Pricing EV charging service with demand charge

Lee, Zachary J.; Pang, John Z. F.; Low, Steven H.

doi:10.1016/j.epsr.2020.106694

Cited by 46 publications

(30 citation statements)

References 6 publications

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“…First, we reviewed the extant literature to capture the most important potential behavioral issues for EV drivers. This led to identification of Range anxiety, 6 , 49 , 50 , 51 , 52 Dealership practices, 53 , 54 , 55 Cost, 6 , 52 , 56 , 57 , 58 Service time, 6 , 52 , 56 , 58 Availability issues, 59 , 60 User interaction, 61 , 62 , 63 station Functionality, 11 , 58 , 64 and Location. 11 Second, to find evidence of the importance of these topics from the data, we hand-coded 8,953 randomly selected reviews to validate the 8 topics from prior literature and used these to generate 34 sub-topics for classification.…”

Section: Methodsmentioning

confidence: 99%

Topic classification of electric vehicle consumer experiences with transformer-based deep learning

Marchetto

Dharur

et al. 2021

Patterns

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Summary The transportation sector is a major contributor to greenhouse gas (GHG) emissions and is a driver of adverse health effects globally. Increasingly, government policies have promoted the adoption of electric vehicles (EVs) as a solution to mitigate GHG emissions. However, government analysts have failed to fully utilize consumer data in decisions related to charging infrastructure. This is because a large share of EV data is unstructured text, which presents challenges for data discovery. In this article, we deploy advances in transformer-based deep learning to discover topics of attention in a nationally representative sample of user reviews. We report classification accuracies greater than 91% (F1 scores of 0.83), outperforming previously leading algorithms in this domain. We describe applications of these deep learning models for public policy analysis and large-scale implementation. This capability can boost intelligence for the EV charging market, which is expected to grow to US$27.6 billion by 2027.

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Section: Methodsmentioning

confidence: 99%

Topic classification of electric vehicle consumer experiences with transformer-based deep learning

Marchetto

Dharur

et al. 2021

Patterns

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“…Our work is unique from these and other MPC approaches in that we consider unbalanced three-phase infrastructure constraints, non-ideal battery behavior, and provide a modular approach with many objectives and regularizers that can be mixed and matched for a particular application. We also use a unique approach to accounting for demand charge which allows us to trade off between demand charge and energy cost by prorating the demand charge over the remaining days in the month, this approach was first proposed in [20].…”

Section: Related Workmentioning

confidence: 99%

“…[21] clusters sessions based on their charging behavior using time series of charging current collected from the ACN. [20] proposes a pricing scheme to allocate costs (including demand charge) to charging sessions. [22], [23] use data to quantify fleet-level flexibility within a charging facility, and Fig.…”

Section: Related Workmentioning

confidence: 99%

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Adaptive Charging Networks: A Framework for Smart Electric Vehicle Charging

Lee

Lee²,

Lee³

et al. 2021

IEEE Trans. Smart Grid

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We describe the architecture and algorithms of the Adaptive Charging Network (ACN), which was first deployed on the Caltech campus in early 2016 and is currently operating at over 100 other sites in the United States. The architecture enables real-time monitoring and control and supports electric vehicle (EV) charging at scale. The ACN adopts a flexible Adaptive Scheduling Algorithm based on convex optimization and model predictive control and allows for significant over-subscription of electrical infrastructure. We describe some of the practical challenges in real-world charging systems, including unbalanced three-phase infrastructure, non-ideal battery charging behavior, and quantized control signals. We demonstrate how the Adaptive Scheduling Algorithm handles these challenges, and compare its performance against baseline algorithms from the deadline scheduling literature using real workloads recorded from the Caltech ACN and accurate system models. We find that in these realistic settings, our scheduling algorithm can improve operator profit by 3.4 times over uncontrolled charging and consistently outperforms baseline algorithms when delivering energy in highly congested systems.

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“…en, from the perspective of consumer satisfaction, grid company profits, and grid load variation, a TOU optimization model for EVs is constructed [26]. On the contrary, a pricing scheme is proposed assigning a per-session price to each charging session to accommodate the energy cost, demand charge, and system congestion [27]. e current pricing strategies for the charging stations mainly focus on the fixed electricity price or TOU price, which may cause congestion in local charging stations and undesirable peak loads on the grid.…”

Section: Introductionmentioning

confidence: 99%

Multiobjective Optimization of Large‐Scale EVs Charging Path Planning and Charging Pricing Strategy for Charging Station

Hou

Luo

et al. 2021

Complexity

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With the increasing number of electric vehicles (EVs), the charging demand of EVs has brought many new research hotspots, i.e., charging path planning and charging pricing strategy of the charging stations. In this paper, an integrated framework is proposed for multiobjective EV path planning with varied charging pricing strategies, considering the driving distance, total time consumption, energy consumption, charging fee such factors, while the charging pricing strategy is designed based on the objectives of maximizing the total revenues of the charging stations and balancing the profits of the charging stations. First, the energy consumption model of EVs, the M/M/S queuing model of charging stations, and the charging model of charging piles are established. A novel charging path planning algorithm is proposed based on bidirectional Martins’ algorithm, which can assist EV users to select charging stations and plan charging paths. Then, a particle swarm optimization (PSO) algorithm is applied to solve the optimal solution of charging station pricing designation. Finally, the method proposed in the paper is simulated on the street map of Shenzhen to verify the efficacy of the multiobjective charging path planning for EVs and the feasibility of the charging pricing strategy.

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Pricing EV charging service with demand charge

Cited by 46 publications

References 6 publications

Topic classification of electric vehicle consumer experiences with transformer-based deep learning

Topic classification of electric vehicle consumer experiences with transformer-based deep learning

Adaptive Charging Networks: A Framework for Smart Electric Vehicle Charging

Multiobjective Optimization of Large‐Scale EVs Charging Path Planning and Charging Pricing Strategy for Charging Station

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