Impact of Integrity Attacks on Real-time Pricing in Smart Grids Rui - - PowerPoint PPT Presentation

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Impact of Integrity Attacks on Real-time Pricing in Smart Grids Rui - - PowerPoint PPT Presentation

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Impact of Integrity Attacks on Real-time Pricing in Smart Grids Rui Tan, Varun Badrinath Krishna, David K. Y. Yau, Zbigniew Kalbarczyk Presented by: Tianyuan Liu 10/27/2016 Background Real-time Pricing (RTP) in Smart Grid Balance the

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Impact of Integrity Attacks on Real-time Pricing in Smart Grids

Rui Tan, Varun Badrinath Krishna, David K. Y. Yau, Zbigniew Kalbarczyk Presented by: Tianyuan Liu 10/27/2016

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SLIDE 2

Background

Real-time Pricing (RTP) in Smart Grid

  • Balance the demand and supply
  • (Smart) consumers behave differently under different prices

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Day-ahead predicted price Real-time price

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SLIDE 3

Quick Model & Solution

  • Supply

𝑑 πœ‡ = βˆ‘ 𝑑%(πœ‡)

  • %
  • Demand

d πœ‡ = βˆ‘ 𝑓%(πœ‡)

  • %

+ π‘π‘π‘‘π‘“π‘šπ‘—π‘œπ‘“%

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SLIDE 4

Quick Model & Solution

  • Minimize the scheduling error

min

34 𝑑 πœ‡5 βˆ’ 𝑒 πœ‡5

𝑑 πœ‡5 ← 𝑒 πœ‡59:

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πœ‡5 = 𝑑9:(𝑒 πœ‡59: )

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SLIDE 5

Unstable RTP

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SLIDE 6

Unstable RTP

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SLIDE 7

Unstable RTP

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𝑑 πœ‡5 ← 𝑒 πœ‡59:

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SLIDE 8

Unstable RTP

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SLIDE 9

Unstable RTP

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SLIDE 10

Unstable RTP

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SLIDE 11

Unstable RTP Summary

Diverging price may lead to

  • Overloaded power network
  • Wasted power
  • Increased operational cost

System vulnerable to integrity attacks

  • False data injection
  • Take down the whole Smart Grid system

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SLIDE 12

Stabilizing RTP

Control-theoretic approach

  • Stabilizing the scheduling error
  • Scheduling error ~ supply – elastic demand

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SLIDE 13

Stabilizing RTP

πœ‡5 = πœ‡59: + 2πœƒ 𝑔 πœ‡> (𝑑59: βˆ’ 𝑓59:)

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SLIDE 14

Integrity Attack Models

Scaling attack

  • Inject false price value
  • πœ‡5 β†’ π›Ώπœ‡5
  • 0 < 𝛿 < 1, decreased price -> increased demand
  • 𝛿 > 1, increased price -> decreased demand

Delay attack

  • Use old price information
  • πœ‡5 β†’ πœ‡59E

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SLIDE 15

Analytic Results – Scaling Attack

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  • 𝛿: scaling factor
  • 𝜍: ratio of compromised smart meters
  • β„Ž β‰œ IΜ‡(3K)

L̇(3K) : marginal demand-supply ratio at fixed operating point

Region of stability

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SLIDE 16

Analytic Results – Delay Attack

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  • 𝜐: delay (hours)
  • 𝜍: ratio of compromised smart meters
  • β„Ž β‰œ IΜ‡(3K)

L̇(3K) : marginal demand-supply ratio at fixed operating point

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SLIDE 17

Simulation

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GridLAB-D

  • Physical characteristics
  • 1405 consumers
  • Simulating consumers with/without attacks

Results

  • The system is stable without attack or with weak attack
  • The system can be destabilized only if attackers are super strong (large 𝜍, 𝜐

and small 𝛿)

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SLIDE 18

Discussion

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Contributions

  • Comprehensive theoretical analysis
  • Good resilience under attacks

Limitations

  • Simple Model? Less important security issue?

Results too good? Or not a problem to worry about? How do you like the idea of applying control-theoretic approach here?