Measurement Based Dynamic Load Modeling in Power Systems

Transcription

1 Measurement Based Dynamic Load Modeling in Power Systems Alireza Rouhani 1 Bilgehan Donmez 2 Ali Abur Northeastern University 1 Currently with Dominion Power (Received his PhD in Spring 2017) 2 Cuurently with AMSC (part-time PhD Student)

2 Importance of Load Modeling ZIP models are not suitable for Voltage Stability Analysis Voltage collapse, FIDVR events Evolution of distribution feeders (smart grid, DER, etc.) Source: NERC LMTF 2

3 Difficulty In Model Development Day-to-day variations in load composition Various equipment scattered along the feeder. What feeder impedance to use? How to break down the load into its components? Induction motors Lighting Power electronics Distributed generation Static load Feeder impedance Protection settings Source: NERC LMTF 3

4 Two Approaches 1. Component Based Comprehensive models: requires detailed engineering analysis Significant effort needed to survey the load composition Accurate given that the right model can be constructed Good fit for offline transmission planning studies 2. Measurement Based Simple but accurate load model Ease of implementation in online applications (e.g., online dynamic state estimation, real-time transient stability analysis) Model accuracy is a function of measurement frequency Automatic real-time tracking of model parameters 4

5 Component Based Models CMLD: The best component based model we currently have Works great given that engineering analysis are done to figure out the correct load composition and parameters Great for offline voltage/transient stability analysis 5

6 Measurement Based Models Real-time parameter tracking First, second, third order models depending on load complexity Any measurement data can be used: SCADA, PMU, DFR, smart meters, etc. The time constant determined by measurement frequency [5] H. Renmu, M. Jin, and D. Hill, Composite load modeling via measurement approach, IEEE Trans. Power Syst., vol. 21, no. 2, pp , May

7 First Order Model [2] 1. A step in voltage triggers a step in power 2. The power settles to a new steady-state value 3. The recovery appears to be of exponential form (typically undamped) 4. The size of the step and steady-state value are nonlinearly related to voltage [2] D. Karlsson and D. J. Hill, Modelling and Identification of Nonlinear Dynamic Loads in Power Systems," IEEE Transactions on Power Systems, vol. 9,no. 1, Feb

8 First Order Model Examples (1) Voltage recovery depends on percentage of motor load Source: AMSC 8

9 First Order Model Examples (2) Voltage recovery depends on distribution impedance Source: AMSC 9

10 Real-Time Dynamic Parameter Estimation for an Exponential Dynamic Load Model This work [1] considers a first order model (the exponential dynamic load model) and aims to identify and track its parameters on-line. Unscented Kalman Filter (UKF) is used as a dynamic state/parameter estimator to track the unknown parameters associated with exponential dynamic load model. [1] A. Rouhani and A. Abur, "Real-Time Dynamic Parameter Estimation for an Exponential Dynamic Load Model," IEEE Transactions on Smart Grid, vol. 7,no. 3, pp , July

11 Real-Time Dynamic Parameter Estimation for an Exponential Dynamic Load Model Exponential dynamic load model: The active power consumed by the load are assumed to be related to the voltage in the following non-linear manner : Similar equations are valid for reactive power. 11

12 Real-Time Dynamic Parameter Estimation for an Exponential Dynamic Load Model Proposed approach: UKF is implemented and used to track the state vector which includes the unknown parameters associated with the exponential dynamic load model : Dynamic state variables of generators again will be updated by discretions of the generators. The measurement is assumed to : UKF 12

13 Real-Time Dynamic Parameter Estimation for an Exponential Dynamic Load Model Testing of the proposed approach using simulated measurements: Here the parameters associated with the load are assumed to be: α s = t p 0.32, α = 1.65 & T = 70[sec]. Now considering the following scenario the proposed approach will be evaluated: 1. is reduced from to is increased from 1.65 to is increased from 70 sec. to 80 sec. 13

14 Real-Time Dynamic Parameter Estimation for an Exponential Dynamic Load Model Using simulated measurements: 14

15 Real-Time Dynamic Parameter Estimation for an Exponential Dynamic Load Model Testing the proposed approach by using actual recorded measurements: Here, the proposed approach is evaluated based on actual recorded measurement data where synchronized voltage and power measurements are acquired every 6 seconds for a utility distribution feeder. The total duration of the recordings is 1440 minutes or 24 hours. The following figure shows that the voltage of the feeder (V) increases during this period (from almost 115 kv to kv). 15

16 Real-Time Dynamic Parameter Estimation for an Exponential Dynamic Load Model Test results: 16

17 Real-Time Dynamic Parameter Estimation for an Exponential Dynamic Load Model Validation of the proposed approach using actual recorded measurements: In order to quantify the accuracy, the following error metric is used to evaluate the performance of the UKF in tracking the real-time model of the active power demand: The following figure illustrates how the MSE value is gradually reduced finally settling below an acceptable level of 0.25, validating the satisfactory performance of UKF. MSE time [min] 17

18 Second Order Model [3] Consider a second order model where T s are time constants and K s are gain coefficients: Better capability to represent load behaviors during system disturbances [3] C. Lin et al., Dynamic Load Models in Power Systems Using the Measurement Approach," IEEE Transactions on Power Systems, vol. 8,no. 1, Feb

19 Order Comparison [3], [4] [4] E. Welfonder, H. Weber and B. Hall, Investigations of the frequency and voltage dependence of load part systems using a digital self-acting measuring and identfication system," IEEE Transactions on Power Systems, vol. 4, pp ,

20 Other Models [5] A combination of ZIP model and induction motor model This 14 parameter model provides good generalization capability Good voltage sag match during faults Can be interpreted physically [5] H. Renmu, M. Jin, and D. Hill, Composite load modeling via measurement approach, IEEE Trans. Power Syst., vol. 21, no. 2, pp , May

21 Use Case for Measurement Based Models Simple models Suitable for online applications (Dynamic State Estimation, Real-time Transient Stability) Can be a complementary tool to the CMLD model for feeders with adequate sensors Any measurement data can be used: SCADA, PMU, DFR, smart meters, etc. 21

22 What is next? Take advantage of high frequency meters (PMUs, smart meters, etc.) to develop real-time measurement models Case studies using recorded system data Source: AMSC 22

23 References 23 [1] A. Rouhani and A. Abur, "Real-Time Dynamic Parameter Estimation for an Exponential Dynamic Load Model," IEEE Trans. Smart Grid, vol. 7,no. 3, pp , July [2] D. Karlsson and D. J. Hill, Modelling and Identification of Nonlinear Dynamic Loads in Power Systems," IEEE Transactions on Power Systems, vol. 9,no. 1, Feb [3] C. Lin et al., Dynamic Load Models in Power Systems Using the Measurement Approach," IEEE Transactions on Power Systems, vol. 8,no. 1, Feb [4] E. Welfonder, H. Weber and B. Hall, Investigations of the frequency and voltage dependence of load part systems using a digital self-acting measuring and identfication system," IEEE Transactions on Power Systems, vol. 4, pp , [5] H. Renmu, M. Jin, and D. Hill, Composite load modeling via measurement approach, IEEE Trans. Power Syst., vol. 21, no. 2, pp , May 2006.