A stochastic-interval model for optimal scheduling of PV-assisted multi-mode charging stations. (15th August 2022)

• Record Type: Journal Article
• Authors: Tostado-Véliz, Marcos; Kamel, Salah; Hasanien, Hany M.; Arévalo, Paul; Turky, Rania A.; Jurado, Francisco
• Is Part Of: Energy. Volume 253(2022)
• Publication Date: 2022-08-15
• Subjects: Photovoltaic -- Charging station -- Electric vehicle -- Renewable energy -- Interval optimization -- Robust optimization; Power resources -- Periodicals; Power (Mechanics) -- Periodicals; Energy consumption -- Periodicals; 333.7905
• DOI: 10.1016/j.energy.2022.124219
• Languages: English
• ISSNs: 0360-5442

Abstract

Abstract: Nowadays, photovoltaic-assisted charging stations are becoming popular worldwide because its capacity to accommodate more clean energy, reduce carbon emissions, alleviate peak charging loads and provide wider charging infrastructures worldwide. When these infrastructures are operated locally, energy management becomes a challenge due to the large number and heterogeneity of uncertainties involved. This aspect is especially noticeable in the case of charging demand, which is difficult to predict. To address this issue, this paper develops a novel stochastic-interval model for optimal scheduling of multi-mode photovoltaic-assisted charging stations. The developed model uses interval formulation to model uncertainties from photovoltaic generation and energy price, while a comprehensive stochastic model is proposed for charging demand. The developed optimal scheduling model is solved using a developed iterative model, which avoids using interval arithmetic explicitly. This methodology encompasses two Mixed-integer linear programming problems and one Quadratic-programming problem, that can be efficiently addressed by conventional solvers, and allows adopting optimistic or pessimistic strategies. A case study is presented on a benchmark mid-size charging station to validate the developed model. As a sake of example, the system profit grows by 9% and decreases by 3% adopting optimistic and pessimistic point of view, respectively. Likewise, total PV generation increases by 150 kWh/day and reduces by 50 kWh/day. Similar conclusions are extracted for other parameters like monetary balances, PV peak power or satisfied EV demand. Highlights: A novel stochastic-interval model for robust scheduling of PVaCSs is developed. A comprehensive stochastic model for multi-mode EV charging is presented. A multi-stage solution procedure for the developed model is described. The new proposal allows to consider optimistic and pessimistic strategies. A case study validates the developed formulation and modelling.