Exploring Complex Energy Networks Florian D orfler @ETH for - - PowerPoint PPT Presentation

Exploring Complex Energy Networks Florian D¨

• rfler

@ETH for “Complex Systems Control” compute actuate throttle sense speed

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@ETH for “Complex Systems Control”

system control

“Simple” control systems are well understood. “Complexity” can enter in many ways . . .

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A “complex” distributed decision making system

. . .

physical interaction local subsystems and control sensing & comm.

local system local control local system local control

Such distributed systems include large-scale physical systems, engineered multi-agent systems, & their interconnection in cyber-physical systems.

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Timely applications of distributed systems control

• ften the centralized perspective is simply not appropriate

robotic networks decision making social networks sensor networks self-organization pervasive computing traffic networks smart power grids

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My main application of interest – the power grid

NASA Goddard Space Flight Center

Electric energy is critical for

• ur technological civilization

Energy supply via power grid Complexities: multiple scales, nonlinear, & non-local

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Paradigm shifts in the operation of power networks

Traditional top to bottom operation: ◮ generate/transmit/distribute power ◮ hierarchical control & operation Smart & green power to the people: ◮ distributed generation & deregulation ◮ demand response & load control

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Challenges & opportunities in tomorrow’s power grid

www.offthegridnews.com

1 increasing renewables & deregulation 2 growing demand & operation at capacity

⇒ increasing volatility & complexity, decreasing robustness margins Rapid technological and scientific advances:

1 re-instrumentation: sensors & actuators 2 complex & cyber-physical systems

⇒ cyber-coordination layer for smarter grids

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Outline

Introduction Complex network dynamics Synchronization Voltage collapse Distributed decision making Microgrids Wide-area control Conclusions

Modeling: a power grid is a circuit

1 AC circuit with harmonic

waveforms Ei cos(θi + ωt)

2 active and reactive power flows 3 loads demanding constant

active and reactive power

4 synchronous generators

& power electronic inverters

5 coupling via Kirchhoff & Ohm

Gij + i Bij i j Pi + i Qi i mech. torque electr. torque

injection = power flows ◮ active power: Pi =

• j BijEiEj sin(θi − θj) + GijEiEj cos(θi − θj)

◮ reactive power: Qi = −

j BijEiEj cos(θi − θj) + GijEiEj sin(θi − θj)

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complex network dynamics: synchronization

Synchronization in power networks

sync is crucial for AC power grids – a coupled oscillator analogy sync is a trade-off

θi(t)

weak coupling & heterogeneous

θi(t)

strong coupling & homogeneous

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Synchronization in power networks

sync is crucial for AC power grids – a coupled oscillator analogy sync is a trade-off

θi(t)

weak coupling & heterogeneous Blackout India July 30/31 2012

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Our research: quantitative sync tests in complex networks

Sync cond’: (ntwk coupling) ∩ (transfer capacity) > (heterogeneity)

˙ θ(t) θ(t)

102

302

+ 0.1% load

sync cond’ violated . . .

Reliability Test System 96 two loading conditions

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Our research: quantitative sync tests in complex networks

Sync cond’: (ntwk coupling) ∩ (transfer capacity) > (heterogeneity)

˙ θ(t) θ(t)

102

302

˙ θ(t) θ(t)

+ 0.1% load Reliability Test System 96 two loading conditions Ongoing work & next steps: ◮ analysis: sharper results for more detailed models ◮ analysis to design: hybrid control & remedial actions

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complex network dynamics: voltage collapse

Voltage collapse in power networks

reactive power instability: loading > capacity ⇒ voltages drop recent outages: Qu´ ebec ’96, Northeast ’03, Scandinavia ’03, Athens ’04 “Voltage collapse is still the biggest single threat to the transmission sys-

• tem. It’s what keeps me

awake at night.” – Phil Harris, CEO PJM.

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Voltage collapse on the back of an envelope

reactive power balance at load:

voltage

Esource Eload B Qload

(fixed) (variable)

Eload Esource Qload

* * * *

reactive power

Qload = B Eload(Eload − Esource) ∃ high load voltage solution ⇔ (load) < (network)(source voltage)2/4

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Our research: extending this intuition to complex networks

IEEE 39 bus system (New England)

• Ongoing work & next steps:

existence & collapse cond’: (load) < (network)(source voltage)2/4 analysis to design: reactive compensation & renewable integration 12 / 22

distributed decision making: plug’n’play control in microgrids

Microgrids

Structure

◮ low-voltage distribution networks ◮ grid-connected or islanded ◮ autonomously managed

Applications

◮ hospitals, military, campuses, large vehicles, & isolated communities

Benefits

◮ naturally distributed for renewables ◮ flexible, efficient, & reliable

Operational challenges

◮ volatile dynamics & low inertia ◮ plug’n’play & no central authority

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Conventional control architecture from bulk power ntwks

• 3. Tertiary control (offline)

Goal: optimize operation Strategy: centralized & forecast

• 2. Secondary control (slower)

Goal: maintain operating point Strategy: centralized

• 1. Primary control (fast)

Goal: stabilization & load sharing Strategy: decentralized

Microgrids: distributed, model-free,

• nline & without time-scale separation

⇒ break vertical & horizontal hierarchy

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Plug’n’play architecture

flat hierarchy, distributed, no time-scale separations, & model-free

Microgrid

… … … … … …

source # 1 source # 2 source # n Secondary Primary Tertiary Secondary Primary Tertiary Secondary Primary Tertiary

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Plug’n’play architecture

flat hierarchy, distributed, no time-scale separations, & model-free Microgrid: physics & power flow

• Di ˙

θi =P ∗

i − Pi − Ωi

ki ˙ Ωi =Di ˙ θi−

• j ⊆ inverters

aij · Ωi Di − Ωj Dj

• Di ∝ 1/αi

τi ˙ Ei =−CiEi(Ei − E∗

i ) − Qi − ei

κi ˙ ei =−

• j ⊆ inverters

aij ·

• Qi

Qi − Qj Qj

• −εei
• Primary control:

mimic oscillators Tertiary control: marginal costs ∝ gains Secondary control: diffusive averaging

• f injections

Ωi/Di Qi Ei ˙ θi Pi Qi/Qi Qi/Qi

. . . . . .

Ωi/Di

. . . . . .

Ωk/Dk Qk/Qk Qj/Qj Ωj/Dj Pi =

• j BijEiEj sin(θi − θj) + GijEiEj cos(θi − θj)

Qi = −

• j BijEiEjcos(θi − θj) + GijEiEj sin(θi − θj)
• source # i

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Experimental validation of control & opt. algorithms

in collaboration with microgrid research program @ University of Aalborg

DC Source LCL filter DC Source LCL filter DC Source LCL filter

DG

DC Source LCL filter

DG

DG

DG

Load 1 Load 2

Z

Z

Z

Z

Z

10 20 30 40 50 300 305 310 315 320 325 330

Voltage Magnitudes Time (s) Voltage (V)

10 20 30 40 50 100 150 200 250 300 350 400 450 500

Reactive Power Injections Time (s) Power (VAR)

10 20 30 40 50 49.5 49.6 49.7 49.8 49.9 50 50.1

Voltage Frequency Time (s) Frequency (Hz)

10 20 30 40 50 200 400 600 800 1000 1200

A ctive Power Injection Time (s) Power (W)

t = 22s: load # 2 unplugged t = 36s: load # 2 plugged back t ∈ [0s, 7s]: primary & tertiary control t = 7s: secondary control activated

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Experimental validation of control & opt. algorithms

in collaboration with microgrid research program @ University of Aalborg

DC Source LCL filter DC Source LCL filter DC Source LCL filter

DG

DC Source LCL filter

DG

DG

DG

Load 1 Load 2

Z

Z

Z

Z

Z

10 20 30 40 50 300 305 310 315 320 325 330

Voltage Magnitudes Time (s) Voltage (V)

10 20 30 40 50 100 150 200 250 300 350 400 450 500

Reactive Power Injections Time (s) Power (VAR)

10 20 30 40 50 49.5 49.6 49.7 49.8 49.9 50 50.1

Voltage Frequency Time (s) Frequency (Hz)

10 20 30 40 50 200 400 600 800 1000 1200

A ctive Power Injection Time (s) Power (W)

t = 22s: load # 2 unplugged t = 36s: load # 2 plugged back t ∈ [0s, 7s]: primary & tertiary control t = 7s: secondary control activated

Ongoing work & next steps: ◮ time-domain modeling & control design ◮ integrate market/load dynamics & control

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distributed decision making: wide-area control

Inter-area oscillations in power networks

Blackout of August 10, 1996, resulted from instability of the 0.25 Hz mode

10 1 2 3 4 5 6 7 8 9 11 12 13 14 15 16 South Arizona SoCal NoCal PacNW Canada North Montana Utah

Source: http://certs.lbl.gov

0.25 Hz

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Remedies against inter-area oscillations

conventional control

Physical layer: interconnected generators Fully decentralized control:

effective against local oscillations ineffective against inter-area oscillations

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Remedies against inter-area oscillations

wide-area control

Physical layer Fully decentralized control Distributed wide-area control identification of architecture? sparse control design? optimality?

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Trade-off: control performance vs sparsity of architecture

K(γ) = arg min

K

• J(K)

+ γ · card(K)

• ptimal control = closed-loop performance + γ · sparse architecture

performance

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Case Study: IEEE 39 New England Power Grid

single wide-area control link = ⇒ nearly centralized performance

15 5 12 11 10 7 8 9 4 3 1 2 17 18 14 16 19 20 21 24 26 27 28 31 32 34 33 36 38 39 22 35 6 13 30 37 25 29 23

1 10 8 2 3 6 9 4 7 5

F

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Ongoing work & next steps: cyber-physical security: corruption of wide-area signals data-driven & learning: what if we don’t have a model?

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wrapping up

Summary & conclusions

Complex systems control distributed, networks, & cyber-physical Apps in power networks complex network dynamics distributed decision making Surprisingly related apps coordination of multi-robot networks learning & agreement in social networks and many others . . .

. . .

physical interaction local subsystems and control sensing & comm.

local system local control local system local control

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Acknowledgements

Synchronization John Simpson-Porco Misha Chertkov Francesco Bullo Enrique Mallada Changhong Zhao Matthias Rungger Voltage dynamics Marco Todescato Basilio Gentile Sandro Zampieri Wide-area control Diego Romeres Mihailo Jovanovic Xiaofan Wu Microgrids Quobad Shafiee Josep Guerrero Sairaj Dhople Abdullah Hamadeh Brian Johnson Jinxin Zhao Hedi Boattour Robotic coordination Bruce Francis Cyber-physical security Fabio Pasqualetti Port-Hamiltonian Frank Allg¨

• wer

Jorgen Johnsen Social networks Mihaela van der Schaar Yuanzhang Xiao . . . Group @ ETH Bala Kameshwar Poolla plus some students on

• ther prof’s payrolls . . .

more people to join . . .

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thank you