A Multi-Timescale and Bilevel Coordination Approach for Matching Uncertain Wind Supply With EV Charging Demand

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

doi:10.1109/tase.2016.2585180

Cited by 70 publications

(35 citation statements)

References 22 publications

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“…Since each individual EV only needs to update its own charging decisions by solving a small subproblem at each iteration, the EBDC method is scalable. Considering that it will be time-consuming to solve the centralized optimization problem (21), even if the global information regarding the day-ahead wind power generation of the buildings and the daily driving cycles of EVs can be acquired, the EBDC is compared with the two heuristic charging strategies (myopic and greedy) in this section. Similarly, S = 50 sample paths are generated in each case study to evaluate the performance of different charging strategies.…”

Section: Complexity and Scalabilitymentioning

confidence: 99%

Decentralized EV-Based Charging Optimization With Building Integrated Wind Energy

Yong

Jia

Guan

et al. 2019

IEEE Trans. Automat. Sci. Eng.

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Electric vehicles (EVs) have experienced a rapid growth due to the economic and environmental benefits. However, the substantial charging load brings challenging issues to the power grid. Modern technological advances and the huge number of high-rise buildings have promoted the development of distributed energy resources, such as building integrated/mounted wind turbines. The issue to coordinate EV charging with locally generated wind power of buildings can potentially reduce the impacts of EV charging demand on the power grid. As a result, this paper investigates this important problem and three contributions are made. First, the real-time scheduling of EV charging is addressed in a centralized framework based on the ideas of model predictive control, which incorporates the volatile wind power supply of buildings and the random daily driving cycles of EVs among different buildings. Second, an EV-based decentralized charging algorithm (EBDC) is developed to overcome the difficulties due to: 1) the possible lack of global information regarding the charging requirements of all EVs and 2) the computational burden with the increasing number of EVs. Third, we prove that the EBDC method can converge to the optimal solution of the centralized problem over each planning horizon. Moreover, the performance of the EBDC method is

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Section: Complexity and Scalabilitymentioning

confidence: 99%

Decentralized EV-Based Charging Optimization With Building Integrated Wind Energy

Yong

Jia

Guan

et al. 2019

IEEE Trans. Automat. Sci. Eng.

Self Cite

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“…Taking into account this disadvantage, V2G mode is not considered, as in references [6,[10][11][12][13] which only address PEV recharging. In [6], an approach to coordinate the charging of PEV is proposed.…”

Section: Coordination Among Multiple Aggregatorsmentioning

confidence: 99%

“…Moreover, the impact of PEV charging on the distribution grid is analyzed in terms of the profile of the voltage, the life loss of the transformer, and the power/energy losses of the grid. The objective of [10] is to coordinate PEV's charging so it matches the wind supply. The solution of the problem is addressed through a two-stage simulation-based approach.…”

Section: Coordination Among Multiple Aggregatorsmentioning

confidence: 99%

“…Based on these bands, the DSO determines a POU tariff with the aim of encouraging aggregators to respond to the optimum charging curve, previously calculated by the DSO, by considering the uncertainty in the input data. This procedure determines the POU tariff, which is calculated through (10).…”

Section: Prediction-of-use Tariff By Aggregatormentioning

confidence: 99%

“…According to (10), the POU tariff is composed of two terms, explained below. The first term represents a base charge that the aggregator must pay to the DSO for all the energy consumed.…”

Section: Prediction-of-use Tariff By Aggregatormentioning

confidence: 99%

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A Framework for Determining a Prediction-Of-Use Tariff Aimed at Coordinating Aggregators of Plug-In Electric Vehicles

et al. 2019

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The objective of this article is to propose a framework for defining a day-ahead prediction-of-use tariff (POU) that promotes aggregators of the plug-in electric vehicles (PEVs) to operate as closely as possible to an optimal charging curve previously calculated by the distribution system operator (DSO). The DSO calculates this optimal charging curve to flatten the load curve of the distribution transformers as much as possible by coordinating the daily recharging of PEVs. The objective is to establish the optimal power profile of the PEV aggregators needed to flatten the power curve supplied by the transformer, so that PEV customers’ needs can be met throughout the day. The proposed framework is applied in a case study accounting for uncertainties associated with charging through Monte Carlo simulation, in order to find the POU tariff. The results demonstrate that applying the POU tariff determines the transformer’s minimum power limit necessary to meet all PEV users’ needs. Additionally, the day-ahead POU tariff does not generate new demand peaks, since it does not concentrate the energy supply of flexible loads in pre-established time bands. Finally, simulation reflects the significant effect of the PEV charging on the distribution system in terms of enhancing the voltage profile, maximizing the transformer life, and reducing the power/energy losses.

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Charging Behavior of Electric Vehicles

Jia

2023

Cyber–Physical–Human Systems

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A Multi-Timescale and Bilevel Coordination Approach for Matching Uncertain Wind Supply With EV Charging Demand

Cited by 70 publications

References 22 publications

Decentralized EV-Based Charging Optimization With Building Integrated Wind Energy

Decentralized EV-Based Charging Optimization With Building Integrated Wind Energy

A Framework for Determining a Prediction-Of-Use Tariff Aimed at Coordinating Aggregators of Plug-In Electric Vehicles

Charging Behavior of Electric Vehicles

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