Yury D. Kononov (kononov@isem.irk.ru), Vladimir N. Tyrtyshnyi (tyrty@mail.ru), Dmitry Yu. Kononov (dima@isem.irk.ru)

Melentiev Energy Systems Institute of Siberian Branch of the Russian Academy of Sciences

The paper studies a comprehensive comparative performance evaluation of regional energy supply options, which ranks among relevant open problems of long-term electric power industry forecasting. We demonstrate that it is essential to assess and account for investment risks as uncertainty undergoes a significant growth. To this end we propose to employ stochastic optimization models. The description of such a model is provided. The model calculations prove conclusive of a notable effect the nature of input data uncertainty has on forecasting results.

forecasting, uncertainty, stochastic programming, optimization, risks