Monday, January 26-Friday, January 30, 9:00 am - 1:00 pm ET each day (5 classes)
Location: 4-231
Register by January 24. Email Pablo Duenas (pduenas@mit.edu)

For the 17th consecutive year, this 5-session hands-on learning experience continues to evolve, exploring how mathematical modeling can inform and accelerate the transition toward net-zero targets. With a primary focus on electricity systems, the course highlights their central role in a carbon-constrained economy that must deliver reliable, affordable energy while accommodating rapid demand growth, especially from data-center development. Participants will examine critical challenges shaping future power systems, including large-scale carbon-free energy deployment, the expanding potential of demand response, and the accelerating rise of data centers as dominant electricity consumers. Addressing these challenges requires advanced mathematical models to optimize and analyze complex decisions, from grid and generation expansion to flexibility, to ensure the system can reliably meet sustained load growth. In addition to providing theoretical insights, the course offers practical tools that enable participants to perform their own case studies. Real-world applications will illustrate how quantitative modeling can inform key stakeholders, guide public understanding, and support collective action toward a secure, clean, and data-center-ready energy future.

No prior experience is required, although basic familiarity with Python and Julia programming can be helpful. Participants are welcome to attend individual sessions.

Monday, January 26

Part 0: How mathematical optimization models contribute to achieving energy targets on time

• Models to inform policymakers, stakeholders, and public opinion
• Introduction to fundamentals of optimization techniques

Part 1: Covering electricity demand daily

• Unit-Commitment (UC): daily dispatch of electricity generation units
• Managing uncertainty through stochastic optimization of UC

Tuesday, January 27

Part 2: Guaranteeing annual electricity production

• Medium-term operation planning
• Managing uncertainty through stochastic hydro-thermal coordination

Part 3: The network as the backbone of electric systems

• Understanding the role of the electricity network
• Managing network constraints with Locational Marginal Pricing

Wednesday, January 28

Part 4: Models for informing utility-scale investments

• Basic concepts: optimal mix problem by screening curves
• MACRO: an expansion model for studying low-carbon energy futures

Part 5: Electricity transmission, storage, and generation expansion planning

• openTEPES: informing infrastructure needs for growing demands

Thursday, January 29

Part 6: Powering AI and data centers

• Explaining data centers from an energy perspective
• Placement and connection of data centers

Friday, January 30

Part 7: Flexibility and dynamics of data centers

• How much flexibility can a data center provide
• Exploring the dynamics of data centers

Instructors

Pablo Duenas – Research Scientist at MIT Energy Initiative, pduenas@mit.edu
Deep Deka – Program Manager of Data Center Power Forum at MIT Energy Initiative, deepj87@mit.edu
Andres Ramos – Professor at Universidad Pontificia Comillas, arght@mit.edu
Javier Garcia-Gonzalez – Professor at Universidad Pontificia Comillas, javiergg@mit.edu
Ruaridh McDonald – Energy Systems Research Lead at MIT Energy Initiative, rmacd@mit.edu