Charge scheduling framework with multiaggregator collaboration for direct charging and battery swapping station in a coupled distribution‐transportation network. (5th April 2022)

• Record Type: Journal Article
• Authors: Kumar, Tushar; Kumar, Nikhil; Thakur, Tripta; Nema, Savita
• Other Names: Bicer Yusuf guestEditor.
• Is Part Of: International journal of energy research. Volume 46:Number 8(2022)
• Page Start: 11139
• Page End: 11162
• Publication Date: 2022-04-05
• Subjects: aggregator collaboration -- battery swap stations -- charge scheduling; Power resources -- Periodicals; Power (Mechanics) -- Periodicals; Power resources -- Research -- Periodicals; 621.042
• DOI: 10.1002/er.7915
• Languages: English
• ISSNs: 0363-907X

Abstract

Summary: The distribution system is anticipated to accommodate a high number of electric vehicles (EVs) and possibly multiple charging stations in the coming years to offer charging and vehicle‐to‐grid capabilities to EV users. As the use of EVs for public transportation is increasing, problems associated with the increase in peak grid demand needs to be tackled judiciously. Without effective charging scheduling, peak EV loads can threaten the grid operation and higher prices during the peak hours can discourage EV adoption by users. This paper proposes a charging management scheme for direct charging and battery swapping stations (BSS) with interaggregator collaboration that takes into account EV mobility patterns and grid constraints. A unified scheduling framework for EVs and E‐buses is developed in a coupled transportation and distribution network under an interaggregator collaboration environment to maximize profit of the aggregator while not breaching grid operating standards. Photovoltaic (PV) support for BSS has been considered, where depleted E‐bus batteries can be charged through solar PV power. Different charging priority schemes are implemented and compared to select the best scheme from the perspective of both the aggregator and the customer. Realistic battery degradation cost model is used to estimate the profit of aggregators. Moreover, the impact of the peak load on the grid is assessed through the IEEE 33 bus distribution test system coupled to the 25‐node transportation network. The proposed framework improves the average wait time by 37.5% compared to the uncontrolled condition and the scheduling is improved by 5%. On multiple measures, the impact of PV support is compared to the scenario with no PV. Furthermore, when the number of EVs scheduled through PV assistance increased, so did the profitability of each aggregator, implying that the grid schedules a lower percentage of EVs when PV support is available.