Problems Samples & Perspectives on Cyber-Physical Energy - PowerPoint PPT Presentation

Problems Samples & Perspectives on Cyber-Physical Energy Networks ETH D-INFK Seminar @ Oct 31 2016 Florian D¨ orfler

@ETH for “Complex Systems Control” system control “Simple” control systems are well understood. “Complexity” can enter in many ways . . . 2 / 43

A “complex” distributed decision making system local subsystems and control local s y s tem local c ontrol . . . local s y s tem local control physical interaction 8 37 10 9 30 25 3 8 26 2 29 1 3 27 6 1 39 28 35 18 22 4 9 17 21 8 5 24 7 12 14 15 23 36 31 6 s ensing & comm. 2 13 16 7 11 10 19 3 20 2 33 3 34 4 5 Such distributed systems include large-scale physical systems, engineered multi-agent systems, & their interconnection in cyber-physical systems. 3 / 43

Timely applications of distributed systems control often the centralized perspective is simply not appropriate robotic networks decision making social networks sensor networks self-organization pervasive computing traffic networks smart power grids 4 / 43

my main application of interest —– the power grid

Paradigm shifts in the operation of power networks purpose of electric power grid: generate/transmit/distribute conventional operation : hierarchical & centralized things are changing . . . IBM’s smart grid vision 5 / 43

Renewable/distributed/non-rotational generation on the rise Source: Renewables 2014 Global Status Report 6 / 43

A few (of many) game changers . . . synchronous generator distributed generation scaling (ensure stable/robust op) (not always coordinated) (no sync through physics) generation ! � power electronics transmission ! The results . . . (injects mostly garbage) distribution ! low-inertia, over-voltages, etc. 7 / 43 based ¡Schedule). ¡These ¡“market ¡induced” ¡effects ¡

Many other paradigm shifts 1 controllable fossil fuel sources ⇒ stochastic renewable sources 2 generation follows load ⇒ controllable load follows generation 3 monopolistic energy markets ⇒ deregulated energy markets 4 . . . many technological advances 8 / 43

Summary: challenges & opportunities in tomorrow’s grid Public policy & environmental concerns: 1 increasing renewables & deregulation 2 more decentralization & uncertainty ⇒ increasing volatility & complexity www.offthegridnews.com Rapid technological and scientific advances: 1 re-instrumentation: sensors & actuators 2 complex & cyber-physical systems ⇒ cyber-coordination layer for smarter grids 9 / 43

Exciting work @ intersection of domains & disciplines . . . on scientific end & driven by very rapid technological advances ◮ power electronics market low-inertia mechanisms issues robust & ◮ battery storage systems ancillary bidding stochastic stability services & pooling disturbances certificates economics complex dynamics ◮ plug-in electric vehicles privacy networks demand prosumers renewable load response ◮ real-time & wide-area modeling models locational large-scale phasor measurements marginal power grid & nonlinear prizing ◮ communication science ◮ wind turbine, PV, & CPS massive distributed & computation solar manufacturing decentralized distributed ◮ microgrid deployment control optimization remote real-time estimation stochastic ◮ energy-efficient buildings mixed integer networked data programs optimal data-driven online vs. offline ◮ smart meters & uncertainty power electronics nonlinear & load control household appliances relaxations ◮ . . . 10 / 43

Problem samples today coordination of online power distributed generation flow optimization decentralized & optimal wide-area control 11 / 43

Outline Introduction Basics of Power System Physics & Operation Coordination of Distributed Generation Decentralized Wide-Area Control Online Feedback Optimization Conclusions

energy is not packetized . . . two slides on the basics

Modeling: a power grid is a circuit 1 AC circuit with harmonic waveforms E i cos( θ i + ω t ) j i G ij + i B ij 2 active and reactive power flows 3 loads demanding constant i active and reactive power P i + i Q i 4 sources: generators & inverters mech. electr. torque torque inject power akin to physics/control injection = � 5 coupling via Kirchhoff & Ohm power flows � ◮ active power: P i = j B ij E i E j sin( θ i − θ j ) + G ij E i E j cos( θ i − θ j ) Q i = − � ◮ reactive power: j B ij E i E j cos( θ i − θ j ) + G ij E i E j sin( θ i − θ j ) 12 / 43

Power balance, frequency, & droop control idealized power balance: 50 49 51 48 52 generation = load + losses l oads + losses generation (does not hold due to unknown Hz loads, renewables, & losses) sync’d frequency ω sync ∼ imbalance droop control: control power ˙ θ injection ∝ frequency deviation − D i ˙ P i = P ref θ i i stabilizes grid & synchronizes ω sync frequencies: ˙ θ i ( t → ∞ ) = ω sync P ref P ref 1 2 . . . but ω sync is wrong frequency 13 / 43

Outline Introduction Basics of Power System Physics & Operation Coordination of Distributed Generation Decentralized Wide-Area Control Online Feedback Optimization Conclusions

Conventional power systems control hierarchy 3. Tertiary control (offline) goal: optimize operation architecture: centralized & forecast strategy: scheduling (OPF) 2. Secondary control (slower) goal: maintain operating point architecture: centralized strategy: I-control (AGC) 1. Primary control (fast) goal: stabilization & load sharing architecture: decentralized strategy: P-control (droop) Is this top-to-bottom architecture still appropriate in tomorrow’s grid? Power System 14 / 43

Plug’n’play architecture flat hierarchy, distributed, no time-scale separations, & model-free s ource # 1 s ource # 2 s ource # n … … T r ansceive r T r ansceive r T r ansceive r … S e c ond ary S e c ond ary S e c ond ary Tertiary Tertiary Tertiary Control Control Control Control Control Control P rimary P rimary P rimary Control Control Control Power System 15 / 43

approach from an optimal energy routing perspective

Energy management as offline resource allocation & scheduling problem 16 / 43

Energy management as offline resource allocation & scheduling problem { cost of generation, losses, . . . } minimize subject to physical constraints: equality constraints: power balance equations operational constraints: inequality constraints: flow/injection/voltage constraints logic constraints: commit generators yes/no . . . 16 / 43

A simple problem instance: optimal economic dispatch � � i P ref dispatch generation: min u i ∈ U i i J i ( u i ) subject to + u i = 0 i � �� � � �� � generation cost load = generation 1 primal feasibility = imbalance: � i P ref + u i = 0 ∼ ω sync (measurable) i 2 identical marginal costs at optimality: J ′ i ( u i ) = J ′ j ( u j ) ∀ i , j (consensus) simple distributed optimization algorithm: 1 dual update on violation: 2 consensus on x i = J ′ i ( u i ): � λ + = λ + 1 x + = x i + j ∈ neighbors a ij x j k · ω sync i � � � k i ˙ λ i = ˙ J ′ i ( u i ) − J ′ ⇒ altogether in real-time: − i ( u i ) θ i j ∈ neighbors a ij � �� � � �� � ⇒ inject u i ( t ) = λ i ( t ) local dual update distributed consensus (comm-based) 17 / 43

Plug’n’play architecture � � P i = j B ij E i E j sin( θ i − θ j ) + G ij E i E j cos( θ i − θ j ) power system � Q i = − j B ij E i E j cos( θ i − θ j ) + G ij E i E j sin( θ i − θ j ) physics : p ower fl ow & devices droop control: ˙ P i θ i trade o ff � D i ˙ θ i = P ref − P i − λ i power injections i & frequency or voltage ˙ λ i θ i J ′ i ( u i ) J ′ i ( u i ) � secondary & k i ˙ λ i = ˙ � a ij ( J ′ i ( u i ) − J ′ i ( u i )) . . . . . . θ i − J ′ j ( u j ) tertiary control: J ′ k ( u k ) j ∈ ne ighbors integral errors & di ff usive averaging 18 / 43

Plug’n’play architecture similar control strategies for voltage magnitude � � P i = j B ij E i E j sin( θ i − θ j ) + G ij E i E j cos( θ i − θ j ) power system � Q i = − j B ij E i E j cos( θ i − θ j ) + G ij E i E j sin( θ i − θ j ) physics : p ower fl ow & devices droop control: ˙ P i Q i E i θ i trade o ff � D i ˙ θ i = P ref − P i − λ i power injections i τ i ˙ E i = − C i E i ( E i − E ∗ i ) − Q i − e i & frequency or voltage ˙ λ i Q i e i θ i J ′ i ( u i ) J ′ i ( u i ) � secondary & � k i ˙ λ i = ˙ a ij ( J ′ i ( u i ) − J ′ i ( u i )) . . . . . . θ i − J ′ j ( u j ) tertiary control: J ′ k ( u k ) j ∈ neighbors Q j /Q j Q i /Q i integral errors & � � Q i − Q j � κ i ˙ e i = − a ij · − ε e i di ff usive averaging . . . . . . Q i Q j Q j /Q j Q k /Q k j ⊆ neighbors 18 / 43