Optimization and State Estimation in electrical microgrids. Microgrids are electrical subsystems that are part of the main grid, characterized by several features such as distributed generation, use of renewable energy technologies and coverage of residential, commercial and industrial customers. The improvement of methods and tools to study and analyze the operation of microgrids is crucial for the optimal performance of modern power systems better known as Smart Grids. This optimal performance is threatened by cybersecurity attacks to the microgrids and the failure in optimization analysis. Additional study based on real datasets is needed in order to improve the techniques related to design cyber defense and optimization codes. On the other hand, in a post-pandemic scenario the Massive Open Online Courses (MOOCs) became a valuable tool for students and professionals towards learning new knowledge and practices in the engineering field of study. This paper focuses on the main ideas associated with the design of a MOOC on Electrical Microgrids State Estimation, Optimization and Cybersecurity. As part of the MOOC simulations for cybersecurity will cover the utilization of real datasets associated with the electrical power system of the Dominican Republic by means of deep learning tools offered by the MATLAB software. Also, algorithms for optimization of power flow in a stand-alone microgrid are based on the use of the real dataset for voltage, current and energy. The cybersecurity simulations display the capacity of the MATLAB software to analyze sets of data in order to classify the different results into a variety of categories, training a neural network and allowing for the analysis of new data points in order to classify whether or not an electrical system is subject to a cyberattack. This research has been developed with the support of the Engineering Postdoctoral Fellowship eFellows program, from the American Society of Engineering Education, funded by the National Science Foundation (NSF).

Keywords:

Microgrid, Cybersecurity, Optimization, Smart Grid, Massive Open Online Course (MOOC).