Robust Scheduling of EV Charging Load With Uncertain Wind Power Integration

Huang, Qilong; Jia, Qing‐Shan; Guan, Xiaohong

doi:10.1109/tsg.2016.2574799

Cited by 81 publications

(20 citation statements)

References 22 publications

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“…Various methods have been developed for the optimization problem of the FO, such as linear programming [103], nonlinear programming [104], branch-andbound/branch-and-cut algorithms [105], stochastic programming [106], Benders decomposition [107], and robust optimization [108], just to name a few. When there is a renewable energy system, Kou et al [109] and Guo et al [110] applied model predictive control.…”

Section: Centralized Schedulingmentioning

confidence: 99%

A review on charging behavior of electric vehicles: data, model, and control

Jia

2020

Control Theory Technol.

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The adoption and usage of electric vehicles (EVs) have emerged recently due to the increasing concerns on the greenhouse gas issues and energy revolution. As a part of the smart grid, EVs can provide valuable ancillary services beyond consumers of electricity. However, EVs are gradually considered as nonnegligible loads due to their increasing penetration, which may result in negative effects such as voltage deviations, lines saturation, and power losses. Relationship and interaction among EVs, charging stations, and micro grid have to be considered in the next generation of smart grid. Therefore, the topic of smart charging has been the focus of many works where a wide range of control methods have been developed. As one of the bases of simulation, the EV charging behavior and characteristics have also become the focus of many studies. In this work, we review the charging behavior of EVs from the aspects of data, model, and control. We provide the links for most of the data sets reviewed in this work, based on which interested researchers can easily access these data for further investigation.

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Section: Centralized Schedulingmentioning

confidence: 99%

A review on charging behavior of electric vehicles: data, model, and control

Jia

2020

Control Theory Technol.

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“…A robust stochastic shortest path model to stochastically match the EV charging load with the wind supply was studied in Ref. [13]. Ref.…”

Section: Introductionmentioning

confidence: 99%

“…Moreover, on the uncertainties of EVs included in MES, some work used the deterministic modeling to formulate the scheduling of MES, without further discussing the stochastic model of EV availability and driving pattern [7,9,11]; in other work, although the stochastics were considered, the coordination between electricity and thermal multi-energy was involved simply. As for the costs of EVs' batteries degradation, little research included it in the coupled scheduling optimization of multi-energy, which is important for realizing the high efficiency operation control for CHP units with the unmatched heating and electric power demands [12][13][14][15][16][17][18][19].…”

Section: Introductionmentioning

confidence: 99%

Stochastic Model Predictive Control Based Scheduling Optimization of Multi-Energy System Considering Hybrid CHPs and EVs

2019

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Recently, the increasing integration of electric vehicles (EVs) has drawn great interest due to its flexible utilization; moreover, environmental concerns have caused an increase in the application of combined heat and power (CHP) units in multi-energy systems (MES). This paper develops an approach to coordinated scheduling of MES considering CHPs, uncertain EVs and battery degradation based on model predictive control (MPC), aimed at achieving the most economic energy scheduling. After exploiting the pattern of the drivers’ commuting behavior, the stochastic characteristics of available charging/discharging electric power of aggregated EVs in office or residential buildings are analyzed and represented by the scenarios with the help of scenario generation and reduction techniques. At each step of MPC optimization, the solution of a finite-horizon optimal control is achieved in which a suitable number of available EVs scenarios is considered, while the economic objective and operational constraints are included. The simulation results obtained are encouraging and indicate both the feasibility and the effectiveness of the proposed approach.

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“…Many studies have been conducted to consider the uncertainties in weather forecasting for optimal EV scheduling or load management . Su et al constructed a MILP model to optimize the day‐ahead plug‐in electric vehicle charging, showing that a controlled charging schedule yielded a much better economical return than an uncontrolled one.…”

Section: Introductionmentioning

confidence: 99%

Synergic integration of desalination and electric vehicle loads with hybrid micro‐grid sizing and control: An Island Case Study

Rajarathnam

Vassallo

2019

Energy Storage

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This study addresses the feasibility, sizing, and scheduling of an island microgrid involving photovoltaic, wind turbine, diesel generator, and battery energy storage systems. In addition, the end-use demand is expanded beyond residential electricity loads, as the full electrification on domestic water supply and transportation, in the forms of desalination and electric vehicles (EVs), are proposed in this study with integration to a renewable energy system focused micro-grid. A mixed integer linear programming model is constructed to explore the mutual interactions of desalination plant, vehicle-to-grid enabled EVs and residential load deferring on a micro-grid. A case study in Bruny Island, Australia was incorporated in the study, based on the existing infrastructure and future planning. Multiple scenarios were run utilizing real-world, fine resolution time-series data. Results were compared to investigate the impact of the polygeneration configuration and potential economic savings on household affordability. Our study displayed the advantage of electrifying and integrating household resources into the micro-grid, such as transportation and water filtration. The integration created a greater diversity in electricity end-uses, allowed for better scheduling and Renewable System management. In addition, the cost-benefit analysis of stationary battery storage units was modeled against the other forms of energy storage, such as electric vehicles and seawater desalination plant, with findings suggesting the potential economic advantage of storage other than grid connected batteries in a residential precinct.

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Robust Scheduling of EV Charging Load With Uncertain Wind Power Integration

Cited by 81 publications

References 22 publications

A review on charging behavior of electric vehicles: data, model, and control

A review on charging behavior of electric vehicles: data, model, and control

Stochastic Model Predictive Control Based Scheduling Optimization of Multi-Energy System Considering Hybrid CHPs and EVs

Synergic integration of desalination and electric vehicle loads with hybrid micro‐grid sizing and control: An Island Case Study

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