This course aims to provide students knowledge on the optimization models and tools which are mandatory to optimally design and operate modern energy systems.

Learning outcomes

At the end of this course, the student will be able to:

• Acquire a broad and extensive knowledge of optimization techniques, required in modern energy optimization problems
• Select the most appropriate optimization model and optimizer when confronted to a given optimization problem, identify the best values of the optimizer parameters for a given use-case.
• Translate a problem from its text version to a mathematical formulation.
• Identify the possible barriers when confronted to real-life problems, and propose relevant simplifications.
• Model, validate and optimize energy systems.
• Use and/or develop energy systems simulation tools to characterize, analyze and optimize the system performances.

Contents

The course is divided into two parts. The first one addresses exact optimization methods for energy systems:

• Course: Introduction to Optimization, Linear Programming Problems, Mixed Integer Linear Programming Problems , Non Linear Programming Problems and fundamentals of convex optimization
• Labs on dedicated optimization modelling programming languages. For instance: the Economic Dispatch Problem with DC network constraints (Linear Programming Problem), the Unit Commitment problem (Mixed Integer Programming Problem), Energy scheduling with non-linear equality constraints (convex relaxations and approximations)

The second part deals with meta-heuristics employed in energy systems optimization:

• Course: General concepts for Meta-Heuristics. Single-solution based Meta-Heuristics, population-based Meta-Heuristics, hybrid Meta-Heuristics, extensions and perspectives. Introduction to energy system modelling (process scheme of the system, identification, and characterization of its elements (variables, characteristic equations, parameters), solution of the equations.
• Project: Modelling, validation and optimization (using literature and commercial  software/in-house code) of typical energetic processes

Basics in Mathematics for Engineers (linear algebra, fundamentals of mathematical analysis) are recommended.

Learning outcomes
At the end of this course, the student will:

• Understand the relationships between (person) mobility and urbanism, social practices, environment, economy, vehicle technology, and infrastructure.
• Learn and apply basic methods used in the field of mobility: socio-economic evaluation of mobility projects, fundamentals of planning in the mobility sector
• Obtain solid knowledge on methods and algorithms used in the field of transport modelling.

Contents
The course will cover the following material:

• Mobility and its evolutions : definitions, determinants of personal mobility (socio-demographic, urban factors), measuring mobility, electric mobility, the new mobility value chain (actors and business cases), autonomous vehicles
• Mobility and environment: impact of the transportation system on the environment, well-towheel and life cycle analyses (individual cars, company fleets), battery recycling
• Socio-economic evaluation and planning methods in transport: Monetization, Total Cost of Ownership, socio-economic evaluation methods (Cost-Benefit Analyses, Multi-criteria/Multi-Actor approaches, etc.), fundamentals of planning in transport.
• Basic concepts in transport modelling: a general vision of the interest of modeling techniques to solve practical transport problems (such as the improvement of public transport) will be presented. The objective will also consist in introducing the basic concepts and methodologies
  that will be useful for understanding the subsequent models.
• Introduction to the 4-step model: the different stages will be introduced gradually, and illustrated with simple examples, with the overarching goal of ensuring a critical analysis of the advantages and disadvantages associated with the underlying assumptions. In particular, the focus will be put on: demand modelling using data analytics, network modelling and the traffic assignment problem through optimization techniques.

Seminars given by professionals of the mobility sector will be organized to illustrate practical use-cases in cities. Guided labs on dedicated software will be organized to illustrate these aspects.

Learning outcomes

At the end of this activity, the student will have developed skills to use, in an integrated and collaborative way, knowledge, methodologies, and tools for:

• Collecting and analyzing energy-related data at the level of an energy community.
• Proposing technical solutions for energy production, storage, distribution, and use.
• Sizing energy technologies, organizing their interoperability for minimizing costs an environmental impact.
• Integrating solutions in the built environment.
• Evaluatin g the global performances of the proposed solutions

Contents

A case study of an Energy Community project will be the starting point of the integrated project. A file containing initial available information on the Energy Community project will be provided (information concerning e.g. buildings, energy consumption, mobility needs, local energy resources, integration in the built environment, location). Students will have to analyze the completeness and relevance of the initial information before mobilizing resources to complete such information (information needed for next steps). Students will then make proposals of energy infrastructures (including their sizing and scheduling/dispatch strategies) for fitting the energy needs and will evaluate the solutions economically, in terms of sustainability and urban planning.

Students will have to define a group organization for tackling both the different subtopics and successive steps successfully. They will benefit from identified supports in the academic team as well as outside the university.

Learning outcomes

The course is divided into two parts: Sustainable Urban Planning and GIS Systems and Geomatics. At the end of this course the student will:

• Gain an advanced understanding on the inner logic of urban planning and tackle its challenges (environmental, societal, etc.) by critically reflecting and integrating criteria, such as the morphology, the density, the governance, the building typology, etc.

• Be able to collect, analyze and synthesize planning information and concisely communicate findings and city strategies;

• Be acquainted with the main theories and approaches of spatial planning;

• Be aware of the different dimensions of spatial planning and gain insights for the spatial planning evolution and the assessment of contemporary urban planning issues;

• Master the fundamental knowledge needed to address geomatic problems;

• Be acquainted with spatial data representation and analysis using GIS and python;

• Be able to create digital maps to communicate spatial data in a meaningful way;
• Acquire a self-learning attitude to solve geomatics problems using deductive reasoning

Contents

The Sustainable Urban planning part addresses the following contents:

• Introduction to city planning and process of urbanization from a global perspective (social, political, economic forces).
• Key historical and contemporary debates in urban planning.
• Theories and models of urban planning.
• Interdisciplinary and discipline-based discussions on the practice of sustainability & key debates in sustainable city planning.
• Case-study analysis and hands-on application
• Empirical study and data collection/analysis/interpretation

The contents of the second part on Geographic Information Systems and Geomatics are as follows:

• Introduction to relational databases and queries: structure of a relational database, database joins, SQL queries.
• Geographical Information Systems and geoprocessing: spatial reference systems and projections, raster and vector models, DEM and DTM, topological model and predicates, data representation, geoprocessing operations, handling networks.
• Hands-on introduction to RDBMS: interacting with a relational database in SQL and writing SQL queries, exporting to data frames;
• Hands-on introduction to Geographical Information Systems and geoprocessing using QGIS and python packages: Using spatial reference systems: srs specification and transforms, on the fly reprojection; Accessing data sources: data types, common formats, geodata sources; Raster datasets: handling data (georeferenced images, geoservers), data representation, georeferencing images, using Digital Elevation Models and Digital Terrain Models; Vector datasets: handling data (geographic databases, geoservers), coding data, using symbology and labels, selecting data subsets (spatial and non-spatial selections), applying geoprocessing operations, topologic and non-topologic editing; input of field data;

Guided labs on dedicated software will be organized to illustrate these aspects.

The growth and development of cities in the 21st century presents significant challenges, including sustainable development, the planning and design of urban space and social wellbeing. With thousands of smart-city initiatives around the world, smart urbanism is now one of the dominant models of urban development. Projects for smart cities involve the regeneration of existing urban areas as well as the creation of large new settlements, and have a major positive impact on the many environmental, social and economic systems that underpin the planet. Meanwhile, and with a strong overlap with smart city initiatives, cities around the world are reacting to broader environmental challenges, such as climate change through measures aimed at developing sustainable solutions. The global scale of such challenges has been recognized within the Sustainable Development Goals (SDG’s) under the heading of ‘Sustainable Cities and Communities’. Here, the promotion of safe, inclusive and sustainable cities is outlined as a central pillar of creating a sustainable urban future. How can transport systems make our cities prettier, healthier and livable? Which approaches to transport planning and dimensions of sustainable urban mobility exist? To remake cities by democratizing transit, leaders and innovators must have a comprehensive understanding of the three pillars of modern smart mobility – technologies, technicalities, policies – and how they interact with complex, real-world systems.

The course is based on a mix of case-studies, tasks and a cross-sectoral project of the urban planning and mobility approaches, among other activities, allowing a comprehensive theoretical and practical knowledge and seeks to generate dialogue between the urban planning and in-depth engineering skills leading edge mobility solutions.

Objectives

This course aims to bridge the knowledge gap between the dynamic on-the-ground reality brought on by technology innovation, academic content, and practice needed to respond systemically and more equitably to global urban challenges through new mobility disruptions.

The course captures this multidisciplinary, contested, emergent character of the field by both critically and constructively engaging an urban transportation planning process:

• Problem identification: coping with the dilemmas of urban mobility and city planning by contrasting paradigms in research and practice;
• System analysis: mainstream methods to develop a rudimentary understanding of the urban mobility system; major criticisms and potential solutions; how to challenge the choices that underlie models and co-create alternatives.
• Strategy development: how to overcome barriers and realize opportunities for transformative change in the urban mobility system, transition management as one emerging, but also controversial approaches.

Learning outcomes

At the end of the course, the student will be able to:

• analyze the interaction between urban planning and mobility strategies in a sustainable approach;
• assess the different transport modes and technologies;
• design sustainable solutions to improve the mobility in terms of spatial planning and infrastructure considering urban and social aspects;
• develop critical overviews of urban planning models and reflections for future urban developments in line with the climate change challenges.

Learning outcomes

At the end of the course, students will be able to:

• Understand the main issues of plug-in and hybrid electric vehicles (introduction), vehicle dynamics, nature of transmission system, electric propulsion
• Describe the structure of electric drive systems and their role in vehicular applications.
• Describe the operation of DC motor drives to satisfy four-quadrant operation to meet mechanical load requirement.
• Understand the basic principles of self-synchronous AC drives.
• Learn speed control of induction motor drives and how to enhance the dynamic performance implementing vector control
• Acquire practical skills with a simulation project. You will be able to evaluate the energy performance of a vehicle for a given driving cycle

Contents

• Vehicle Motorization: Performance curves: electric and thermal; power train architectures; performance evaluation of vehicles; normalized measure of energy/fuel consumption and emissions.
• Electrical Vehicle Drives: automotive EV classification; electric railway vehicles; main sizing aspects; DC motor drives: basic principle of speed control, field-weakening, energetic optimization; Self-synchronous AC motor drives; induction motor drives: scalar control, slip regulation, current-controlled voltage-fed inverter drive; induction motor-based vector control of EV.
• Project of Electric Vehicles: In this project, the students will first implement energy-based models of the different subsystems of an electric vehicle. Then, the performance (e.g. consumption, EV battery degradation, etc.) will be assessed for one or more driving cycles assuming an energy management strategy.

A background in electrical machines and power electronics is strongly recommended for selecting this course.

Learning outcomes

• At the end of the course, the student will be able to:
• evaluate and choose technological solutions according to the contemporary challenges of a society in transition
• refine a technological solution by integrating the low-tech spirit and/or inclusive, collaborative and open design principles
• participate in a Design Thinking process
• consider a solution in the context of an economy of functionality and cooperation

Contents

The course addresses the following contents

• Engineering for a Society in transition: Beyond climates, contemporary issues for a Society in transition, Resources (material and immaterial) and needs linked to a territory or a community, Towards a sobriety in technology, the LowTech mindset (concepts, principles and values, successes and failures, criteria) is not DIY, Other design mindsets (Inclusive design, design for recycling, open (open-source and appropriable) and collaborative science and technology), Design Thinking and co-innovation, Scalability; circular economy; economy of functionality and cooperation ;
• Case studies. Two main possibilities: case studies of projects already completed, application to a student project in progress, through the prism of all the issues considered in the UE (Climate changes and Society in transition).

Learning outcomes

At the end of the course, students will be able to:

• Explore and create solutions adapted to market opportunities;
• Create and exploit innovative business ideas and market opportunities;
• Turn market opportunity into a business plan;
• Demonstrate understanding   and   application   of   the tools   necessary   to   create sustainable and viable businesses;
• Analyze and understand the main implications of developing sustainable entrepreneurial solution the context of Smart Cities;
• Craft a Smart City Business Model Canvas.

Contents

The course will provide students with key insights into entrepreneurship and its practical implementation within newly created organizations. It focuses on different stages related to the entrepreneurial process, including business model innovation, monetization, marketing, small business management as well as strategies that improve sustainability of new business ventures. The course will also show the main implications of entrepreneurship for smart cities by highlighting the role and components of the smart city business model. Confronted to a mixture of theoretical foundations and case studies based on real-life examples, students will develop an understanding of successes, opportunities and risks of entrepreneurship within the context of smart cities.

Learning outcomes

There is a need for engineering students to develop a global perspective of their technical field and profession, in the context of multinational operations, companies, standards and regulations. This course aims at developing an increased awareness of international issues for the engineering world: demographic growth, natural and energetical resources, transport, education and the role of states, international institutions or organizations; international law and regulations.

Contents

The course will cover:

• Theory and principles of international relations
• Selected internationally engineer-specific law : labor law, product liability, trademark law, copyright law, competition law, patent law, industrial law, corporate law, environmental law (Framework Convention on Climate Change);
• Geopolitics of energy and natural resources; demographical and social challenges; education
• European Institutions, World Trade Organization, Diplomacy.