Balancing Cost and Dissatisfaction in Online EV Charging under Real-time Pricing

Yi, Hanling; Lin, Qiulin; Chen, Minghua

doi:10.1109/infocom.2019.8737558

Cited by 22 publications

(10 citation statements)

References 14 publications

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“…Furthermore, there is an interesting algorithmic framework, called CR-Pursuit. It leads to competitive algorithms with given CRs and has proven useful in Lin et al (2019) and Yi et al (2019). Motivated by these pioneering results, we propose online peak-minimizing storage-discharging algorithms parameterized by prescribed CRs, which we "pursue" with online actions in each decision-making round.…”

Section: Online Competitive Analysismentioning

confidence: 99%

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Optimal Online Peak Minimization Using Energy Storage

Lin¹,

Chen²,

Qin³

2021

Preprint

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We study the problem of online peak minimization under inventory constraints. It is motivated by the emerging scenario where large-load customers utilize energy storage to reduce the peak procurement from the grid, which accounts for up to 90% of their electric bills. The problem is uniquely challenging due to (i) the coupling of online decisions across time imposed by the inventory constraints and (ii) the noncumulative nature of the peak procurement. In this paper, we develop an optimal online algorithm for the problem that attains the best possible competitive ratio (CR) among all deterministic and randomized algorithms. We show that the optimal CR can be computed in polynomial time, by solving a linear number of linear-fractional problems. We also generalize our approach to develop an anytime-optimal online algorithm that achieves the best possible CR at any epoch, given the inputs and online decisions so far. The algorithm retains the optimal worst-case performance and achieves adaptive average-case performance. Simulation results based on real-world traces show that our algorithms improve peak reduction by more than 19% as compared to baseline alternatives.

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Section: Online Competitive Analysismentioning

confidence: 99%

“…Although the idea behind CR-Pursuit is simple and intuitive, designing an online algorithm under the framework is non-trivial (Yi et al 2019.…”

Section: Overview Of the Cr-pursuit Framework And The Challengesmentioning

confidence: 99%

Optimal Online Peak Minimization Using Energy Storage

Lin¹,

Chen²,

Qin³

2021

Preprint

Self Cite

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“…Online competitive analysis. Online competitive analysis has been useful in electric vehicle charging [33,41,44] and economic dispatching [9,43]. The competitive analyses of cost minimization and benefit maximization have different challenges, as discussed in [29] and [41].…”

Section: Related Workmentioning

confidence: 99%

“…Online competitive analysis has been useful in electric vehicle charging [33,41,44] and economic dispatching [9,43]. The competitive analyses of cost minimization and benefit maximization have different challenges, as discussed in [29] and [41]. Although we can easily transform the problem from one to the other in the offline scenario, solutions and results for one problem may not directly apply to its counterpart.…”

Section: Related Workmentioning

confidence: 99%

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Optimal Online Algorithms for Peak-Demand Reduction Maximization with Energy Storage

Mo,

Lin,

Chen

et al. 2021

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The high proportions of demand charges in electric bills motivate large-power customers to leverage energy storage for reducing the peak procurement from the outer grid. Given limited energy storage, we expect to maximize the peak-demand reduction in an online fashion, challenged by the highly uncertain demands and renewable injections, the non-cumulative nature of peak consumption, and the coupling of online decisions. In this paper, we propose an optimal online algorithm that achieves the best competitive ratio, following the idea of maintaining a constant ratio between the online and the optimal offline peak-reduction performance. We further show that the optimal competitive ratio can be computed by solving a linear number of linear-fractional programs. Moreover, we extend the algorithm to adaptively maintain the best competitive ratio given the revealed inputs and actions at each decision-making round. The adaptive algorithm retains the optimal worst-case guarantee and attains improved average-case performance. We evaluate our proposed algorithms using real-world traces and show that they obtain up to 81% peak reduction of the optimal offline benchmark. Additionally, the adaptive algorithm achieves at least 20% more peak reduction against baseline alternatives.

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