Mathematical Frameworks for Dynamic Reserve Policies

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The objective of this award is to develop a new paradigm for the determination of reserve requirements for either day-ahead unit commitment (UC) models or reliability UC models to maintain a reliable electric power system. Specifically, this work will 1) develop novel mathematical programming frameworks for the determination of reserve zones and reserve levels embedded in unit commitment models; 2) develop mathematical frameworks that adaptively determine reserve requirements for various reliability standards; 3) embed dynamic reserve policies inside stochastic programming algorithms in order to improve scalability and solution quality; 4) compare the techniques to existing reserve rules as well as stochastic programming techniques; 5) validate the concepts by demonstrating improvements in reliability, economic efficiency, and scalability; 6) validate these techniques by examining their ability to facilitate the integration and management of high levels of variable generation (renewable) resources. If successful, the mathematical frameworks for reserve policies will lead to improvements in electric power systems operations and planning by improving efficiency and systems reliability, with a reduction in grid outages. This framework will also improve the ability to manage renewable resources and, thus, allow for higher levels of variable renewable resources, which will help reduce emissions and it will facilitate the development of a sustainable economy for energy independence and security. The process to balance reserve policies with stochastic programming algorithms will facilitate the integration and adoption of stochastic programming in electric power systems. The mathematical models and polyhedral studies developed will also contribute to large scale network flow or scheduling problems with uncertainties and reliability standards, e.g., transportation systems and telecommunication systems.