Hostname: page-component-6766d58669-nf276 Total loading time: 0 Render date: 2026-05-23T09:49:03.317Z Has data issue: false hasContentIssue false
  • English
  • Français

STOCHASTIC MARKET EQUILIBRIUM MODEL FOR GENERATION PLANNING

Published online by Cambridge University Press:  31 August 2005

J. Barquín ,
E. Centeno  and
J. Reneses
J. Barquín
Affiliation:
Instituto de Investigación Tecnológica, Pontificia Comillas University of Madrid, 28015 Madrid, Spain, E-mail: barquin@iit.upco.es
E. Centeno
Affiliation:
Instituto de Investigación Tecnológica, Pontificia Comillas University of Madrid, 28015 Madrid, Spain, E-mail: barquin@iit.upco.es
J. Reneses
Affiliation:
Instituto de Investigación Tecnológica, Pontificia Comillas University of Madrid, 28015 Madrid, Spain, E-mail: barquin@iit.upco.es
Get access Rights & Permissions [Opens in a new window]

Abstract

It is widely accepted that medium-term generation planning can be advantageously modeled through market equilibrium representation. There exist several methods to define and solve this kind of equilibrium in a deterministic way. Medium-term planning is strongly affected by uncertainty in market and system conditions; thus, extensions of these models are commonly required. The main variables that should be considered as subject to uncertainty include hydro conditions, demand, generating units' failures, and fuel prices. This paper presents a model to represent a medium-term operation of the electrical market that introduces this uncertainty in the formulation in a natural and practical way. Utilities are explicitly considered to be maximizing their expected profits, and biddings are represented by means of a conjectural variation. Market equilibrium conditions are introduced by means of the optimality conditions of a problem, which has a structure that strongly resembles classical optimization of hydrothermal coordination. A tree-based representation to include stochastic variables and a model based on it are introduced. This approach to market representation provides three main advantages: Robust decisions are obtained; technical constraints are included in the problem in a natural way, additionally obtaining dual information; and large-size problems representing real systems in detail can be addressed.

Information

Type
Papers from the 8th International PMAPS Conference
Information
Probability in the Engineering and Informational Sciences , Volume 19 , Issue 4 , October 2005 , pp. 533 - 546
Copyright
© 2005 Cambridge University Press

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

Article purchase

Temporarily unavailable

References

REFERENCES

Barquín, J., Centeno, E., Malillos, E., & Román, J. (1999). Medium-term hydro operation in a competitive electricity market. IEEE International Conference on Electric Power Engineering PowerTech.
Barquín, J., Centeno, E., & Reneses, J. (2004). Medium-term generation programming in competitive environments: A new optimization approach for market equilibrium computing. IEE Proceedings—Generation, Transmission and Distribution 151(1): 119–126.Google Scholar
Bushnell, J. (1998). Water and power: Hydroelectric resources in the era of competition in the western US. Power Conference on Electricity Restructuring, University of California, Energy Institute.
Day, C.J., Hobbs, B.F., & Pang, J.S. (2002). Oligopolistic competition in power networks: A conjectured supply function approach. IEEE Transactions on Power Systems 17(3): 597–607.Google Scholar
Ferrero, W., Shahidehpour, S.N., & Ramesh, V.C. (1997). Transaction analysis in deregulated power systems using game theory. IEEE Transactions on Power Systems 12(3): 1340–1347.Google Scholar
Gorenstin, B.G., Campodonico, N.M., Costa, J.P., & Pereira, M.V.F. (1992). Stochastic optimization of a hydrothermal system including network constraints. IEEE Transactions on Power Systems 7(2): 791–797.Google Scholar
Hobbs, B.F. (2001). Linear complementary models of Nash–Cournot competition in bilateral and POOLCO power markets. IEEE Transactions on Power Systems 16(2): 194–202.Google Scholar
Morton, D.P. (1993). Algorithmic advances in multi-stage stochastic programming. Ph.D. thesis, Stanford University, Stanford, CA.
Nguyen, D.H.M. & Wong, K.P. (2002). Natural dynamic equilibrium and multiple equilibria of competitive power markets. IEE Proceedings—Generation, Transmission and Distribution 149(2): 133–138.Google Scholar
Otero-Novas, I., Meseguer, C., Batlle, C., & Alba, J.J. (2000). Simulation model for a competitive generation market. IEEE Transactions on Power Systems 15(1): 250–256.Google Scholar
Pereira, M.V.F. & Pinto, L.M.V.G. (1991). Multi-stage stochastic optimization applied to energy planning. Mathematical Programming 52: 359–375.Google Scholar
Rivier, M., Ventosa, M., & Ramos, A. (1999). A generation operation planning model in deregulated electricity markets based on the complementary problem. International Conference on Complementary Problems (ICCP).
Ventosa, M., Ramos, A., & Rivier, M. (1999). Modeling profit maximization in deregulated power markets by equilibrium constraints. Proceedings of the 13th Power System Computing Conference (PSCC).
Yu, Z., Sparrow, F.T., & Bowen, B.H. (1998). A new long-term hydro production scheduling method for maximizing the profit of hydroelectric systems. IEEE Transactions on Power Systems 13(1): 66–71.Google Scholar