Incorporating Fuel Constraints and Electricity Spot Prices into the Stochastic Unit Commitment Problem

The electric power industry is going through deregulation. As a result, the load on the generating units of a utility is becoming increasingly unpredictable. Furthermore, electric utilities may need to buy power or sell their production to a power pool that serves as a spot market for electricity. These trading activities expose utilities to volatile electricity prices. In this paper, we present a stochastic model for the unit commitment that incorporates power trading into the picture. Our model also accounts for fuel constraints and prices that may vary with electricity prices and demand. The resulting model is a mixed-integer program that is solved using Lagrangian relaxation and Bender's decomposition. Using this solution approach, we solve problems with 729 demand scenarios on a single processor to within 0.1% of the optimal solution in less than 10 minutes. Our numerical results indicate that significant savings can be achieved when the spot market is entered into the problem and when stochastic policy is adopted instead of a deterministic one.

[1]  John W. Tukey,et al.  Data Analysis and Regression: A Second Course in Statistics , 1977 .

[2]  John A. Muckstadt,et al.  An Application of Lagrangian Relaxation to Scheduling in Power-Generation Systems , 1977, Oper. Res..

[3]  D. Bertsekas,et al.  Solution of Large-Scale Optimal Unit Commitment Problems , 1982, IEEE Transactions on Power Apparatus and Systems.

[4]  Arthur I. Cohen,et al.  A Branch-and-Bound Algorithm for Unit Commitment , 1983, IEEE Transactions on Power Apparatus and Systems.

[5]  A. Merlin,et al.  A New Method for Unit Commitment at Electricite De France , 1983, IEEE Transactions on Power Apparatus and Systems.

[6]  Allen J. Wood,et al.  Power Generation, Operation, and Control , 1984 .

[7]  K. Kiwiel Methods of Descent for Nondifferentiable Optimization , 1985 .

[8]  Dimitri P. Bertsekas,et al.  Dynamic Programming: Deterministic and Stochastic Models , 1987 .

[9]  Alan J. King,et al.  A Standard Input Format for Multiperiod Stochastic Linear Programs , 1987 .

[10]  Walter L. Snyder,et al.  Dynamic Programming Approach to Unit Commitment , 1987, IEEE Transactions on Power Systems.

[11]  Francisco D. Galiana,et al.  Towards a more rigorous and practical unit commitment by Lagrangian relaxation , 1988 .

[12]  Laurence A. Wolsey,et al.  Integer and Combinatorial Optimization , 1988 .

[13]  F. N. Lee,et al.  Short-term thermal unit commitment-a new method , 1988 .

[14]  Francisco D. Galiana,et al.  Unit commitment by simulated annealing , 1990 .

[15]  R. Tyrrell Rockafellar,et al.  Scenarios and Policy Aggregation in Optimization Under Uncertainty , 1991, Math. Oper. Res..

[16]  Jochem Zowe,et al.  A Version of the Bundle Idea for Minimizing a Nonsmooth Function: Conceptual Idea, Convergence Analysis, Numerical Results , 1992, SIAM J. Optim..

[17]  Mokhtar S. Bazaraa,et al.  Nonlinear Programming: Theory and Algorithms , 1993 .

[18]  Gerald B. Sheblé,et al.  Unit commitment literature synopsis , 1994 .

[19]  Ross Baldick,et al.  The generalized unit commitment problem , 1995 .

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

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

[22]  Anastasios G. Bakirtzis,et al.  A genetic algorithm solution to the unit commitment problem , 1996 .

[23]  S. Hunt,et al.  Competition and Choice in Electricity , 1996 .

[24]  Citizens Power Llc The US power market : restructuring and risk management , 1997 .

[25]  Ching-Lien Huang,et al.  Application of genetic-based neural networks to thermal unit commitment , 1997 .

[26]  John R. Birge,et al.  Introduction to Stochastic Programming , 1997 .

[27]  S. R. Huang,et al.  Effectiveness of optimum stratified sampling and estimation in Monte Carlo production simulation , 1997 .

[28]  Werner Römisch,et al.  Optimal Power Generation under Uncertainty via Stochastic Programming , 1998 .