Competitive Online Scheduling Algorithms with Applications in Deadline-Constrained EV Charging

Alinia, Bahram; Talebi, Majid; Hajiesmaili, Mohammad H.; Yekkehkhany, Ali; Crespi, Noël

doi:10.1109/iwqos.2018.8624184

Cited by 18 publications

(16 citation statements)

References 21 publications

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“…Online fractional model: In our recent study [19], we proposed two online algorithms referred to as WFAIR and WRAND for EV scheduling problem. The proposed algorithms provide a competitive ratio of 2 − 1/U where U is "scarcity level".…”

Section: Worst-case Analysis In Similar Scheduling Problemsmentioning

confidence: 99%

“…However, the result only holds when all EVs have the same maximum charging rate. Also, [19] does not study the integral revenue model. In this paper, we propose FOCS as an online algorithm.…”

Section: Worst-case Analysis In Similar Scheduling Problemsmentioning

confidence: 99%

“…In addition, our algorithm outperforms the existing scheduling algorithm in Caltech ACN [7] by 35% for integral revenue model. This paper represents follow-up work to our previous study [19], where we address a simplified version of the problems studied in this paper in fractional revenue model. The competitive analysis in [19] is done under the assumption that all EVs have the same maximum charging rate.…”

Section: Introductionmentioning

confidence: 99%

“…This paper represents follow-up work to our previous study [19], where we address a simplified version of the problems studied in this paper in fractional revenue model. The competitive analysis in [19] is done under the assumption that all EVs have the same maximum charging rate. Also, [19] does not study the integral revenue model.…”

Section: Introductionmentioning

confidence: 99%

“…The competitive analysis in [19] is done under the assumption that all EVs have the same maximum charging rate. Also, [19] does not study the integral revenue model. In this paper, we extend the results and propose a 2-competitive online algorithm with no assumption on the input parameters.…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

Online EV Scheduling Algorithms for Adaptive Charging Networks with Global Peak Constraints

Alinia

Hajiesmaili

Lee

et al. 2022

IEEE Trans. Sustain. Comput.

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This paper tackles online scheduling of electric vehicles (EVs) in an adaptive charging network (ACN) with local and global peak constraints. Given the aggregate charging demand of the EVs and the peak constraints of the ACN, it might be infeasible to fully charge all the EVs according to their charging demand. Two alternatives in such resource-limited scenarios are to maximize the social welfare by partially charging the EVs (fractional model) or selecting a subset of EVs and fully charge them (integral model). The technical challenge is the need for online solution design since in practical scenarios the scheduler has no or limited information of future arrivals in a time-coupled underlying problem. For the fractional model, we devise both offline and online algorithms. We prove that the offline algorithm is optimal. Using competitive ratio as the performance measure, we prove the online algorithm achieves a competitive ratio of 2. The integral model, however, is more challenging since the underlying problem is strongly NP-hard due to 0/1 selection criteria of EVs. Hence, efficient solution design is challenging even in offline setting. For offline setting, we devise a low-complexity primal-dual scheduling algorithm that achieves a bounded approximation ratio. Built upon the offline approximate algorithm, we propose an online algorithm and analyze its competitive ratio in special cases. Extensive trace-driven experimental results show that the performance of the proposed online algorithms is close to the offline optimum, and outperform the existing solutions.

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Section: Worst-case Analysis In Similar Scheduling Problemsmentioning

confidence: 99%

Section: Worst-case Analysis In Similar Scheduling Problemsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Online EV Scheduling Algorithms for Adaptive Charging Networks with Global Peak Constraints

Alinia

Hajiesmaili

Lee

et al. 2022

IEEE Trans. Sustain. Comput.

Self Cite

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Robustness Comparison of Scheduling Algorithms in MapReduce Framework

Daghighi

Chen

2021

Lecture Notes in Networks and Systems

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A feedback‐integrated framework for resilient and distributed scheduling of electric vehicles under uncertain charging characteristics

Kandpal

Verma

2022

IET Energy Syst Integration

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Emerging innovation in smart charging for plug-in electric vehicles (EVs) has the potential to achieve significant economic benefits. In several works, smart charging encourages the use of EVs as a flexible resource by modifying their power consumption through a demand response (DR) program. However, it is promptly assumed that EVs are always responsive and accept the smart charging signals with no fault. In practice, due to uncertainties such as random EV mobility, volatile battery charging characteristics or charging component failures, some EVs would be unable to accept the assigned charging signals dispatched from a central server. Therefore, this article proposes a feedback loop to predict EV charging behaviours and thereby adaptively tune the time-based control signals dispatched to individual EVs. Moreover, a parallel-operating distributed DR algorithm is proposed which aims optimal EV scheduling under charging uncertainties while reducing the need of private information sharing. The proposed distributed algorithm allows increased EV user privacy, fast convergence properties and optimal operation under communication disruptions and delays. The effectiveness of the proposed methods are also numerically exhibited for varying penetration of EVs within a lowvoltage (LV) distribution test network.

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Competitive Online Scheduling Algorithms with Applications in Deadline-Constrained EV Charging

Cited by 18 publications

References 21 publications

Online EV Scheduling Algorithms for Adaptive Charging Networks with Global Peak Constraints

Online EV Scheduling Algorithms for Adaptive Charging Networks with Global Peak Constraints

Robustness Comparison of Scheduling Algorithms in MapReduce Framework

A feedback‐integrated framework for resilient and distributed scheduling of electric vehicles under uncertain charging characteristics

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