Demonstration of Using Flywheels and FACTS
Control for Transient Stabilization and
Interactions with Transactive Energy Market
Kevin Bachovchin
Milos Cvetkovic
Martin Wagner
kbachovc@andrew.cmu.edu
Carnegie Mellon University
mcvetkov@mit.edu
Massachusetts Institute of
Technology
mwagner1@andrew.cmu.edu
Carnegie Mellon University
10th CMU Electricity Conference
April 1, 2015
Joint work with
Prof. Marija Ilic
milic@ece.cmu.edu
Outline
 Motivation for fast control
 Transient stabilization using flywheel energy storage systems
 Competitive control; cancel effect of wind disturbance
 Demo on the Smart Grid in a Room Simulator (SGRS)
 Interaction of flywheel control with transactive energy market
 Transient stabilization using FACTS devices
 Cooperative control logic based on ectropy
 Implications for SGRS numerical integration
2
Motivation for Fast Control
 Transactive energy control is a steady-state scheduling
concept
 Does not guarantee dynamic stability
 Interest in implementing more renewable energy sources
into future power grids
 Renewables introduce more uncertainty, intermittency and
unpredictability => a challenge for control design
 Large sudden deviations in wind power can cause
 high deviations in frequency and voltage
 transient instabilities
 Possible solution: fast energy storage
 flywheel energy storage systems
 FACTS devices
3
Objective
 Use flywheels for transient stabilization of power grids in
response to large sudden wind disturbances
 Design nonlinear power electronic control so that the
flywheel absorbs the disturbance and the rest of the system
is minimally affected
P
P
P
P
4
Variable Speed Drives for Flywheels
 Use AC/DC/AC converter to regulate the speed of the flywheel (and hence the
energy stored) to a different frequency than the grid frequency
 Controllable inputs are the switch positions in the power electronics
Source: K. D. Bachovchin, M. D. Ilic, "Transient Stabilization of Power Grids Using
Passivity-Based Control with Flywheel Energy Storage Systems," IEEE Power & Energy
Society General Meeting, Denver, USA, July 2015..
5
Controller Implementation
 Time-scale separation to simplify the control design
 Regulate both the flywheel speed and the currents into the power
electronics using nonlinear passivity-based control
Source: K. D. Bachovchin, M. D. Ilic, "Transient Stabilization of Power Grids Using
Passivity-Based Control with Flywheel Energy Storage Systems," IEEE Power & Energy
Society General Meeting, Denver, USA, July 2015..
6
Transient Stabilization Using Flywheels
P
P
P
P
 Want to choose set points so that the wind disturbance power goes
to the flywheel and rest of the system is minimally affected
7
Simulation Results: Flywheel
 Since the power output of the wind generator decreases during the
disturbance, the flywheel set point decreases
Wind Generator Mechanical Torque
Flywheel Speed
angular velocity(rad/sec)
0.26
torque(N*m)
0.255
0.25
0.245
0.24
0.235
0.23
0
0.2
0.4
time(sec)
0.6
0.8
1290

1280
ref
1270
1260
1250
1240
1230
0
0.2
0.4
time(sec)
0.6
0.8
8
Simulation Results: Power Electronics
 The set points for the power electronic currents are chosen so that
the total current out of Bus 2 remains constant during the
disturbance
Power Electronic and Wind Currents
Power Electronic Currents
2.4
current(A)
current(A)
-251
-252
i1d
-253
-254
iref
1d
0.2
0.4
0.6
1.8
0
0.8
0.2
0.4
0.6
0.8
20.02
36
i1q
35
iref
1q
current(A)
current(A)
2
1.6
0
37
34
33
i1d+iSdW
2.2
0
0.2
0.4
t(sec)
0.6
0.8
i1q+iSqW
20.01
20
19.99
19.98
0
0.2
0.4
t(sec)
0.6
0.8
9
Simulation Results: Rest of System
 With the control, the effect on the rest of the system is very
minimal and lasts only a short time
0.0745
voltage(V)
0.066
voltage(V)
Bus 2 Voltage: Direct Component
Bus 1 Voltage: Direct Component
Vd (Control)
Vd (No Control)
0.065
0.064
0.2
0.4
0.6
Vq (Control)
Vq (No Control)
0.2
0.4
t(sec)
0.6
0.2
0.4
0.6
0.8
2
0.8
Vq (Control)
1.995
1.99
0
0
Bus 2 Voltage: Quadrature Component
voltage(V)
voltage(V)
0.073
2.005
2
1.995
Vd (No Control)
0.0735
0.8
Bus 1 Voltage: Quadrature Component
1.99
0.074
0.0725
0
Vd (Control)
Vq (No Control)
0
0.2
0.4
t(sec)
0.6
0.8
10
Linking Multi Time-Scale Simulations
 Communication for multi time-scale simulation with ALM and
fast dynamics for generators
Source: M. R. Wagner, K. D. Bachovchin, M. D. Ilić, "Computer Architecture and Multi
Time-Scale Implementations for Smart Grid in a Room Simulator," EESG Working
Paper No. R-WP-1-2014, March 2015.
11
Importance of Reactive Power
 Typically the market only specifies the active power set point
 However the reactive power is critically important to the
equilibria and stability of the system
Power Factor PF = 0.99 (Without Shunt Capacitor)
Power Factor PF=0.2 (Without Shunt Capacitor)
3
3
Manifold of Active Power Balancing Eqn at Bus 2
Manifold of Active Power Balancing Eqn at Bus 2
Manifold of Reactive Power Balancing Eqn at Bus 2
2
2
[0.9615, -0.18 rad]
1
2 (rad)
2 (rad)
1
0
0
-1
-1
[0.182, -1.27 rad]
-2
-2
-3
-3
0
0.5
1
1.5
2
2.5
3
Voltage V2 (pu)
3.5
4
4.5
5
0
0.5
1
1.5
2
2.5
3
3.5
4
4.5
Voltage V2 (pu)
Source: X. Miao, K. D. Bachovchin, M. D. Ilić, "Effect of Load Type and Unmodeled
Dynamics in Load on the Equilibria and Stability of Electric Power System," EESG
Working Paper No. R-WP-1-2014, March 2015.
12
5
Flores Island – Market
 Based on prices, market computes active power set points P*
from each component
13
Flores Island – Dynamics
 Since currently the market does not specify reactive power set
points Q*, data for Q* is randomly created
 Place a voltage source inverter and the variable speed drive on
the hydro and diesel generator buses
 Control the sum of the power out of the hydro and diesel
generators to match the active and reactive power set points
14
Simulation Results – Combining Dynamics and ALM
Unstable Case:
Reactive Power Load Consumption
10
5
0
0
20
40
60
80
100
power(pu)
power(pu)
Stable Case:
Reactive Power Load Consumption
10
5
0
0
20
40
80
100
t(min)
t(min)
Wind Generator Bus Voltages
Wind Generator Bus Voltages
1.2
1.2
1
1
0.8
vB2d
vB2q
0.6
0.4
voltage(V)
voltage(pu)
60
0.8
vB2d
0.6
vB2q
0.4
0.2
0
0.2
-0.2
0
-0.2
-0.4
0
20
40
60
t(min)
80
100
-0.6
0
20
40
60
t(min)
80
15
100
Transient Stabilization of Interactions Using FACTS
Implications for SGRS
distributed integration
of differential equations
Large scale
interconnected
power system
Transient stability problem
•
•
•
Nonlinear dynamics
Multiple time scales
Large regions
Unstable
Stable
Interactions are captured using
an energy-based model
•
•
Accumulated energy as a measure of
stability
Managing energy to ensure stability
Cooperative power
electronics (FACTS) control
•
Fast thyristor switching
•
Flow control
Common Modeling Approach for FACTS Control
 Create a simplified power system model
 Control logic is case dependent
 Loaded as test case for transients simulator
 Create a structure preserving system model by combining dynamic
models of individual components
 Coupling achieved through states on ports of components 𝑦𝑖 , 𝑝𝑖
 Competitive control design
Module description
𝑥𝑖 = 𝑓𝑖 𝑥𝑖 , 𝑦𝑖 , 𝑢𝑖 , 𝑑𝑖
𝑦𝑖 = 𝑦𝑖𝑗 (𝑥𝑗 ) 𝑦𝑖𝑘 (𝑥𝑘 )
𝑝𝑖 = 𝑝𝑖𝑗 (𝑥𝑖 )
Component-based
approach to modeling
𝑝𝑖𝑘 (𝑥𝑖 )
Proposed Modeling Approach
 Represent components of power systems using a two-level model which
separates their internal dynamics from the dynamics of their interactions
 Internal dynamics are described using internal dynamic states 𝑥
 Interaction dynamics are described using interaction variables 𝑧
Two-level module description
𝑥𝑖 = 𝑓𝑖 𝑥𝑖 , 𝑧𝑖 , 𝑃𝑖𝑘 , 𝑃𝑖𝑗 , 𝑢𝑖 , 𝑑𝑖
𝑧𝑖 = 𝑔𝑖 𝑥𝑖 , 𝑧𝑖 , 𝑃𝑖𝑘 , 𝑃𝑖𝑗 , 𝑢𝑖 , 𝑑𝑖
Interaction
variable-based
modeling
M. Cvetkovic, M. Ilic, “A Two-level Approach to Tuning FACTS for Transient Stabilization”,
IEEE PES General Meeting, July 2014.
Interaction variable 𝒛 is the
accumulated energy inside a module.
Proposed Cooperative Controller
Accumulated (stored) energy
in an uncontrolled system
Generator
FACTS
 Redistribute energy of disturbance
 Cooperative control is expressed in
terms of higher (interaction) level
dynamics
 Enabled by using time scale
separation between interaction and
internal level dynamics
Accumulated (stored) energy in a system
controlled by power electronics
Generator
FACTS
M. Cvetkovic, M. Ilic, “Ectropy-based Nonlinear Control of FACTS for Transient Stabilization”,
IEEE Transactions on Power Systems, Vol. 29, No. 6, November 2014, pp. 3012-3020.
SGRS Hierarchical Distributed Simulation of Dynamics
 Aggregation od dynamics using interaction variables separates the rate
of exchange of information and the rate of internal state computations
Interactions coming from other modules at time n
Interaction variable update at a slower rate n
Interaction at n+1
Zoom-out level
States at k+1
Internal states update at a faster rate k
Zoom-in level
Interactions going toward other modules at time n+1
M. Cvetkovic, M. Ilic, “Dynamic Simulation of Power System Transients in Energy State Space”, in preparation.
Response of Uncontrolled System
Short circuit at bus 3
in duration of 0.35 sec
M. Cvetkovic, M. Ilic, “Cooperative Line-flow Power Electronics Control for Transient Stabilization”, IEEE Conference on Decision and Control, December 2014.
Controlled System Response
𝑃𝜏3 =
1
𝜏3
𝑃𝜏2 𝑑𝑡
𝜏2
𝑧2 = 0
FACTS energy has
reached zero
States converge
to a different
equilibrium
22
M. Cvetkovic, M. Ilic, “Cooperative Line-flow Power Electronics Control for Transient Stabilization”, IEEE Conference on Decision and Control, December 2014.
Questions?