Alternative Title


Subject Area



Electricity market has been transitioning from a conventional and deterministic operation to a stochastic operation under the increasing penetration of renewable energy. Industry-level solutions toward the future electricity market operation ask for both accuracy and efficiency while maintaining model interpretability. Hence, reliable stochastic optimization techniques come to the first place for such a complex and dynamic problem.

This work starts at proposing a solution strategy for the uncertainty-based power system planning problem, which acts as a preliminary and instructs the electricity market operation. Considering 100% renewable penetration in the future, it analyzes the cost-effectiveness of renewable energy from a long-term point of view. After the uncertainty-based system planning problem is well tackled, we turn our direction to the hierarchical market operation considering the transmission and distribution coordination. We develop a three-stage unit commitment model for the market operation of transmission and distribution coordination under the uncertainties of renewable generation and demand variations. With the study of new electricity market operations, current market protocols should be updated according to the growing variable resources in the power system, especially the ancillary service participation. To better let industry and research communities leverage our works, we develop an flexible software platform for the full-stack electricity market operations including both the day-ahead and real-time operations. Various operation novelties, such as ancillary service provision from variable resources, multi-level energy storage participation, and model updates on the conventional non-spinning reserve, are integrated in the new model, considering the profitability of the variable renewable energy.

Degree Date

Summer 2021

Document Type


Degree Name



Electrical and Computer Engineering


Jianhui Wang



Creative Commons License

Creative Commons Attribution-Noncommercial 4.0 License
This work is licensed under a Creative Commons Attribution-Noncommercial 4.0 License