Navigating the Roadmap for Clean, Secure

and Efficient Energy Innovation

Presented at EMP-E Conference

17-18 May 2017, Brussels

Gustav Resch, TU Wien

Ruud Egging, NTNU, Trondheim

Insights in the LCE 21 projects

Linking models - (SET-Nav)

The SET-Nav project at a glance

Linking models: why, what and issues

SET-Nav model integration platform

OUTLINE

SET-NAV OVERVIEW

Title: SET-Nav - Navigating the Roadmap for Clean, Secure and Efficient Energy Innovation

Funding: European Union’s Horizon 2020 Research and Innovation Programme (H2020)

Started: April 2016

Duration: 36 Months

Coordinator: Technische Universität Wien, Energy Economics Group (TU Wien)

Participants: 16

SET-NAV KEY P ILLARS

• Combining theory of technology innovation, diffusion & spill-overs with large-scale

numerical energy-economy-engineering models.

• Developing the methodological framework & technical infrastructure for effective

model integration to adequately capture interdependencies across levels, energy

carriers, and sectors.

Enhancing modelling capacities

Stakeholder dialogue &

dissemination

Strategic policy analysis

enhancing innovation

towards a clean, secure and efficient

energy system

SET-NAV METHODOLOGICAL FRAMEWORK

The overall work is clustered into 10 modules that complement each other under the umbrella of SET-Nav efficient Management

SET-Plan Themes covered by

SET-Nav

Case Studies

Sce

nar

ios

of

the

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Work package

(according to work plan, section 3)

Global perspectives

(WP4)

Demand perspective (WP5)

Infrastructure(WP6)

Supply perspective (WP7)

Macro-economic

aspects (WP8)

1

● ●2

●3

● ● ●4

● ● ● ●5

● ● ●6

● ● ● ●7

● ● ● ●8

● ● ● ● ● ● ● ●9

● ●10

● ● ● ● ● ●11

● ● ● ●12

●13

● ●

(1) Engaging consumers through better understanding, information and market transformation

(2) Activating consumers through innovative technologies, products and services

(3) Increasing energy efficiency in buildings

(4) Increasing energy efficiency in heating & cooling

(5) Increasing energy efficiency in industry & services

(6) Modernising the European electr.

grid and establishing synergiesbetween the various energy networks

(7) Unlocking the potential of

energy storage and conversion of electricity to other energy carriers

(8) Providing the energy system with

flexibility, security and cost-effectiveness

(9) Development and demonstration

of holistic system optimisation at local/urban level

(10) Accelerating the development

of renewable electricity and

heating/cooling technologies

(11) Enabling carbon capture, CO2

utilisation & storage technologies and increas-ed efficiency of the fossil fuel-based power sector and energy intensive industry

(12) Supporting safe and efficient

operation of nuclear systems, develop-

ment of innovative reactor concepts etc.

(13) Developing sustainable biofuels,

fuel cells and hydrogen and alternative fuels

for the European transport fuel mixChal

leng

e 3:

Secu

re, c

ost

-eff

ecti

ve, c

lean

an

d co

mpe

titi

ve s

uppl

yCh

alle

nge

3: S

yste

m o

ptim

isat

ion

Chal

leng

e 2:

Dem

and

focu

s-

incr

easi

ng e

nerg

y ef

ficie

ncy

acro

ss th

e en

ergy

sys

tem

Chal

leng

e 1:

Act

ive

cons

umer

at th

e

cent

re ..

.

L INK BETWEEN

CASE STUDIES & SET-PLAN THEMES ( E X T R A C T )

Linking models:

why, what and issues

Need consistent & coherent quantitative representation of pathways across models

Models have different

Approaches (econ perspective; math approach)

Foci: strengths & weaknesses

Scopes, scales, granularities, info struct (stat-dynam; det-stoch; open-closed loop)

Exogenous (parameters) vs endogenous variables: boundary conditions

units of measurement, e.g., physical vs monetary representation;

Features give complementary strengths but pose challenges for exchanging data

Use each model's strengths by using one model’s results as another’s boundary conditions

but generally mismatch in scopes, granularities, etc.

LINKING MODELS - WHY

Macro-economic models

ASTRA – ECTRIC – FRAUNHOFER

NEMESIS – ECTRIC – SEURECO

REMES – CGE – SINTEF

System and sector models

ASTRA-Transport - FRAUNHOFER

CCTSMOD – CCTS - DIW

EMPIRE – EL - NTNU

eLOAD - EL - FRAUNHOFER

EGMM – NG - REKK

ENERTILE – EL - FRAUNHOFER

FORECAST-INDUSTRY-FRAUNHOFER

GREEN-X – EL – TUW

INVERT-EE-Lab – HEAT - TUW

GGM – NG – DIW

MultiMod – ENER - DIW

PowerACE – EL - Fraunhofer

RAMONA – NG – NTNU

TEPES – EL – COMILLAS

Risk / robustness assessment tools

NEXUS security – OPTIM – ETHZ

RCT – SIMUL – NTUA

LINKING MODELS – WHICH

TEPES

EMPIRE

Analysis

Electricity generation,

Electricity capacities

RAMONA

Grid Investments

EGMM

Gas production cost,

Gas infrastructure

Gas demand, Gas prices

NEXUS security

Gas operation, Gas infrastructure

Electricity generation, Electricity capacities

Gas operation, infrastructure,

investments, costs, emissions

Electricity generation, capacities, investments,

fuel mix, costs, emissions

System security/reliability indices electricity/gas

Detailed grid costs Grid investments Gas demand,

Gas prices

CS 7.4 Unlocking Flexibi l i ty in Power & Gas Supply Systems

CS 8.2 MACROECONOMIC CONSEQUENCES OF SUSTAINABLE

ENERGY SECTOR INNOVATION

Energy-systemmodels

Inputs

Investments

O&M costs

Energy comsumption

Energy prices

Policy variables

Other indicators

Conversion into usable

inputs for the macro-

economic models

(technology to economic

activities, price index, etc.)

ASTRA

NEMESIS

REMES

Outputs

GDP andcomponents

Employment

Sectoral indicators

Trade

Incomes

Etc.

Feedback broader economy to energy systems models – SOFT LINK

One model’s results are another’s inputs

generally mismatch in scope, granularity, info structure, units of measurem

Scope (what is covered?)

Geography

Sectors

Time horizon

Granularity (in what level of detail is it covered?)

Geographical

Technologies

Time

Information structure: deterministic vs stochastic

Or inherent differences in approach

social welfare vs game theoretic

system optimization vs general equilibrium

LINKING MODELS - CHALLENGES

SOFT – HARD – INTEGRATED (**)

Soft: manual exchange and/or adjustments

Hard: (automated) scripts

Integrated: single model

Hard vs integrated

may not get the same results(!)

example P.I. Helgesen TIMES-Norway – REMES link: last period investment

Development phase: soft

Study phase: hard

More on this on Focus Session 2, tomorrow afternoon

Data exchange templates: next

LINKING MODELS - HOW

SET-Nav model integration platform

Aim: We want to improve the current implementation of the

IIASA web databases for a more effective model integration platform

A better user experience:

More flexible output and visualization of results (tables, graphs, maps)

Workflow improvements:

Excel upload/download

Application programming interface (API) for workflow integration

(Python, R)

Direct import/export interface with GAMS (gdx format)

Functionality improvements:

Version control functionality and a detailed changelog

Current Status: Modelling platform online and operational BUT delay

in the web user interface development

SET NAV MODEL INTEGRATION PLATTFORM: AIMS & STATUS

Example – Case study 6.2: Analysis of centralised vs. decentralised electricity supply

Model integration workflow:

1. Model results export, upload to model integration platform

2. Handover between models, easy-to-use visualization, version control, comparison to reference data and other models, sanity checks, ...

3. Download of data from model integration platform, launch model run

Enertile TEPES

Optimal dispatch & generation mix, prices Transfer capacity between regions

Investment requirements and system costs

Conceptual model linkage

Actual data workflow

Model integration platform

2

1 3

SET NAV MODEL INTEGRATION PLATTFORM: MODEL INTEGRATION WORKFLOW

List of models & scenarios

History of previous versions of same model/scenario identifier

SET NAV MODEL INTEGRATION PLATTFORM: WORKFLOW OUTLINE: LIST OF MODELS/SCENARIOS

Filters

SET NAV MODEL INTEGRATION PLATTFORM: DATA ANALYSIS & VISUALISATION

Navigating the Roadmap for Clean, Secure

and Efficient Energy Innovation

Thank you!

Visit our Website

www.set-nav.eu

Email us

contact@set-nav.eu

Follow us

@SET_Nav #SET_Nav

Group SET-Nav

Project Coordinator

Dr. Gustav Resch Vienna University of Technology Institute of Energy Systems and Electric Drives TU Wien, EEG - Energy Economics Group Website: www.eeg.tuwien.ac.at E-mail: resch@eeg.tuwien.ac.at Tel: +43-1-58801-370354