2021
DOI: 10.1109/tase.2020.2995914
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A Computing Budget Allocation Method for Minimizing EV Charging Cost Using Uncertain Wind Power

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Cited by 12 publications
(4 citation statements)
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“…A set of experiments indicate that our proposed allocation procedure is significantly more efficient than existing sampling procedures, especially when the number of competing alternatives is large, under various sizes of the good enough subset. How to apply the proposed allocation procedure to improve the computational efficiency in reinforcement learning [36]- [38] deserves future research.…”
Section: Discussionmentioning
confidence: 99%
“…A set of experiments indicate that our proposed allocation procedure is significantly more efficient than existing sampling procedures, especially when the number of competing alternatives is large, under various sizes of the good enough subset. How to apply the proposed allocation procedure to improve the computational efficiency in reinforcement learning [36]- [38] deserves future research.…”
Section: Discussionmentioning
confidence: 99%
“…[22] introduced a charging price strategy that distinguished the busyness of charging stations, motivating users to adjust charging time to improve the utilization of charging infrastructure and reduced the waiting time at charging stations. In [23][24], a scheduling strategy was proposed to charge multiple vehicles through matching with intermittentt renewable generation while minimizing the total charging cost and. Paper [25] proposed an appointment-based mobile charging scheduling strategy to provide an economical and efficient EVs charging service.…”
Section: Routing Optimization Of Electric Vehicles For Charging With ...mentioning
confidence: 99%
“…5. 𝑥 𝑖𝑗 + 𝐶 𝑖,𝑘 (23) where 𝑛 is the total number of the traffic network node, 𝑠 𝑖𝑗 𝑜𝑘 , 𝑠 𝑖𝑗 𝑘𝑑 represents the impedances of the road section with 𝑖 and as both endpoints from the origin to the 𝐶𝑆 𝑘 and from the 𝐶𝑆 𝑘 to the destination respectively, 𝑥 𝑖𝑗 is the 0- (24) where 𝑛 ̃ is the total number of EVs in period 𝑡 , 𝑃 𝑖,𝑡 is the charging power of 𝐸𝑉 𝑖 in period 𝑡. 2) Charging Decision Function: With the lowest total cost as the objective to plan the optimal charging strategy for users, the charging decision function proposed is as follows, with the constraint of the waiting time tolerance, remaining mileage coverage, and driving speed 𝐶 𝑜𝑏𝑗 = 𝑚𝑖𝑛{𝐶…”
Section: 𝐶 𝑟 (𝑖) = {mentioning
confidence: 99%
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