Robust Frequency Regulation Capacity Scheduling Algorithm for Electric Vehicles

Yao, Enxin; Wong, Vincent W. S.; Schober, Robert

doi:10.1109/tsg.2016.2530660

Cited by 106 publications

(81 citation statements)

References 22 publications

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“…Supporting universal charging protocols: Charging the PEVs with faster rate and lower time by drawing the maximum charging power from the charging pole [12], prolonging the PEV battery's lifetime with constant power feeding [15], and requiring smaller communication overheads to contact (switch off or on) with a small subset of PEVs [16]. Further, with this method the infrastructures only need to work with one charging rate which needs very cheap infrastructure and services.…”

Section: B Contributionsmentioning

confidence: 99%

“…Besides, in the liberalized ancillary service market, to facilitate PEVs participation and satisfy the minimum regulation capacity (e.g., 0.1 MW), the aggregators are served as an interface between the independent system operator (ISO) and a fleet of PEVs [12]. The more the PEVs can provide regulation capacity, the more they can acquire financial rebate.…”

Section: System Modelmentioning

confidence: 99%

“…Now, we need to provide a coordinated charging mechanism between the PEVs to result in a public social optimal solution to the global problem (11). The main challenge to provide a fully decentralized solution to (11) is that each customer k requires to know the aggregate PEVs power consumption (12). A common state of the art solution to this, is that each PEV submit its action profile to all other PEVs (either directly, or through the aggregator).…”

Section: Nash Folk Regret-based Strategymentioning

confidence: 99%

“…It is inference from Theorem 1 in [6] that for optimization problem (12), optimal charging profile is in general not unique. So, let denote the set of optimal charging profiles with an arbitrary optimal charging profile X o as O := {X ∈ X | X ∼ = X o }.…”

Section: A Proof Of Theoremmentioning

confidence: 99%

See 3 more Smart Citations

Agent-Based Decentralized Optimal Charging Strategy for Plug-in Electric Vehicles

Latifi

Rastegarnia

Khalili

et al. 2019

IEEE Trans. Ind. Electron.

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Abstract-This paper presents a game theoretic decentralized electric vehicle charging schedule for minimizing the customers' payments, maximizing the grid efficiency, and providing maximum potential capacity for ancillary services. Most of the available methods for electric vehicle charging assume that the customers are rational, there is low-latency perfect two-way communication infrastructure without communication/computation limitation between the distribution company and all the customers, and they have perfect knowledge about the system parameters. To avoid these strong assumptions and preserve the customers' privacy, we take advantages of the regret matching and the Nash Folk theorems. In the considered game, the players (customers) interact and communicate locally with only their neighbors. We propose a mechanism for this game which results in a full Nash Folk theorem. We demonstrate and prove that the on-off charging strategy provides maximum regulation capacity. However, our mechanism is quite general, takes into account the battery characteristics and degradation costs of the vehicles, provides a real time dynamic pricing model, and supports the vehicle-to-grid (V2G) and modulated charging protocols. Moreover, the developed mechanism is robust to the data disruptions and takes into account the long/short term uncertainties.Index Terms-Plug-in electric vehicles (PEVs), decentralized charging, Nash Folk strategy, regret matching.

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Section: B Contributionsmentioning

confidence: 99%

Section: System Modelmentioning

confidence: 99%

Section: Nash Folk Regret-based Strategymentioning

confidence: 99%

Section: A Proof Of Theoremmentioning

confidence: 99%

See 2 more Smart Citations

Agent-Based Decentralized Optimal Charging Strategy for Plug-in Electric Vehicles

Latifi

Rastegarnia

Khalili

et al. 2019

IEEE Trans. Ind. Electron.

View full text Add to dashboard Cite

show abstract

“…These strategies can be separated into two main groups: centralized strategies, which use an EV aggregator as a middleman between the ISO and the EVs (e.g. : [23]- [27]) and decentralized strategies, which do not use an aggregator to coordinate individual EV charging commands (e.g. : [16]- [22]).…”

Section: Introductionmentioning

confidence: 99%

Real-Time Charging Strategies for an Electric Vehicle Aggregator to Provide Ancillary Services

Wenzel

Negrete-Pincetic

Olivares

et al. 2018

IEEE Trans. Smart Grid

123

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Abstract-Real-time charging strategies, in the context of vehicle to grid (V2G) technology, are needed to enable the use of electric vehicle (EV) fleets batteries to provide ancillary services (AS). In this paper we develop tools to manage charging and discharging in a fleet to track an Automatic Generation Control (AGC) signal when aggregated. We propose a real-time controller that considers bidirectional charging efficiency and extend it to study the effect of looking ahead when implementing Model Predictive Control (MPC). Simulations show that the controller improves tracking error as compared with benchmark scheduling algorithms, as well as regulation capacity and battery cycling.

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Impact of load sharing schemes on the stability delay margins computed by Rekasius substitution method in load frequency control system with electric vehicles aggregator

Naveed

Sönmez

Ayasun

2021

Int Trans Electr Energ Syst

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The impact of load sharing between the electric vehicles (EVs) aggregator and the conventional generator on stability delay margins in a two-area load frequency control (LFC) system is investigated in this work. A frequency-domain Rekasius substitution method is used to compute stability delay margins for different values of proportional-integral (PI) controller gains. The proposed method computes complex roots on the imaginary axis of the quasi-characteristic equation. The substitution first converts the quasi-characteristic equation of the LFC with EVs aggregator (LFC-EVs) system including delay-dependent exponential terms into an ordinary polynomial. Then, the Routh-Hurwitz stability method is applied to find those imaginary roots and the corresponding stability delay margins. The qualitative impact of different sharing schemes between the conventional generator and EVs aggregator and the impact of EVs gains on stability delay margins are thoroughly analyzed, and the results are validated by time domain simulations and quasi-polynomial mapping-based root finder algorithm. It is observed that for any given PI controller gains, stability delay margins decrease when the participation of EVs into the frequency regulation increases.

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Robust Frequency Regulation Capacity Scheduling Algorithm for Electric Vehicles

Cited by 106 publications

References 22 publications

Agent-Based Decentralized Optimal Charging Strategy for Plug-in Electric Vehicles

Agent-Based Decentralized Optimal Charging Strategy for Plug-in Electric Vehicles

Real-Time Charging Strategies for an Electric Vehicle Aggregator to Provide Ancillary Services

Impact of load sharing schemes on the stability delay margins computed by Rekasius substitution method in load frequency control system with electric vehicles aggregator

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