Emerging Needs for Evolving Hybrid Energy Cyber‐Physical System (e‐CPS)
Sudip. K. Mazumder
Professor, Department of Electrical and Computer Engineering
Director, Laboratory for Energy and Switching‐Electronics Sys.
University of Illinois, Chicago
President
NextWatt LLC
Funding Agency: National Science Foundation
Department of Energy
Advanced Research Project Agency – Energy
Ninth Annual Carnegie Mellon Conference on the Electricity Industry
Pittsburgh, USA
February 4, 2014
Challenges of e‐CPS
Power, communication, information, sensing, financial networks Some estimates: 100 X+
Interop
erability
Dimensi
onality
Data integrity, known/unknown undesirable pattern Security
recognition Optimization in the presence of interoperability constraints
Nano
seconds to hours
Scale
e‐CPS
Controll
ability
Optimal
ity
Observa
bility
Spatio‐temporal power management and control with distributed functionalities under nominal and singular conditions Estimation in the presence of (real‐time) data proliferation, uncertainties
e‐CPS: Great, but how do we tackle the giant?
Think of a “slightly” bigger giant: The Universe
which works so amazingly
E8: A theory of everything
Courtesy John Stembridge/Atlas of Lie Groups Project
“Understanding the interplay between small and large is the key to the solution”
e‐CPS modeling challenges

Unified dynamical modeling that captures hybrid, functional, event‐
driven, and stochastic dynamics with cross‐disciplinary inter‐
dependencies

Representation of multi‐scale, multi‐dimensional, and nonlinear spatio‐temporal dynamics at varying levels of abstraction, granularity and aggregation

Generalized approach(es) to contingency, stability, robustness, and reliability analyses that is (are) scalable, seamless, computationally efficient, and can provide offline and real‐time measure and predictions
e‐CPS control challenges

Optimal networked‐control to sustain distributed operation under communication‐network’s capacity and power‐network’s stability bounds in the presence of nodal and network uncertainty, stochasticity, and destabilization

Jointly optimize control of the power network over the communication network and control of the information‐flow network itself

Unification of event‐driven control, protection using rapid solid‐
state switching, and distributed coordination

Robustness of control in the presence of uncertainty in the truthfulness and accurate representation of the measured/ estimated data
Few Illustrations ….
Illustration 1
Network architectures for control of inverters Centralized
Distributed
Master Controller
“Collective” Cluster Controller
Communication
Network
PN3
PN1
LC3
LC1
PN3
PN1
LC4
LC2
PN4
PN2
Power
Network
PN: Power Node
PN4
PN2
Power
Network
PN: Power Node LC: Local Controller
Control distribution
X
Cost: X *  gX 
 P X
T
*
X

Constraints:

 X t 0 ,α i i 1 h ,
,
X t 0  Tw   f 

 Tw ,Ai i 1 h ,Bi i 1 h 
X t 0  Tw   X max ,
i 1 αi  1,
h
T




and 0  αi  1
X 1 t 0  Tw1
i 11 1i
h
S.K. Mazumder, K. Acharya, and M. Tahir, “Joint optimization of control performance and network resource utilization in homogeneous power networks”, IEEE Transactions on Industrial Electronics, vol. 56, no. 5, pp. 1736‐1745, 2009.


 
   
 1, and 0  1i  1
 
Cost: X *  X T P X *  X
N
N
N
N
N
Constraints:




X N t 0  TwN
i N1  Ni
h
X1*  g1'  X1 , D1 
 X 1 t 0 , 1i i 1 h ,Tw1 , 
1
,
 f1 
 A1i i 1 h , B1i i 1 h ,W1 
1
1


 X 1max ,
Module N
X N t 0  TwN
8
 
 X 1 P1 X 1*  X 1
X 1 t 0  Tw1
Cost and Constraint Decomposition
Cost:

*
1
Constraints:
Centralized Control
J  X*  X
Module 1

X *N  g'N  X N , DN 
 
   
 X N t 0 ,  N i i 1 h ,TwN , 
N
,
 f N 
 AN i i 1 h , B N i i 1 h ,W N 
N
N


 X N max ,
 1, and 0   N i  1
Module power control for distributed implementation
Power
Stage
Time delayed information from other modules Optimal
Performance
Control
Local
sensor
information
Reachable Switching
Sequences
Stability
Prediction
Hybrid Dynamical
Model
Information to other modules Information Processing
S.K. Mazumder and K. Acharya, “Multiple Lyapunov function based reaching criteria for orbital existence of switching power converters”, IEEE Transactions on Power Electronics, vol. 23, no. 3, pp. 1449‐1471, 2008.
Communication network throughput optimization 

maximize  si U (rsi ) 
l rsi

l L ( s i )


Rmin(si )  rsi
s.t




rs i
si
 l s l s
(d (q)  dl(t ) ) 
 si l
minimize  (qi)  (t )i 

Dmax (si ) 
2dl
l  2dl
si :lL( si )




maximize
si (1   si )  (1   si ) ( si )
dl(q) , dl(t )
minimize g (λ, ρ, ψ)
s.t l ,  l , si  0 l , s i
 si

As in the case of the control sub‐problem, communication network optimization can also be performed centrally or in a distributed manner
si
l ,  l  0
 si  0
s.t 0 si 1 si
(   )R e  e
k
minimize
l
l
l
Pl
~
tl
 l ~
tl ck

l
s.t ~
tl   ck ~min (l )  l
maximize
 l
maximize g P ( )
s.t  l  0 l
log( l (P))
l
s.t
0  Pl  Pmax
l
 l ,  l , s i
M. Tahir and S.K. Mazumder, “Delay constrained optimal resource utilization of wireless networks for distributed control systems”, IEEE Communication Letters, vol. 12, no. 4, pp. 289‐291, 2008.
Non‐cooperative control‐communication scenario
1
Stability
Margin
800
Optimal Network
Throughput (Mbps)
Delay Margin (μs)
1000
600
400
200
0
0
Performance
Margin
200
400
600
800
Time Delay (μs)
Variations of the stability and performance margins in terms of delay margin 0.9
0.8
0.7
0.6
0.5
500
1000
1500
2000
2500
Time Delay (μs)
Achievable optimal network throughput as a function of the network time delay
Tradeoff, with regard to network time delay, between control performance and stability and resource utilization of the communication network
Joint optimization
PN
X(t )
Control
Parameters
X(t )
PN Optimization Framework
A 
J *PN

 
( )
d th
SINR l 
∑
Jo
+
1 
A 0i 
Bi  PN
Model
×
+
Weight
Calculation
Stability
Margin
Calculation
PN
Optimization
Block
( )
1
Joint
J *o
Optimization
Block
 
*(  )
d
 
( )
d th
CN Optimization Framework
×
J *CN
PN: power network
CN: communication network
SINR: Signal‐to‐interference noise ratio
CN
Optimization
Block
c l (P)
r* , Pl* 
CN
SINR l 
CN Model
5
0.25
4
0.2
3
0.15
2
0.1
Centralized
1
0.05
Distributed
0
0
1
2
3
4
5
Number of Modules
6
7
Normalized Convergence Time Load‐sharing Error (%)
Results (centralized vs. distributed control)
Illustration 2
ARPA‐E project – Collaboration between UIC, Silicon Power, Cree World’s first all‐optical 15‐kV/10‐kHz/2‐kA SiC ETO
Control of system at device level
Control of
EM signature,
switching loss,
dv/dt, di/dt stress (also
enables series/parallel
connection of PSDs)
• Intensity and/or wavelength of optical signal
controls PSD switching dynamics
• Duration, frequency, and placement of optical
pulse controls PSD switching sequence
Control of system
performance and
stability, EM signature,
switching loss
PSD commutation stress
Electromagnetically-shielded
Optical control signal
Switching-sequence and
switching-transition
controller
Light
source
High
power
stage
Electromagnetic
immunity and optical
isolation
Optical detector
and sensor
Optical sensor signal
OTPT
PSD Power
Optically-triggered
Semiconductor Device (PSD)
Circuit
RF band
Optics
Hz
100
102
104
106
108
1010
1012
1014
1016
Dynamic control of device dynamics for rapid fault mitigation, power quality, and multi‐scale power management
Illustration 3
Boolean microgrid*
* Patent pending
Power transfer over a pulsating‐
dc voltage link (PDVL)
Source
Microgrid
Load
Discretized power and data delivery
A simple realization for discretized power flow
17
Thank You!
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