| Title |
Power plant investment planning by stochastic programming / |
| Authors |
Sakalauskas, Leonidas ; Žilinskas, Kęstutis |
| DOI |
10.3846/tede.2010.46 |
| Full Text |
|
| Is Part of |
Ūkio technologinis ir ekonominis vystymas= Technological and economic development of economy. 2010, vol. 16, no. 4. ISSN 1392-8619 |
| Keywords [eng] |
Stochastic programming ; Monte Carlo method ; Power planning ; Stochastic gradient ; Statistical criteria ; ε-feasible direction |
| Abstract [eng] |
Although the problem of rational power generation has been extensively studied, traditional approaches for power optimization do not offer good solutions to this purpose, especially in a competitive electricity market environment where many factors are uncertain. In this paper, within the framework of two-stage linear stochastic programming, the method for power planning has been developed, with uncertain factors taken into account, through a continuously distributed set of scenarios. The objective is to find the structure of the power plants capacity in the region which minimizes the sum of the investment and the expected operating costs over the long-term planning horizon, taking into account the environmental impact. The structure of the considered task corresponds to a power investment planning problem that often arises in the developing regions. The method is developed for solving the stochastic optimization problem by the sequence of Monte- Carlo sampling estimators. The procedures developed make it possible to solve stochastic problems with an admissible accuracy by means of an acceptable amount of computations. As follows from numerical experiments the approach presented enables us to decrease the total expected costs of power planning versus deterministic planning solution. |
| Type |
Journal article |
| Language |
English |
| Publication date |
2010 |
| CC license |
|
