A Hierarchical Decomposition Approach for Coordinated Dispatch of Plug-in Electric Vehicles

Yao, Weifeng; Zhao, Junhua; Wen, Fushuan; Xue, Yusheng; Ledwich, Gerard

doi:10.1109/tpwrs.2013.2256937

Cited by 203 publications

(109 citation statements)

References 25 publications

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“…Accordingly, EV owners need to plug EVs into the power system concerned and to follow the charging/discharging scheduling in given time periods. EVs in the VPP are managed by a day-ahead submitting mechanism [29], in which EV owners submit the next-day usage information of EVs according to their preference, including the plug-in time, the plug-out time, the expected initial state of charge (SOC) and the target SOC when plugging out. The EMS receives the submitted information and determines the charging/discharging schedules of EVs of the next day.…”

Section: The Dispatching Framework Of the Vppmentioning

confidence: 99%

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Optimal Dispatch of a Virtual Power Plant Considering Demand Response and Carbon Trading

Liu

Zheng

et al. 2018

Energies

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Abstract:The implementation of demand response (DR) could contribute to significant economic benefits meanwhile simultaneously enhancing the security of the concerned power system. A well-designed carbon emission trading mechanism provides an efficient way to achieve emission reduction targets. Given this background, a virtual power plant (VPP) including demand response resources, gas turbines, wind power and photovoltaics with participation in carbon emission trading is examined in this work, and an optimal dispatching model of the VPP presented. First, the carbon emission trading mechanism is briefly described, and the framework of optimal dispatching in the VPP discussed. Then, probabilistic models are utilized to address the uncertainties in the predicted generation outputs of wind power and photovoltaics. Demand side management (DSM) is next implemented by modeling flexible loads such as the chilled water thermal storage air conditioning systems (CSACSs) and electric vehicles (EVs). On this basis, a mixed integer linear programming (MILP) model for the optimal dispatching problem in the VPP is established, with an objective of maximizing the total profit of the VPP considering the costs of power generation and carbon emission trading as well as charging/discharging of EVs. Finally, the developed dispatching model is solved by the commercial CPLEX solver based on the YALMIP/MATLAB (version 8.4) toolbox, and sample examples are served for demonstrating the essential features of the proposed method.

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Section: The Dispatching Framework Of the Vppmentioning

confidence: 99%

“…The initial SOCs of the EVs are random numbers uniformly distributed between 0.15 and 0.35. The departure and arrival time points of the EVs respectively follow the probability density distributions described by (44) and (45) [29]:…”

Section: Parameter Settingmentioning

confidence: 99%

Optimal Dispatch of a Virtual Power Plant Considering Demand Response and Carbon Trading

Liu

Zheng

et al. 2018

Energies

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“…In this paper, the summation of distribution fees and tariffs, which is denoted as w, is assumed to have a positive correlation with the amount of the charging power. Equation (4) can be calculated through (5)- (8).…”

Section: Aggregated Ev Charging and Discharging Modelmentioning

confidence: 99%

“…For example, optimal dispatch and control approaches for EVs are discussed in [8][9][10][11]; the vehicle-to-grid (V2G) potentials of EVs in energy trading and ancillary services are studied in [12][13][14][15][16]; the coordination of EVs and the volatile renewable energy sources are examined in [17][18][19][20]; the impacts of flexible EVs on generation expansion planning [21,22]; the EV (2) It has been confirmed that battery discharging has a negative impact on the battery life [34,35], and this means that the EV customer will suffer from extra economic loss than in the ordinary charging mode without V2G, if no compensation is provided. The degradation process of EV batteries are modeled in detail and analyzed based on factors such as the driving patterns of EVs, impacts of the depth-of-discharge and ambient temperatures in [35,36].…”

Section: Introductionmentioning

confidence: 99%

Bidding Strategy for Aggregators of Electric Vehicles in Day-Ahead Electricity Markets

et al. 2017

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Abstract:To make full use of the flexible charging and discharging capabilities of the growing number of electric vehicles (EVs), a bidding strategy for EV aggregators to participate in a day-ahead electricity energy market is proposed in this work. The proposed bidding strategy is able to reduce the operating cost of the EV aggregators and to handle the uncertainties of day-ahead market prices properly at the same time. Agreements between the EV owners and the aggregators are discussed, and a hierarchical market structure is proposed. While assuming the aggregators as economic rational entities, the bidding strategy is established based on the market prices, extra battery charging/discharging costs and the expected profits. The bidding clearing system will display the current/temporal market clearance results of the day-ahead market before the final clearance, and hence the market participants can revise their bids and mitigate the risks, to some extent, of forecasted market price forecast errors. Numerical results with a modified IEEE 30-bus system have demonstrated the feasibility and effectiveness of the proposed strategy.

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“…Specific to distribution systems, the integration of distributed energy resources (DERs) such as distributed generations (DGs) [1], energy storage devices [2,3], active loads [4][5][6][7][8][9][10][11] and electric vehicles (EVs) [10][11][12], has greatly changed the operation conditions of the distribution system concerned. DERs are believed to play critical roles in shifting peak loads [7], improving power qualities, enhancing operation efficiency [13] and mitigating possible congestions at distribution system levels [14] in future power systems.…”

Section: Introductionmentioning

confidence: 99%

Congestion management with demand response considering uncertainties of distributed generation outputs and market prices

Wen

Liu

et al. 2016

J. Mod. Power Syst. Clean Energy

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In recent years, much attention has been devoted to the development and applications of smart grid technologies, with special emphasis on flexible resources such as distributed generations (DGs), energy storages, active loads, and electric vehicles (EVs). Demand response (DR) is expected to be an effective means for accommodating the integration of renewable energy generations and mitigating their power output fluctuations. Despite their potential contributions to power system secure and economic operation, uncoordinated operations of these flexible resources may result in unexpected congestions in the distribution system concerned. In addition, the behaviors and impacts of flexible resources are normally highly uncertain and complex in deregulated electricity market environments. In this context, this paper aims to propose a DR based congestion management strategy for smart distribution systems. The general framework and procedures for distribution congestion management is first presented. A bi-level optimization model for the day-ahead congestion management based on the proposed framework is established. Subsequently, the robust optimization approach is introduced to alleviate negative impacts introduced by the uncertainties of DG power outputs and market prices. The economic efficiency and robustness of the proposed congestion management strategy is demonstrated by an actual 0.4 kV distribution system in Denmark.

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A Hierarchical Decomposition Approach for Coordinated Dispatch of Plug-in Electric Vehicles

Cited by 203 publications

References 25 publications

Optimal Dispatch of a Virtual Power Plant Considering Demand Response and Carbon Trading

Optimal Dispatch of a Virtual Power Plant Considering Demand Response and Carbon Trading

Bidding Strategy for Aggregators of Electric Vehicles in Day-Ahead Electricity Markets

Congestion management with demand response considering uncertainties of distributed generation outputs and market prices

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