A Framework for Analyzing the Impact of Data Integrity/Quality on Electricity Market Operations

Transcription

1 A Framework for Analyzing the Impact of Data Integrity/Quality on Electricity Market Operations Dae-Hyun Choi (Advisor: Prof. Le Xie) Department of Electrical and Computer Engineering Texas A & M University February 4, /51

2 1 Introduction 2 Research Goals 3 Part I: Data Attack on Look-Ahead Dispatch 4 Part II: Sensitivity Analysis of LMP to Data Corruption 5 Conclusions 2/51

3 Smart Grid: A Cyber-Physical System Image Source: 3/51

4 Advanced Grid Sensors Improve Smart Grid Operations Transmission Operator Distribution Operator Distributed Resources Industrial Customer Energy Storage Substation Commercial Customer Residential Customer Other Substation Other Substation Microgrid Communication s Smart Switching Devices Sensor Advanced Computing Image Source: EPRI 4/51

5 Advanced Grid Sensors Improve Smart Grid Operations Transmission Operator Distribution Operator Distributed Resources Industrial Customer Energy Storage Substation Commercial Customer Residential Customer Other Substation Other Substation Microgrid Communication s Smart Switching Devices Sensor Advanced Computing Image Source: EPRI 5/51

6 Data Quality in Future Grid Exabytes year (1 exabyte=250 million DVDs) Distribution automation Workforce management Substation automation Building energy management Transmission Sensors Smart in-home devices AMI deployment Distribution management Image Source:EPRI Big data explosion! Big data analytics Data quality analysis Pre-processing for big data analytic Transmission sensors SCADA/PMU data 6/51

7 Data Integrity in SCADA System Stuxnet Worm, 2010 Nuclear power plant attacked via SCADA systems Stuxnet: Malware more Stuxnet: Computer worm complex, targeted and opens new era of warfare dangerous than ever * SCADA (Supervisory Control And Data Acquisition) A Silent Attack, but Not a Subtle One SmartGrid Update Report* Even small changes in the data could affect the stability of the grid and even jeopardize human safety SCADA Weak Cybersecurity+Data Integrity Violation Grid Malfunction * 7/ 51

8 Motivation Noise EMS (Energy Management System) SCADA Network RTU (Remote Terminal Unit) Adversary MMS (Market Management System) MTU (Master Terminal Unit) Control Center SCADA system Data quality/integrity violation blackouts & financial losses 8/51

9 Background: Power System State Estimation Three-bus system Base case power flow solution G G G G = (, ) = (, ) = (, ) Measurement model = + = (,,,, ) = (,,,, ) = (,,,,, ) Weighted least squares method minimize = s.t = ( ) State estimation solution = (,,,,, ) 9/51

10 Background: Electricity Market Operations State Estimation Base Case Power Flow Solution s.t. Real-time economic dispatch Min ( ) Balance constraint of supply and demand Operational system constraints Day-ahead Day(k-1) Hour(j) Ex-ante real-time price Day(k) Present 5-min(i) Ex-post real-time price Time Static dispatch Look-ahead dispatch Time (5-min) : Forecasted load for static dispatch : Forecasted load for look-ahead dispatch 10/51

11 Problem Statement Normal Condition Control/Computation Layer (Control Center) Economic Dispatch State Estimation Measurement Layer (Sensor Network) SCADA Network Physical Layer (Power Network) 11/51

12 Problem Statement Normal Condition Control/Computation Layer (Control Center) Economic Dispatch After Data Corruption Control/Computation Layer (Control Center) Economic Dispatch State Estimation Measurement Layer (Sensor Network) SCADA Network State Estimation Bad/Malicious Data SCADA Network Physical Layer (Power Network) Physical Layer (Power Network) 1 What are the impacts of data integrity/quality on real-time market prices, namely locational marginal price (LMP), via state estimation? 2 What are analytical tools for quantifying such impacts? 11/51

13 Previous and Missing Work I. Data Integrity Attack on Physical and Economical Grid Operations Attack modeling & analysis based on continuous data manipulation: [1, Liu et al., 2009], [2, Kosut et al., 2010], [3, Kim et al., 2011] Attack modeling & analysis based on discrete data manipulation: [4, Kim et al., 2013] Data attack on static economic dispatch: [5, Xie et al., 2011] Data attack on look-ahead economic dispatch:? II. LMP Sensitivity Analysis Subject to Power System Condition Impact of physical system conditions (e.g., load variations) on LMP sensitivity: [6, Conejo et al., 2005], [7, Li et al., 2007] Impact of sensor data quality on LMP sensitivity:? 12/51

14 Research Goals A Market Participant s Perspective Part I: Data Integrity Attack on Look-Ahead Economic Dispatch Ramp-induced data (RID) attack [Choi, Xie, TSG2013] Undetectable and profitable RID attack strategy Economic impact of RID attack A System Operator s Perspective Part II: Sensitivity Analysis of LMP to Data Corruption Impact of continuous data quality on real-time LMP [Choi, Xie, TPS2014] Impact of discrete data quality on real-time LMP [Choi, Xie, SmartGridComm2013] 13/51

15 Part I: Malicious Ramp-Induced Data (RID) Attack A Market Participant s Perspective RID Attack on Look-Ahead Dispatch in Real-Time Market 1 Attack Modeling - Generation capacity withholding - Covert change of generators inter-temporal ramp constraints 2 Performance Evaluation - Undetectability - Profitability 14/51

16 State Estimation Model Economic Dispatch State Estimation Sensor Data z Power Network Measurement Model z = Sx+e z: measurements vector, e N(0,R) [ ] I S = : system factor matrix H d x: (nodal power injection) states vector Weighted Least Squares Estimate ˆx(z) = (S T R 1 S) 1 S T R 1 z = Bz Bad Data Detection (Chi-squares test) where r = z Sˆx(z) J(ˆx(z)) = r T R 1 r H1 H 0 η χ 15/51

17 Economic Dispatch Model Look-Ahead Dispatch Model Economic Dispatch State Estimation Sensor Data Power Network Pgi(z) s.t. min K C i (P gi [k]) P gi [k] k=1 i G P gi [k] = i G N D n [k] k = 1,...,K n=1 P gi [k] P gi [k 1] R i T k = 1,...,K Pg min i Fl min P gi [k] P max g i F l [k] F max l k = 1,...,K k = 1,...,K l = 1,...,L Attack Target: P gi [0] is updated with ˆP gi (z) at every dispatch interval! 16/51

18 Data Attack Model P*gi(za) (za) Economic Dispatch State Estimation Bad/Malicious Data Power Network Pgi(za) za Attack Measurement Model z a = Sx+e+a z a : corrupted measurement vector a: injected attack vector A Domino Effect of Data Attack z a ˆP gi (z a ) λ(z a ) 17/51

19 Two Main Features of RID Attack: (1) Undetectability After data attack, we have New estimator: ˆx(z a ) = Bz a = ˆx(z)+Ba New residual: r 2 = r+(i SB)a 2 r }{{} 2 + (I SB)a }{{ 2 } Without attack With attack For undetectability, the attacker s goal is to Construct a such that the contribution of (I SB)a 2 still makes the following healthy detection condition hold true: r 2 < η 18/51

20 Two Main Features of RID Attack: (2) Profitability Without Attack With Attack Pgi,max Pgi,max Ri - Ri P*gi[1] P*gi[1] P*gi,a[1] P*gi[0]= Pgi[0] Ri P*gi[0] -CM(a) Pgi,a[0] : Ri - (Shortage Power) Pgi,min :(Excess Power) Pgi,min Capacity withholding condition: C M (a) > R i T L increasing LMP and profit 19/51

21 Attack Strategy: Compute the Attack Vector a s.t. where max δ a span(a) (I SB)a 2 ɛ Undetectable Condition αc M (a)+βc B (a) L R i T δ Profitable Condition δ > 0 C M (a) = E[ˆP gi,a [0] Pg i [0]] = B i a C B (a) = E[ˆP gj,a [0]+R j T Pg max j [0]] = [B j a+r j T] j G c M j G c M α = 1, β = 0: Marginal unit attack (Case I) α = 0, β = 1: Binding unit attack (Case II) α = 1, β = 1: Coordinated attack (Case III) 20/51

22 Simulation Setup G G G G : Compromised Injection Sensor G 3 Figure : IEEE 14-Bus System. Unit Type P min P max Ramp Rate Marginal Cost Coal(1) 0MW 200MW 10MW/5min 30/MWh Wind(2) 0MW 300MW 150MW/5min 20/MWh Nuclear(3) 0MW 300MW 8MW/5min 40/MWh Coal(6) 50MW 250MW 15 MW/5min 55/MWh Oil(8) 60MW 150MW 60 MW/5min 60/MWh Table : Generator Parameters. 21/51

23 Attack Performance Case Static (PE(3)%) Look-ahead (PE(3)%) J (η χ =37.6) I II III Case I: P 3 injection sensor compromised Case II: P 1 injection sensor compromised Case III: P 1, P 3 injection sensors compromised Observation 1 Attack profitability (PE(3) > 100%) Attack undetectability (J < η χ =37.6) 22/51

24 Ramp-Induced Data Attack Increases LMPs LMP increase (\MWh) Time interval (5 min) Figure : LMP Increase of Look-ahead Dispatch with Case 1 Attack. 23/51

25 Attack Relative Magnitude vs Attack Performance Attack Relative Magnitude (ARM) Static (PE(3)) Look-ahead (PE(3)) J ( a z % ) Observation 2 Increasing ARM increasing attack profit at the expense of increasing J 24/51

26 Ramp Rate & Data Accuracy vs Attack Profit Ramp Rate (MW/5min) Variance (σ 2 ) Static (PE(3)) Look-ahead (PE(3)) Observation 3 A slower ramp rate unit targeted increasing attack profit Observation 4 A less accurate sensor compromised increasing attack profit 25/51

27 Part I: Remarks Main Contributions 1 Problem formulation of a novel ramp-induced data attack covert generation capacity withholding 2 An optimization-based undetectable/profitable attack strategy 3 Economic impacts on real-time electricity market operations 26/51

28 Part II: Sensitivity Analysis of LMP to Data Corruption A System Operator s Perspective SCADA Telemetry Topology Processor Observability Analysis State Estimation (b) (a) SCED SCADA Bad Data Topology Error Processing Processing EMS MMS Impact flow of continuous data Impact flow of discrete data Data corruption (a): Power Flow Estimate (b): Network Topology Estimate Part II-A: impact of undetectable error in (a) on LMP Part II-B: impact of undetectable error in (b) on LMP 27/51

29 Research Goal Develop analysis tools to study the impact of data quality on LMP G G 5 G G G 5 G G : Injection sensor : Flow sensor : Voltage magnitude sensor : LMP change G 3 2 G : On circuit breaker : Line exclusion : Off circuit breaker : LMP change G 3 (a) Continuous data corruption (Part II-A) (b) Discrete data corruption (Part II-B) 28/51

30 Part II-A: LMP Sensitivity to Continuous Data Corruption Ex-ante Pricing Ex-post Pricing SCED Ex-ante Dispatch ( ) ( ) {, ( ( ))} ( ) = ( ( )) State Estimation G Power System = ( ) + G Composite function of the Ex-ante and Ex-post LMP vectors: LMP = π(ˆx(z)) Proposed sensitivity matrix: Λ = π z = π }{{} ˆx Λ A ˆx z }{{} Λ B Λ A : Sensitivity matrix of LMPs to state estimates (Economic Impact) Λ B : Sensitivity matrix of state estimates to sensor data (Cyber Impact) A unified closed-form LMP sensitivity matrix Λ 29/51

31 Continuous Data Corruption Manipulates LMP s.t. min P gi N b i=1 C i (P gi ) N b N b λ(z a ) : P gi = τ(z a ) : i=1 µ(z a ) : F min l i=1 L di min ˆP g i (z a ) P gi N b Domino effect: z a i=1 ˆP max g i (z a ) i = 1,...,N b S li (P gi L di ) F max l l = 1,...,N l {ˆPmin } max g i (z a ), ˆP g i (z a ) {λ(z a ),µ(z a )} LMP(z a ) LMP(z a ) = λ(z a )1 Nb S T [µ max (z a ) µ min (z a )] 30/51

32 Derivation of Λ A : KKT Condition Perturbation Approach KKT equations (i) C B i(p gi ) Bg f λ + τ j A ji + µ l S li = 0 P gi j=1 l=1 N b N b (ii) P gi = L di i=1 i=1 N b (iii) A ji P gi = Ĉ j i=1 i = 1,...,N b j = 1,...,B g N b (iv) S li [P gi L di ] = D l l = 1,...,B f. i=1 Perturbed KKT equations ( ) Ci (P gi ) Bg (i) dp gi dλ + A ji dτ j P gi P gi j=1 }{{} M i N b N b (ii) dp gi = dl di i=1 i=1 N b (iii) A ji dp gi = dĉ j i=1 B f + S li dµ l = 0 i = 1,...,N b l=1 j = 1,...,B g N b N b (iv) S li dp gi = S li dl di l = 1,...,B f. i=1 i=1 For example, (ii) N b i=1 P g i = N b i=1 L d i = N b i=1 dp g i = N b i=1 dl d i 31/51

33 Derivation of Λ A : KKT Condition Perturbation Approach Perturbed KKT equations in matrix form dp M 1 Nb Υ g 1 T N b 0 0 dλ Υ T dτ 0 0 s = [ T T U 1 U ] [ dld 2 }{{} dĉ s }{{} dµ s Φ Ξ Sensitivity of lagrangian multipliers to estimated capacity limit dp g [ ] dλ dτ s = dld } Ξ 1 {{ Φ } = Λ dĉ p = [ ] Λ Ld ΛĈs = s Λ dµ p s Finally, Λ A is constructed with λ Ĉ s and µ s Ĉ s P g L d λ L d τ s L d µ s L d ] P g Ĉ s λ Ĉ s τ s Ĉ s µ s Ĉ s 32/51

34 Derivation of Λ B : Iterative State Estimation Equation Gauss-Newton iterative equation for state estimation dˆx k+1 = [G(ˆx k )] 1 H T (ˆx k )R 1 dz k }{{} Ψ(ˆx k ) [ dˆθ k+1 dˆv k+1 ] [ Ψˆθ(ˆx = k ) ΨˆV(ˆx k ) ] dz k Sensitivity of linearized real power estimates to sensor data dẑ r = Desired sensitivity matrix BS Pθ B Pθ B Fθ Λ B = dˆθ = BS Pθ B Pθ B Fθ BS Pθ B Pθ B Fθ Ψˆθ Ψˆθdz 33/51

35 Simulation Setup G G G G : Injection sensor : Flow sensor : Voltage magnitude sensor G 3 Figure : IEEE 14-bus system with a given measurement configuration. Table : Generator parameters in the IEEE 14-bus system. Bus P min (MW) P max (MW) ai(/mwh) bi(/(mw) 2 h) gi gi /51

36 Simulation Results Using a closed-form LMP sensitivity matrix Λ = Λ A Λ B, π n / z m Bus 1 2 Bus 2 The most vulnerable bus 3 Bus 3 Bus 4 3 Bus 5 Bus 10 The most impacting measurement 8 Bus Real Power Flow Measurement (a) π n / z m Bus Bus 2 Bus Bus 4 Bus Bus 10 Bus Reactive Power Flow Measurement (b) 35/51

37 Key Observations 1 Sensitivity grouping property Identical positive or negative sensitivity bus group to data corruption 2 Economically sensitive physical and cyber assets Buses with LMP highly sensitive to data corruption. Significantly influential sensors on LMP change. 3 Impact of different types of sensor data on LMP A more significant impact of real power sensor data on LMP sensitivity 36/51

38 Part II-B: LMP Sensitivity to Network Topology Error Two types of topology error 1 2 Line Exclusion/ Inclusion 1 2 Bus Split/ Merging closed open 2 37/51

39 Part II-B: LMP Sensitivity to Network Topology Error Two types of topology error Attack scenario [4, Kim et al., 2013] ,, (a) Without attack Line Exclusion/ Inclusion Bus Split/ Merging , 0 0, (b) With attack (line exclusion) closed open 2 [Continuous data sensor] Injection sensor Flow sensor [Discrete data sensor] Circuit breaker sensor ( Closed=0 ) Circuit breaker sensor ( Open=1 ) 37/51

40 Topology Data Attack Manipulates LMP s.t. min p i i G C i p i N b N b λ(z a ) : P gn = n=1 τ(z a ) : p min i µ(z a ) : F min l n=1 L dn p i p max i N b n=1 Ĥ l,n (z a )(P gn L dn ) F max l i G l = 1,...,N l Domino effect: z a Ĥl,n(z a ) {λ(z a ),µ(z a )} LMP(z a ) LMP(z a ) = λ(z a )1 Nb Ĥ(z a) T [µ max (z a ) µ min (z a )] 38/51

41 LMP Sensitivity Analysis to Topology Error Proposition 1 (A Closed-Form Shadow Price Expression) The shadow price µ l for the congested transmission line l: where µ l = C(j,i) H l (i,j) C(j,i) = C j C i : Marginal Unit Energy Costs Difference H l (i,j) = H l,i H l,j : Distribution Factors Difference 39/51

42 LMP Sensitivity Analysis to Topology Error (cont d) Corollary 2 (A Closed-Form LMP Sensitivity Index to Topology Error) LMP sensitivity index with respect to the line k status error (k l): where LMP k l = C(j,i)v k l ] T LMP k l = [ LMP l,1 k,..., LMPk l,nb v k l = [v k l,1,...,vk l,n b ] T, v k l,n = Hk l,n H k l (i,j) H l,n H l (i,j) Benefit: less computational time than exhaustive numerical simulations 40/51

43 LMP Sensitivity Analysis to Topology Error (cont d) Corollary 3 (a) vl,n k > 0 decreasing LMP at bus n with topology error A quick prediction of post-lmp direction by topology error (b) vl,n k > vk l,m LMP sensitivity at bus n is higher than at bus m A quick comparison of LMP sensitivity magnitude (c) Increasing C(j,i) increasing LMP sensitivity at any bus Guidelines for a bidding strategy of generation company 41/51

44 Simulation Setup Table : Generator parameters of the IEEE 14-bus system Bus P min P max Marginal Cost 1 0MW 330MW 30/MWh 2 0MW 140MW 20/MWh 3 0MW 100MW 40/MWh 6 0MW 100MW 55/MWh 8 0MW 100MW 60/MWh 3 Line 5-6 is congested 2 : congested line 5-6 closed open open: line 4-5 exclusion Line 4-5 is excluded due to data corruption Figure : IEEE 14-bus system including bus-breaker model. 42/51

45 Topology Errors Significantly Change LMPs LMP(/MWh) Without Topology Change With Topology Change Bus Location (a) LMP Sensitivity(/MWh) SCED Result Analytical Result Bus Location (b) 43/51

46 LMP Sensitivities Under the Same Line 5-6 Congestion LMP Sensitivity(/MWh) 6 The most impacting 5 line/cbs exclusion 1 5 exclusion 2 4 exclusion 6 12 exclusion 3 The most sensitive bus Bus Location The highest sensitivity at bus 6 to line 1-2, 1-5 and 2-4 exclusions Line 1-2 exclusion (blue plot) changes sensitivities the most 44/51

47 Part II: Remarks Main Contributions 1 New analytical frameworks to study real-time LMP sensitivity with respect to data corruption 2 Derivation of closed-form LMP sensitivity analysis tools economically sensitive buses to data corruption influential sensors and transmission lines on LMP change 3 Easily integrated with the existing EMS/MMS 45/51

48 Conclusions Impact of Data Integrity/Quality on Economic Dispatch Bad/Malicious Data State Estimation Economic Dispatch? Part I Data Attack on Look-Ahead Dispatch A market participant s perspective Feasible ramp-induced data (RID) attack strategy for: Undetectability Profitability Part II LMP Sensitivity to Data Corruption A system operator s perspective Analytical tools for LMP sensitivity quantification with respect to: Continuous Data Corruption Discrete Data Corruption 46/51

49 The Bigger Picture Data Quality, Integrity, Privacy-Aware Multi-Scale Decision Making Tool MMS EMS Sensor Current Research - Data quality - Data integrity DMS µgems BEMS HEMS Future Research - Data quality - Data integrity - Data privacy DMS: Distribution Management System, µgems: Microgrid Energy Management System BEMS: Building Energy Management System, HEMS: Home Energy Management System 47/51

50 Multidisciplinary Approach to Future Work A Unified System-Wide Monitoring Tool for Multi-Scale Spatial Data Quality Analysis Smart Meter Solar Power Electric Vehicle Energy Storage Design of interface between EMS and DMS Performance index for the impact analysis of distribution data quality Power system engineering, operations research/optimization Smart Grid Cyber Security and Privacy Data integrity attack modeling and countermeasures Smart meter data privacy-preserving algorithm Power system engineering, computer networking, cyber security, statistical signal processing 48/51

51 References [1] Y. Liu, M. K. Reiter, and P. Ning, False data injection attacks against state estimation in electric power grids, Proc. 16th ACM Conf. Comput. Commun. Security, [2] O. Kosut, L. Jia, R. Thomas, and L. Tong, Malicious data attacks on smart grid state estimation: Attack strategies and countermeasures, 2010 First International Conference on Smart Grid Communications, October [3] T. T. Kim, and H. V..Poor, Strategic Protection Against Data Injection Attacks on Power Grids, IEEE Trans. Smart Grid, vol. 2, no. 2, pp , May [4] J. Kim and L. Tong, On Topology Attack of a Smart Grid: Undetectable Attacks and Counter Measures, IEEE J. Selected Areas in Communications, July [5] L. Xie, Y. Mo, and B. Sinopoli, Integrity Data Attacks in power market operations, IEEE Trans. Smart Grid, vol. 2, no. 4, pp , December [6] A. J. Conejo, E. Castillo, R. Minguez, and F. Milano, Locational marginal price sensitivities, IEEE Trans. Power Syst, vol. 20, no. 4, pp , November [7] F. Li and R. Bo, DCOPF-based LMP simulation: Algorithm, comparison with ACOPF, and sensitivity, IEEE Trans. Power Syst, vol. 22, no. 4, pp , November /51

52 Publications (During Ph.D. Study) Journal [1] D.-H Choi and L. Xie, Sensitivity Analysis of Real-Time LocationalMarginal Price to SCADA Sensor Data Corruption, IEEE Trans. Power Syst (accepted). [2] D.-H Choi and L. Xie, Ramp-Induced Data Attacks on Look-ahead Dispatch in Real-time Power Markets, IEEE Trans. Smart Grid, vol. 4, no. 3, pp , September [3] L. Xie, D.-H. Choi, S. Kar and H. Vincent Poor Fully Distributed State Estimation for Wide-Area Monitoring Systems, IEEE Trans. Smart Grid, vol. 3, no. 3, pp , September [4] S. Wang, L. Cui, J. Que, D.-H. Choi, X. Jiang, S. Cheng and L. Xie A Randomized Response Model for Privacy Preserving Smart Metering, IEEE Trans. Smart Grid, vol. 3, no. 3, pp , September Conference Proceedings [5] D.-H Choi and L. Xie, Impact Analysis of Locational Marginal Price Subject to Power System Topology Errors, 2013 Fourth International Conference on Smart Grid Communications, October [6] D.-H Choi and L. Xie, Malicious Ramp-Induced Temporal Data Attack in Power Market with Look-Ahead Dispatch 2012 Third International Conference on Smart Grid Communications, November 2012 (The Best Paper Award). [7] D.-H Choi and L. Xie, Fully Distributed Bad Data Processing for Wide Area State Estimation, 2011 Second International Conference on Smart Grid Communications, October [8] L. Xie, D.-H. Choi and S. Kar, Cooperative distributed state estimation: Local observability relaxed, Proc. IEEE Power and Energy Society General Meeting, Detroit, Book Chapter [9] L. Xie, D.-H. Choi, S. Kar and H. Vincent Poor Bad/malicious data detection in distributed power system state estimation, in E. Hossain, Z. Han, and H. V. Poor, editors, Smart Grid Communications and Networking, Cambridge University Press, 2012 (to appear). 50/51

53 Thank You! 51/51