5th International forum on Research and Technology for Society and Industry - RTSI 2019, Florence (Italy). 09-12 September 2019
Summary:
Reducing costs and risks to foster rural electrification in developing countries is one of the major hurdles for researchers, firms and practitioners aiming to develop business with rural minigrids, which are local networks that supply energy demand by exploiting local sources. Predictive rolling-horizon operating strategies have proven to reduce operating costs and better cope with uncertainties in renewable sources and demand than standard priority-list approaches, when forecasts are accurate enough. Their main drawback is the need for more powerful controlling devices, which, however, is becoming acceptable for modern operation purposes. Yet, calculating the optimal design of the minigrid operated with predictive strategies can easily require much larger computational requirements than with priority-list rules. In the present paper, we describe a two-stage procedure to reduce the computational requirements of design methodologies based on predictive strategies. First, an optimization is developed using a priority-list strategy, namely load-following, whose results are used to refine the second-stage optimization based on an advanced rolling-horizon strategy. Results suggest significant reduction in computational requirements, up to 84% with respect to standard predictive approaches, with negligible reduction in the optimality of results.
Keywords: off-grid energy system; isolated hybrid system; optimization; MILP; microgrid
DOI: 
https://doi.org/10.1109/RTSI.2019.8895530
Published in RTSI 2019, pp: 1-6, ISBN: 978-1-7281-3816-9
Publication date: 2019-09-09.
Citation:
D. Fioriti, G. Lutzemberger, D. Poli, A. Micangeli, P. Dueñas, Optimal design of off-grid power systems operated by a rolling-horizon strategy: a method to reduce computational requirements, 5th International forum on Research and Technology for Society and Industry - RTSI 2019, Florence (Italy). 09-12 September 2019. In: RTSI 2019: Conference proceedings, ISBN: 978-1-7281-3816-9
