Halbe, Johannes with Stefan Gausling and Jan Adamowski  "Vision Modeling and Assessment Using System Dynamics – Application to a Sustainable Energy System Based upon Power-to-Gas", 2018 August 7 - 2018 August 9

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THE 36™ INTERNATIONAL CONFERENCE OF THE SYSTEM DYNAMICS SOCIETY

REYKJAVIK, ICELAND

Vision Modeling and Assessment Using System
Dynamics

Application to a Sustainable Energy System Based upon Power-to-Gas

Johannes Halbe, McGill University
Stefan Gausling, University of Osnabruck
Jan Adamowski, McGill University

0:00-

Problem Statement

¢ Visions of the future depend upon different
values or interests

* Unclear visions and goals can render policy
development and implementation ineffective

* Vision modeling allows for the assessment of
VISIONS
- internal consistency (e.g., existence of trade-offs)
- plausibility (are realistic constraints considered?)
- desirability (are sustainability benefits reached?)

0:30-

& Approach or Dynamic Hypothesis

Vision Analysis and Assessment Framework

Steps of the VDA Framework Methods used as part of the
VDA Framework
Vision Design
Step 1.1: Definition of needs, requirements Functional organization
and functions analysis

Step 1.2: Organizational analysis of

‘ ‘ wsystem Organization“
alternative system designs

Step 1.3: Structural analysis of system Causal Loop Diagrams (CLDs)
designs (optional)
»system Structure“

Vision Assessment |
Step 2.1: Dynamic modeling and sas System
assessment of system designs ee Dynamics
a »System
Step 2.2: Model testing and validation »System States Processes“

2:00-

System

——— '
G2) Progress and Insights to Share

— 4

Greenhouse gas emissions Space Requirements
power supply [Mio ta] Renewables [km2]

* Application to the vis 2

of a fully renewable ~ ad | ——
energy system 2100 antl ™ Pix 3000 RE+
(power, heat, mobility) ou. = i

electricity [€/kWh]
0,00 0,05 0.10

™ Pix 4000 RE
| — ue © Without PX R2
2050 ee 2050 ™ PIX 3000 RE R2
1 2100
* Model testing — - —

0,00 0,05 0,10 0,15 0,20

Approaches for handling uncerta

. Sensitivity Model-to-
IA framework Scenario analysis (global model Expert
analysis . assessment
/ local) analysis
— Statistical uncertainty + ++ ++
$ Scenario uncertainty + ++ ++
- Recognized ignorance + + ++
Model boundary ++ + ++
7 Model structure + + + +
2 Model technique + ++ +
8 Input variables + ae
Parameters + + +
Model outcomes + +H ++ +

Metadata

Resource Type:
Document
Description:
Scenario analysis is a powerful approach to support decision-making under complexity. Realistic energy policy scenarios have considerable time and data requirements though, as they usually consider temporal pathways of multiple energy carriers and technologies as well as related social, ecological and economic aspects. Instead of the development of process-oriented knowledge, an innovative vision modeling approach is proposed that aims at the generation of goal-oriented knowledge by investigating the dynamic coherence of an energy system vision. Such a vision model is proposed as a preceding step of a more detailed temporal policy scenario approach by answering the questions of whether a vision of a system design is desirable at all or counterintuitive side-effects occur. Vision modeling reduces complexity by focusing on a specific design of a future supply system. This article presents a stepwise methodological framework for systematically developing vision models consisting of requirements, functional and structural system analyses for vision design and quantitative system dynamics modeling for vision assessment. A special focus is devoted to the handling of uncertainties in the modeling process. An application of the framework is presented for a visionary renewable energy system for power and heat energy supply based upon the Power–to-Gas technology.
Rights:
CC BY-NC-SA 4.0
Date Uploaded:
March 10, 2026

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