Dynamic stochastic fractional programming for sustainable management of electric power systems

Abstract

A dynamic stochastic fractional programming (DSFP) approach is developed for capacity-expansion planning of electric power systems under uncertainty. The traditional generation expansion planning focused on providing a sufficient energy supply at minimum cost. Different from using least-cost models, a more sustainable management approach is to maximize the ratio between renewable energy generation and system cost. The proposed DSFP method can solve such ratio optimization problems involving issues of capacity expansion and random information. It has advantages in balancing conflicting objectives, handling uncertainty expressed as probability distributions, and generating flexible capacity-expansion strategies under different risk levels. The method is applied to an expansion case study of municipal electric power generation system. The obtained solutions are useful in generating sustainable power generation schemes and capacity-expansion plans. The results indicate that DSFP can support in-depth analysis of the interactions among system efficiency, economic cost and constraint-violation risk.