Price restricted optimal bidding model using derated sensitivity factors by considering risk concept

Ahmadian, Saeed; Vahidi, B.; Jahanipour, Jahandar; Hoseinian, Seyed Hossein; Rastegar, Hasan

doi:10.1049/iet-gtd.2015.0014

Cited by 14 publications

(7 citation statements)

References 34 publications

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“…Generation capacity can be used for B. This is because, after an adverse event, the high‐capacity generators can inject more spinning and nonspinning reserves into the system and satisfy more demand (Ahmadian, Vahidi, Jahanipour, Hoseinian, & Rastegar, ; Matos, Bessa, Botterud, & Zhou, ). Factor C is time‐to‐recovery.…”

Section: Numerical Studiesmentioning

confidence: 99%

Design and Assessment Methodology for System Resilience Metrics

Najarian

Lim

2019

Risk Analysis

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By providing objective measures, resilience metrics (RMs) help planners, designers, and decisionmakers to have a grasp of the resilience status of a system. Conceptual frameworks establish a sound basis for RM development. However, a significant challenge that has yet to be addressed is the assessment of the validity of RMs, whether they reflect all abilities of a resilient system, and whether or not they overrate/underrate these abilities. This article covers this gap by introducing a methodology that can show the validity of an RM against its conceptual framework. This methodology combines experimental design methods and statistical analysis techniques that provide an insight into the RM's quality. We also propose a new metric that can be used for general systems. The analysis of the proposed metric using the presented methodology shows that this metric is a better indicator of the system's abilities compared to the existing metrics.

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Section: Numerical Studiesmentioning

confidence: 99%

Design and Assessment Methodology for System Resilience Metrics

Najarian

Lim

2019

Risk Analysis

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“…Thus, the attacker's decision-making problem turns into bi-level optimization problem. Using the dual formulation of (9b) and the strong duality condition [24], the set of equations in (9b) can be turned into the MPEC problem that will be discussed in the next subsection.…”

Section: B Attacking Electricity Marketmentioning

confidence: 99%

“…Replacing the dual constraints (differentiating with respect to decision variables) with the strong duality condition (at the optimal point) of (9b) leads to a MPEC problem. It must be mentioned that the strong duality for the power system physical constraints are already proven in [24], [25], and [29]. To be specific first we need to introduce the Lagrangian multipliers and Lagrangian function.…”

Section: Correlation Between Market and Compromised Data Using Mpecmentioning

confidence: 99%

Modelling Cyber Attacks on Electricity Market Using Mathematical Programming With Equilibrium Constraints

et al. 2019

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With the development of communication infrastructure in smart grids, cyber security reinforcement has become one of the most challenging issues for power system operators. In this paper, an attacker is considered a participant in the virtual bidding procedure in the day-ahead (DA) and realtime (RT) electricity markets to maximize its profit. The cyber attacker attempts to identify the optimal power system measurements to attack along with the false data injected into measurement devices. Towards the maximum profit, the attacker needs to specify the relation between manipulated meters, virtual power traded in the markets, and electricity prices. Meanwhile, to avoid being detected by the system operator, the attacker considers the physical power system constraints existing in the DA and RT markets. Then, a bilevel optimization model is presented which combines the real electricity market state variables with the attacker decision-variables. Using the mathematical problem with equilibrium constraints, the presented bilevel model is converted into a single level optimization problem and the optimal decision variables for the attacker are obtained. Finally, simulation results are provided to demonstrate the performance of the attacker, which also provides insights for security improvement. INDEX TERMS Cyber-security, electricity market, false data injection, mathematical programming with equilibrium constraints.

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“…Another reason for the slow uptake of the automated CAD systems is that DNN-based models tend to fail silently and have no risk-management mechanism [10,11]. In other words, they cannot inform doctors when they are not confident about their predictions.…”

Section: Introductionmentioning

confidence: 99%

Risk-Aware Machine Learning Classifier for Skin Lesion Diagnosis

Mobiny

Singh

Nguyen

2019

JCM

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Knowing when a machine learning system is not confident about its prediction is crucial in medical domains where safety is critical. Ideally, a machine learning algorithm should make a prediction only when it is highly certain about its competency, and refer the case to physicians otherwise. In this paper, we investigate how Bayesian deep learning can improve the performance of the machine–physician team in the skin lesion classification task. We used the publicly available HAM10000 dataset, which includes samples from seven common skin lesion categories: Melanoma (MEL), Melanocytic Nevi (NV), Basal Cell Carcinoma (BCC), Actinic Keratoses and Intraepithelial Carcinoma (AKIEC), Benign Keratosis (BKL), Dermatofibroma (DF), and Vascular (VASC) lesions. Our experimental results show that Bayesian deep networks can boost the diagnostic performance of the standard DenseNet-169 model from 81.35% to 83.59% without incurring additional parameters or heavy computation. More importantly, a hybrid physician–machine workflow reaches a classification accuracy of 90 % while only referring 35 % of the cases to physicians. The findings are expected to generalize to other medical diagnosis applications. We believe that the availability of risk-aware machine learning methods will enable a wider adoption of machine learning technology in clinical settings.

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Price restricted optimal bidding model using derated sensitivity factors by considering risk concept

Cited by 14 publications

References 34 publications

Design and Assessment Methodology for System Resilience Metrics

Design and Assessment Methodology for System Resilience Metrics

Modelling Cyber Attacks on Electricity Market Using Mathematical Programming With Equilibrium Constraints

Risk-Aware Machine Learning Classifier for Skin Lesion Diagnosis

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