Abstract
Recently, electric power systems are facing increasing uncertainties in demand, energy price and environmental constraints in future. In such a situation, a plan for expansion of the system capacity must be made robust against the aforesaid uncertainties. Large-scale power plants require long time for construction, so that, the decision of construction must be made under much uncertainty in future. On the other hand, small-scale plants require less time for construction, i.e., the decision can be made under less uncertainty in spite of their relatively higher costs.
The present paper is concerned with an optimal combination of the large-scale and the small-scale plants having the aforesaid characteristics under uncertainty of the demand in future. First, the demand growth in future is described by scenarios of demand growth branching like a tree. Then, the optimization problem of the system expansion is formulated into a stochastic linear-programming problem. An optimal solution of the problem is obtained by using the scenario-aggregation algorithm proposed by Rockafellar and Wets. The simulation results, yielded by using a parallel computation on Transputers, show that there is a possibility of constructing the small-scale power plants to cope with the uncertainty even if they are more expensive than the large-scale plants.
