Economic dispatch with deliverable ramping capability constraint for high wind penetration

Ramping products are planned or in effect in multiple power markets overseen by independent system operators (ISOs) in the United States (US). They are intended to provide flexibility in security-constrained economic dispatch, given higher uncertainty in net load (load minus non-dispatchable renewable power) due to higher wind penetration. When conventional generators respond to unforeseen wind ramps, transmission congestion can arise. In such situations, reserves may not be deliverable as power across the network. This paper presents an extension of the original Midcontinent Independent System Operator (MISO) ramping capability formulation. The key characteristic of this proposed deliverable ramping capability formulation is that it recognizes the magnitude of net load uncertainty by location. An example is given in which the economic dispatch with deliverable ramping capability avoids congestion.

[1]  A. Papavasiliou,et al.  Reserve Requirements for Wind Power Integration: A Scenario-Based Stochastic Programming Framework , 2011, IEEE Transactions on Power Systems.

[2]  A. Feijoo,et al.  Simulation of Correlated Wind Speed Data for Economic Dispatch Evaluation , 2012, IEEE Transactions on Sustainable Energy.

[3]  David L. Woodruff,et al.  Pyomo: modeling and solving mathematical programs in Python , 2011, Math. Program. Comput..

[4]  Feng Gao,et al.  Wind generation scheduling and coordination in ERCOT Nodal market , 2012, 2012 IEEE Power and Energy Society General Meeting.

[5]  Nivad Navid,et al.  Market Solutions for Managing Ramp Flexibility With High Penetration of Renewable Resource , 2012, IEEE Transactions on Sustainable Energy.

[6]  Victor M. Zavala,et al.  Unit Commitment With Wind Power Generation: Integrating Wind Forecast Uncertainty and Stochastic Programming. , 2009 .

[7]  E. Ela,et al.  Effective ancillary services market designs on high wind power penetration systems , 2012, 2012 IEEE Power and Energy Society General Meeting.

[8]  R. Fischl,et al.  Secure Allocation of Reguiating Reserve in the Presence of Bus-Demand Uncertainty , 1978, IEEE Transactions on Power Apparatus and Systems.

[9]  M. Shahidehpour,et al.  Security-Constrained Unit Commitment With Volatile Wind Power Generation , 2008, IEEE Transactions on Power Systems.

[10]  H. L. Durrwachter,et al.  Integration of Wind Generation Into the ERCOT Market , 2012, IEEE Transactions on Sustainable Energy.

[11]  Jhi-Young Joo,et al.  Integration of intermittent resources with price-responsive loads , 2009, 41st North American Power Symposium.

[12]  Chen-Ching Liu.  A New Method for the Construction of Maximal Steady-State Security Regions of Power Systems , 1986, IEEE Transactions on Power Systems.

[13]  S. Nahavandi,et al.  Prediction Intervals for Short-Term Wind Farm Power Generation Forecasts , 2013, IEEE Transactions on Sustainable Energy.

[14]  M. Rothleder,et al.  Enhanced system reliability using flexible ramp constraint in CAISO market , 2012, 2012 IEEE Power and Energy Society General Meeting.

[15]  Jian Ma,et al.  Impact of wind and solar generation on the California ISO's intra-hour balancing needs , 2011, 2011 IEEE Power and Energy Society General Meeting.

[16]  John R. Birge,et al.  A stochastic model for the unit commitment problem , 1996 .

[17]  P. Carpentier,et al.  Stochastic optimization of unit commitment: a new decomposition framework , 1996 .

[18]  A. Llombart,et al.  Statistical Analysis of Wind Power Forecast Error , 2008, IEEE Transactions on Power Systems.

[19]  Jianhui Wang,et al.  Time Adaptive Conditional Kernel Density Estimation for Wind Power Forecasting , 2012, IEEE Transactions on Sustainable Energy.

[20]  J. Williams,et al.  Resource transition model under MISO MIP based Look Ahead Commitment , 2012, 2012 IEEE Power and Energy Society General Meeting.

[21]  R. B. Chedid,et al.  Probabilistic Model of a Two-Site Wind Energy Conversion System , 2002, IEEE Power Engineering Review.

[22]  B. Kirby,et al.  Operating Reserve Reductions from a Proposed Energy Imbalance Market with Wind and Solar Generation in the Western Interconnection , 2012 .

[23]  Carlos Abreu Ferreira,et al.  Development and testing of improved statistical wind power forecasting methods. , 2011 .

[24]  R. Harley,et al.  Increased Wind Revenue and System Security by Trading Wind Power in Energy and Regulation Reserve Markets , 2011, IEEE Transactions on Sustainable Energy.

[25]  Chen-Ching Liu,et al.  A New Method for the Construction of Maximal Steady-State Security Regions of Power Systems , 1986, IEEE Power Engineering Review.