Computational scheduling methods for integrating plug-in electric vehicles with power systems: A review

Yang, Zhile; Li, Kang; Foley, Aoife

doi:10.1016/j.rser.2015.06.007

Cited by 194 publications

(92 citation statements)

References 190 publications

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“…Depending on the perspective (FOs or GOs), the process of charging management can be formulated as different optimization problems. Mathematically, these problems are modeled and solved using different approaches, such as linear programming, quadratic programming, dynamic programming, mixed-integer programming, nonlinear programming, stochastic programming, robust optimization, heuristic and meta-heuristic algorithms, and model predictive control, which have been intensively reviewed by several articles [72,73]. In this part, we only briefly review the objectives of different charging strategies and elaborate on what additional constraints (with respect to SMs) need to be considered when modeling the charging strategies based on the basic characteristics of the PEVF.…”

Section: Charging Management Strategiesmentioning

confidence: 99%

An Overview of Modeling Approaches Applied to Aggregation-Based Fleet Management and Integration of Plug-in Electric Vehicles †

You¹,

Hu²,

Ziras³

2016

Energies

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Abstract:The design and implementation of management policies for plug-in electric vehicles (PEVs) need to be supported by a holistic understanding of the functional processes, their complex interactions, and their response to various changes. Models developed to represent different functional processes and systems are seen as useful tools to support the related studies for different stakeholders in a tangible way. This paper presents an overview of modeling approaches applied to support aggregation-based management and integration of PEVs from the perspective of fleet operators and grid operators, respectively. We start by explaining a structured modeling approach, i.e., a flexible combination of process models and system models, applied to different management and integration studies. A state-of-the-art overview of modeling approaches applied to represent several key processes, such as charging management, and key systems, such as the PEV fleet, is then presented, along with a detailed description of different approaches. Finally, we discuss several considerations that need to be well understood during the modeling process in order to assist modelers and model users in the appropriate decisions of using existing, or developing their own, solutions for further applications.

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Section: Charging Management Strategiesmentioning

confidence: 99%

An Overview of Modeling Approaches Applied to Aggregation-Based Fleet Management and Integration of Plug-in Electric Vehicles †

You¹,

Hu²,

Ziras³

2016

Energies

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“…However, the current power grids in many countries are not fully prepared for high EV penetration, and the construction of additional grid capacity is constantly outpaced by EV diffusion [6][7][8][9]. If this situation continues, it will compromise grid reliability and cause problems, such as voltage and frequency fluctuations and power losses [10][11][12]. This is especially true for densely populated areas, as shown in Figure 2 [13,14], where the grid capacity is already strained with the existing infrastructure.…”

Section: Introductionmentioning

confidence: 99%

An Agent-Based Model for Zip-Code Level Diffusion of Electric Vehicles and Electricity Consumption in New York City

Ahkamiraad¹,

Wang²

2018

Energies

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Current power grids in many countries are not fully prepared for high electric vehicle (EV) penetration, and there is evidence that the construction of additional grid capacity is constantly outpaced by EV diffusion. If this situation continues, then it will compromise grid reliability and cause problems such as system overload, voltage and frequency fluctuations, and power losses. This is especially true for densely populated areas where the grid capacity is already strained with existing old infrastructure. The objective of this research is to identify the zip-code level electricity consumption that is associated with large-scale EV adoption in New York City, one of the most densely populated areas in the United States (U.S.). We fuse the Fisher and Pry diffusion model and Rogers model within the agent-based simulation to forecast zip-code level EV diffusion and the required energy capacity to satisfy the charging demand. The research outcomes will assist policy makers and grid operators in making better planning decisions on the locations and timing of investments during the transition to smarter grids and greener transportation.

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“…Thus, a reliable optimization algorithm [20][21][22][23] is more suitable for the trajectory planning. Furthermore, both of the overshoot and error should be considered in the optimization object, so a multiobjective optimization based on PSO [24][25][26][27][28][29] (particle swarm optimization) is adopted in this paper.…”

Section: Introductionmentioning

confidence: 99%

Online‐Offline Optimized Motion Profile for High‐Dynamic Positioning of Ultraprecision Dual Stage

2018

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The wafer stage in dual-stage lithographic system is an air-bearing servo motion platform requiring high positioning accuracy and high transient performance. However, the residual vibration, resulting from almost zero damping, high velocity, parallel decoupling structure, and direct drive, brings about unacceptable overshoot and settling time. To suppress these unfavorable elements in high dynamic motion, a novel motion profile planning method combined with input shaping is proposed in this paper. Firstly, a trajectory named all free S-curve (AFS-curve) is derived, which has less constraints and better performance than traditional S-curve profile. Then, AFS-curve combined with a zero vibration shaper (ZV) is developed to further suppress residual vibration. Due to the very complex parameter adjustment, the online tuning may cause system oscillation that leads to damage of the precision stage. This paper, furthermore, proposes an online-offline method to optimize the parameters in the motion profile. Online step is performed to collect input and output data. Offline step includes the system model identification based on I/O data and parameter self-learning based on particle swarm optimization (PSO). The simulation and experimental results indicate that the proposed method achieves significant reduction of the positioning time and the overshoot in the dual-stage system.

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Computational scheduling methods for integrating plug-in electric vehicles with power systems: A review

Cited by 194 publications

References 190 publications

An Overview of Modeling Approaches Applied to Aggregation-Based Fleet Management and Integration of Plug-in Electric Vehicles †

An Overview of Modeling Approaches Applied to Aggregation-Based Fleet Management and Integration of Plug-in Electric Vehicles †

An Agent-Based Model for Zip-Code Level Diffusion of Electric Vehicles and Electricity Consumption in New York City

Online‐Offline Optimized Motion Profile for High‐Dynamic Positioning of Ultraprecision Dual Stage

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