Electric vehicle charging schedule considering user's charging selection from economics

Chen, Jie; Huang, Xiaoqing; Tian, Shiming; Cao, Yijia; Huang, Bicheng; Luo, Xiaoyue; Yu, Wenlong

doi:10.1049/iet-gtd.2019.0154

Cited by 36 publications

(17 citation statements)

References 21 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In addition, for encouraging users to participate in the DR market, aggregator was used to compensate the cost of battery degradation and share benefits earned from the DR market with users, as calculated in Equations ( 6) and ( 7) [5], respectively. The penalty factor is presented in (8)…”

Section: Objective Functionmentioning

confidence: 99%

“…In the discussion on structure of the optimization problem, reference [8] verifies the effectiveness of decentralized methods in saving computing burden. Additionally, reference [19] designs a hybrid centralized-decentralized charging control scheme which shows that the decentralized algorithm can lower the communication load between the EVs and the system controller.…”

Section: Introductionmentioning

confidence: 99%

“…Methods for the charging/discharging scheduling of EVs can be divided into two categories, centralized [7] and decentralized management architectures [8]. In existing scheduling optimization algorithms, centralized scheduling methods are the most common, which are used to realize the coordination of multiple EVs.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Decentralized V2G/G2V Scheduling of EV Charging Stations by Considering the Conversion Efficiency of Bidirectional Chargers

et al. 2021

View full text Add to dashboard Cite

With a rapid increase in the awareness of carbon reduction worldwide, the industry of electric vehicles (EVs) has started to flourish. However, the large number of EVs connected to a power grid with a large power demand and uncertainty may result in significant challenges for a power system. In this study, the optimal charging and discharging scheduling strategies of G2V/V2G and battery energy storage system (BESS) were proposed for EV charging stations. A distributed computation architecture was employed to streamline the complexity of an optimization problem. By considering EV charging/discharging conversion efficiencies for different load conditions, the proposed method was used to maximize the operational profits of each EV and BESS based on the related electricity tariff and demand response programs. Moreover, the behavior model of drivers and cost of BESS degradation caused by charging and discharging cycles were considered to improve the overall practical applicability. An EV charging station with 100 charging piles was simulated as an example to verify the feasibility of the proposed method. The developed algorithms can be used for EV charging stations, load aggregators, and service companies integrated with distributed energy resources in a smart grid.

show abstract

Section: Objective Functionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Decentralized V2G/G2V Scheduling of EV Charging Stations by Considering the Conversion Efficiency of Bidirectional Chargers

et al. 2021

View full text Add to dashboard Cite

show abstract

“…García-Villalobos et al (2014) [28] present a review of different strategies, algorithms, and methods to implement smart charging control systems and identify significant projects around the world about PEV integration. Habib, Kamran, and Rashid (2015) [29] Focusing on users' charging preferences in particular, Chen et al (2019) [32] propose a multi-objective scheduling method for PEV charging events. Korkas et al (2018) [33] present an adaptive learning-based approach for nearly optimal dynamic charging of PEV fleets respecting user preferences.…”

Section: Related Workmentioning

confidence: 99%

Simulating Electric Vehicle Diffusion and Charging Activities in France and Germany

Ensslen

Will

Jochem

2019

WEVJ

View full text Add to dashboard Cite

Plug-in electric vehicles (PEV) are considered to reduce oil dependency, noise, and local air pollution as well as greenhouse gas emissions caused by road transportation. Today, the early market penetration phase has started and can be observed in many countries. But how could the diffusion and adoption of PEV be modeled to create consistent scenarios? With which PEV driving and charging behavior can these scenarios be associated and what load-shifting potentials can be derived? This work provides an answer to these questions by describing a hybrid modeling approach of a PEV diffusion scenario consisting of a top-down macro-econometric Bass model, answering the question as to at what point in time how many PEV will be on the market, and a bottom-up micro-econometric binary logistic PEV adoption model answering who is likely to adopt. This set of methods is applied to representative mobility data sets available for France and Germany in order to simulate driving and charging behaviors of potential French and German PEV adopters. In addition, a sampling method is presented, which reduces computational times while intending to remain representative of the population of PEV adopters considered. This approach enables the consideration of PEV at a detailed level in an agent-based energy system model focusing on European day-ahead markets. Results show that PEV diffusion dynamics are slightly higher in France than in Germany. Furthermore, average plug-in times, average active charging periods, average load-shifting potentials, and average energy charged per PEV differ slightly between France and Germany. Computational times can be reduced by our approach, resulting in the ability to better integrate PEV diffusion, adoption, and representative charging demand in bottom-up energy system models that simulate European wholesale electricity markets.

show abstract

“…Electric vehicles have become the main direction of developing new energy sources owing to the low pollution and the high environmental protection [1]. Because of the uncertain distribution of space and time of the electric vehicles, the grid is bound to lay out charging facilities for accepting the charging demand of electric vehicles according to the scale of electric vehicles [2]- [3]. Therefore, it is important to predict the development scale of electric vehicles.…”

Section: Introductionmentioning

confidence: 99%

Electric Vehicles Scale Evolution Model Considering Social Attributes

Zhou

Cai

et al. 2020

IEEE Access

Self Cite

View full text Add to dashboard Cite

Predicting the scale of electric vehicles at different time is the basis for the power grid to effectively accept charging loads. The development of electric vehicles is not only affected by economy, technology and policy, but also affected by the factors from social networks. This paper proposes the electric vehicle purchase decision model considering the influences of technology, economy and society. A two-dimensional space is used to characterize the random influences of related economic and social groups on consumer decisions, which can dynamically simulate the interaction between social environment and consumer decisions. Thus, based on such two-dimensional social space, an electric vehicle scale evolution model is established, so that it can be used to analyze the impact of the structural characteristics of social networks on the scale evolution. A large community in Changsha is taken as an example to analyze the scale development trend of electric vehicles under different social networks. Case studies demonstrate the feasibility and effectiveness of the proposed methodology.

show abstract

Electric vehicle charging schedule considering user's charging selection from economics

Cited by 36 publications

References 21 publications

Decentralized V2G/G2V Scheduling of EV Charging Stations by Considering the Conversion Efficiency of Bidirectional Chargers

Decentralized V2G/G2V Scheduling of EV Charging Stations by Considering the Conversion Efficiency of Bidirectional Chargers

Simulating Electric Vehicle Diffusion and Charging Activities in France and Germany

Electric Vehicles Scale Evolution Model Considering Social Attributes

Contact Info

Product

Resources

About