Real-time stochastic optimal scheduling of large-scale electric vehicles: A multidimensional approximate dynamic programming approach

Pan, Zhenning; Yu, Tao; Chen, Li; Yang, Bo; Wang, B.; Guo, Weisi

doi:10.1016/j.ijepes.2019.105542

Cited by 29 publications

(11 citation statements)

References 27 publications

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“…In the scenario of large EV integration, uncoordinated operation may deteriorate power network operations, likely to result in transformer overloading or dissatisfied power quality. As a result, large-scale EV real-time charging optimization (RTCO) should be considered, because individual EV charging behavior, conventional load profiles, renewable generation output, and real-time price (RTP) are highly stochastic, which is typically a resource scheduling approach with multivariable epistemic uncertainties [25,26]. Table 2 shows the review of the study on electric vehicle access to the distribution network.…”

Section: Figure 3 Overview Of Ev Charge Scheduling Approachesmentioning

confidence: 99%

Optimization Strategies for Electric Vehicle Charging and Routing: A Comprehensive Review

Shanmugam,

Thomas

2024

Gazi University Journal of Science

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Based on information from recent research, by 2045, Electric Vehicles (EV) will dominate the roads with presence of more than 80% of its kind. Hence, these vehicles' grid level penetration will increase proportionally, which challenges the existing grid infrastructure in terms of its reliability and energy management capabilities. New techniques to store and consume massive quantities of energy from the power grid, as well as infusing the captive energy within the EV in response to grid demands, are emerging with the advent of electric vehicles. Everything could be handled smoothly only if we schedule the EV operation (charging/discharging) more optimally and efficiently using scheduling algorithms. Despite the existence of many routings and charging schedule computations, nature-inspired optimization approaches might play a critical role in responding to such routing challenges. Researchers have created several optimum scheduling approaches, such as Dynamic Programming, Differential Evolutionary Optimization Techniques, Collaborative Optimization Scheduling, Two-stage optimal scheduling strategy, and so on. The optimum schedule review examines the operation of an EV fleet while considering uncertainty sources and varied EV operating circumstances by integrating heuristic and meta-heuristic techniques. This paper exhibits a deep review on the various EV optimal scheduling techniques and adopted algorithms which are the emerging best practices like predictive analytics, dynamic routing, user centric planning, multi-objective optimization, etc. that reflect the industry's focus on leveraging advanced technologies, data-driven decision-making, and collaborative approaches to enhance the efficiency and sustainability of electric vehicle routing and charging scheduling.

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Section: Figure 3 Overview Of Ev Charge Scheduling Approachesmentioning

confidence: 99%

Optimization Strategies for Electric Vehicle Charging and Routing: A Comprehensive Review

Shanmugam,

Thomas

2024

Gazi University Journal of Science

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“…In studies [9][10][11][12][13][14][15], a heterogeneous EV fleet is considered. However, the studies assume that the required EV specific details are known by the control system, and this assumption is not further discussed.…”

Section: Properties Of the Evsmentioning

confidence: 99%

“…Bidirectional communication between upper and lower layer is assumed in most studies shown in Table 1. Generally, for example in [9–13, 20, 24–32], the communication from the lower layer to the upper layer is used to describe the required charging demand, and the communication from the upper layer to the lower layer is used to ensure that the grid‐related limitations or objectives are being considered. Alternatively, in [33, 34], Stackelberg games are considered, where the upper layer indirectly controls the decisions of the lower layers using their decisions as a feedback.…”

Section: Related Researchmentioning

confidence: 99%

Communicational aspects in hierarchical real‐time control of electric vehicle charging: Available information and its value

Simolin

Rauma

Rautiainen

et al. 2023

IET Generation Trans & Dist

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Smart electric vehicle (EV) charging is a widely studied topic, and multiple different solutions have been proposed in the scientific literature to execute various demand response objectives. However, most studies use assumptions and simplifications that may not hold in real‐life situations, and thus, the proposed solutions may have a diminished practical value. Here, communicational aspects of hierarchical EV charging control are studied. The study considers the availability, acquisition, and value of certain information in case of real‐time control. The paper illustrates potential pitfalls when using certain assumptions or simplifications in the simulations and proposes a novel algorithm to deal with the lack of preliminary information that exists in real‐life situations. The objective of the proposed algorithm is to allow a lower‐layer controller to track the real‐time controllability of the EVs. This information is necessary in hierarchical control solutions with multiple lower‐layer controllers controlled by an upper‐layer controller to ensure efficient operation. The simulation results shows that a lower layer controller can estimate the controllability of the EV charging in realistic conditions within 1%–4 % accuracy despite the initially unknown characteristics of EVs.

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“…For example, Yao et al [40] presented a three-layer control architecture where the top and middle layers are centralized aggregators. References [41], [42] employed the same architecture. In [43]- [45], the top layer is a decentralized aggregator: the nodes are aggregated by centralized aggregators, and then distributed algorithms, more specifically, the game theory approach is applied to regulate the centralized aggregators.…”

Section: A Control Algorithms and Architecturesmentioning

confidence: 99%

Electric Vehicle Load Management: An Architecture for Heterogeneous Nodes

et al. 2022

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Integrating electric vehicle (EV) charging infrastructures into the utility grids requires solutions for coordinated charging of EVs. Load management systems aim at computing coordinated charging schedules for electric vehicles based on predetermined charging objectives. Contributions on coordinated charging for EVs predominantly assume that the EVs or the charging stations are controllable entities in the load management systems. However, in practice, charging infrastructures may consist of controllable and uncontrollable entities. This paper proposes architecture and control strategies for EV charging infrastructures consisting of controllable and uncontrollable entities. Simulations based on realworld charging sessions show how the share of uncontrollable entities in a charging infrastructure affects the performance of different control strategies in the system architecture. We show that a certain number of uncontrollable entities in a charging infrastructure does not affect the scheduling objectives significantly. EV fleet and charging infrastructure operators can develop pragmatic investment and operation strategies based on the proposed control strategy and architecture.INDEX TERMS electric vehicles, scheduling strategy, aggregator, control architecture.

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Real-time stochastic optimal scheduling of large-scale electric vehicles: A multidimensional approximate dynamic programming approach

Cited by 29 publications

References 27 publications

Optimization Strategies for Electric Vehicle Charging and Routing: A Comprehensive Review

Optimization Strategies for Electric Vehicle Charging and Routing: A Comprehensive Review

Communicational aspects in hierarchical real‐time control of electric vehicle charging: Available information and its value

Electric Vehicle Load Management: An Architecture for Heterogeneous Nodes

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