What you'll learn:
- Build economic dispatch models from scratch using Python (Pyomo) and GAMS
- Model energy storage systems and quantify their economic value in grid operations
- Implement COâ‚‚ constraints and analyze carbon pricing impacts on dispatch decisions
- Integrate wind generation with storage to optimize renewable energy utilization
- Scale models from simple 1-bus systems to complex 24-bus reliability test systems
- Debug optimization models and interpret solver outputs for operational insights
- Analyze constraint impacts on solution convexity and system costs
- Export results to Excel and create visualizations for investment analysis
WHO I AM: I hold a PhD in Quantitative Energy and Economics from Imperial College London. I teach practical, real-world data science specifically for the economics, energy and finance sectors.
REGULAR ENHANCEMENTS: This course is reviewed periodically with updates to reflect the modern energy market.
STUDENT BONUS: Note: Students who enroll in this course will receive access to the Energy Data Scientist community.
What You'll Learn:
How to build and solve economic dispatch models using Python (Pyomo) and GAMS for power system optimization
How to model energy storage systems and analyze their economic impact on grid operations
How to incorporate COâ‚‚ constraints and carbon pricing into dispatch optimization models
How to integrate renewable energy (wind) into economic dispatch with storage solutions
How to scale from simple 1-bus systems to complex 24-bus reliability test systems
How to debug optimization models and interpret solver outputs for operational insights
How to analyze convexity of objective functions and constraint impacts on solutions
How to export results to Excel and create visualizations for investment decision-making
Perfect For:
Power system engineers optimizing grid operations and dispatch strategies
Energy analysts evaluating storage economics and carbon reduction pathways
Utility professionals planning renewable integration and storage investments
Energy consultants advising on grid flexibility and decarbonization
Graduate students in electrical engineering or energy systems
Energy economists modeling electricity markets and storage value
Grid operators managing real-time dispatch with environmental constraints
Anyone working on power system optimization and energy transition
Why This Matters:
Economic dispatch is the backbone of power system operations, determining which generators run when to minimize costs while meeting demand. With energy storage becoming cost-competitive and carbon constraints tightening, traditional dispatch models need updating. The global energy storage market is projected to reach $120 billion by 2030, and professionals who can model storage value streams are essential. Understanding how to optimize dispatch with storage can reduce system costs by 20-30% while enabling 50%+ renewable penetration. As grids worldwide integrate batteries, pumped hydro, and emerging storage technologies, the ability to model their economic impact becomes critical for investment decisions worth billions. Whether optimizing utility-scale operations or designing microgrids, these skills are vital for energy analysts ($80,000-140,000), power system engineers ($90,000-160,000), and energy consultants ($100,000-180,000+). Master the optimization techniques used by ISOs, utilities, and energy trading desks worldwide.
