A DECOMPOSITION PROCEDURE BASED ON APPROXIMATE NEWTON DIRECTIONS

The efficient solution of large-scale linear and nonlinear optimization problems may require exploiting any special structure in them in an efficient manner. We describe and analyze some cases in which this special structure can be used with very little cost to obtain search directions from decomposed subproblems. We also study how to correct these directions using (decomposable) preconditioned conjugate gradient methods to ensure local convergence in all cases. The choice of appropriate preconditioners results in a natural manner from the structure in the problem. Finally, we conduct computational experiments to compare the resulting procedures with direct methods, as well as to study the impact of different preconditioner choices.

Insuring unit failures in electricity markets

An electric energy producer participates in futures markets in the hope of hedging the risk of trading in the pool. However, this producer is required to supply the energy associated with all its signed forward contracts even if some of its units are forced out due to unexpected failures. In this case, the producer must purchase some of the energy needed to meet its futures market commitments in the pool, which may result in high losses if the pool prices happen to be higher than the forward contract prices. To mitigate these losses, the producer can take out insurance against the forced outages of its units. Using a stochastic programming model, this paper analyzes the convenience of signing an insurance against unit failure by an electric energy producer and its impact on forward contracting decisions. Results from a realistic case study are provided and analyzed.Insurance contract Electricity markets Stochastic programming Unit forced outage rate