New Control Functions for 100% Renewable Generation:
An Industry PerspectiveUlrich Muenz Siemens Corporate Technology, Princeton, NJ
joint work with A. Mešanović, A. Szabo, D. Unseld, J. Bamberger, C. Ebenbauer, R. Findeisen
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High renewable generation is achieved today with hydro power,
but large scale hydro power is not available in many countries
Source: IEA: Energy Policies of IEA Countries: New Zealand 2017 Review
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High wind and PV generation is available everywhere,
but Wind & PV generation is still below 25%
Source: IEA: Energy Policies of IEA Countries: Australia 2018 Review
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New Control Functions have to solve diverse challenges
to close the gap to 100% wind and solar generation
ChallengeNew Control
FunctionsNew HW
➢ Generation
meets load
➢ Long distance
power transfer
➢ Low inertia
grids
Storage
HVDC
Synchronous
Condensers
Demand
Side
Management
Robust
Optimal
Power Flow
Adaptive
Power
Oscillation
Damping10MW
Electricity generation from Wind & PV as a
percentage of the total generation in Island Grids
Japan
100MW 1GW 10GW 100GW
20%
40%
60%
80%
100%
?
UKIrelandOahu
Kodiak
Bonaire
King Island
EI Hierro
HawaiMaui
Power system size
in peak demand
Wind & PV
Gap
Data from:
RMI Renewable Microgrids: Profiles from islands and remote communities across the globe
https://www.hawaiianelectric.com/clean-energy-hawaii/clean-energy-facts/about-our-fuel-mix
https://www.renewable-ei.org/en/statistics/electricity/
https://www.gov.uk/government/collections/electricity-statistics
http://www.eirgridgroup.com/site-files/library/EirGrid/Generation_Capacity_Statement_20162025_FINAL.pdf
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New Control Functions for 100% Renewable Generation:
An Industry Perspective
New Control Functions
Robust Optimal Power Flow
Adaptive Power Oscillation Damping
Mitigate risks of
volatile generation
at minimal cost
Dynamically maximize
stability reserves
Cost &
constraint
Power
system
model
Uncer-
tainty
Control
action
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Sept 2018Page 9
New Control Functions for 100% Renewable Generation:
An Industry Perspective
New Control Functions
Robust Optimal Power Flow
Adaptive Power Oscillation Damping
Cost &
constraint
Power
system
model
Uncer-
tainty
Control
action
Mitigate risks of
volatile generation
at minimal cost
Dynamically maximize
stability reserves
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Sept 2018Page 10
Volatile generation requires Robust Optimal Power Flow to
mitigate risks of volatile generation at minimal cost
Benefits
• Increased resiliency, e.g. for market clearance
• Fast response without communication
• Ready for brown-field applications
Status quo Change Solution
Robust OPF:
• Optimize both
setpoints and
droops/reserve to
enable fast power
flow adjustment
without fast
communication
• Recalculation of
R-OPF depending
on uncertainty
➢ Fast and scalable
optimization
algorithms.
Uncertain
power flows:
• Determined by non-
dispatchable, volatile
generation
• High line loading
because generation
far from load
➢ Fast adaptation of
power flow
required.
Predictable
power flows:
• Determined by
slowly changing,
dispatchable
generation
• Low line loadings
because generation
close to load
➢ OPF calculations
hours in advance.
4 7532
1
6
9
810
1
2
34
droop
droop
droop
droop droop
droop
Online Robust Optimal Power Flow
Generator Wind Park controller
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Robust Optimal Power Flow in a nutshell
Cost &
Constraint
Power
System Model
Control
Action
Uncertain
Renewable
Cost:• Disptach / redispatch costs
• Primary reserve costs
• Power line losses
Constraint:• DC voltage limits
• Generator active power limits
• Converter active power limits
• Power line limits
• Droop gain limits
• Frequency limits
AC/DC Converter parameters:
• VC0 – voltage reference
• P0 – active power reference
• kV, kP – voltage and frequency droop gains
Generators:
• P0: active power reference
• ΔPt: redispatch
• kP: primary reserve / droop
AC Grid:
DC Grid:
Uncertainty:
• active power infeed in a convex set
for t ∈ [t0, t0+T], e.g. T=15 min;
∙ 1 ∙ ∞
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Robust Optimal Power Flow combines uncertainties and
droop controllers in one optimization problem
❖ Minimize cost and maximize resiliency
• Power flow satisfies constraints for all variations
• Optimal usage of available resources (decentralized droop controllers) to minimize
cost and for fast reaction without (fast) communication
Robust OPF
Cost &
constraint
Power
system
model
Uncer-
tainty
Control
action
Cost &
constraint
Power
system
model
No
uncer-
tainty
No
control
action
Cost &
constraint
Power
system
model
Uncer-
tainty
No
control
action
Cost &
constraint
Power
system
model
Contin-
gency
No
control
action
OPF Stochastic OPFSecurity constraint OPF
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Robust Optimal Power Flow achieves primary reserve
sharing across asynchronous AC power systems
1 Load -100 MW at AC bus 6• Default droops lead to frequency limit violation in AC B
• Solved by slightly modifying droops of generators (AC A&C) and HVDC converters
➢ no re-dispatch required
2 Generation +200W at DC bus 5• Default droops lead to DC power line overloading between bus 5&6
• Solved by slightly modifying droops of generators (AC A&C) and HVDC converters
➢ no re-dispatch required
Cigré B4 DC Test Grid
• 3 AC and 3 DC grids
• Generation center AC A+B & Load center
AC B interconnected through DC system
• 4GW total generation and load
• Default droops: 5%
Cost
f / V / …Constraint Satisfied Constraint Violated
Operation with
high uncertainty
Operation with
high uncertainty
and redispatch
Operation with high
uncertainty and primary
reserve adaptation
3 Key Findings
• Robust OPF provides solution for complex primary reserve provisioning
• Improved resiliency at minimal cost
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Robust Optimal Power Flow is fast even for realistic power
systems sizes
Application Example: Danish 2020 Transmission System
1 Setup: Danish 2020 Transmission System
• 2 asynchronous AC systems interconnected through HVDC system;
• 300 buses, 4GW total generation and load.
2 Key Findings
• Robust OPF runs in less than 5 minutes for a 300 bus system with 200
uncertain infeeds
• Computation time is comparable for 1- and 2-norm undertainties, but
slightly larger for ∞-norm uncertainties
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New Control Functions for 100% Renewable Generation:
An Industry Perspective
New Control Functions
Robust Optimal Power Flow
Adaptive Power Oscillation Damping
Cost &
constraint
Power
system
model
Uncer-
tainty
Control
action
Mitigate risks of
volatile generation
at minimal cost
Dynamically maximize
stability reserves
Unrestricted © Siemens AG 2018
Sept 2018Page 16
High renewable integration requires Adaptive Power
Oscillation Damping to maximize stability reserves
Benefits
• Increased resiliency through stability reserve optimization;
• Low communication bandwidth required;
• Ready for brown-field applications.
Status quo Change Solution
Adaptive power
oscillation damping:
• Online estimation of
eigenmodes
• Online optimization
of PSS controller
parameters;
➢ Fast and reliable
optimization
algorithms.
Time-varying
eigenmodes[1]:
• Weather dependent
generation
• Faster power
system dynamics
because of PE
inverters;
• Reduced stability
reserve;
➢ Increased risk of
black-outs & poorly
damped oscillations.
Time-invariant
eigenmodes:
• Dispatchable
generation
• Power system
stabilizer (PSS)
damp oscillations
➢ Manual tuning of
PSS to damp
interarea modes.
[1] S. Al Ali, T. Haase, I. Nassar, and H. Weber, “Impact of increasing wind power generation on the north-south inter-area
oscillation mode in the European ENTSO-E system,” IFAC Proceedings Volumes, vol. 47, no. 3, pp. 7653–7658, 2014.
Adaptive Power Oscillation Damping
PSS
PSS
PSS
PSS
Communication
AC grid
Generator
Wind Park
PSS
Solar
PSS
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Sept 2018Page 17
Tim
e D
om
ain
Optim
ized P
ara
ms
Adaptive Power Oscillation Damping in a nutshell
Power System
1110
0100
1110
0100
Modeling, calibration & verification
Power system
status and
forecasts
Optimized
controller
parameters
Fourier
Tra
nsfo
rm
Tim
e D
om
ain
Initia
l P
ara
mete
rs
Optim
ized F
ourier
Tra
nsfo
rm
Digital Twin of the Power System
Cloud: MindSphere
• Large Island (Europe) • Small Island (IREN2)
Nonlinear model: Linearized model: H-inf Optimization:
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Sept 2018Page 18
Application example shows significant increase of power
oscillation damping for IEEE39 benchmark model
[2] P. Kundhu, Power System Stability and Control, McGraw-Hill, 1993.
[3] A. Moeini, I. Kamwa, P. Brunelle, G. Sybille, "Open Data IEEE Test Systems Implemented in SimpowerSystems for Education and Research in Power Grid Dynamics and Control," Power Engineering Conference (UPEC), 2015 50th International Universities,
1-4 Sept. 2015, Staffordshire University, UK. (https://www.mathworks.com/matlabcentral/fileexchange/54771-10-machine-new-england-power-system-ieee-benchmark)
[4] IEEE committee report, "Dynamic models for steam and hydro turbines in power system studies," IEEE Transactions on Power Apparatus and Systems, Vol. PAS-92, No. 6, 1973, pp. 1904-1915.
[5] "Recommended Practice for Excitation System Models for Power System Stability Studies," IEEE® Standard 421.5-1992, August, 1992.
IEEE 39 bus benchmark model
❖ Initial Parameters ❖ Optimized Parameters
❖ IEEE 39 bus model
from[3] with component
models from[4,5] and
PSS from[2].
❖ HVDC line between
buses 16 and 27.
❖ 216 states❖ 128 controller
parameters
Optimization
Problem
Characterization
1 8
10
2 3 5 4 7
6
9
=
˷˷˷
=
˷˷˷
C1
C2
1
2
3
4
56
7
8
9
10
11
12
13
14
1516
1718
19
20
2122
23
24
2526
2728 29
30
31 323334
35
36
37
38
39
Generator =˷˷˷ HVDC converter station
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Detailed power plant models can be optimized with this
approach
❖ 10 tunable controller parameters per generator❖ 19 states per generator
Decomposition of Generator
Synchronous
Generator
VT
Ɯ
Pm
Turbine +
Governor
Ɯ
Efd
AVR +
Exciter
Ɯ
PSS
VT
Vref
VPSS
Automatic Voltage Regulator
+ Exciter Model
PSS
Turbine + Governor
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Successful field test of Adaptive Power Oscillation
Damping in Wildpoldsried, Germany
❖ Optimization /wo Droops ❖ Optimization /w Droops❖ Initial Parameters ❖ Manually Tuned Parameters
❖ Status
• Modelling, calibration, and validation of simulations
• Validation of oscillation damping with standard HW
❖ Outlook
• Evaluation in customer projects
• Extension to larger island grids, e.g. Hawaii
• Evaluation for transmission systems
Load bank
Transformer
Batteries
Inv1
Inv2
Inv3
Inv4
Inv5
Inv6
time (s) time (s) time (s) time (s)
P (
kW
)
P (
kW
)
P (
kW
)
P (
kW
)
Unrestricted © Siemens AG 2018
Sept 2018Page 21
New Control Functions for 100% Renewable Generation:
An Industry Perspective
10MW
Electricity generation from Wind & Solar as a
percentage of the total generation in Island Grids
Japan
100MW 1GW 10GW 100GW
20%
40%
60%
80%
100%
UKIrelandOahu
Kodiak
Bonaire
King Island
EI Hierro
HawaiMaui
Power system size
in peak demand
Wind & PV
Gap
Data from:
RMI Renewable Microgrids: Profiles from islands and remote communities across the globe
https://www.hawaiianelectric.com/clean-energy-hawaii/clean-energy-facts/about-our-fuel-mix
https://www.renewable-ei.org/en/statistics/electricity/
https://www.gov.uk/government/collections/electricity-statistics
http://www.eirgridgroup.com/site-files/library/EirGrid/Generation_Capacity_Statement_20162025_FINAL.pdf
High renewable generation is achieved
today only with hydro power
Wind & PV generation is still below 25%
New Control Functions are needed to
bring Wind & PV up to 100%
Robust Optimal Power Flow mitigates
risks of volatile generation at minimal cost
Adaptive Power Oscillation Damping
dynamically maximizes stability reserves
1
2
3
4
5
Unrestricted © Siemens AG 2018
Sept 2018Page 22
Ulrich Muenz
Head of Research Group
Autonomous Systems and Control / US / CT RDA FOA ASY-US
755 College Road East
Princeton, NJ 08540
USA
Mobile: +1 609 216 0170
E-mail: [email protected]
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