An improved particle swarm optimisation algorithm applied to battery sizing for stand-alone hybrid power systems. (January 2016)

• Record Type: Journal Article
• Authors: Shang, Ce; Srinivasan, Dipti; Reindl, Thomas
• Is Part Of: International journal of electrical power & energy systems. Volume 74(2016:Jan.)
• Page Start: 104
• Page End: 117
• Publication Date: 2016-01
• Subjects: Stand-alone -- Hybrid power system -- Storage -- Sizing -- Dispatch -- PSO; Electrical engineering -- Periodicals; Electric power systems -- Periodicals; Électrotechnique -- Périodiques; Réseaux électriques (Énergie) -- Périodiques; Electric power systems; Electrical engineering; Periodicals; 621.3
• DOI: 10.1016/j.ijepes.2015.07.009
• Languages: English
• ISSNs: 0142-0615

Abstract

Highlights: Battery-integrated dispatch model with analysis of system non-differentiability. Dispatch-coupled battery sizing which prevents over- and under-sizing. Numeric analysis on different levels of solar and wind energy penetration. An improved particle swarm optimisation which outperforms the standard one. Abstract: Stand-alone hybrid power systems with renewable energies are an economic alternative to the main electricity grid where the extension of the grid is too costly or the small local consumption would not justify it. Properly sizing the battery of the systems is an important step to guarantee their reliability and low cost. This paper accepts the dispatch-coupled sizing method by integrating the battery into the operation of the generation units in the system, and formulates this application problem using optimal control. Two major renewable energy sources – solar photovoltaic panels and wind turbines – are considered, together with traditional diesel generators. Penetration level is used as the lever to indicate the different integration degrees of renewables in the system. A particle swarm optimisation (PSO) algorithm is adapted and improved for this specific application. The numeric results of the system planning are benchmarked using an economic indicator, the levelised cost of electricity, to address the real-world system.