The course focuses on the modeling of the dynamics of energy systems and markets at different spatial scales and on medium-long terms under a complex set of constraints. This modeling is crucial today for the planning of sustainable energy strategies at regional, national and international level. The objective of the course is to provide the students the capability to discriminate between different models and scenarios for energy planning, as well as to develop their own simplified models, and to analyze and compare the results of complex energy models and different development scenarios. The course starts presenting the context of the Paris Agreement, the needs for energy modeling and the different classifications of the existing models. The various possible approaches to face the complex challenge of developing a model connecting the input to the output are then compared: top-down vs. bottom-up ("engineering-economic" models), partial vs. global equilibrium models; particular attention is devoted to the data-driven modeling, bottom-up, partial equilibrium MARKAL - TIMES family of models. The analysis of the micro-scale models aims at showing how different deterministic or statistical, machine learning and stochastic models can be built to investigate the energy demand at domestic level. Then the meso-scale models are presented, with particular reference to Multi-Criteria Decision Algorithms and data classification learning processes. Finally, the macro-scale models are discussed. The different methodologies and approaches are presented: assumptions in input, interconnections between energy demand, energy supply and technologies, mathematical methods adopted (e.g. linear and non-linear programming), as well as an assessment of the propagation of uncertainties from input to output. The attention is focused in particular on the World Energy Model (WEM) by the International Energy Agency (IEA) and the Global Multi-regional MARKAL (GMM) model by the World Energy Council (WEC). The assumptions of the two models, resulting in so-called scenarios, are compared in detail, as well as their yearly published results, considering energy balances at regional level and also specific energy markets (e.g, the oil market). In order to get familiar with the most important indicators, the students will be requested to post-process some of the data tables contained in IEA and WEC reports. In parallel with these theoretical developments, the students have the opportunity to develop their own macro-scale model with a hands-on approach and to apply it to the analysis of a case study (a country or a region), divided in small groups. The course is inter-disciplinary in nature, being culturally located at the crossroads between economics and engineering.

After this course, the students will understand the rationale behind energy models at local/regional/world level, they will know the structure of the different models existing in the literature and will be able to distinguish and classify them. They will know the input needed for the different energy models (e.g. the MARKAL – TIMES and the WEM on the macro-scale), with special attention to the main scenarios considered in the IEA and WEC reports, and they will be able to properly comment and compare the relative outputs, and in particular the outlook of the main energy markets for the next few decades. The student will also know the main algorithms adopted for the solution of the constrained optimization problems hidden in the models, and be able to apply them in order to develop the model of a regional energy balance in small teams. Thus they will empower their capabilities to work in a group exchanging ideas with their peers, while thanks to the final presentation to the whole classroom they will improve their communication skills.

A background on the fundamentals of all major energy technologies (oil, coal, gas, renewables, nuclear, etc.) is taken for granted.

1. Introduction and description of the course/content; the Paris agreement; taxonomy of energy models (15 h) 2. Micro-scale models (6 h ) - Deterministic approach –Data driven approach - Probabilistic approach 3. Meso-scale models (6 h + 3 h in the lab) - Community Energy Planning - Components of urban energy systems:drivers and constraints - Reference Energy System at urban level - Accounting simulation/optimization models for the energy modeling at the meso-scale - MCDA methods for decision making in the energy field -– classification models 4. Macro-scale models (15 h + 12 h in the lab): - History and classification - Reference Energy Systems, Macro-economics, Modeling techniques - The IEA World Energy Outlook: inputs, assumptions, scenarios and results - The WEC World Energy Scenarios: inputs, assumptions, scenarios and results - Comparison of IEA and WEC outcomes and discussion 5. Examples of regional energy outlooks, also based on students presentations (3 h)

The course will address the theoretical part in formal lectures. Several hours of computational lab are also foreseen, for a subset of which the students will individually work on PCs, developing their own model to address a case study assigned by the teacher.

Selected up-to-date papers published in International Journals on the topic subject of the course. In addition: - World Energy Outlook from 2012 on, by IEA - World Energy scenarios from 2015 on, WEC - L. Schrattenholzer, “THEORY AND PRACTICES FOR ENERGY EDUCATION, TRAINING, REGULATION AND STANDARDS – Energy Planning Methodologies and Tools”, Encyclopedia of Life Support Systems (EOLSS) - R. Loulou, G. Goldstein, K. Noble, Documentation for the MARKAL family of models, Energy Technology Systems Analysis Programme 2004. - R. Loulou et al, Documentation for the TIMES model – PART 1 - Energy Technology Systems Analysis Programme 2005. - World Energy Model Documentation – 2013 Version., OCSE/IEA 2013 - World Energy Model Methodology and Assumptions, OCSE/IEA 2011

Modalità di esame: Prova orale obbligatoria; Prova scritta tramite PC con l'utilizzo della piattaforma di ateneo; Elaborato progettuale in gruppo;

The final grade is obtained combining two different assessments: 1. A written exam, contributing up to 18/30 to the final grade, including 3-4 questions on different theoretical topics addressed during the lectures, for a duration of 2h. The written exam aims at verifying the student’s capability to personally summarize and discuss the main features and issues of the energy modeling at the different spatial scales, presented in the course. 2. The preparation of a short report, and its presentation in oral form, on the analysis of a regional balance of a selected country, and on the simulation of alternative scenarios for its energy mix evolution up to 2050, to be submitted by the end of the course. The individual project, contributing up to 12/30 to the final grade, aims at assessing the student’s capability to select and use the most suitable numerical models and tools, among those presented during the course, for the energy modeling at the macro-scale.

Modalità di esame: Prova orale obbligatoria; Prova scritta tramite PC con l'utilizzo della piattaforma di ateneo; Elaborato progettuale in gruppo;

The final grade is obtained combining two different assessments: 1. A written exam, contributing up to 18/30 to the final grade, including 3-4 questions on different theoretical topics addressed during the lectures, for a duration of 2h. The written exam aims at verifying the student’s capability to personally summarize and discuss the main features and issues of the energy modeling at the different spatial scales, presented in the course. 2. The preparation of a short report, and its presentation in oral or poster form, on the analysis of a regional balance of a selected country, and on the simulation of alternative scenarios for its energy mix evolution up to 2050, to be submitted by the end of the course. The individual project, contributing up to 12/30 to the final grade, aims at assessing the student’s capability to select and use the most suitable numerical models and tools, among those presented during the course, for the energy modeling at the macro-scale.