Transforming the Grid: AI, Renewables, Storage, EVs, and Prosumers
The electric grid is undergoing a dramatic change. The increasing adoption of renewable energy sources such as wind and solar, plus growing use of storage, electric vehicles, and smart devices, is generating new demands on the grid to manage intermittency and uncertainty. The rapidly decreasing cost of power electronics, communications and sensing are enabling widespread deployment of novel measurement and control devices.
System operators, utilities, and aggregators are now able to access vast amounts of real-time data to plan, monitor, and operate the grid. The digitization of the electric grid by leveraging the availability of massive data, inexpensive cloud computing, and rapidly evolving artificial intelligence, and machine learning to create new strategies to operate and plan the grid will result in a transformation of the ecosystem. This will lead to new marketplaces, pricing strategies, novel services, and opportunities for increased automation and autonomy.
The aim of this course is to introduce students to the modern electric grid and focuses on transforming technologies including
- Artificial intelligence (AI)
- Machine Learning (ML)
- Storage technologies
- Electric vehicles
- Renewable energy
What you will learn
- The history of the grid covering its evolution and future transformation
- The basic functioning of the grid and the flows of electric power, money, and information
- How to integrate renewables in future grids and the role of information
- Advantage of a variety of storage technologies to manage renewable energy intermittency to ensure reliance
- The growing and interactive role of prosumers as both energy consumers and providers
- The emergence of EV charging demand and managing its impact
- Pricing and rate models for storage and renewable scenarios
- AI and ML applications to learn consumer behavior and demand response, perform forecasting, improve network management and scalably determine asset locations
- Real local, national, and global AI and ML case studies
Continuing Education Units
By completing this course, you’ll earn 0.5 Continuing Education Unit (CEU). CEUs cannot be applied toward any Stanford degree. CEU transferability is subject to the receiving institution’s policies.
Please contact the Program Manager at
650.273.5459 or submit an inquiry to ask a question or to receive more information.
60 day access to the online course starts upon payment.
Course materials are available for download from the online videos page. All materials are available for printing and review upon enrollment.
Online participants are asked to complete a final exam at the end of each course to maintain the integrity of the program. A score of 85% must be achieved to successfully pass the exam. A digital record of completion will be emailed to participants when they pass the exam.
It is required that participants complete the course evaluation once they have passed the final exam.
This course may not currently be available to learners in some states and territories.