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| Format: | Preprint |
| Veröffentlicht: |
2024
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| Online-Zugang: | https://arxiv.org/abs/2408.14169 |
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| _version_ | 1866914924023775232 |
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| author | Kalakanti, Arun Kumar Rao, Shrisha |
| author_facet | Kalakanti, Arun Kumar Rao, Shrisha |
| contents | Dynamic pricing is a promising strategy to address the challenges of smart charging, as traditional time-of-use (ToU) rates and stationary pricing (SP) do not dynamically react to changes in operating conditions, reducing revenue for charging station (CS) vendors and affecting grid stability. Previous studies evaluated single objectives or linear combinations of objectives for EV CS pricing solutions, simplifying trade-offs and preferences among objectives. We develop a novel formulation for the dynamic pricing problem by addressing multiple conflicting objectives efficiently instead of solely focusing on one objective or metric, as in earlier works. We find optimal trade-offs or Pareto solutions efficiently using Non-dominated Sorting Genetic Algorithms (NSGA) II and NSGA III. A dynamic pricing model quantifies the relationship between demand and price while simultaneously solving multiple conflicting objectives, such as revenue, quality of service (QoS), and peak-to-average ratios (PAR). A single method can only address some of the above aspects of dynamic pricing comprehensively. We present a three-part dynamic pricing approach using a Bayesian model, multi-objective optimization, and multi-criteria decision-making (MCDM) using pseudo-weight vectors. To address the research gap in CS pricing, our method selects solutions using revenue, QoS, and PAR metrics simultaneously. Two California charging sites' real-world data validates our approach. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2408_14169 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Dynamic Pricing for Electric Vehicle Charging Kalakanti, Arun Kumar Rao, Shrisha Distributed, Parallel, and Cluster Computing Artificial Intelligence Dynamic pricing is a promising strategy to address the challenges of smart charging, as traditional time-of-use (ToU) rates and stationary pricing (SP) do not dynamically react to changes in operating conditions, reducing revenue for charging station (CS) vendors and affecting grid stability. Previous studies evaluated single objectives or linear combinations of objectives for EV CS pricing solutions, simplifying trade-offs and preferences among objectives. We develop a novel formulation for the dynamic pricing problem by addressing multiple conflicting objectives efficiently instead of solely focusing on one objective or metric, as in earlier works. We find optimal trade-offs or Pareto solutions efficiently using Non-dominated Sorting Genetic Algorithms (NSGA) II and NSGA III. A dynamic pricing model quantifies the relationship between demand and price while simultaneously solving multiple conflicting objectives, such as revenue, quality of service (QoS), and peak-to-average ratios (PAR). A single method can only address some of the above aspects of dynamic pricing comprehensively. We present a three-part dynamic pricing approach using a Bayesian model, multi-objective optimization, and multi-criteria decision-making (MCDM) using pseudo-weight vectors. To address the research gap in CS pricing, our method selects solutions using revenue, QoS, and PAR metrics simultaneously. Two California charging sites' real-world data validates our approach. |
| title | Dynamic Pricing for Electric Vehicle Charging |
| topic | Distributed, Parallel, and Cluster Computing Artificial Intelligence |
| url | https://arxiv.org/abs/2408.14169 |