Minimizing Cost-Plus-Dissatisfaction in Online EV Charging Under Real-Time Pricing

Lin, Qiulin; Yi, Hanling; Chen, Minghua

doi:10.1109/tits.2021.3114537

Cited by 14 publications

(7 citation statements)

References 40 publications

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“…This paper focuses on the online settings where EVs arrive one by one and one must decide the scheduling of each EV upon its arrival without knowing the future information. In online EV charging, one stream of works studies how to charge EVs under unknown future electricity prices [9,29,11,5,10,21,18]. In [9], the goal is to minimize determine whether or not to accept the request and, if so, when (scheduling) and where (station assignment) to charge the vehicle.…”

Section: Related Workmentioning

confidence: 99%

“…Dynamic pricing allows these platforms to adjust their charging demands to match available resources, i.e., the transformers' capacity, while carefully scheduling which users receive resources at which time can further improve revenue and efficiency. Yet despite considerable prior work on both pricing and scheduling problems for adaptive EV charging networks [18,32,33], there is still little rigorous theoretical understanding on how they can be jointly optimized to improve social welfare and ensure platform profitability.…”

Section: Introductionmentioning

confidence: 99%

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Near-optimal Online Algorithms for Joint Pricing and Scheduling in EV Charging Networks

Roozbeh¹,

Sun²,

Joe‐Wong³

et al. 2023

Preprint

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With the rapid acceleration of transportation electrification, public charging stations are becoming vital infrastructure in a smart sustainable city to provide on-demand electric vehicle (EV) charging services. As more consumers seek to utilize public charging services, the pricing and scheduling of such services will become vital, complementary tools to mediate competition for charging resources. However, determining the right prices to charge is difficult due to the online nature of EV arrivals. This paper studies a joint pricing and scheduling problem for the operator of EV charging networks with limited charging capacity and time-varying energy cost. Upon receiving a charging request, the operator offers a price, and the EV decides whether to admit the offer based on its own value and the posted price. The operator then schedules the real-time charging process to satisfy the charging request if the EV admits the offer. We propose an online pricing algorithm that can determine the posted price and EV charging schedule to maximize social welfare, i.e., the total value of EVs minus the energy cost of charging stations. Theoretically, we prove the devised algorithm can achieve the order-optimal competitive ratio under the competitive analysis framework. Practically, we show the empirical performance of our algorithm outperforms other benchmark algorithms in experiments using real EV charging data.

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Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Near-optimal Online Algorithms for Joint Pricing and Scheduling in EV Charging Networks

Roozbeh¹,

Sun²,

Joe‐Wong³

et al. 2023

Preprint

View full text Add to dashboard Cite

show abstract

“…For this reason we consider the continuous version of the EV game. Furthermore, we consider a symmetric setting where all users experience same dissatisfaction and hence are charged same prices akin to setting considered in Lin et al (2021). Formally, the game is as follows, Player i selects a quantity q i ∈ [0, M ] in peak time (peak time plug-in) and M − q i (assuming M is the all-day demand and same for all players) in non-peak time.…”

Section: Mediation/co-ordinationmentioning

confidence: 99%

“…The motivation for capturing dissatisfaction explicitly is justified because price as an instrument to control charging behavior is only possible when users are (or are not) willing to pay to avoid dissatisfaction. In fact, more recently user dissatisfaction is explicitly modeled within an algorithmic charging decision-making set-up (Lin et al, 2021). Similarly, Wu et al (2022) uses the term inconvenience cost in the same sense and illustrate optimal mechanisms for EV charging at public stations.…”

mentioning

confidence: 99%

Pricing and Electric Vehicle Charging Equilibria

et al. 2022

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“…Competitive online optimization is a fundamental tool for decision making with uncertainty. We have witnessed its wide applications spreading from EV charging [1][2][3][4], micro-grid operations [5,6], energy storage scheduling [7,8] to data center provisioning [9,10], network optimization [11,12], and beyond. Theoretically, there are multiple paradigms of general interest in the online optimization literature.…”

Section: Introductionmentioning

confidence: 99%

Competitive Online Optimization with Multiple Inventories: A Divide-and-Conquer Approach

Lin¹,

Su²,

Chen³

2022

Preprint

Self Cite

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We study a competitive online optimization problem with multiple inventories. In the problem, an online decision maker seeks to optimize the allocation of multiple capacity-limited inventories over a slotted horizon, while the allocation constraints and revenue function come online at each slot. The problem is challenging as we need to allocate limited inventories under adversarial revenue functions and allocation constraints, while our decisions are coupled among multiple inventories and different slots. We propose a divide-and-conquer approach that allows us to decompose the problem into several single inventory problems and solve it in a two-step manner with almost no optimality loss in terms of competitive ratio (CR). Our approach provides new angles, insights and results to the problem, which differs from the widely-adopted primal-and-dual framework. Specifically, when the gradients of the revenue functions are bounded in a positive range, we show that our approach can achieve a tight CR that is optimal when the number of inventories is small, which is better than all existing ones. For an arbitrary number of inventories, the CR we achieve is within an additive constant of one to a lower bound of the best possible CR among all online algorithms for the problem. We further characterize a general condition for generalizing our approach to different applications. For example, for a generalized one-way trading problem with price elasticity, where no previous results are available, our approach obtains an online algorithm that achieves the optimal CR up to a constant factor.

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Minimizing Cost-Plus-Dissatisfaction in Online EV Charging Under Real-Time Pricing

Cited by 14 publications

References 40 publications

Near-optimal Online Algorithms for Joint Pricing and Scheduling in EV Charging Networks

Near-optimal Online Algorithms for Joint Pricing and Scheduling in EV Charging Networks

Pricing and Electric Vehicle Charging Equilibria

Competitive Online Optimization with Multiple Inventories: A Divide-and-Conquer Approach

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