On-demand dynamic charging pricing strategy for Electric Vehicles

Electric Vehicles (EVs) charging pricing plays an important role in reducing the charging demand during peak hours and increasing Charging Station Operator (CSO) profits. However, the existing studies have overlooked the charging pile availability and the current Charging Stations (CS) occupancy. This study proposes a novel on-demand dynamic pricing strategy considering limited charging spaces and CS occupancy using the low and high occupancy thresholds, with low and high cost adjustments in the charging costs. The idle occupancy at the CSs with a limited number of spaces can reduce the CSO profit; therefore, the idle time penalty is also introduced. The real-world EV charging data of 6 CSs from 3 districts of Jiaxing city, China, is used. The case study also includes analysis of occupancy thresholds, cost adjustments, idle time penalty limits, and penalty costs. The findings show that the proposed strategy, including both algorithms, improved CSO profits across most EV charging sites as compared to Time-of-Use (ToU) pricing. The profits are increased by 8.019% with algorithm 1 and 9.603% with algorithm 2 for the Bus Station location. The Government Agency site achieved a 4.284% and 6.109% increase, while the Shopping Mall also increased by 3.315% and 5.107%, respectively. The Tourist Attraction location also experienced profit rises of 0.657% and 2.710%. Expressway Service District C and Financial Industrial Park showed a slight decrease of −0.237% and −0.299% with Algorithm 1, and improved by 1.824% and 1.442% using Algorithm 2, respectively. The results highlight that algorithm 2 consistently improves profit across all six CS locations.