Temporal vs. Stochastic Granularity in Thermal Generation Capacity Planning with Wind Power
Shan Jin (shan.jin.cgmail.com)
Abstract: We propose a stochastic generation expansion model, where we represent the long-term uncertainty in the availability and variability in the weekly wind pattern with multiple scenarios. Scenario reduction is conducted to select a representative set of scenarios for the long-term wind power uncertainty. We assume that the short-term wind forecast error induces an additional amount of operating reserves as a predefined fraction of the wind power forecast level. Unit commitment (UC) decisions and constraints for thermal units are incorporated into the expansion model to better capture the impact of wind variability on the operation of the system. To reduce computational complexity, we also consider a simplified economic dispatch (ED) based model with ramping constraints as an alternative to the UC formulation. We find that the differences in optimal expansion decisions between the UC and ED formulations are relatively small. We also conclude that the reduced set of scenarios can adequately represent the long-term wind power uncertainty in the expansion problem. The case studies are based on load and wind power data from the state of Illinois.
Keywords: Generation Expansion Planning, Wind Energy, Unit Commitment, Electricity Markets, Stochastic Programming
Category 1: Applications -- Science and Engineering (Facility Planning and Design )
Category 2: Stochastic Programming
Citation: IEEE Transactions on Power Systems, accepted, Dec 2013.
Entry Submitted: 07/25/2013
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