Self-scheduling of generation companies via stochastic optimization considering uncertainty of units

This paper provides a novel self-scheduling model for price-taker generation companies (GENCOs) participating in a day-ahead energy market. Also, this paper models the effect of uncertainty of generating units' forced outage considered by stochastic optimization approach in the self-scheduling. This approach allows the producer to maximize its profit while controlling the risk of profit variability. A scenario generation technique is considered to produce the scenarios for modeling the uncertainty source. Moreover, a well-known scenario reduction tool is applied to reduce the computational burden of the problem. A proposed methodology solves a set of stochastic mixed-integer linear programming (MILP) problems. The framework is effectively applied to a test system and the effect of GENCOs' unavailability and risk are obtained and discussed.

[1]  A. Bakirtzis,et al.  Optimal Self-Scheduling of a Thermal Producer in Short-Term Electricity Markets by MILP , 2010, IEEE Transactions on Power Systems.

[2]  P. Luh,et al.  Optimization based bidding strategies in the deregulated market , 1999, Proceedings of the 21st International Conference on Power Industry Computer Applications. Connecting Utilities. PICA 99. To the Millennium and Beyond (Cat. No.99CH36351).

[3]  A. Conejo,et al.  Optimal response of a thermal unit to an electricity spot market , 2000 .

[4]  N. Growe-Kuska,et al.  Scenario reduction and scenario tree construction for power management problems , 2003, 2003 IEEE Bologna Power Tech Conference Proceedings,.

[5]  Nima Amjady,et al.  OPTIMUM STOCHASTIC SELF-SCHEDULING FOR GENCOS CONSIDERING POOL AUCTION AND BILATERAL CONTRACTS , 2011 .

[6]  Mohammad Abedini,et al.  Impact of Wind Uncertainties on Generation Dispatch and Unit Commitment , 2012 .

[7]  Mohammad Rouhi,et al.  A New Hybrid Algorithm for Optimization Using PSO and GDA , 2012 .

[8]  Hamidreza Zareipour,et al.  Stochastic self-scheduling of generation companies in day-ahead multi-auction electricity markets considering uncertainty of units and electricity market prices , 2013 .

[9]  Richard E. Rosenthal,et al.  GAMS -- A User's Guide , 2004 .

[10]  Lina P Garces,et al.  Weekly Self-Scheduling, Forward Contracting, and Offering Strategy for a Producer , 2010, IEEE Transactions on Power Systems.

[11]  Y. Ho,et al.  An Ordinal Optimization-Based Bidding Strategy for Electric Power Suppliers in the Daily Energy Market , 2001, IEEE Power Engineering Review.

[12]  Weerakorn Ongsakul,et al.  Optimal risky bidding strategy for a generating company by self-organising hierarchical particle swarm optimisation , 2011 .

[13]  Gerald B. Sheblé,et al.  A profit-based unit commitment GA for the competitive environment , 2000 .

[14]  Zuyi Li,et al.  Market Operations in Electric Power Systems : Forecasting, Scheduling, and Risk Management , 2002 .

[15]  Hamidreza Zareipour,et al.  Stochastic security‐constrained joint market clearing for energy and reserves auctions considering uncertainties of wind power producers and unreliable equipment , 2013 .

[16]  Hamidreza Zareipour,et al.  A new hybrid stochastic‐robust optimization approach for self‐scheduling of generation companies , 2016 .

[17]  S. M. Shahidehpour,et al.  Risk and profit in self-scheduling for GenCos , 2004, IEEE Transactions on Power Systems.

[18]  S.N. Singh,et al.  Fuzzy Adaptive Particle Swarm Optimization for Bidding Strategy in Uniform Price Spot Market , 2007, IEEE Transactions on Power Systems.

[19]  M. Shahidehpour,et al.  Price-based unit commitment: a case of Lagrangian relaxation versus mixed integer programming , 2005, IEEE Transactions on Power Systems.