The current electric system was built with a focus on large, continuous-duty baseload power generators fueled primarily by coal and nuclear generation - and without anticipating today's large load growths such as AI data centers, EV fast-charging, and manufacturing loads. The electric grid was designed to meet local needs rather than regional or national ones, leading to a shortage of transmission capacity for integrating renewable energy sources like wind and solar and connecting these new gigawatt-scale loads. This shortage has created a backlog of interconnection applications for utility-scale wind, solar, storage, and large-load projects to reach wholesale power markets. The problem is compounded by the fact that transmission permitting is largely a state issue, with each state prioritizing its own interests. As a result, renewable developers and large-load customers face high network upgrade costs to connect to the transmission system, creating a chicken-and-egg cycle that impedes the clean energy transition. This course provides a comprehensive understanding of electric-grid planning, focusing on the integration of emerging generation technologies - including solar, wind, geothermal, and energy storage - alongside large loads. Key issues covered include policy, economics, environmental impacts, and the latest tools and techniques for electric-grid planning. Students will learn to evaluate the economic principles of electricity systems, conduct cost-benefit analyses of emerging generation technologies and large-load siting, and identify financing options for these technologies. Using a project-based learning approach, students will tackle three real-world problems: the U.S., California, and a local context. This hands-on method provides practical experience in designing and implementing electricity systems that integrate emerging-generation resources and accommodate large, flexible or inflexible loads. By the end of the course, students will understand the challenges and opportunities presented by integrating emerging generation and large loads into the grid and will be equipped to design effective solutions. Open-source Python and MATLAB tools and datasets will be provided.
3 units · Letter or Credit/No Credit
The current electric system was built with a focus on large, continuous-duty baseload power generators fueled primarily by coal and nuclear generation - and without anticipating today's large load growths such as AI data centers, EV fast-charging, and manufacturing loads. The electric grid was designed to meet local needs rather than regional or national ones, leading to a shortage of transmission capacity for integrating renewable energy sources like wind and solar and connecting these new gigawatt-scale loads. This shortage has created a backlog of interconnection applications for utility-scale wind, solar, storage, and large-load projects to reach wholesale power markets. The problem is compounded by the fact that transmission permitting is largely a state issue, with each state prioritizing its own interests. As a result, renewable developers and large-load customers face high network upgrade costs to connect to the transmission system, creating a chicken-and-egg cycle that impedes the clean energy transition. This course provides a comprehensive understanding of electric-grid planning, focusing on the integration of emerging generation technologies - including solar, wind, geothermal, and energy storage - alongside large loads. Key issues covered include policy, economics, environmental impacts, and the latest tools and techniques for electric-grid planning. Students will learn to evaluate the economic principles of electricity systems, conduct cost-benefit analyses of emerging generation technologies and large-load siting, and identify financing options for these technologies. Using a project-based learning approach, students will tackle three real-world problems: the U.S., California, and a local context. This hands-on method provides practical experience in designing and implementing electricity systems that integrate emerging-generation resources and accommodate large, flexible or inflexible loads. By the end of the course, students will understand the challenges and opportunities presented by integrating emerging generation and large loads into the grid and will be equipped to design effective solutions. Open-source Python and MATLAB tools and datasets will be provided.
Offered in Autumn 2026 at Stanford University.