INNOVATIVE GRID-IMPACTING TECHNOLOGIES

ENABLING A CLEAN, EFFICIENT AND SECURE ELECTRICITY SYSTEM IN EUROPE

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Bettina Burgholzer Energy Economics Group (EEG) Vienna University of Technology

14th IAEE European Energy Conference Rome, 31 October 2014

Cost/Benefit Analysis of further Expansion of the Austrian

Transmission Grid to enable further RES-E Integration

Overview

About the project

Methodology of bottom-up model

Austrian Case description

Selected results of scenarios

2020: without/with expansion of Salzburg Transmission Power Line

2050: implementation of FACTS & DLR

Conclusions

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About the project

GridTech is a project co-funded by the European Commission under the Intelligent Energy Europe Programme.

GridTech’s main goal:

 Conduct a fully integrated assessment of new grid-impacting technologies and their implementation into the European electricity system.

Contract number:

IEE/11/017 / SI2.616364

Full title:

Impact Assessment of New Technologies to Foster RES-Electricity Integration into the European Transmission System

Duration:

May 2012 - April 2015

Budget:

EUR 1,958,528

EC contribution:

EUR 1,468,896

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Pan-European study (top-down approach)

Regional case studies (bottom-up approach)

Results and recommendations

Innovative technologies

screening

Cost-benefit methodology

RES integration: market issues

Transmission expansion:

non-technical barriers

Project objectives & structure Assess the non-technical barriers for transmission expansion and market compatible renewable electricity integration in Europe

Develop a robust cost-benefit analysis methodology on investments

Apply and verify the cost-benefit analysis methodology for investments in the transmission grid

Achieve a common understanding among key actors and target groups on best practise criteria

Deliver tailor-made recommendations and action plans

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Pan-European study

EU30+ zonal model Top-down approach:

Target countries (bottom-up modelling)

• Used tool: MTSIM, developed and analyses done by Ricerca sul Sistema Energetico - RSE S.p.A. Milano

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Bottom-up methodology: Input/Output and Optimisation EDisOn (Electricity Dispatch Optimization): Linear Optimisation Problem (LOP) formulated in MATLAB® and solved by Gurobi-Solver! (cp. (Burger et al., 2007), (Shahidehpour et al., 2002))

Target function: Minimisation of the total system costs

Constraints: • Demand • Capacity • Ramping Limits • Reservoirs balance • Spill of hydro and RES-E

generation technologies • DC power flow

(PTDF-Matrix, cp. (Van den Bergh et al., 2014))

Input

•hourly based: Demand, Wind, PV, RoR and PHES inflow

•power plant data

•primary energy prices, CO2 certificate prices and specific CO2 emissions

•WP4: Exchanges and Prices

Market Model

•Linear Optimisation Problem (LOP)

•minimisation of the total generation costs

•s.t. technical constraints

Solving Model with

Gurobi- Solver

Output

hourly based RoR, PHS and thermal

production, Exchanges, Flows, Storage level, wholesale electricity

prices, etc.

CBA

Calculation of benefits

(welfare, CR, changes in fossil fuel needs and CO2 emissions)

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Economic outputs How to model? How to measure?

Flexibility

FACTS Sensitivity analyses Increase of the overall welfare

DLR NTC=f(Temperature) Increase of the overall welfare

HVDC Point to point Increase of the overall welfare

Should we combine flexibility and controllability to one economic output?

Security of supply Not Supplied Energy (NSE) NSE (VoLL=Value of Lost Load)

How to model security of supply / not supplied energy? And how to measure?

Controllability FACTS, HVDC NSE, Spillage, …

Congestion rent (Price differences between the nodes of a

transmission line) x (load flow) Million € per year

Congestion losses Redispatch costs Redispatch costs

Social welfare producer and consumer surplus

Price, demand, production costs Million € per year

Fossil fuel need Calculation of the need of fossil fuel Primary energy demand of the different

kinds of thermal power plants (generation/(lowercalorificvalue*eff))

CO2-Emission Calculation of the total CO2 emission of each

country and per node

CO2-emissions per year

(Million tons of CO2)

RES curtailment Energy in excess Energy remunerated

(e.g. at market prices / feed-in tariff)

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Modelling FACTS & DLR

• Parameters: • DLR ∈ ℝ𝐻

• 𝛼𝑚𝑎𝑥 = 30°

• Decision Variable: 𝛼𝑙𝑝𝑠𝑡,ℎ

• Constraints:

8

0 1000 2000 3000 4000 5000 6000 7000 8000 0

20

40 50 60

80

100

120

140

hours

%

DLR Wind

Wind Util

< 0.5 100 %

< 0.8 105 %

< 1 115 %

with

Temperature

Source: presentation at RWTH Aachen

(Puffer, 2010).

Source: dena-Netzstudie II,

2010.

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Incidence Complex conductance

phase angle

Austrian Case description 24 nodes: 17 correlate with

the main substations within Austria and 7 neighbouring ones

35 transmission power lines (TPL): All parallel transmission power lines between the nodes are taken together to one representative transmission power line.

Grid Technology Focus

2020 HVAC line Salzburg

2030 HVAC line Carinthia DLR and FACTS

2050 HVDC line (e.g. Brenner) DLR and FACTS Storage (PHS)

TIR_w TIR_e SBG_s

SBG_n

OOE_e

NOE

NOE_n

W

NOE_s

STMK_s

KTN_e

KTN_w

OTIR

STMK_w

STMK

OOE_w

VBG

DE1

DE2

CZ

HU

SI

IT

CH

380 kV 220 kV

110 kV

Germany

Czech Republic

Hungary

Slovenia

Italy

Switzerland

4028

1030

2600

1900

3500

2518

1866

2685

428

1700

1700

3092

3092

518

492

518

518

518

2300

400

2417

518

2573

2148

298

550

1321

389

104

1296

2573

5400

5400

777

5400

*

*

*

*

*

*

* interaction with Pan-European study by RSE

2030

2020

Source: Austrian Power Grid,

Masterplan 2030.

Source: own illustration.

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Selected results: wo/w TPL expansion 2020

(2020A) without expansion

(2020B) with expansion

CO2 certificate price

10 EUR/ton CO2

Total demand: 73.67 TWh

Total installed Capacity: 31 GW, of which are …

Wind: 3.2 GW

PV: 1.2 GW

RoR: 5.6 GW

PHS: 10.2 GW

wo/w Salzburg expansion:

Not Supplied Energy (NSE): 0 MWh

0,02

0,01

0,33

0,30

- 0,05 0,10 0,15 0,20 0,25 0,30 0,35

(2020A)

(2020B)

RES curtailment (GWh)

10

0

10

20

30

40

50

60

1 501 1001 1501 2001

EU R

/M W

h

time (hours)

mean (2020A): 34,97 / mean (2020B): 34,88

(2020A) (2020B)

Peak Price duration curve

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6686

0

0 5000 10000 15000 20000 25000

(2020A)

(2020B)

(cumulated number of hours)

Load factor of TPLs > 70 % Salzburg TPL

Selected results: wo/w TPL expansion 2020

(2020A) without expansion

(2020B) with expansion

CO2 certificate price

10 EUR/ton CO2

Total demand: 73.67 TWh

Total installed Capacity: 31 GW, of which are …

Wind: 3.2 GW

PV: 1.2 GW

RoR: 5.6 GW

PHS: 10.2 GW

wo/w Salzburg expansion: