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Modeling for Operations and Planning Wind Energy

This proposal discusses the need for a new modeling framework to overcome the challenges in integrating wind energy into existing power systems. It outlines the objectives, proposed approach, and two layers model for addressing wind uncertainty, load uncertainty, and equipment failures. The proposal also mentions potential references and acknowledges individuals from MISO, Siemens PTI, and ISO-NE for their contributions.

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Modeling for Operations and Planning Wind Energy

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  1. Modeling for Operations and Planning Wind Energy A PSERC pre-proposal Marija Ilic, Carnegie Mellon Alejandro Dominguez-Garcia, UIUC August 4, 2008

  2. Motivation for this project • Much emphasis on wind integration in the existing electric power systems • Studies generally use models which do not capture unique wind characteristics (wide variability, stochastic wind output) • Consequently, many problems--- ranging from having no confidence in potential of wind energy to plans to design system support which may result in huge investment consts

  3. Some typical unsolved problems • It is possible to show that if one treats wind simply as a negative load without enhancing operations and planning, it would be hard to make the economic case for it due to: --The need for significantly higher spinning reserves --Problems with having to turn off must-run power plants; --Cost of additional transmission/distribution lines --Cost of Var support --Related impact on power quality --Unit commitment to account for Texas-like events --Valuing different rates of response (new economic dispatch products)

  4. Our proposal • Take a step back, and pose a new modeling framework which lends itself to overcoming the problems with treating wind as a negative load. • The objective is to use this modeling framework for: --More realistic potential of wind power given today’s industry practice --Introducing forward-looking scheduling (economic dispatch) of available resources for meeting the same reliability of service --Tradeoffs between replacement reserve (30 min) and spinning reserve --Tradeoffs between regulation, economic dispatch, and short-term unit commitment (1-4 h)

  5. Modeling steps • Model load as a Markov process, whose parameters can be estimated using historic data (much on this done, see [1] ) • Model wind as a Markov process , whose parameters can be estimated using various advanced regression methods (Noha Nabil at CMU is already working on this [2]) • Model failures of equipment as a Markov process (UIUC effort [3]) • Introduce a two layers stochastic model which combines “smooth” Markov models of wind and load with the discrete-space Markov model into a single Markov reward model (UIUC and CMU new effort)

  6. Proposed approach • Develop models using typical data (load available from ISO websites, wind from several open sources/ISOs(?)) • Illustrate analysis results on the data provided by the industry (at least using the equivalent NPCC model [4]) • Merge the models developed with the novel UC/scheduling/AGC approach formulated as a sequential model-predictive control problem (MPC) to illustrate how such an approach would utilize wind more efficiently than today • Attempt the question of “optimal” wind penetration • Attempt the question of “optimal” transmission investment for integrating reliably wind (desired level and/or “optimal”)

  7. Two Layers Model • Layer 1 includes stochastic models for: • Wind uncertainty • Load uncertainty • Equipment failures • Layer 2 integrates all stochastic models of layers 1 into a Markov Reward model: • Transitions are governed by 1-3 • Each state of the Markov Reward model has associated a monetary reward (or penalty)

  8. References • [1] Skantze, P., Ilic, M., Kluwer 2001. • [2] Noha Nabil, poster presentation http://www.ece.cmu.edu/~electricityconference/ • [3] HØyland, A., Rausand, M., Wiley 1994. • [4] Allen, Lang, Ilic, IEEE Trans. PAS, Aug 2008.

  9. Acknowledgments • Dale Osborn (MISO) • Robert de Mello (Siemens PTI) • Eugene Litvinov (ISO-NE)

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