PORTALE DELLA DIDATTICA

PORTALE DELLA DIDATTICA

PORTALE DELLA DIDATTICA

Elenco notifiche



Models and scenarios for energy planning

02OKGND, 02OKGNF, 02OKGXY

A.A. 2025/26

Course Language

Inglese

Degree programme(s)

Master of science-level of the Bologna process in Ingegneria Energetica E Nucleare - Torino
Master of science-level of the Bologna process in Ingegneria Per L'Ambiente E Il Territorio - Torino
Master of science-level of the Bologna process in Ingegneria Energetica E Nucleare - Torino

Course structure
Teaching Hours
Lezioni 31,5
Esercitazioni in laboratorio 28,5
Tutoraggio 10
Lecturers
Teacher Status SSD h.Les h.Ex h.Lab h.Tut Years teaching
Savoldi Laura Professore Ordinario IIND-07/D 19,5 0 0 0 11
Co-lectures
Espandi

Context
SSD CFU Activities Area context
ING-IND/19 6 D - A scelta dello studente A scelta dello studente
2025/26
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.
This course explores the modeling of energy systems and markets at the macroscale, focusing on medium- to long-term dynamics under a complex set of technical, economic, and environmental constraints. Such modeling plays a critical role in shaping sustainable energy strategies at regional, national, and international levels. Students will develop the ability to critically assess and differentiate between various energy models and planning scenarios, gaining insights into their underlying assumptions, methodologies, and applications. The course emphasizes both theoretical understanding and practical skills, enabling students to construct simplified models, analyze results from complex energy system models, and compare different development pathways. Through this process, students will learn to interpret model outputs to support informed decision-making in energy policy and planning. Interdisciplinary in nature, the course bridges the fields of economics and engineering, offering a comprehensive perspective on the challenges and opportunities in energy system modeling and sustainable development.
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.
By the end of this course, students will understand the rationale behind energy system models at the macroscale, recognizing their importance in supporting energy planning and policy decisions. They will gain comprehensive knowledge of the structure, key inputs, and outputs of various energy models in the literature and will be able to distinguish, classify, and critically evaluate them based on their characteristics and applications. Students will acquire practical skills to develop simple model instances using the TEMOA modeling framework and confidently navigate, modify, and analyze complex model instances for advanced energy system studies. They will also acquire basic knowledge of methodologies commonly used in the energy planning field, including modeling to generate alternatives (MGA) for exploring diverse solution spaces, multi-objective optimization for balancing competing objectives in energy systems, stochastic optimization for managing uncertainty in long-term planning, and local sensitivity analysis for understanding model behavior and parameter influence. By integrating these skills, students will be equipped to apply energy models and methodologies to real-world planning challenges, interpret results effectively, and derive actionable insights to inform policy and strategic decision-making.
A background on the fundamentals of all major energy technologies (oil, coal, gas, renewables, nuclear, etc.) is taken for granted.
Students selecting this course are expected to have a solid understanding of the fundamental principles and operations of major energy technologies, including but not limited to oil, coal, natural gas, renewable energy sources (such as solar, wind, and hydro), and nuclear power. Familiarity with the basic technical, economic, and environmental aspects of these technologies will be assumed.
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)
This course provides an in-depth exploration of energy system modeling and its applications in energy planning, focusing on sustainable strategies and climate change mitigation. Students will learn to analyze and model energy systems at the macro scale, incorporating key technologies, policies, and methodologies. The course integrates both theoretical foundations and practical skills, using computational tools like TEMOA, while addressing the challenges of uncertainty and multi-sectoral interactions in energy systems. Below are the main topics covered throughout the course: - Introduction to Energy Systems and Policy: Overview of the energy paradox, global energy policies, and climate change challenge. Introduction to energy modeling and its role in addressing these challenges. - Energy System Modeling Techniques: Exploration of bottom-up and top-down models, linear programming, multi-criteria decision analysis (MCDA), integrated assessment models (IAMs), and optimization techniques such as PROMETHEE and multi-objective optimization. - Modeling Tools and Methodologies: Introduction to the TEMOA modeling tool, scenario modeling, and techniques for managing uncertainty (e.g., sensitivity analysis, structural uncertainty, and parameter uncertainty). - Energy System Components and Analysis: Building reference energy systems (supply and demand), emissions accounting, and the integration of technologies and commodities, with a focus on energy demand in buildings and statistical analysis for energy consumption. - Sustainability and Multi-sectorial Analysis: Assessing sustainability metrics, exploring the land-energy-water nexus, critical raw materials, energy storage, and applying machine learning for time series analytics in energy systems. - Project Development and Group Work: Students will engage in group projects, with hands-on exercises including database construction, modeling future scenarios, and post-processing results. Instructors will provide 20 hours of tutoring to guide the development of projects, culminating in oral presentations and report submissions.
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.
The course is structured to provide a balanced mix of theoretical knowledge and hands-on experience, ensuring that students gain a deep understanding of energy system modeling and its practical applications in energy planning. The theoretical part of the course will be covered through approximately 45 hours of lectures, which will focus on the fundamental concepts of energy system dynamics, modeling methodologies, and the role of models in medium- to long-term energy planning. In addition to the lectures, students will participate in around 15 hours of computational labs, where they will get hands-on experience with energy modeling tools. For a subset of these lab sessions, students will work individually on PCs to develop their own energy models. A central component of the course will be the group projects, where students will collaborate to develop their own analysis. These projects will allow students to apply the knowledge gained from lectures and labs to address practical energy planning challenges. To support the development of these projects, 20 hours of tutoring sessions are foreseen. The tutoring sessions will foster a collaborative learning environment, helping students deepen their understanding and develop their problem-solving skills.
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
Selected papers published in international journals on the topic subject of the course.
Dispense;
Lecture notes;
Modalità di esame: Prova orale obbligatoria; Elaborato progettuale in gruppo;
Exam: Compulsory oral exam; Group project;
... The final grade is obtained combining two different assessments: 1. Written exam (about half of the final grade), including 3-4 questions on different theoretical topics addressed during the lectures. 2. Preparation of a short report and a poster presentation to all other students of the course on the analysis of a regional energy balance, to be submitted about 2 weeks before the end of the course (about half of the final grade).
Gli studenti e le studentesse con disabilità o con Disturbi Specifici di Apprendimento (DSA), oltre alla segnalazione tramite procedura informatizzata, sono invitati a comunicare anche direttamente al/la docente titolare dell'insegnamento, con un preavviso non inferiore ad una settimana dall'avvio della sessione d'esame, gli strumenti compensativi concordati con l'Unità Special Needs, al fine di permettere al/la docente la declinazione più idonea in riferimento alla specifica tipologia di esame.
Exam: Compulsory oral exam; Group project;
The assessment for this course will be based on an oral presentation of the group projects. Students will be required to submit a report detailing the project development process, including the methods used, results obtained, and a discussion of the conclusions drawn from the analysis. This report must be uploaded in the “elaborates” section of the teaching portal at least one week before the oral presentation. The report will be assessed for clarity, technical accuracy, depth of the analysis, and the quality of the results presented. The report will be evaluated on the ability to clearly explain the methodology, results, and implications of their developed models, as well as their understanding of the theoretical concepts discussed throughout the course. Each group will present their project to the teaching staff during an oral presentation, discussing the case study they addressed and the outcomes of their analysis. The presentation will allow students to demonstrate their problem-solving skills, their ability to apply the modeling techniques learned in class, and their understanding of the energy system’s dynamics and constraints. In addition to the presentation, theoretical questions will be asked, related to energy modeling approaches, optimization techniques, and relevant methodologies. The questions will test the students’ ability to critically engage with the theoretical concepts behind the models they have applied.
In addition to the message sent by the online system, students with disabilities or Specific Learning Disorders (SLD) are invited to directly inform the professor in charge of the course about the special arrangements for the exam that have been agreed with the Special Needs Unit. The professor has to be informed at least one week before the beginning of the examination session in order to provide students with the most suitable arrangements for each specific type of exam.
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