Dynamic stochastic fractional programming for sustainable management of electric power systems

• Published in: International journal of electrical power & energy systems Vol. 53; pp. 553 - 563
• Main Authors: Zhu, H., Huang, G.H.
• Format: Journal Article
• Language: English
• Published: Oxford Elsevier Ltd 01-12-2013; Elsevier
• Subjects: Applied sciences; Chance-constrained programming; Dynamical systems; Dynamics; Electric power generation; Electric power plants; Electric power systems; Electrical engineering. Electrical power engineering; Electrical power engineering; Exact sciences and technology; Fractional programming; Generation expansion planning; Management; Miscellaneous; Operation. Load control. Reliability; Power networks and lines; Programming; Renewable energy; Stochastic uncertainty; Stochasticity; Sustainability; Uncertainty; Chance-constrained programming; Fractional programming; Generation expansion planning; Renewable energy; Sustainability; Stochastic uncertainty; Performance evaluation; Cost minimization; Costs; Probabilistic approach; Optimization method; Probability distribution; Power generation planning; Sustainable development; Stochastic programming; Case study; Energy supply; Electrical network; Electric power; Economic aspect; Production capacity; Electric power production; Dynamic programming
• ISSN: 0142-0615; 1879-3517
• DOI: 10.1016/j.ijepes.2013.05.022

•A dynamic fractional programming (DSFP) method is developed for energy management.•Ratio objectives, capacity expansion issues and random inputs can be dealt with.•A case study of power generation system planning demonstrates its advantages.•DSFP can reflect system efficiency and achieve better resources sustainability.•This study explores system tradeoffs and supports policy analysis under uncertainty.
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.