An optimization-based approach for facility energy management with uncertainties, and, Power portfolio optimization in deregulated electricity markets with risk management

In a deregulated electric power system, multiple markets of different time scales exist with various power supply instruments. A load serving entity (LSE) has multiple choices from these instruments to meet its load obligations. In view of the large amount of power involved, the complex market structure, the risks in such volatile markets, the stringent constraints to be satisfied, and the long time horizon, a power portfolio optimization problem is of critical importance for an LSE to serve its load, maximize its profit, and manage its risks. In this paper, a midterm power portfolio optimization problem with risk management is presented. Key instruments are considered, risk terms based on semi-variances of spot market transactions are introduced, and penalties on load obligation violations are added to the objective function to improve algorithm convergence and constraint satisfaction. To overcome the inseparability of the resulting problem, a surrogate optimization framework is developed, enabling a decomposition and coordination approach. Numerical testing results show that our method effectively provides decisions for various instruments to maximize profit and manage risks, and it is computationally efficient

[1]  Chongqing Kang,et al.  Risk evaluation of electricity price and its sensitivity analysis after deregulation , 2002, Proceedings. International Conference on Power System Technology.

[2]  Laureano F. Escudero,et al.  SEGEM: a simulation approach for electric generation management , 1998 .

[3]  Amy V. Puelz.  Asset and liability management: a stochastic model for portfolio selection , 1997, Proceedings of the IEEE/IAFE 1997 Computational Intelligence for Financial Engineering (CIFEr).

[4]  R. Masiello,et al.  Managing market risk in energy , 2003 .

[5]  P. Luh,et al.  Optimal integrated generation bidding and scheduling with risk management under a deregulated daily power market , 2002, 2002 IEEE Power Engineering Society Winter Meeting. Conference Proceedings (Cat. No.02CH37309).

[6]  Hans Byström,et al.  The hedging performance of electricity futures on the Nordic power exchange , 2000 .

[7]  H. Markowitz.  Portfolio Selection: Efficient Diversification of Investments , 1971 .

[8]  James E. Smith,et al.  Valuing Risky Projects: Option Pricing Theory and Decision Analysis , 1995 .

[9]  Zuwei Yu.  A spatial mean-variance MIP model for energy market risk analysis , 2003 .

[10]  Yaming Ma,et al.  A neural network-based method for forecasting zonal locational marginal prices , 2004, IEEE Power Engineering Society General Meeting, 2004..

[11]  Lihua Yu,et al.  Decision Aids for Scheduling and Hedging (DASH) in deregulated electricity markets: a stochastic programming approach to power portfolio optimization , 2002, Proceedings of the Winter Simulation Conference.

[12]  P. Luh,et al.  The surrogate gradient algorithm for Lagrangian relaxation method , 1997, Proceedings of the 36th IEEE Conference on Decision and Control.

[13]  P. Luh,et al.  Nonlinear approximation method in Lagrangian relaxation-based algorithms for hydrothermal scheduling , 1995 .

[14]  X. Zhao,et al.  Surrogate Gradient Algorithm for Lagrangian Relaxation , 1999 .

[15]  S. N. Siddiqi,et al.  Project valuation and power portfolio management in a competitive market , 2000 .

[16]  Gang George Yin,et al.  Markowitz's mean-variance portfolio selection with regime switching: from discrete-time models to their continuous-time limits , 2004, IEEE Transactions on Automatic Control.

[17]  G. Sheblé,et al.  Price and volume risk management for power producers , 2005, 2004 International Conference on Probabilistic Methods Applied to Power Systems.

[18]  Yaakov Bar-Shalom,et al.  Estimation and Tracking: Principles, Techniques, and Software , 1993 .