Stochastic Optimization of Sub-Hourly Economic Dispatch With Wind Energy

We present a stochastic programming framework for a multiple timescale economic dispatch problem to address integration of renewable energy resources into power systems. This framework allows certain slow-response energy resources to be controlled at an hourly timescale, while fast-response resource...

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Bibliographic Details
Published in:IEEE transactions on power systems 2016-03, Vol.31 (2), p.949-959
Main Authors: Gangammanavar, Harsha, Sen, Suvrajeet, Zavala, Victor M.
Format: Article
Language:English
Subjects:
Approximation methods
Biological system modeling
Computation
Computational modeling
Costs
Decomposition
Economic dispatch
Economics
Electric utilities
Energy resources
Generators
Mathematical models
Mathematical programming
Optimization
Power dispatch
Programming
stochastic decomposition
Stochastic processes
Stochasticity
sub-hourly dispatch
Time
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Summary:We present a stochastic programming framework for a multiple timescale economic dispatch problem to address integration of renewable energy resources into power systems. This framework allows certain slow-response energy resources to be controlled at an hourly timescale, while fast-response resources, including renewable resources, and related network decisions can be controlled at a sub-hourly timescale. To this end, we study two models motivated by actual scheduling practices of system operators. Using an external simulator as driver for sub-hourly wind generation, we optimize these economic dispatch models using stochastic decomposition, a sample-based approach for stochastic programming. Computational experiments, conducted on the IEEE-RTS96 system and the Illinois system, reveal that optimization with sub-hourly dispatch not only results in lower expected operational costs, but also predicts these costs with far greater accuracy than with models allowing only hourly dispatch. Our results also demonstrate that when compared with standard approaches using the extensive formulation of stochastic programming, the sequential sampling approach of stochastic decomposition provides better predictions with much less computational time.
ISSN:0885-8950
1558-0679
DOI:10.1109/TPWRS.2015.2410301