Optimal charging of vehicle-to-grid fleets via PDE aggregation techniques

Floch, Caroline Le; Meglio, Florent Di; Moura, Scott J.

doi:10.1109/acc.2015.7171839

Cited by 19 publications

(16 citation statements)

References 14 publications

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“…The potential of MC in minimizing an EV aggregator's (EVA's) charging costs is analyzed in Le Floch, Di Meglio, and Moura 249 using an optimization problem based on partial differential equations subject to individual EVs’ charging requirements and constraints on provision of contracted FR services. Similarly, Hu et al 162 and Wang et al 165 analyze the impacts of considering power consumption limits set by the system operator on EVA's charging costs, whereas Clairand, Rodríguez-García, and Álvarez-Bel 165 incorporate penalties associated to the violation of such power consumption limits in the objective function of an EVA.…”

Section: Value Of Managed Charging In Analysis/modeling Studiesmentioning

confidence: 99%

“…Several studies show that MC can reduce EV charging costs for the EV owners and aggregators by about 10–60% depending on intra-day price variability, charging schemes, 162,170 V2G capability, 186,200 participation in FR, 246,249 and participation in unmonetized DS services. Results also show that if EV charging is controlled to provide unmonetized DS services, the charging costs can increase by ∼5–90% (compared to cost minimization without considering DS operation).…”

Section: Value Of Managed Charging In Analysis/modeling Studiesmentioning

confidence: 99%

See 1 more Smart Citation

Assessing the value of electric vehicle managed charging: a review of methodologies and results

Anwar

Muratori

Jadun

et al. 2022

Energy Environ. Sci.

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Section: Value Of Managed Charging In Analysis/modeling Studiesmentioning

confidence: 99%

Section: Value Of Managed Charging In Analysis/modeling Studiesmentioning

confidence: 99%

Assessing the value of electric vehicle managed charging: a review of methodologies and results

Anwar

Muratori

Jadun

et al. 2022

Energy Environ. Sci.

View full text Add to dashboard Cite

show abstract

“…Many methods to reduce the cost of charging PEV fleets have been proposed in literature [17][18][19][20][21][22]. An optimal charging scheduling strategy for charging a large PEV fleet to reduce cost and supplying the requested power to the grid is developed using partial differential equation in [17]. This method assumes prior knowledge of the large PEV fleet.…”

Section: Literature Reviewmentioning

confidence: 99%

Autonomous PEV Charging Scheduling Using Deep-Q Network and Dyna-Q Reinforcement Learning

Wang¹

2021

Preprint

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This paper proposes a demand response method that aims to reduce the long-term charging cost of a plug-in electric vehicle (PEV) while overcoming obstacles such as the stochastic nature of the user’s driving be- haviour, traffic condition, energy usage, and energy price. The problem is formulated as a Markov Decision Process (MDP) with unknown transition probabilities and solved using deep reinforcement learning (RL) techniques. Existing methods using machine learning either requires initial user behaviour data, or converges far too slowly. This method does not require any initial data on the PEV owner’s driving behaviour and shows improvement on learning speed. A combination of both model-based and model-free learning called Dyna-Q algorithm is utilized. Every time a real experience is obtained, the model is updated and the RL agent will learn from both real data set and “imagined” experience from the model. Due to the vast amount of state space, a table-look up method is impractical and a value approximation method using deep neural networks is employed for estimating the long-term expected reward of all state-action pairs. An average of historical price is used to predict future price. Three different user behaviour without any initial PEV owner behaviour data are simulated. A purely model-free DQN method is shown to run out of battery during trips very often, and is impractical for real life charging scenarios. Simulation results demonstrate the effectiveness of the proposed approach and its ability to reach an optimal policy quicker while avoiding state of charge (SOC) depleting during trips when compared to existing PEV charging schemes for all three different users profiles.

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“…Floch et al [74] examine modelling and control of a large population of grid-connected plug-in electric vehicles (PEVs), and develop a partial differential equation (PDE)based technique for aggregating large populations of gridintegrated PEVs. Yoon et al [65] propose a flexible V2G smart charging coordination schemes for office buildings equipped with Electric Vehicle (EV) charging stations.…”

Section: ) Vehicle-to-grid (V2g) Interaction Modelsmentioning

confidence: 99%

Deployment of IoV for Smart Cities: Applications, Architecture, and Challenges

et al. 2019

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The Internet of Vehicles (IoV) is a convergence of the mobile Internet and the Internet of Things (IoT), where vehicles function as smart moving intelligent nodes or objects within the sensing network. This paper gives two contributions to the state-of-the-art for IoV technology research. First, we present a comprehensive review of the current and emerging IoV paradigms and communication models with an emphasis on deployment in smart cities. Currently, surveys from many authors have focused concentration on the IoV as only serving applications for intelligent transportation like driver safety, traffic efficiency, and infotainment. This paper presents a more inclusive review of the IoV for also serving the needs of smart cities for large-scale data sensing, collection, information processing, and storage. The second component of the paper presents a new universal architecture for the IoV which can be used for different communication models in smart cities to address the above challenges. It consists of seven layers: vehicle identification layer, object layer, inter-intra devices layer, communication layer, servers and cloud services layer, big data and multimedia computation layer, and application layer. The final part of this paper discusses various challenges and gives some experimental results and insights for future research direction such as the effects of a large and growing number of vehicles and the packet delivery success rate in the dynamic network structure in a smart city scenario. INDEX TERMS Internet of Vehicles, IoV, layer architecture, smart city, applications, big data.

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Optimal charging of vehicle-to-grid fleets via PDE aggregation techniques

Cited by 19 publications

References 14 publications

Assessing the value of electric vehicle managed charging: a review of methodologies and results

Assessing the value of electric vehicle managed charging: a review of methodologies and results

Autonomous PEV Charging Scheduling Using Deep-Q Network and Dyna-Q Reinforcement Learning

Deployment of IoV for Smart Cities: Applications, Architecture, and Challenges

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