Mathematical Frameworks for Dynamic Reserve Policies Mathematical Frameworks for Dynamic Reserve Policies The US electric grid, and many national and regional grids globally, will experience an unprecedented penetration of variable renewable resources, such as wind and solar. California has a Renewable Portfolio Standard (RPS) mandating 33% renewables by 2020 and future RPS goals may reach up to the 50% level or beyond. Combined with existing demand uncertainty, contingencies, and area interchange error, there are increasing needs to manage uncertainty in order to ensure reliability and to ensure economic efficiency in an industry that is roughly trillion dollars a year in the USA alone. The proposed research offers a new paradigm for the determination of reserve requirements for either dayahead unit commitment (UC) models or reliability UC models. In this proposal, the objectives are: 1) to develop novel mathematical programming frameworks for the determination of reserve zones and reserve levels embedded in UC models; 2) to develop mathematical frameworks that adaptively determine reserve requirements for various reliability standards; 3) to embed dynamic reserve policies inside stochastic programming algorithms in order to improve scalability and solution quality; 4) to compare the proposed techniques to existing reserve rules as well as stochastic programming techniques; 5) to validate the proposed concepts by demonstrating improvements in reliability, economic efficiency, and scalability; 6) to validate these techniques by examining their ability to facilitate the integration and management of high levels of variable generation resources.
|Effective start/end date||8/15/13 → 7/31/16|
- National Science Foundation (NSF): $300,000.00
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