A two-stage linear programming optimization framework for isolated hybrid microgrids in a rural context: The case study of the "El Espino" community. (1st December 2019)

• Record Type: Journal Article
• Authors: Balderrama, Sergio; Lombardi, Francesco; Riva, Fabio; Canedo, Walter; Colombo, Emanuela; Quoilin, Sylvain
• Is Part Of: Energy. Volume 188(2019)
• Publication Date: 2019-12-01
• Subjects: Microgrids -- Rural electrification -- Isolated energy systems -- Energy -- Open energy modeling; Power resources -- Periodicals; Power (Mechanics) -- Periodicals; Energy consumption -- Periodicals; 333.7905
• DOI: 10.1016/j.energy.2019.116073
• Languages: English
• ISSNs: 0360-5442

Abstract

Abstract: Efforts towards ensuring clean and affordable electricity for all have been progressing slowly in rural, off grid areas of developing countries. In this context, hybrid microgrids may offer reliable and potentially clean electricity for isolated locations. Nevertheless, the process of planning and operation of these systems faces several challenges, often due to the uncertainties related to the renewable resources and to the stochastic nature of electricity consumption in rural contexts. This paper tackles this problem and contributes to the literature in bridging the gap between field practices and two-stage stochastic modeling approaches by identifying an open-source modeling framework which is then applied to real local data. As reference case-study, we consider a microgrid built in 2015 in Bolivia. Overall, the optimal system results from a compromise between the Net Present Cost, the peak capacity installed and the flexibility (to balance variable generation). Different approaches to size isolated microgrids are tested, with the conclusion that methods accounting for the uncertainty in both demand and renewable generation may lead to a more robust configuration with little impacts on the final cost for the community. Highlights: The historical monitoring data from a remote PV-diesel microgrid in Bolivia for rural electrification is presented. A novel two-stage optimization framework is developed for the robust design of such systems under uncertainty. Components models and performance curves are derived from the monitoring data. Among different model formulations, stochastic MILP models generate the most robust system configuration. Recommendations are formulated regarding the optimal configuration with respect to the existing system.