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Emerging: Smart Grid & Energy Management

Learning to dynamically price electricity demand based on multi-armed bandit

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Authors:
Ahmadreza Moradipari, Cody Silva, Mahnoosh alizadeh
Submitted On:
29 November 2018 - 1:42am
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[1] Ahmadreza Moradipari, Cody Silva, Mahnoosh alizadeh, "Learning to dynamically price electricity demand based on multi-armed bandit ", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/3823. Accessed: Mar. 21, 2019.
@article{3823-18,
url = {http://sigport.org/3823},
author = {Ahmadreza Moradipari; Cody Silva; Mahnoosh alizadeh },
publisher = {IEEE SigPort},
title = {Learning to dynamically price electricity demand based on multi-armed bandit },
year = {2018} }
TY - EJOUR
T1 - Learning to dynamically price electricity demand based on multi-armed bandit
AU - Ahmadreza Moradipari; Cody Silva; Mahnoosh alizadeh
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/3823
ER -
Ahmadreza Moradipari, Cody Silva, Mahnoosh alizadeh. (2018). Learning to dynamically price electricity demand based on multi-armed bandit . IEEE SigPort. http://sigport.org/3823
Ahmadreza Moradipari, Cody Silva, Mahnoosh alizadeh, 2018. Learning to dynamically price electricity demand based on multi-armed bandit . Available at: http://sigport.org/3823.
Ahmadreza Moradipari, Cody Silva, Mahnoosh alizadeh. (2018). "Learning to dynamically price electricity demand based on multi-armed bandit ." Web.
1. Ahmadreza Moradipari, Cody Silva, Mahnoosh alizadeh. Learning to dynamically price electricity demand based on multi-armed bandit [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/3823

A Fixed-Point Iteration for Steady-State Analysis of Water Distribution Networks

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Authors:
M. Bazrafshan, N. Gatsis, M. Giacomoni, and A. F. Taha
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28 November 2018 - 11:11am
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[1] M. Bazrafshan, N. Gatsis, M. Giacomoni, and A. F. Taha, "A Fixed-Point Iteration for Steady-State Analysis of Water Distribution Networks", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/3821. Accessed: Mar. 21, 2019.
@article{3821-18,
url = {http://sigport.org/3821},
author = {M. Bazrafshan; N. Gatsis; M. Giacomoni; and A. F. Taha },
publisher = {IEEE SigPort},
title = {A Fixed-Point Iteration for Steady-State Analysis of Water Distribution Networks},
year = {2018} }
TY - EJOUR
T1 - A Fixed-Point Iteration for Steady-State Analysis of Water Distribution Networks
AU - M. Bazrafshan; N. Gatsis; M. Giacomoni; and A. F. Taha
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/3821
ER -
M. Bazrafshan, N. Gatsis, M. Giacomoni, and A. F. Taha. (2018). A Fixed-Point Iteration for Steady-State Analysis of Water Distribution Networks. IEEE SigPort. http://sigport.org/3821
M. Bazrafshan, N. Gatsis, M. Giacomoni, and A. F. Taha, 2018. A Fixed-Point Iteration for Steady-State Analysis of Water Distribution Networks. Available at: http://sigport.org/3821.
M. Bazrafshan, N. Gatsis, M. Giacomoni, and A. F. Taha. (2018). "A Fixed-Point Iteration for Steady-State Analysis of Water Distribution Networks." Web.
1. M. Bazrafshan, N. Gatsis, M. Giacomoni, and A. F. Taha. A Fixed-Point Iteration for Steady-State Analysis of Water Distribution Networks [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/3821

Persistent Hyperspectral Observations of the Urban Lightscape

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Authors:
J. Baur, G. Dobler, F. Bianco, M. Sharma, A. Karpf
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27 November 2018 - 6:33pm
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slides for lecture MHI L.2.3.

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[1] J. Baur, G. Dobler, F. Bianco, M. Sharma, A. Karpf, "Persistent Hyperspectral Observations of the Urban Lightscape", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/3817. Accessed: Mar. 21, 2019.
@article{3817-18,
url = {http://sigport.org/3817},
author = {J. Baur; G. Dobler; F. Bianco; M. Sharma; A. Karpf },
publisher = {IEEE SigPort},
title = {Persistent Hyperspectral Observations of the Urban Lightscape},
year = {2018} }
TY - EJOUR
T1 - Persistent Hyperspectral Observations of the Urban Lightscape
AU - J. Baur; G. Dobler; F. Bianco; M. Sharma; A. Karpf
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/3817
ER -
J. Baur, G. Dobler, F. Bianco, M. Sharma, A. Karpf. (2018). Persistent Hyperspectral Observations of the Urban Lightscape. IEEE SigPort. http://sigport.org/3817
J. Baur, G. Dobler, F. Bianco, M. Sharma, A. Karpf, 2018. Persistent Hyperspectral Observations of the Urban Lightscape. Available at: http://sigport.org/3817.
J. Baur, G. Dobler, F. Bianco, M. Sharma, A. Karpf. (2018). "Persistent Hyperspectral Observations of the Urban Lightscape." Web.
1. J. Baur, G. Dobler, F. Bianco, M. Sharma, A. Karpf. Persistent Hyperspectral Observations of the Urban Lightscape [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/3817

Cyber attacks on Smart Energy Grids Using Generative Adversarial Networks


Recently, cyber-attacks to smart energy grid has become a critical subject for Energy System Operators (ESOs). To keep the energy grid cyber-secured, the attacker’s behavior, resources and goals must be modeled properly. Then, the counter-measurement actions can be designed based on the attacker's model. In this paper, a new zero-sum game based on the Generative Adversarial Networks (GANs) is presented. The attacker to energy smart grid pursues two objects.

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Authors:
Heidar Malki, Zhu Han
Submitted On:
27 November 2018 - 5:25am
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poster_GlobalSIP.pdf

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[1] Heidar Malki, Zhu Han, "Cyber attacks on Smart Energy Grids Using Generative Adversarial Networks", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/3809. Accessed: Mar. 21, 2019.
@article{3809-18,
url = {http://sigport.org/3809},
author = {Heidar Malki; Zhu Han },
publisher = {IEEE SigPort},
title = {Cyber attacks on Smart Energy Grids Using Generative Adversarial Networks},
year = {2018} }
TY - EJOUR
T1 - Cyber attacks on Smart Energy Grids Using Generative Adversarial Networks
AU - Heidar Malki; Zhu Han
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/3809
ER -
Heidar Malki, Zhu Han. (2018). Cyber attacks on Smart Energy Grids Using Generative Adversarial Networks. IEEE SigPort. http://sigport.org/3809
Heidar Malki, Zhu Han, 2018. Cyber attacks on Smart Energy Grids Using Generative Adversarial Networks. Available at: http://sigport.org/3809.
Heidar Malki, Zhu Han. (2018). "Cyber attacks on Smart Energy Grids Using Generative Adversarial Networks." Web.
1. Heidar Malki, Zhu Han. Cyber attacks on Smart Energy Grids Using Generative Adversarial Networks [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/3809

Kernel-Based Learning for Smart Inverter Control


Distribution grids are currently challenged by frequent voltage excursions induced by intermittent solar generation. Smart inverters have been advocated as a fast-responding means to regulate voltage and minimize ohmic losses. Since optimal inverter coordination may be computationally challenging and preset local control rules are subpar, the approach of customized control rules designed in a quasi-static fashion features as a golden middle. Departing from affine control rules, this work puts forth non-linear inverter control policies.

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Authors:
Aditie Garg, Mana Jalali, Vassilis Kekatos
Submitted On:
27 November 2018 - 4:30am
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[1] Aditie Garg, Mana Jalali, Vassilis Kekatos, "Kernel-Based Learning for Smart Inverter Control", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/3808. Accessed: Mar. 21, 2019.
@article{3808-18,
url = {http://sigport.org/3808},
author = {Aditie Garg; Mana Jalali; Vassilis Kekatos },
publisher = {IEEE SigPort},
title = {Kernel-Based Learning for Smart Inverter Control},
year = {2018} }
TY - EJOUR
T1 - Kernel-Based Learning for Smart Inverter Control
AU - Aditie Garg; Mana Jalali; Vassilis Kekatos
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/3808
ER -
Aditie Garg, Mana Jalali, Vassilis Kekatos. (2018). Kernel-Based Learning for Smart Inverter Control. IEEE SigPort. http://sigport.org/3808
Aditie Garg, Mana Jalali, Vassilis Kekatos, 2018. Kernel-Based Learning for Smart Inverter Control. Available at: http://sigport.org/3808.
Aditie Garg, Mana Jalali, Vassilis Kekatos. (2018). "Kernel-Based Learning for Smart Inverter Control." Web.
1. Aditie Garg, Mana Jalali, Vassilis Kekatos. Kernel-Based Learning for Smart Inverter Control [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/3808

SPARSE ERROR CORRECTION FOR PMU DATA UNDER GPS SPOOFING ATTACKS


Time-synchronized phasor measurements from phasor measurement units (PMUs) are valuable for real time monitoring and control. However, their reliance on civilian GPS signals makes them vulnerable to GPS signal spoofing attacks which can be launched by an adversary to falsify PMU data entries.

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Authors:
Shashini De Silva, Travis Hagen, Jinsub Kim, Eduardo Cotilla-Sanchez
Submitted On:
25 November 2018 - 9:43pm
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[1] Shashini De Silva, Travis Hagen, Jinsub Kim, Eduardo Cotilla-Sanchez, "SPARSE ERROR CORRECTION FOR PMU DATA UNDER GPS SPOOFING ATTACKS", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/3775. Accessed: Mar. 21, 2019.
@article{3775-18,
url = {http://sigport.org/3775},
author = {Shashini De Silva; Travis Hagen; Jinsub Kim; Eduardo Cotilla-Sanchez },
publisher = {IEEE SigPort},
title = {SPARSE ERROR CORRECTION FOR PMU DATA UNDER GPS SPOOFING ATTACKS},
year = {2018} }
TY - EJOUR
T1 - SPARSE ERROR CORRECTION FOR PMU DATA UNDER GPS SPOOFING ATTACKS
AU - Shashini De Silva; Travis Hagen; Jinsub Kim; Eduardo Cotilla-Sanchez
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/3775
ER -
Shashini De Silva, Travis Hagen, Jinsub Kim, Eduardo Cotilla-Sanchez. (2018). SPARSE ERROR CORRECTION FOR PMU DATA UNDER GPS SPOOFING ATTACKS. IEEE SigPort. http://sigport.org/3775
Shashini De Silva, Travis Hagen, Jinsub Kim, Eduardo Cotilla-Sanchez, 2018. SPARSE ERROR CORRECTION FOR PMU DATA UNDER GPS SPOOFING ATTACKS. Available at: http://sigport.org/3775.
Shashini De Silva, Travis Hagen, Jinsub Kim, Eduardo Cotilla-Sanchez. (2018). "SPARSE ERROR CORRECTION FOR PMU DATA UNDER GPS SPOOFING ATTACKS." Web.
1. Shashini De Silva, Travis Hagen, Jinsub Kim, Eduardo Cotilla-Sanchez. SPARSE ERROR CORRECTION FOR PMU DATA UNDER GPS SPOOFING ATTACKS [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/3775

Coincident Peak Prediction Using a Feed-Forward Neural Network


A significant portion of a business' annual electrical payments can be made up of coincident peak charges: a transmission surcharge for power consumed when the entire system is at peak demand. This charge occurs only a few times annually, but with per-MW prices orders of magnitudes higher than non-peak times. A business is incentivized to reduce its power consumption, but accurately predicting the timing of peak demand charges is nontrivial. In this paper we present a decision framework based on predicting the day-ahead likelihood of peak demand charges.

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Authors:
Daniel Kirschen, Baosen Zhang
Submitted On:
28 November 2018 - 2:08pm
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Up-to-date slides.

Coincident_Peak_Prediction_Slides.pdf

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[1] Daniel Kirschen, Baosen Zhang, "Coincident Peak Prediction Using a Feed-Forward Neural Network", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/3773. Accessed: Mar. 21, 2019.
@article{3773-18,
url = {http://sigport.org/3773},
author = {Daniel Kirschen; Baosen Zhang },
publisher = {IEEE SigPort},
title = {Coincident Peak Prediction Using a Feed-Forward Neural Network},
year = {2018} }
TY - EJOUR
T1 - Coincident Peak Prediction Using a Feed-Forward Neural Network
AU - Daniel Kirschen; Baosen Zhang
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/3773
ER -
Daniel Kirschen, Baosen Zhang. (2018). Coincident Peak Prediction Using a Feed-Forward Neural Network. IEEE SigPort. http://sigport.org/3773
Daniel Kirschen, Baosen Zhang, 2018. Coincident Peak Prediction Using a Feed-Forward Neural Network. Available at: http://sigport.org/3773.
Daniel Kirschen, Baosen Zhang. (2018). "Coincident Peak Prediction Using a Feed-Forward Neural Network." Web.
1. Daniel Kirschen, Baosen Zhang. Coincident Peak Prediction Using a Feed-Forward Neural Network [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/3773

LARGE-SCALE ADAPTIVE ELECTRIC VEHICLE CHARGING


Large-scale charging infrastructure will play an important role in supporting the adoption of electric vehicles. In this presentation, we describe a unique physical testbed for large-scale, high-density EV charging research which we call the Adaptive Charging Network (ACN). We describe the architecture of the ACN including its hardware and software components. We also present a practical framework for online scheduling, which is based on model predictive control and convex optimization.

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Authors:
Zachary J. Lee, Daniel Chang, Cheng Jin, George S. Lee, Rand Lee, Ted Lee, Steven H. Low
Submitted On:
24 November 2018 - 3:10am
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Global SIP Presentation.pdf

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[1] Zachary J. Lee, Daniel Chang, Cheng Jin, George S. Lee, Rand Lee, Ted Lee, Steven H. Low, "LARGE-SCALE ADAPTIVE ELECTRIC VEHICLE CHARGING", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/3768. Accessed: Mar. 21, 2019.
@article{3768-18,
url = {http://sigport.org/3768},
author = {Zachary J. Lee; Daniel Chang; Cheng Jin; George S. Lee; Rand Lee; Ted Lee; Steven H. Low },
publisher = {IEEE SigPort},
title = {LARGE-SCALE ADAPTIVE ELECTRIC VEHICLE CHARGING},
year = {2018} }
TY - EJOUR
T1 - LARGE-SCALE ADAPTIVE ELECTRIC VEHICLE CHARGING
AU - Zachary J. Lee; Daniel Chang; Cheng Jin; George S. Lee; Rand Lee; Ted Lee; Steven H. Low
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/3768
ER -
Zachary J. Lee, Daniel Chang, Cheng Jin, George S. Lee, Rand Lee, Ted Lee, Steven H. Low. (2018). LARGE-SCALE ADAPTIVE ELECTRIC VEHICLE CHARGING. IEEE SigPort. http://sigport.org/3768
Zachary J. Lee, Daniel Chang, Cheng Jin, George S. Lee, Rand Lee, Ted Lee, Steven H. Low, 2018. LARGE-SCALE ADAPTIVE ELECTRIC VEHICLE CHARGING. Available at: http://sigport.org/3768.
Zachary J. Lee, Daniel Chang, Cheng Jin, George S. Lee, Rand Lee, Ted Lee, Steven H. Low. (2018). "LARGE-SCALE ADAPTIVE ELECTRIC VEHICLE CHARGING." Web.
1. Zachary J. Lee, Daniel Chang, Cheng Jin, George S. Lee, Rand Lee, Ted Lee, Steven H. Low. LARGE-SCALE ADAPTIVE ELECTRIC VEHICLE CHARGING [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/3768

Fast Nonconvex SDP Solver for Large-scale Power System State Estimation

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24 November 2018 - 12:25am
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Fast Nonconvex SDP Solver for Large-scale Power System State Estimation

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[1] , "Fast Nonconvex SDP Solver for Large-scale Power System State Estimation", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/3766. Accessed: Mar. 21, 2019.
@article{3766-18,
url = {http://sigport.org/3766},
author = { },
publisher = {IEEE SigPort},
title = {Fast Nonconvex SDP Solver for Large-scale Power System State Estimation},
year = {2018} }
TY - EJOUR
T1 - Fast Nonconvex SDP Solver for Large-scale Power System State Estimation
AU -
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/3766
ER -
. (2018). Fast Nonconvex SDP Solver for Large-scale Power System State Estimation. IEEE SigPort. http://sigport.org/3766
, 2018. Fast Nonconvex SDP Solver for Large-scale Power System State Estimation. Available at: http://sigport.org/3766.
. (2018). "Fast Nonconvex SDP Solver for Large-scale Power System State Estimation." Web.
1. . Fast Nonconvex SDP Solver for Large-scale Power System State Estimation [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/3766

Real-Time Power Outage Detection System using Social Sensing and Neural Networks

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Submitted On:
21 November 2018 - 7:49pm
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Paper Presentation1.pdf

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[1] , "Real-Time Power Outage Detection System using Social Sensing and Neural Networks ", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/3707. Accessed: Mar. 21, 2019.
@article{3707-18,
url = {http://sigport.org/3707},
author = { },
publisher = {IEEE SigPort},
title = {Real-Time Power Outage Detection System using Social Sensing and Neural Networks },
year = {2018} }
TY - EJOUR
T1 - Real-Time Power Outage Detection System using Social Sensing and Neural Networks
AU -
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/3707
ER -
. (2018). Real-Time Power Outage Detection System using Social Sensing and Neural Networks . IEEE SigPort. http://sigport.org/3707
, 2018. Real-Time Power Outage Detection System using Social Sensing and Neural Networks . Available at: http://sigport.org/3707.
. (2018). "Real-Time Power Outage Detection System using Social Sensing and Neural Networks ." Web.
1. . Real-Time Power Outage Detection System using Social Sensing and Neural Networks [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/3707

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