Principles of Modeling, Simulations, and Control for Electric Energy Systems
About this courseSkip About this course
As global energy demand grows, to mitigate climate change we must drive a swift transition to clean energy resources and enhanced electric power grid infrastructure.
In this course, you will explore systemic principles of future electric power system management, such as the role of smart grids, data-enabled machine learning, power electronics-control, and data-driven decision-making. You will learn how energy technologies, including intermittent renewable energy technologies, can be modeled and controlled at both the component and system level to achieve sustainable, well-functioning, and economically sound results.
You will also learn about assumptions underlying today’s hierarchical control and the innovations needed to support end-to-end flexible efficient electricity services by conventional and new resources. A particular emphasis is on data-enabled distributed cooperative systems solutions.
Throughout, you will examine examples of real-world industry problems and solutions, such as methods for achieving stable integration of diverse power resources, demand response, and fast storage at reasonable cost. Modeling can be used for developing the next generation software needed to operate these systems, and for implementing incentives for new technologies in electric energy markets.
This course is designed for people engaging the energy transition across disciplines and professions. It introduces fundamental concepts for those interested in working in power systems planning, operations, and management. Researchers with a background in dynamical systems and control will learn how to model the dynamics and objectives of enabling clean and resilient electricity services as systems problems while making physically meaningful assumptions and using these models as the basis for introducing their own novel data-enabled methods. Experienced professionals, including utility and energy industry executives, will gain insights into cutting-edge research, concepts, and software. Policymakers in government and leaders in non-governmental organizations (NGOs) will find strategies for building resilience in grid infrastructure, driving more equitable access to energy, and driving higher renewable energy penetration in local markets.
At a glance
- Institution: MITx
- Subject: Computer Science
- Level: Advanced
This course was designed for students from various disciplines, but its core concepts are rooted in electrical engineering and computer science.
Learners should have strong English language skills, an undergraduate-level study of physics, and undergraduate-level study of trigonometry and ordinary differential equations.
A basic understanding of electric circuits and networks is helpful. MIT’s OpenCourseWare offers material related to its Circuits and Electronics course here.
- Language: English
- Video Transcript: English
- Associated skills: Electric Power Systems, Energy Technology, Machine Learning, Dynamical Systems, Operations, Electronics, Decision Making, Management, Infrastructure, Research, Sales, Resilience, Data-Driven Decision-Making, Integration, Planning, Cost Estimation Models
What you'll learnSkip What you'll learn
In this course, you will learn:
- fundamental concepts for power systems planning, operations, and management
- what may not work when using today’s hierarchical control and how to evolve it into flexible end-to-end electricity service
- strategies for building resilience and enhancing energy access using existing grid infrastructure
- how energy technologies, including intermittent renewable energy technologies, can be modeled and controlled at both the component and system level to ensure reliability and efficiency
- the role of smart grids, data-enabled machine learning, power electronics-control, and data-driven decision-making in sustainable electric energy grids