Electrical Vehicle Charging Services Planning and Operation with Interdependent Power Networks and Transportation Networks: A Review of the Current Scenario and Future Trends

Jawad, Shafqat; Liu, Junyong

doi:10.3390/en13133371

Cited by 28 publications

(7 citation statements)

References 183 publications

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“…Although numerous valuable publications and reviews have been published on this topic [22], [23]. the number of studies dealing with signaled intersections is limited.…”

Section: Related Workmentioning

confidence: 99%

Placement and sizing strategies for dynamic wireless charging stations on signalized intersection corridors

Doğan

2022

Pamukkale J Eng Sci

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Dynamic wireless charging stations are a potential solution to the range problem of the limited battery capacity of electric vehicles. However, the infrastructure of these stations is costly, so it is important to position the wireless charging tracks (WCT) of the stations. This study proposes strategies in two different groups for positioning and sizing the charging stations on signalized corridors. The first group consists of two predefined strategies, while the second includes strategies using Gray Wolf Optimization (GWO) and Whale Optimization Algorithm (WOA). The performance of these strategies was tested taking into account various BEV ratios (𝑟 𝐸𝑉 ) and maximum WCT lengths. Analysis results showed GWO and WOA is presented high-efficiency placement plans in the majority of cases studied. Surprisingly, however, with increasing 𝑟 𝐸𝑉 , the predefined strategies showed better performances in some cases than that of GWO and WOA. Another notable finding is that the efficiency of the station can be increased by using more WCTs at the corridor entrances. This study presents results that have the potential to contribute to the solution of the problem of positioning and sizing wireless charging stations for intersection corridors and were not highlighted in previous studies. Dinamik kablosuz şarj istasyonları, elektrikli araçların sınırlı pil kapasitesinin menzil sorununa potansiyel bir çözümdür. Ancak bu istasyonların altyapısı maliyetli olduğundan, istasyonların kablosuz şarj hatlarının (WCT) konumlandırılması önemlidir. Bu çalışma, sinyalize koridorlarda şarj istasyonlarının konumlandırılması ve boyutlandırılması için iki farklı grupta stratejiler önermektedir. İlk grup önceden tanımlanmış iki stratejiden oluşurken, ikincisi Gri KurtOptimizasyonu (GWO) ve Balina Optimizasyon Algoritması (WOA) kullanan stratejileri içerir. Bu stratejilerin performansı, çeşitli BEV oranları 𝑟 𝐸𝑉 ve maksimum WCT uzunlukları dikkate alınarak test edildi. Analiz sonuçları, incelenen vakaların çoğunda GWO ve WOA'ya yüksek verimli yerleşim planları sunulduğunu gösterdi. Ancak şaşırtıcı bir şekilde artan 𝑟 𝐸𝑉 ile önceden tanımlanmış stratejiler bazı durumlarda GWO ve WOA'dan daha iyi performans gösterdi. Bir diğer dikkat çekici bulgu ise koridor girişlerinde daha fazla WCT kullanılarak istasyonun verimliliğinin artırılabileceğidir. Bu çalışma, kavşak koridorları için kablosuz şarj istasyonlarının konumlandırılması ve boyutlandırılması sorununun çözümüne katkı potansiyeline sahip ve önceki çalışmalarda vurgulanmayan sonuçlar sunmaktadır.

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“…Although numerous valuable publications and reviews have been published on this topic [22], [23]. the number of studies dealing with signaled intersections is limited.…”

Section: Related Workmentioning

confidence: 99%

Placement and sizing strategies for dynamic wireless charging stations on signalized intersection corridors

Doğan

2022

Pamukkale J Eng Sci

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“…In this section, different models concerning EV aggregator integration and modeling are exposed. Different approaches are considered by several authors [3,7,8], namely the participation in reserve markets. The main objective is to increase the aggregator's profit from participating in frequency regulation.…”

Section: Related Workmentioning

confidence: 99%

Participation of an EV Aggregator in the Reserve Market through Chance-Constrained Optimization

et al. 2020

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The adoption of Electric Vehicles (EVs) will revolutionize the storage capacity in the power system and, therefore, will contribute to mitigate the uncertainty of renewable generation. In addition, EVs have fast response capabilities and are suitable for frequency regulation, which is essential for the proliferation of intermittent renewable sources. To this end, EV aggregators will arise as a market representative party on behalf of EVs. Thus, this player will be responsible for supplying the power needed to charge EVs, as well as offering their flexibility to support the system. The main goal of EV aggregators is to manage the potential participation of EVs in the reserve market, accounting for their charging and travel needs. This work follows this trend by conceiving a chance-constrained model able to optimize EVs participation in the reserve market, taking into account the uncertain behavior of EVs and their charging needs. The proposed model, includes penalties in the event of a failure in the provision of upward or downward reserve. Therefore, stochastic and chance-constrained programming are used to handle the uncertainty of a small fleet of EVs and the risk profile of the EV aggregator. Two different relaxation approaches, i.e., Big-M and McCormick, of the chance-constrained model are tested and validated for different number of scenarios and risk levels, based on an actual test case in Denmark with actual driving patterns. As a final remark, the McCormick relaxation presents better performance when the uncertainty budget increases, which is appropriated for large-scale problems.

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“…Various studies have examined the nexuses of electrified transportation. The study provides a detailed analysis of coupled networks [6]. An origin-destination method was introduced to capture the load demand and the traffic flows [7].…”

Section: Introductionmentioning

confidence: 99%

Electrical Vehicle Charging Load Mobility Analysis Based on a Spatial–Temporal Method in Urban Electrified-Transportation Networks

Jawad

Liu

2023

Energies

Self Cite

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Charging load mobility evaluation becomes one of the main concerns for charging services and power system stability due to the stochastic nature of electrical vehicles (EVs) and is critical for the robust scheduling of economic operations at different intervals. Therefore, the EV spatial–temporal approach for load mobility forecasting is presented in this article. Furthermore, the reliability indicators of large-scale EV distribution network penetration are analyzed. The Markov decision process (MDP) theory and Monte Carlo simulation are applied to efficiently forecast the charging load and stochastic path planning. A spatial–temporal model is established to robustly forecast the load demand, stochastic path planning, traffic conditions, and temperatures under different scenarios to evaluate the charging load mobility and EV drivers’ behavior. In addition, the distribution network performance indicators are explicitly evaluated. A Monte Carlo simulation is adopted to examine system stability considering various charging scenarios. Urban coupled traffic-distribution networks comprising 30-node transportation and 33-bus distribution networks are considered as a test case to illustrate the proposed study. The results analysis reveals that the proposed method can robustly estimate the charging load mobility. Furthermore, significant EV penetrations, weather, and traffic congestion further adversely affect the performance of the power system.

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Electrical Vehicle Charging Services Planning and Operation with Interdependent Power Networks and Transportation Networks: A Review of the Current Scenario and Future Trends

Cited by 28 publications

References 183 publications

Placement and sizing strategies for dynamic wireless charging stations on signalized intersection corridors

Placement and sizing strategies for dynamic wireless charging stations on signalized intersection corridors

Participation of an EV Aggregator in the Reserve Market through Chance-Constrained Optimization

Electrical Vehicle Charging Load Mobility Analysis Based on a Spatial–Temporal Method in Urban Electrified-Transportation Networks

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