Effect of load profile uncertainty on the optimum sizing of off-grid PV systems for rural electrification. (December 2016)

• Record Type: Journal Article
• Authors: Mandelli, Stefano; Brivio, Claudio; Colombo, Emanuela; Merlo, Marco
• Is Part Of: Sustainable energy technologies and assessments. Volume 18(2017)
• Page Start: 34
• Page End: 47
• Publication Date: 2016-12
• Subjects: LLP loss of load probability -- NPC net present cost -- O&M operation and maintenance -- PV photovoltaic -- SOC state of charge; Renewables -- Electric consumptions -- Stochastic model -- Simulation -- Optimization; Renewable energy sources -- Periodicals; Energy development -- Technological innovations -- Periodicals; Electric power production -- Periodicals; Energy storage -- Periodicals; 333.79
• DOI: 10.1016/j.seta.2016.09.010
• Languages: English
• ISSNs: 2213-1388

Abstract

Abstract: Off-grid systems based on PV and batteries are becoming a solution of great interest for rural electrification. Nevertheless, sizing these systems is not straightforward since it means matching unpredictable energy sources with uncertain demands while providing the best reliability and costs. In our opinion, the effect of users' energy consumptions uncertainty on the sizing of these systems has not been appropriately investigated. This paper addresses this issue and analyzes the effect of load profile uncertainty on the off-grid PV systems optimum design. Specifically a novel sizing methodology has been introduced based on: (i) an effective approach of modelling rural energy needs; (ii) an innovative stochastic method which formulates different possible realistic daily load profiles for un-electrified rural areas; (iii) a PV-battery techno-economic analysis via steady-state simulation; (iv) the evaluation of the optimum system sizing via a numerical method based on net present cost and loss of load probability. Finally, the proposed methodology has been applied to find the optimum size of an off-grid PV system for a peri-urban area of Uganda. The results show that the optimum system configurations are significantly affected by load profiles; consequently an approach to identify the robust solution with regards the assumed uncertainty is proposed.