An inexact optimization model for regional energy systems planning in the mixed stochastic and fuzzy environment

Abstract

In this study, an interval-parameter superiority–inferiority-based regional energy management model has been developed for supporting regional energy management (REM) systems planning under uncertainty. This method is based on an integration of the existing interval mathematical programming, superiority–inferiority-based fuzzy–stochastic programming and mixed integer linear programming techniques. It can explicitly address the system uncertainties that can be expressed as fuzzy-random variables and/or interval numbers. In addition, dynamic interrelationships among system parameters can be successfully reflected through the introduction of fuzzy-random variables and the associated transition probabilities. The developed method has then been applied to a case of long-term REM planning. Useful solutions for the planning of energy management systems have been generated, which can be used for generating decision alternatives and thus help resource managers identify desired policies under various economic and system-reliability constraints. The generated solutions can also provide desired plans for energy resource/service allocation and facility capacity expansion with a minimized system cost, maximized system reliability and maximized energy security. Tradeoffs between system costs and constraint-violation risk levels can also be tackled. Higher costs will increase system stability, while a desire for lower system costs will run into a risk of potential instability of the management system. The results also suggest that the proposed methodology is applicable to practical problems that are associated with uncertain information. Copyright © 2008 John Wiley & Sons, Ltd.