Complexity of Scheduling Charging in the Smart Grid

Weerdt, Mathijs de; Albert, Michael; Conitzer, Vincent; Linden, Koos van der

doi:10.24963/ijcai.2018/658

Cited by 9 publications

(10 citation statements)

References 24 publications

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“…Online integral model: Another direction is to tackle online scenario under integral revenue model, where the scheduling problem becomes fundamentally more challenging. We note that the integral scheduling problem is strongly NP-Hard even in the offline case [39]. Due to combined online and combinatorial challenges, there are very limited studies in this category.…”

Section: Worst-case Analysis In Similar Scheduling Problemsmentioning

confidence: 99%

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%

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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show abstract

“…Both these problems could be alleviated or reduced by smart grid control, for example by using flexibility in the demand for electricity. Several types of (potentially) flexible load have been identified: EVs typically need shorter to charge than the total time they spend parked, and can thus have their charge rate modulated or time periods shifted (De Weerdt et al, 2018). Heat pumps and air conditioners may also be shifted in time, because the indoor temperature changes gradually due to thermal inertia.…”

Section: Autonomous Agents Coordinating Their Resource Usagementioning

confidence: 99%

“…For example, in autonomous warehouses there are multiple robots which collect items for delivery, and these robots are constrained by, e.g., time constraints, locations, and facilities they use (Claes et al, 2017). Another example can be found in power distribution grids, in which aggregators control charging of multiple autonomous electric vehicles which are not allowed to violate distribution grid constraints (De Weerdt et al, 2018). Multiagent decision making with constraints also occurs in online advertising, in which a limited advertising budget should be assigned to online users in order to maximize conversion (Boutilier & Lu, 2016).…”

Section: Introductionmentioning

confidence: 99%

Constrained Multiagent Markov Decision Processes: a Taxonomy of Problems and Algorithms

Nijs¹,

Walraven

Weerdt

et al. 2021

jair

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In domains such as electric vehicle charging, smart distribution grids and autonomous warehouses, multiple agents share the same resources. When planning the use of these resources, agents need to deal with the uncertainty in these domains. Although several models and algorithms for such constrained multiagent planning problems under uncertainty have been proposed in the literature, it remains unclear when which algorithm can be applied. In this survey we conceptualize these domains and establish a generic problem class based on Markov decision processes. We identify and compare the conditions under which algorithms from the planning literature for problems in this class can be applied: whether constraints are soft or hard, whether agents are continuously connected, whether the domain is fully observable, whether a constraint is momentarily (instantaneous) or on a budget, and whether the constraint is on a single resource or on multiple. Further we discuss the advantages and disadvantages of these algorithms. We conclude by identifying open problems that are directly related to the conceptualized domains, as well as in adjacent research areas.

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“…The resulting scheduling problem is computationally challenging under conditions such as a shared constraint on multiple loads with already complete information [27]. It is therefore relevant to benchmark methods not only regarding the costs/benefits and unmet demands of the resulting trading decisions, but also their computational performances (i.e., runtime of the algorithm).…”

Section: Type Of Load and Its Flexibilitymentioning

confidence: 99%

Benchmarking Flexible Electric Loads Scheduling Algorithms

Linden¹,

Romero²,

Weerdt³

2021

Energies

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Due to increasing numbers of intermittent and distributed generators in power systems, there is an increasing need for demand responses to maintain the balance between electricity generation and use at all times. For example, the electrification of transportation significantly adds to the amount of flexible electricity demand. Several methods have been developed to schedule such flexible energy consumption. However, an objective way of comparing these methods is lacking, especially when decisions are made based on incomplete information which is repeatedly updated. This paper presents a new benchmarking framework designed to bridge this gap. Surveys that classify flexibility planning algorithms were an input to define this benchmarking standard. The benchmarking framework can be used for different objectives and under diverse conditions faced by electricity production stakeholders interested in flexibility scheduling algorithms. Our contribution was implemented in a software toolbox providing a simulation environment that captures the evolution of look-ahead information, which enables comparing online planning and scheduling algorithms. This toolbox includes seven planning algorithms. This paper includes two case studies measuring the performances of these algorithms under uncertain market conditions. These case studies illustrate the importance of online decision making, the influence of data quality on the performance of the algorithms, the benefit of using robust and stochastic programming approaches, and the necessity of trustworthy benchmarking.

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Complexity of Scheduling Charging in the Smart Grid

Cited by 9 publications

References 24 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

Constrained Multiagent Markov Decision Processes: a Taxonomy of Problems and Algorithms

Benchmarking Flexible Electric Loads Scheduling Algorithms

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