Certificate of Graduate Studies in Power Systems Engineering

The U.S. electricity grid is poised for a dramatic transformation, enabled by Smart Grid technologies, to address national and worldwide existential necessities: meeting the increasing electric energy needs, reducing the reliance on fossil fuels, and thus restoring and boosting the economy. The 2020 World Energy Outlook from the International Energy Agency estimates that the current value of the U.S. electricity grid is over $1 trillion, and the total cost of replacing it with a Smart Grid is $4~5 trillion. If a Smart Grid is fully deployed across the U.S., the country could expect to save $130 billion annually. The U.S. Department of Energy’s (DoE) Quadrennial Energy Review (QER) found that 1.5 million new jobs across the energy sector will need to be filled by 2030, with many of these positions paying 50 to 80 percent more than the average annual salary. However, achieving the goals of “Smart Grid” is hampered by a growing shortage of qualified electric power and energy engineers. With an aging workforce and looming retirements, the power and energy industry continually faces a hiring gap and reports that they cannot find skilled workers to fill open positions. According to the U.S. Senate Committee on Energy and Natural resources, 77% of energy companies find it difficult to hire qualified employees.

The Electrical and Computer Engineering Department has been regularly teaching power systems related graduate courses, and this COGS puts the existing ECE expertise and newly developed power systems courses together in one package allowing students to gain meaningful background that will prepare them for a career in power and energy area. It consists of four (4) graduate courses to develop graduates with a technical foundation in electric power systems focused on the operation of emerging systems, as described in below Program Curriculum. Through this four-class sequence, students will be able to articulate the core concepts of conventional system analysis, different renewable system analysis, electricity market and different smart grid enabling techniques. Additionally, one of the COGS electives could be a MS or PhD level research / thesis / dissertation, where students work on real-world power systems related problems under the supervision of faculty.

Overall, the Power Systems Engineering COGS will provide students with breadth and depth in theoretical foundations of emerging power systems, as well as practical hands-on project experience. As a result of this COGS, we expect our graduates to be particularly marketable, attracting many employers looking to hire highly qualified smart-grid ready power engineers. In addition, this COGS will give Rowan’s power program greater visibility and will improve our ability to attract students into the electrical and computer engineering program.

Program Structure: This program consists of four courses, two required and two electives, with a total of 12 credit hours. All required and elective courses are 3 credit hours each. One of the electives may be MS or PhD level research / thesis / dissertation, where the student will need to choose a power systems related topic for the project / research.

Required Courses

1. ECE 09.504 ST ECE: Power System Engineering (3 cr). This class must be taken first.
2. ECE 09.510 Advanced Alternate Energy Systems (3 cr)

Electives:

Choose either two courses (strongly recommended) from Knowledge Area 1 OR choose one from Knowledge Area 1 and one from Knowledge Area 2

Knowledge Area 1 (Courses with immediate relevance to Power Systems)

• ECE 09.572 Advanced Smart Grid (3 cr)
• ECE 09.573 Advanced Smart Sensors (3 cr)
• ECE 09.515 Emerging Electricity Market (3 cr)

Knowledge Area 2 (supportive courses of relevance) – If only one course is taken from Knowledge Area 1, choose any one of the following. If you take two courses from Knowledge Area 1 (recommended), then the following is not required

• ECE 09.585 Advanced Engineering Cyber Security (3 cr)
• ECE 09.655 Advanced Computational Intelligence and Machine Learning (3 cr)
• ECE 09.521 Fundamentals in Systems Engineering (3 cr)

• ENGR 01.501 Engineering Economics (3 cr)
• ENGR 01.511 Engineering Optimization (3 cr)
• ENGR 01.598 Engineering Graduate Research (3 cr)

• ENGR 01.599 Master’s Research (3 cr)
• ENGR 01.699 Doctoral Research and Dissertation (3 cr)
• ENGR 01.799 Doctoral Research / Dissertation (3 cr)