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Modeling of the transmission grid using geo allocation and generalized processes

Presentation at the ISESO - Nov 10th 2015

Simon Köppl, Felix Böing, Christoph Pellinger Research Centre for Energy Economics, Munich

http://www.ffe.de/en/

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- merit order -

- basic data - - scenario - analysis -

- measure classification-

Which grid optimizing measures are technically,

legally and at the same time economically –

including socio-ecological factors - representable?

Uncertainty concerning

framework conditions

Possible future scenarios

Impacts

Profiles

&

Spatial discrepancy

of generation/consumption

Influence on

distribution and

transmission

grid

Variety of measures

which? classifiable? comparable?

1. Motivation: objectives in the project MONA 2030

Co-funded by

and the support of 16 partners

Grid structures

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„The ENTSO-E Grid Map is commonly used“

• No geographical location of the facilities

• Data set represents the „Startnetz“

• Unclear illustration of other European countries

1. Motivation: a brief history of using grid data at FfE

„In Germany, data can be obtained by the BNetzA“

• Simplified electrical parameters

• Data only available as a map

Search for a data set of the European transmission grid for a grid

model in a power plant dispatch model with focus on Germany/Austria

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2. Public grid data sets: The modeling of the transmission grid is conducted via the connection of a variety of data sets

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Electrical parameters

For every line and for every station, relevant electrical characteristics have to be known:

• Status: active/passive/not yet built

• Resistance and reactance (R, X)

• Thermal limits

Validatability

Every data set has to be able to be validated:

• Reliability of the source

• Benchmarking in other data sets

Every data set should be public

Goal: consistent transmission grid model, based on public data

2. Public grid data sets: requirements for a consistent grid model

Geographical location

All network components have to be able to be located

• Exact geographical location

• Electrical connections also beyond network levels

Expandability

• Every data set has to be able to be combined with other data sets and to be expanded at a later point

• Integration of future grid projects and development paths

For the integration in a consistent grid model, a data set has to fulfill certain

characteristics

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Open Street Map

Regulation authorities,

e.g.in Germany

Sc

op

e

Vo

lta

ge

leve

ls

2. Public grid data sets: overview of the used data sets

Ele

me

nts

ENTSO-E Grid Map

Germany comprehensively

(+ Europe as a reduced

peripheric grid)

Europe, North Africa, USA,

parts of Asia and South

America

Europa + peripheric

regions in Africa and Asia

Lo

ca

tio

ns

220 kV–750 kV

comprehensively

Other voltages reduced to

display cross-flows

„Numerate model“:

impedances and

component values of all

network elements

No geo data

220 V – 765 kV

But: uncertain level of

detail of the map

Lines with „wires“ and

„cables“

Georeferenced locations

220 kV–750 kV

110 kV/150 kV at cross-

border lines in certain

countries

Voltage level + number of

circuits

Stylized map,

approximate line course

Grid model TSO

Supply region of the TSO

Mostly, 220 kV and 380 kV

Impedances and

component values of the

lines

No geo data, simplified

overview map

Sources: TenneT TSO GmbH, Static Grid model: https://www.tennettso.de/site/en/Transparency/publications/staticgrid-model/static-grid-model. TenneT TSO GmbH, Bayreuth (2015)

Amprion GmbH, Static grid model: http://www.amprion.net/en/static-grid-model. Amprion GmbH, Dortmund (2015)

Bundesnetzagentur, Daten nach 12f Abs.1 EnWG. Bundesnetzagentur, Bonn (2014)

OpenStreetMap, United Kingdom (UK): https://www.openstreetmap.org/about (2015)

ENTSO-E, Interconnected network of ENTSO-E. Brussels (2014)

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2. Public grid data sets: inconsistent grid node designation and different geographical detail

1 Every starting/ending

point of a line has to be

georeferenced

2 Problem: unclear positions

+ inconsistent designation

of grid nodes

3 Additionally: increased

computing effort due to a

lot of grid nodes

„X-Knoten Vierraden-Krajnik“

KW Emsland – UW Emsland

„Staatsgrenze Györ/HU

„Frankfurt/SW“ – „Frankfurt N“

4 Solution: geographical

aggregation of grid nodes

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Determination of the

“catchment area of a grid

node“

Drawing a circle with a specified radius around the grid node

2. Public grid data sets: reducing the complexity - approach for a geographical grid node aggregation

Overlapping of the circles

All circle centers in one

grid region

• Assembling of the circles to

one polygon

• Grid node in the center of

the polygon as „main node“

• Aggregation of the obtained

grid node

Circle centers in different

grid regions

• Formation of different

polygons: one polygon per

grid region

• Afterwards analogous

procedure as seen on the

left

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2. Public grid data sets: data sources for the planned grid expansion in Europe

Standardized collection of

project data

• Location

• Status of implementation

• Grid region

• Type of project:

line/transformer/

Q-compensation/…

• Electrical parameters

• Legal basis of the project

• Time of completion

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European Grid

development plans

• GDP Germany

• GDP Austria

• …

3

Specific project

desciptions

1

National grid

development plans

Ten-Years-Network-Development-Plan

of the ENTSO-E (TYNDP)

Detailed desciptions of grid projects

(e.g. HVDC projects, cross border

interconnectors)

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2. Public grid data sets: Resulting grid node list and line list

ID Name Optional Grid region Voltage Geometry Source

688 Schkopau SCHK DE83 110 kV, 380 kV 01010020787F000007708… OSM

689 X-Knoten Vierraden-Krajnik

DE81,PL 220 kV 01010020787F00004E203… manual

690 Magdeburg 50Hertz DE81 110 kV, 220 kV 01010020787F0000C2D30… OSM

691 Herrenwyk HVDC Baltic Cable

DE21 110 kV, 380 kV 01010020787F0000585CB… OSM

692 Waldeck Waldeck I+II DE24 380 kV 01010020787F00001EF00… OSM

ID_line ID_station1 ID_station2 Voltage I R X Length

00209399 336 755 220 kV 1.360 A 0.3 Ω 1.5 Ω 5.0 km

11209008 370 494 220 kV 30 km

11208783 372 373 380 kV

00208775 419 398 380 kV 3.600 A 1.5 Ω 19.1 Ω 78 km

00400040 21 157 380 kV 2.720 A 0.5 Ω 5.2 Ω 19 km

1 Extract of the grid node list

2 Extract of the lines list

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3. Process Model: inhomogeneous and inconsistent data situation as common challenge in data aggregation

power plant type

fuels

chp

voltage level

…

Electric Storage

storage type

storage medium

voltage level

…

Grid

voltage level

AC/DC

number of circuits

reactance

…

Element:

Identification

data:

Power Plants

• Inconsistent allocation of parameters and scenarios

• Inconsistent aggregation of devices

• Each element is considered as an individual case

The various elements of the power system are classified by different

identification data.

Starting situation:

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3. Process Model: the definition of processes

energy source input technical component energy source output

attribute of a process

optional

examples:

natural gas gas turbine electrical energy (AC) + heat

380kV AC power line 380kV AC

380kV AC power line 380kV AC underground cable

380kV AC power line 380kV AC overhead line

process:

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3. Process Model: creating a tree of processes

Advantages:

• Consistent aggregation on

different levels for all

elements

• Both instances and

parameters are assigned to

processes

• top-down approach for

parameters

• Simple amalgamation of

parameters to form

instances

380kV AC – Power Line – 380kV AC

+ Overhead Line

380kV AC – Power Line – 380kV AC

+ Overhead Line + Quad Package

Electricity – Power Line – Electricity

Power AC – Power Line – Power AC

Extra High Voltage AC – Power Line – Extra

High Voltage AC

220kV AC – Power Line – 220kV AC

380kV AC – Power Line – 380kV AC

380kV AC – Power Line – 380kV AC

+ Overhead Line + Triple Package

Electricity – Transformer – Electricity

Power AC – Transformer – Power AC

Extra High Voltage AC – Transformer –

High Voltage AC

...

Notation:

element = eg. conv. power plants

instance = eg. „CCGT Irsching“

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3. Process Model: Implementation in a database environment using a dynamic allocation algorithm

Sources: Hofmann et al., Wirtschaftlichkeitsvergleich unterschiedlicher Übertragungstechnik im Höchstspannungsnetz anhand der 380-KV-Leitung Wahle-Mecklar. Hannover (2010)

APG Austrian Power Grid (APG), Static grid data in: https://www.apg.at/de/netz/anlagen/leitungsnetz. Vienna (2015)

DIW, Electricity Sector Data for Policy-Relevant Modeling - Data Documentation and Application to the German and European Electricity Markets. Berlin (2014)

YearRegionProcess

Instance

Value

Reactance (X)

220kV AC – Power

Line – 220kV ACEU 2030 0.075

Germany 2030 0.014

Power AC – Power

Line – Power ACEU 2030 0.029

Current (I)

220kV AC – Power

Line – 220kV ACEU 2030 1286

380kV AC – Power Line

– 380kV AC (overhead)Austria 2277

380kV AC – Power Line

– 380kV AC (overhead)Germany 2720

Node 137 –

Node 138

(Germany)

2019

Pa

ram

ete

rE

lem

en

t

380kV AC – Power Line –

380kV AC (overhead,

quadruple bundle)

1

2

3

4

5

6

380kV AC – Power Line

– 380kV AC (overhead)

2030

2030

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4. Integration of grid data in energy system models: the energy system can be modeled as a network of processes

Natural Gas – Gas Turbine –

220kV AC

220kV AC – Power Line –

220kV AC (overhead, triple)

380kV AC – Transformer –

220kV AC

20kV AC – Electric Storage –

20kV AC (Li-Ionen-Battery)

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4. Integration of grid data in energy system models: Providing nodal load and production data

Sources: Pellinger, Christoph et al., Merit-Order der Energiespeicherung im Jahr 2030. Forschungsstelle für Energiewirtschaft e.V., Munich (2012)

Forschungsstelle für Energiewirtschaft e.V.: The FfE Regionalized Energy System Model (FREM). Forschungsstelle für Energiewirtschaft e.V., Munich (2014)

Carr, Luis et al., Erneuerbare Energien - Potenziale und ihre räumliche Verteilung in Deutschland in: Flächennutzungsmonitoring V – Methodik, Analyseergebnisse

Flächenmanagement. Leibniz-Institut für ökologische Raumentwicklung e. V., Forschungsstelle für Energiewirtschaft e.V., Dresden, Munich (2013)

FREM

FfE Regionalized Energy System

Model

2 Allocation to grid nodes:

Geographically / direct allocation /

using the HV grid

1 Flexible and consistent data basis

for geographically and temporally

highly resolved generation/demand

data at all aggregation levels

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5. Resulting transmission grid model

• Detailed presentation of the German and Austrian transmission grid, simple expansion to

other countries, if data is available

• Simplified illustration of other European countries: mostly one grid node per country, modeling

of the transmission grid via cross border capacities (one country as a copper plate)

Characteristics of the grid model

• Final aggregation radius of grid

node 5 km

• 448 grid nodes in DE and AT

• Display of 1134 line sections + 696

projects with line parameters

(voltage, max. current, R, X)

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1. Added value only if detailed grid data is provided

2. Temporal inconsistency due to varying publishing dates of

data sets

3. Varying and not clear designations for substations which

requires an manual allocation in many times

Validation of the grid model with other grid

models is crucial and an ongoing task

Weak spots of the model

6. Critical review

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7. Conclusion and outlook

1 For a consistent model of the transmission grid, it is necessary to

amalgamate different, public data sets.

3 The described process model is a transparent approach to

standardize and complement grid data sets with appropriate

simplifications and assumptions.

5 Besides any smart allocation algorithm, a transmission grid model still

requires a significant amout of work for filling the database with input

data…

4 The process model also allows a simple integration of grid data into

dispatch models.

2 The latest publications of grid data sets (TSOs, ENTSO-E, Open data

projects) improve the quality of transmission grid models a lot.

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Discussion? Questions? Thanks a lot for your attention!

Questions? Discussion

!

? ENTSO-E

Contact:

Simon Köppl, Felix Böing,

Christoph Pellinger

+49 (89) 158121-78

skoeppl@ffe.de

Research center for energy

economics

(Forschungsstelle für

Energiewirtschaft e.V.)

Am Blütenanger 71

80995 Munich

www.ffe.de/en

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Backup: Integration of grid data in energy system models: virtual network in peripheral regions

No detailed data available

„copper plate“

Centroids of

countries

Specific

process

definition

Transfer capacity =

sum of all cross border lines

between countries

Reduced simplified

network

Virtual dummy

transmission lines

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2. Public grid data sets: different development paths for the expansion of the transmission grid

2 For every grid project which is implemented in the grid model, the following

information has to be available:

• Starting and ending point of the line

• electrical parameters (current thermal limit, R/C/X)

e.g. „all projects of the TYNDP

will be built on time“

e.g. „only projects of the Federal

Requirements Plan will be built“

1 For an appropriate integration of grid projects and resilient grid planning, different

development paths have to considered

Example of the NEP

380kV-quadruplicate line (overhead line) from

Altenfeld to Redwitz

After calibration with 12f-data set:

• 2 curcuits with high current cables

• S_th = 2369,45 MVA

Example of the TYNDP

380kV-line from Feroleto (IT) to Maida (IT)

• Number of curcuits?

• Which type of cable?

• Which electrical parameters?

• Overhead line/underground cable?