Uncertainty handling for Electric Vehicle aggregator using IGDT

Pradhan, Jitendra Kumar; Verma, Pranjal; Khemka, Vinit; Anoop, V; Srinivas, Srimath Tirumala Pallerlamudi

doi:10.1109/npsc.2018.8771745

Cited by 4 publications

(2 citation statements)

References 17 publications

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“…Pradhan et al [23] used information gap decision theory to address price uncertainties that could arise at a charging node. The proposed mechanism guarantees a minimum amount of profit in the case of fluctuations in the charging price.…”

Section: B Uncertainty Of Ev Chargingmentioning

confidence: 99%

Detection of Lying Electrical Vehicles in Charging Coordination Using Deep Learning

et al. 2020

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Because charging coordination is a solution for avoiding grid instability by prioritizing charging requests, electric vehicles may lie and send false data to illegally receive higher charging priorities. In this paper, we first study the impact of such attacks on both the lying and honest electric vehicles. Our evaluations indicate that lying electric vehicles have a higher chance of charging, whereas honest electric vehicles may not be able to charge or may charge late. Then, an anomaly-based detector based on a deep neural network is devised to identify lying electric vehicles. The idea is that since each electric vehicle driver has a particular driving pattern, the data reported by the corresponding electric vehicle should follow this pattern, and any deviation due to reporting false data can be detected. To train the detector, we first create an honest dataset for the charging coordination application using real driving traces and information provided by an electric vehicle manufacturer, and we then propose a number of attacks as a basis for creating malicious data. We train and evaluate a gated recurrent unit model using this dataset. Our evaluations indicate that our detector can detect lying electric vehicles with high accuracy and a low false alarm rate even when tested on attacks that are not represented in the training dataset. INDEX TERMS Security, false data injection, charging coordination, electric vehicles, and smart grid. Nomenclature ADASY N Adaptive synthetic sampling AU C Area under the curve CC Charging coordinator DN N Deep neural network EV Electric vehicle GA Genetic algorithm GRU Gated recurrent unit M LP Multilayer perceptron N SGA Non-dominated Sorting Genetic Algorithm RN N Recurrent neural network ROC Receiver operating characteristic SM OT E Synthetic Minority Over-sampling Technique SoC State of charge T CC Time to complete charge

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Section: B Uncertainty Of Ev Chargingmentioning

confidence: 99%

Detection of Lying Electrical Vehicles in Charging Coordination Using Deep Learning

et al. 2020

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“…The first step for quantifying TSA is the identification of the uncertainties that may influence that adequacy and its subsequent mathematical model. Several techniques for modeling the uncertainties have been used: for example, stochastic modeling, 1 fuzzy modeling, 2 information gap decision theory, 3 and robust optimization 4 . Each method needs a certain amount of information regarding uncertain data.…”

Section: Introductionmentioning

confidence: 99%

Transmission expansion planning in presence of electric vehicles at the distribution level

Gouveia

Costa

Sagredo

et al. 2020

Int Trans Electr Energ Syst

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SUMMARY The planning of the transmission network is an issue that, over the years, has received much attention, particularly due to the impact that this infrastructure has on the safe and reliable functioning of electrical systems. The search for solutions addressing climate change has led to several changes in the functioning of electrical systems, particularly concerning the increasing integration of renewable electricity production. However, in recent years, changes in the load side of the electrical system have also emerged. In particular, electric mobility has been developing, and a high penetration of electric vehicles (EVs) is expected in near future. This consumption is supplied by the distribution system but will impact the transmission network. Naturally, the amount of energy used by EVs is subject to uncertainties, which makes the problem complex. Those uncertainties cannot be easily modeled using statistical distributions because of the reduced history of available information. The transmission system operator (TSO) needs an efficient tool to analyze the adequacy of the transmission network to supply the distribution networks with high penetration of EVs. In this paper, a methodology based on symmetric/constrained fuzzy power flow is proposed to find the optimal investment policy at the transmission level while satisfying the technical constraints. The concept of dual variables provided by Lagrange multipliers, the natural result of the nonlinear optimization problem, is used to obtain the most promising reinforcement options considering the actual structure of the transmission network. The proposed model is tested on an IEEE 14‐bus system.

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Energy Bill Minimisation of Dynamic Tariff Bound Residential Consumers by Intentional Load Shifting

Anand¹,

Rajalakshmi²,

Venkatakrishnan³

et al. 2023

Communications in Computer and Information Science

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Uncertainty handling for Electric Vehicle aggregator using IGDT

Cited by 4 publications

References 17 publications

Detection of Lying Electrical Vehicles in Charging Coordination Using Deep Learning

Detection of Lying Electrical Vehicles in Charging Coordination Using Deep Learning

Transmission expansion planning in presence of electric vehicles at the distribution level

Energy Bill Minimisation of Dynamic Tariff Bound Residential Consumers by Intentional Load Shifting

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