Johns Hopkins University 3 credits Electrical & Computer Engineering
The goal of this course is to explore cutting-edge methods in power system optimization and machine learning, aimed at solving industry-grade decision support challenges related to optimal power flow and unit commitment. The course begins by building foundational knowledge of the mathematical formulations underpinning these problems. It then delves into optimization methods for their efficient solution with exact, nearly exact, occasionally exact, pt approximate relaxation/restriction techniques. These methods are then applied within the framework of security-constrained optimal power flow, which in turn is then enhanced to deal with parametric uncertainty and unit commitment decisions. The course concludes with an in-depth review of how machine learning techniques can accelerate power flow optimization, focusing on approaches such as learning to optimize and learning to sample, and their benchmarking with conventional optimization approaches in terms of interpretability, trustworthiness, and guarantees.
Our subject experts help with essays, research papers, case studies, discussion posts, and capstone projects for these programs β written from scratch to your course requirements.
Program and course information is reproduced from each university's published academic catalog for informational purposes and may change. Always confirm current requirements with your university. We are an independent academic support service and are not affiliated with or endorsed by the universities listed.
Expert guides on essay writing, citation styles, study skills, and academic success.
View All Articles
Learn how to write a good research question for a paper. Narrow your topic, identify a research problem, and create a focused question with practical examples.
Learn how to choose a research topic you can actually research. Discover how to narrow broad ideas, find reliable sources, and develop a focused research question.
Learn how to cite sources in a research paper using APA, MLA, and Chicago styles, with in-text citation examples and reference-list tips.