A Modeling Framework for Future Energy Systems Gran Andersson, ETH - - PowerPoint PPT Presentation

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A Modeling Framework for Future Energy Systems Gran Andersson, ETH - - PowerPoint PPT Presentation

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A Modeling Framework for Future Energy Systems Gran Andersson, ETH Zrich ETH Power Systems Laboratory Content Energy Hub - Multi energy-carrier systems Power Node - Incorporation of fluctuating power sources - Incorporation of

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ETH Power Systems Laboratory

A Modeling Framework for Future Energy Systems

Göran Andersson, ETH Zürich

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Content

  • Energy Hub
  • Multi energy-carrier systems
  • Power Node
  • Incorporation of fluctuating power sources
  • Incorporation of demand side participation
  • Incorporation of storage
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The Energy Hub

L = Loads (Output) M = Output side storage flows C = Coupling matrix P = Input power flows Q = Input storage flows

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Hub Equations and Results

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Applications (so far)

  • Long term energy planning of the city of Bern
  • Energy planning of several Swiss municipalities
  • Analysis of e-mobility
  • Energy/Exergy analysis of city of Zürich
  • …
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Status Quo in Power Systems Modelling

Traditional power system modeling is “fractional“:

  • Separate models are used for capturing information of
  • Transmission & distribution grid (topology, voltage & frequency

dynamics, voltage & line limits)

  • Power generation (generator dynamics, ramp constraints, wind and

PV in-feed predictions)

  • Load models (dynamics, load demand predictions)
  • Storage models (capacity, storage levels, dynamics)
  • Modelled interaction between individual power system units

and grid does not necessarily capture all relevant aspects

  • No interaction with other energy carriers modeled (cf Energy Hub)
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  • Example: optimal power dispatch simulations do consider units

that inject or absorb power from the grid.

  • Which of these units are storages (energy-constrained)?
  • Which of these units provide fluctuating power in-feed?
  • What controllability (full / partial / none) does the operator

have over fluctuating generation and demand processes?

Status Quo in Power Systems Modelling

Grid

Power System Unit Pin Pout

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  • Example: optimal power dispatch simulations do consider units

that inject or absorb power from the grid.

  • Which of these units are storages (energy-constrained)?
  • Which of these units provide fluctuating power in-feed?
  • What controllability (full / partial / none) does the operator

have over fluctuating generation and demand processes?

Status Quo in Power Systems Modelling

Grid

Power System Unit

Energy provided / demanded

Pin Pout

Storage

? ?

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Motivation for Power Nodes Modeling Framework

  • Create unified framework for modeling power system units

(incl. relevant operation constraints, power supply and demand processes and the controllability)

  • Diverse storage units (battery, pumped hydro, …)
  • Diverse generation units (fully dispatchable conventional

generators, fluctuating in-feed of wind turbines and PV)

  • Diverse load units (conventional, interruptible, thermal, ...)
  • Operation constraints: ramp rates, storage capacity, current

storage level (SOC)

  • Operation controllability over underlying process (=“flexibility“):

fully controllable, curtailable / sheddable, non-controllable

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The Power Nodes Framework

  • Modeling of three domains and their interactions
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One Power Node

i i i gen load load i i

v w u u x C

i i gen i i

− − + − =

−

ξ η η

1

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One Power Node

i i i gen load load i i

v w u u x C

i i gen i i

− − + − =

−

ξ η η

1

Efficiency factors Storage capacity × state-of- charge Provided / demanded power Shedding term Internal losses Power out- feed from grid Power in- feed to grid

< <

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One Power Node (including constraints)

  • Power constraints defined by: min/max power, ramp rates, storage capacity
  • Operation flexibility defined by: shedding term wi, storage term Ci xi , ξi
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Power Node without storage (e.g. non-controllable load)

  • Power node equation degenerates to

algebraic equality constraint (for classical load: ugen,i = 0)

  • Power node’s power in-feed / out-feed is
  • Partially controllable, if shedding term adjustable (wi (k) > 0)
  • Non-controllable, if shedding term is zero (wi (k) = 0)
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Variety of Power Node modelling definitions

Load Gener- ation Storage

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17 Non-Controllable Local Load

PV with local storage unit, no RES feed-in tariff Power Node Modelling Examples

Grid gen,

u

Grid load,

u

PV gen,

u

Bat load,

u

Bat gen,

u

NCL load,

u

CL gen,

u

PV Panel Battery storage Controllable Local Load

PV gen, PV gen,

  • 1

PV gen,

ξ η = u

Bat gen, 1 Bat gen, Bat load, Bat load, Bat Bat

u u x C

−

− = η η 

CL CL load,CL load,CL CL CL,0 CL

( ) C x u a x x η ζ = − − +  ฀

load,NCL load,NCL load,NCL

u η ξ = −

Power Grid (modelled as a slack power node)

Grid Grid load, Grid gen,

ξ = −u u

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PV with local storage unit, RES feed-in tariff Power Node Modelling Examples

PV Panel Battery storage Non-Controllable Local Load Controllable Thermal Load

RES Grid, load,

u

Bat load,

u

Bat gen,

u

CL load,

u

PV gen,

u

Grid load,

u

Grid gen,

u

NCL load,

u

12

P

Node 1 Node 2

Power Grid (additional variable for FIT energy)

PV gen, PV gen,

  • 1

PV gen,

ξ η = u

Bat gen, 1 Bat gen, Bat load, Bat load, Bat Bat

u u x C

−

− = η η 

Grid RES Grid, load, Grid load, Grid gen,

ξ = − − u u u

(subject to RES FIT)

CL CL load,CL load,CL CL CL,0 CL

( ) C x u a x x η ζ = − − +  ฀

load,NCL load,NCL load,NCL

u η ξ = −

This enables the modelling of differentiated feed-in tariffs

  • incl. options for local PV

energy usage (e.g. Germany).

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Joint Provision of Load Frequency Control Power Node Modelling Examples

Battery storage Controllable Thermal Load

Bat load,

u ∆

Bat gen,

u ∆

CL load,

u ∆

LFC load,

u ∆

Dispatchable Generator

CG gen,

u ∆

LFC load, CL load, Bat load, CG gen, Bat gen,

u u u u u ∆ = ∆ − ∆ − ∆ + ∆

Power Balance:

Bat gen, 1 Bat gen, Bat load, Bat load, Bat Bat

u u x C ∆ − ∆ = ∆

−

η η 

Secondary Frequency Controller

CL CL load,CL load,CL CL CL,0 CL

( ) C x u a x x η ζ ∆ = ∆ − ∆ − ∆ + ∆  ฀

CG gen, CG gen,

  • 1

CG gen,

ξ η ∆ = ∆u

LFC LFC LFC load,

ˆ Y P u ⋅ = ∆

LFC

Y

LFC

ˆ P

: control signal [-100%, +100%] : offered control band [MW] Convenient representation: Control signal modelled as a load to be served

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Demand response (driven by dynamic electricity tariff)

( ) ( ) ( )

[ ]

. . . min

1

t s k v k u k tariff elec u

N k k losses i load

i

∑

− = = ∗

+ ⋅ =

( )

, 1 , ≥ ≤ ≤ − + =

i i i i i load

load i i losses demand load i i

u x x v u x C ξ η 

Power Node Modelling Examples

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Power Node Modeling Example: Predictive power dispatch

  • Conventional generation unit [6]
  • Conventional (uncontrolled) load [1] + load predictions
  • Pumped-hydro storage units [4+5] and flexible loads (DSM) [7]
  • Wind/PV units (curtailable) [2-3] + Wind/PV power in-feed predictions

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Power Node Modeling Example: Predictive power dispatch

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  • Optimal predictive power dispatch (Germany)
  • Tpred. = 72h, Tupd. = 4h, T

sample=15min.

  • Simulation Period: May 2010 (30% Wind, 20% PV) – Calc < 4min.
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  • Optimal predictive power dispatch (Germany, high PV)
  • Tpred. = 72h, Tupd. = 4h, T

sample=15min.

  • Simulation Period: May 2010 (30% Wind, 50% PV, no DSM)
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Power Nodes and Energy Hubs

  • Partial transformation between Power Nodes and

Energy Hubs is possible

  • Converter: natural gas → electricity (uload = 0, Mβ = 0)

( )

( )

α α αβ β

ξ η ξ η ξ η η Q P c L x C u u u u x C

gas gas in gen el gen gas in el gen gen gas in el gen gen el load load gas

− = ⇔ − = + − = + − =

− −

 

1 1

Pα

Qα = Cgas ẋ Eα = Cgas x Pα = ξgas

[ ] [ ]

Q P C M L − = + Converter Lβ Qα Eα

  • El. Grid

Gas Grid

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  • Goal is to better evaluate performance of power system
  • peration and to improve performance
  • Storage utilisation (What is its best use?)
  • Integrating fluctuating power in-feed
  • Integrating demand-side management (DSM)
  • Reduce forced ramping of conventional generators for

load following and balancing of fluctuating power in-feed

  • Examples of performance criteria
  • power system operation cost
  • curtailment of RES in-feed
  • Power system CO2 emissions

Goals of Power Node Approach

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Contributions from

Kai Heussen (DTU) Stephan Koch Andreas Ulbig Martin Geidl Gaudenz Koeppel Thilo Krause Florian Kienzle ……..