Monte Carlo analysis of Plug-in Hybrid Vehicles and Distributed Energy Resource growth with residential energy storage in Michigan

Jung, Jaesung; Cho, Yongju; Cheng, Danling; Önen, Ahmet; Arghandeh, Reza; Dilek, Murat; Broadwater, Robert

doi:10.1016/j.apenergy.2013.03.033

Cited by 29 publications

(17 citation statements)

References 22 publications

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“…Without considering the TOU price, the simulation results are similar to that in [3][4][5]: charging load curves on weekdays and weekends are almost identical, which will increase the burden of distribution network, because the charging peak load of the PrEV cluster overlaps with the original one of the distribution network.…”

Section: Scenario Asupporting

confidence: 61%

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Procedural simulation method for aggregating charging load model of private electric vehicle cluster

Ban

2015

J. Mod. Power Syst. Clean Energy

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The usage of each private electric vehicle (PrEV) is a repeating behavior process composed by driving, parking, discharging and charging, in which PrEV shows obvious procedural characteristics. To analyze the procedural characteristics, this paper proposes a procedural simulation method. The method aggregates the behavior process regularity of the PrEV cluster to model the cluster's charging load. Firstly, the basic behavior process of each PrEV is constructed by referring the statistical datasets of the traditional private non-electric vehicles. Secondly, all the basic processes are set as a simulation starting point, and they are dynamically reconstructed by several constraints. The simulation continues until the steady state of charge (SOC) distribution and behavior regularity of the PrEV cluster are obtained. Lastly, based on the obtained SOC and behavior regularity information, the PrEV cluster's behavior processes are simulated again to make the aggregating charging load model available. Examples for several scenarios show that the proposed method can improve the reliability of modeling by grasping the PrEV cluster's procedural characteristics.

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Section: Scenario Asupporting

confidence: 61%

“…In [3][4][5], the trip distances, initial stage of change (SOC) and charging time were simulated by several independent probability distributions, and then the charging load model was established. In [6], based on the data provided by GPS devices, more accurate results were obtained by conditional probability distribution function.…”

Section: Introductionmentioning

confidence: 99%

Procedural simulation method for aggregating charging load model of private electric vehicle cluster

Ban

2015

J. Mod. Power Syst. Clean Energy

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“…For example, a typical voltage phase angle difference for a distribution feeder at full load might be 0.1 /mile, compared with tens of degrees between transmission nodes. 1 Consequently, transmission PMUs with typical errors near AE1 may not provide enough precision for meaningful distribution measurements.…”

Section: Microsynchrophasor (Mpmu) Technologymentioning

confidence: 99%

Every Moment Counts

Meier¹,

Arghandeh²

2014

Renewable Energy Integration

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Every moment counts in action recognition. A comprehensive understanding of human activity in video requires labeling every frame according to the actions occurring, placing multiple labels densely over a video sequence. To study this problem we extend the existing THUMOS dataset and introduce MultiTHUMOS, a new dataset of dense labels over unconstrained internet videos. Modeling multiple, dense labels benefits from temporal relations within and across classes. We define a novel variant of long short-term memory (LSTM) deep networks for modeling these temporal relations via multiple input and output connections. We show that this model improves action labeling accuracy and further enables deeper understanding tasks ranging from structured retrieval to action prediction.

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“…Likewise, various studies [7][8][9][10][11] have reported ability of storage to further support system operation and thus renewables integration through provision of network services such as congestion management in transmission and distribution systems. In the particular case of distributed storage, various references have demonstrated [12][13][14][15] how design of distributed energy systems can be improved by increasing participation of storage, which enhances system flexibility and facilitates integration of further distributed energy resources such as distributed generation (e.g. solar and wind).…”

Section: Literature Review and Contributionmentioning

confidence: 99%

A MILP model for optimising multi-service portfolios of distributed energy storage

2015

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Energy storage has the potential to provide multiple services to several sectors in electricity industry and thus support activities related to generation, network and system operation. Hence aggregating the value delivered by storage to these sectors is paramount for promoting its efficient deployment in the near future, which will provide the level of flexibility needed to deal with the envisaged high renewables share and the increase in peak demand driven by transport and heating electrification. In this context, we develop a Mixed Integer Linear Programming (MILP) model to schedule operation of distributed storage by coordinating provision of a range of system services which are rewarded at different market prices. The model maximises distributed storage's net profit while providing distribution network congestion management, energy price arbitrage and various reserve and frequency regulation services through both active and reactive power control. We demonstrate benefits associated with the coordination of these services and its impacts on commercial strategies to determine optimal multi-service portfolios in the long term. We also demonstrate the value of reactive power control to support not only distribution network congestion management, but also efficient trading of energy and balancing services which are usually treated through active power-only control.In addition, we use the model to price the service of distribution network congestion management and propose an efficient investment policy to upgrade distribution network capacity in the presence of distributed storage. Finally, several case studies under current market conditions in Great Britain (GB) demonstrate that distributed storage revenues associated with frequency control services are significantly more profitable.

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Monte Carlo analysis of Plug-in Hybrid Vehicles and Distributed Energy Resource growth with residential energy storage in Michigan

Cited by 29 publications

References 22 publications

Procedural simulation method for aggregating charging load model of private electric vehicle cluster

Procedural simulation method for aggregating charging load model of private electric vehicle cluster

Every Moment Counts

A MILP model for optimising multi-service portfolios of distributed energy storage

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