GEN 5756

Electric System Planning with Emerging Generation Technologies and Large Load

Stanford University · UGRD · Fall 2026

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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…

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Class #stanford-5756Fall 2026UGRD3 credits
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