Modeling Tools for Energy Systems Analysis (MOTESA) (2016-2017)

Understanding the potential and effects of different penetration levels of wind and solar power requires detailed characterization of 1) renewable resources, 2) conventional electricity generation infrastructure and 3) the interaction and coordination of the two in a balancing authority, independent system operator or regional transmission organization.

Earlier iterations of the MOTESA project responded to these three requirements by developing and making accessible a few tools that vary in purpose and sophistication, but that each serve as building blocks for more complex models and are downloadable, well documented and user friendly.

This Bass Connections project set out to contribute to the assessment of 1) the economic, reliability and environmental implications of new regulatory constraints (e.g., emissions standards or carbon prices that could result from implementation of the Clean Power Plan); 2) new technologies (e.g., Integrated Solar Combined Cycle, Carbon Capture and Sequestration with Concentrating Solar and coal-drying processes); and 3) new market clearing tools in the U.S. and China. This work required simulating the operations of different power systems (e.g., Duke Energy Progress/Duke Energy Carolinas System and scale versions of midcontinent ISO and PJM power systems) according to models previous MOTESA teams have developed.

Timing

Summer 2016 – Fall 2016

Team Outcomes

Reproducing the Hourly Electrical-load Curve from the Residential Sector of Querétaro México: A Preliminary Step towards Characterizing the Uncertainty of Future Residential Electricity Demand in Latin America and the Caribbean, and Estimating the Potential of Demand-side Policies (Mauricio Hernandez, Dalia Patino-Echeverri, Sunzhe Cao, Rui Shan, Mingquan Li, Jun Zhang, Ildo Luis Sauer)

See earlier related team, Modeling Tools for Energy Systems Analysis (MOTESA) (2014-2015).

/faculty/staff Team Members

  • John Fay, Nicholas School of the Environment-Environmental Sciences and Policy*
  • Shuo Gao, N/A
  • Mingquan Lee, Nicholas School of the Environment
  • Dalia Patino Echeverri, Nicholas School of the Environment-Environmental Sciences and Policy*
  • Colin Rundel, Arts & Sciences-Statistical Science*
  • Walter Simmons, Pratt School of Engineering-Mechanical Engineering & Materials Science

/graduate Team Members

  • Genghua Chen, Master of Environmental Management, Energy and Environment
  • Thomas Fleming, Economics-PHD
  • Mingyuhui Liu, Master of Environmental Management, Energy and Environment
  • Lei Qian, Statistical and Econ Modeling
  • Leonardo Shu, Statistical Science - PHD
  • Kaifeng Xu, Master of Environmental Management, Energy and Environment

/undergraduate Team Members

  • Faisal Alsaadi, Mathematics (AB), Economics (AB2)
  • Andrew Cooper, Statistical Science (AB), Computer Science (BS2)
  • Catherine Fei, Mechanical Engineering (BSE)
  • Anh Trinh, Computer Science (BS), Statistical Science (BS2)
  • Yue (Joyce) Xi, Electrical & Computer Egr(BSE), Computer Science (AB2)