Course Title

Energy Systems Analysis and Modeling

Course Code

EAS 521

Course Type

Elective

Level

PhD

Instructor’s Name

Assoc. Prof.Theodoros Zachariades (Lead Instructor), Dr. Nestor Fylaktos, Dr. Constantinos Taliotis, Dr. Marios Karmellos

ECTS

5

Lectures / week

1 (3 hour)

Laboratories / week

 -

Course Purpose and Objectives

The energy transition required to achieve the global climate targets necessitates investments at an unprecedented rate. However, reaching decisions for infrastructure investments that are sustainable and cost-efficient, without compromising energy security, is not a trivial task. Energy models are simplified representations of reality, comprised of mathematical equations, with the goal of comprehending complex interactions in a given energy system and providing policy insights. This course introduces students to energy systems analysis and the main categories of long-term energy system models. It provides advanced knowledge on a range of energy system models and helps students identify the type of model best suited to address a specific policy question. By participating in this course, students develop the technical and analytical skills required to develop an energy model and carry out an independent analysis, providing insights to policy-makers. A set of case studies is used to illustrate the real-life application of energy models to assist in national energy planning.

Learning Outcomes

Upon completion of the course, students will be able to:

-  Define the different categories of energy system models, comprehend their basic characteristics and the purpose for using each of these to guide energy planning.
-  Conceptualise and represent a network of technologies and energy forms,clearly identifying all interactions within an energy system.
-  Analyse trends in energy demand and carry out demand forecasts with methodologies similar to those used by governments, international organizations and energy companies.
-  Understand the theory and purpose of bottom-up models and develop simple models in a relevant modelling framework, using an optimisation model (i.e. OSeMOSYS).
-  Understand the theory and purpose of top-down models and perform a simple analysis using an already developed input-output model (i.e. JEDI).
-  Comprehend the concept of the Climate, Land-use, Energy and Water strategies (CLEWs) framework.
-  Develop a simplified representation of the interconnections between energy, land and water within an optimisation framework.
-  Carry out an analysis of a chosen national energy system including independent data collection, problem definition, scenario development, results generation and interpretation.

Prerequisites

None

Background Requirements

None

Course Content

3. Top-down modelling – an introduction to energy demand forecast models, input-output models and working on a simple exercise using the JEDI model.

5. Distributed Energy Systems: Theory, perspectives and modelling methods.

7. The Climate, Land-use, Energy and Water strategies (CLEWs) framework: Why is it important to consider interactions across sectors in national and regional planning? Providing an overview with a series of case studies.

9. Case Study 1 on national energy planning with models: modelling tools used to support policy in Cyprus.

11. Cast Study 3 on Geographical Information Systems to assist in energy planning: the example of the OnSSET tool to assist in national electrification efforts.

Teaching Methodology

Lectures, seminars

Bibliography

A. Herbst, F. A. Toro, F. Reitze, and E. Jochem, “Introduction to Energy Systems Modelling,” Swiss J. Econ. Stat. SJES, vol. 148, no. II, pp. 111–135, 2012.

M. Gargiulo and B. Ó. Gallachóir, “Long-term energy models: Principles, characteristics, focus, and limitations,” Wiley Interdiscip. Rev. Energy Environ., vol. 2, no. 2, pp. 158–177, Mar. 2013.

B. Merven, C. Arndt, and H. Winkler, “The development of a linked modelling framework for analysing the socioeconomic impacts of energy and climate policies in South Africa,” United Nations University World Institute for Development Economics Research, WIDER Working Paper 2017/40, Feb. 2017.

P. E. Dodds and W. McDowall, “Methodologies for representing the road transport sector in energy system models,” Int. J. Hydrog. Energy, vol. 39, no. 5, pp. 2345–2358, Feb. 2014.

S. Pfenninger, “Energy scientists must show their workings,” Nat. News, vol. 542, no. 7642, p. 393, Feb. 2017.

Howells, M., Rogner, H., Strachan, N., Heaps, C., Huntington, H., Kypreos, S., Hughes, A., Silveira, S., DeCarolis, J., Bazillian, M., and Roehrl, A.: OSeMOSYS: the open source energy modeling system: an introduction to its ethos, structure and development, Energ. Policy, 39, 5850–5870, 2011.

C. Taliotis, M. Howells, M. Bazilian, H. Rogner, and M. Welsch, “Energy Security prospects in Cyprus and Israel: A focus on Natural Gas,” Int. J. Sustain. Energy Plan. Manag., vol. 3, no. 0, pp. 5–20, Sep. 2014.

Taliotis, C., Shivakumar, A., Ramos, E., Howells, M., Mentis, D., Sridharan, V., Broad, O., Mofor, L., 2016. An indicative analysis of investment opportunities in the African electricity supply sector — Using TEMBA (The Electricity Model Base for Africa). Energy Sustain. Dev. 31, 50–66.

Howells, M., Hermann, S., Welsch, M., Bazilian, M., Segerström, R., Alfstad, T., Gielen, D., Rogner, H., Fischer, G., Van Velthuizen, H., Wiberg, D., Young, C., Roehrl, A., Mueller, A., Steduto, P., and Ramma, I.: Integrated analysis of climate change, land-use, energy and water strategies, Nat. Clim. Change, 3, 621–626.

Mentis, D., Howells, M., Rogner, H., Korkovelos, A., Arderne, C., Eduardo Zepeda, Siyal, S., Taliotis, C., Bazilian, M., Roo, A. de, Tanvez, Y., Alexandre Oudalov, Scholtz, E., 2017. Lighting the World: the first application of an open source, spatial electrification tool (OnSSET) on Sub-Saharan Africa. Environ. Res. Lett. 12, 085003.

M. Karmellos, P.N. Georgiou, G. Mavrotas, 2019. A comparison of methods for the optimal design of Distributed Energy Systems under uncertainty, Energy, Volume 178, 318-333.

T. Zachariadis, E. Taibi, Exploring drivers of energy demand in Cyprus – Scenarios and policy options, Energy Policy. 86 (2015) 166–175.

NREL, Jobs and Economic Development Impact (JEDI) models. https://www.nrel.gov/analysis/jedi/

Assessment

Coursework, essays, presentations

Language

English