Magnus Thor Jonsson is a Professor of Mechanical Engineering at the School of Engineering and Science, University of Iceland, located in Reykjavik, Iceland. He received his MSc. degree in Mechanical Engineering from the University of Iceland, Department of Industrial Engineering in 1982 and his PhD in Mechanical Engineering from the Mechanical Department at the Technical University of Norway in Trondheim in 1987. His major field of study was model based design using nonlinear FEM models. His main research interest is in the field of model based optimization for geothermal power plants. He has published a number of reviewed research papers and led a number of funded research projects in the areas of thermal and structural modeling of geothermal wells and optimization of steam gathering systems for geothermal power plants. Prof. Jonsson has worked as the Department Head of the Mechanical Engineering Department and has been the chairman of the Engineering Research Institute, University of Iceland, chairman of Continuing Education, and the chairman of CAD, CAM and CAE Association in Iceland. He has also been the chairman of the Machine Group of Icelandic Standards. He has been a board member of many steering committees at the University of Iceland and at Icelandic associations such as the Icelandic Society of Engineers and the IT&T User Forum.
Speech Title: Metaheuristic Optimization of Renewable Energy Resources
Abstract: The growth in demand of energy consumption is one of the main concern of this century and the limitation of energy production is mainly due to the environmental cost of using the energy resources. Renewable energy is energy produced by renewable and natural resources such as sunlight, wind, rain, tides, waves and geothermal heat. Despite the advantages of renewable energy, it presents some important drawbacks such as fluctuating generation of energy as most renewable energy resources depend on the climate. Hence, the use of renewable energy requires complex design, planning and control optimization methods.
An energy system can be defined as set of interacting, interrelated or interdependent entities that together transform energy sources into energy services. In the past, the design of energy systems was mainly based on analytical methods and experience but with increased interest towards more efficient and eco-friendly solutions, there is a need for a systematic formulation to determine the optimum design. Optimization of energy systems has been the subject of many papers in recent years with an emphasis on efficiency for distributed energy generation. The formulation is based on system definition, representation of design or decision variables, analysis of the behavior of the system using modelling and optimization of the objectives.
The energy system optimization consists of defining the conceptual solutions based on demand and resources, selecting components from a pre-defined set of elements and connecting the elements together. The goal is to improve the efficiency while minimizing both financial and environmental cost. The problem is a hybrid of multiple modeling paradigms using continuous, discrete, and combinational representations. Due to the complexity of the model, meta-heuristic algorithms are used as an optimization tool where it is crucial to minimize the number of evaluations. The method is generalized for different kind of energy systems but the findings presented in this case study are based on hydro, wind and geothermal systems.
Meta-heuristic algorithms were first introduced in the mid-1980s as a group of searching algorithms able to approach and solve ill-defined and complex optimization problems. Meta-heuristics algorithms are often defined as approximation algorithms, because they have no theoretical guarantee of convergence to the globally optimal solution. Since their introduction they have been continuously developed and used for solving a growing number of problems previously considered impossible to solve. The methods include simulated annealing, tabu search, evolutionary computation, memetic algorithms, ant colony algorithm, differential evolution, honey-bee colony optimization etc.
Ruslan Vladimirovich Sharapov received the System Engineer degree in 1998 at Vladimir State University, Russia. After his graduation, he started his career as post-graduated researcher in the Department of Information systems at Murom institute of Vladimir State University, where he collaborated with the Vladimir Regional Oncology Center in the framework of the "Informatization of health institutions" project. In 2001 he received PhD in St. Petersburg Institute for Informatics and Automation of the Russian Academy of Sciences, Russia. From 2008 to 2012 he was the Deputy Dean of the Industrial Faculty in Murom Institute of Vladimir State University. In 2008 he received the academic title of professor of Russian Academy of Natural Sciences and was appointed as Executive editor of scientific journal «Engineering industry and life safety». From 2011 he focuses on Geodynamic monitoring in area of Nizhny Novgorod nuclear power plant construction. From 2012 Ruslan V. Sharapov works as the Head of the Technosphere Safety Department in Murom Institute of Vladimir State University.
Speech Title: Nuclear Energy: perspectives and dangers
At present, nuclear power supplies about 11% of the world's energy. Now in world operates 448 nuclear power reactors. Also 58 reactors under construction. Today, many countries are developing nuclear power plant projects containing newest technical solutions and modern reliability and safety requirements. Nevertheless, after the Fukushima accident in 2011, the view on nuclear power has changed. Some countries, many of them in Europe, such as Germany and Belgium, have adopted policies of nuclear power phase-out. The prospects for the development of nuclear power in Japan are unclear. At the same time, some Asian countries, such as China, South Korea and India, have committed to rapid expansion of nuclear power. What are the problems of nuclear energy? The experience of accidents in Chernobyl and Fukushima has shown that it is impossible to ensure the absolute safety of nuclear power plants and provide for all possible scenarios. So, it is possible to damage NPP facilities with conventional weapons or as a result of terrorist attacks. There are problems with waste disposal. The profitability of nuclear power plants is declining. Now there is a tendency of aging NPP power units. The first nuclear power plant is already nearing the end of its life cycle and there is a problem of their reconstruction or recycling. Another problem of the nuclear power plant is thermal pollution of the environment.
Mattia received his MEng in Thermal Engineering at the University of Pisa (Italy) in 2010 and he completed his PhD in Mechanical Engineering (Applied Thermodynamics) at the University of Genoa (Italy) in 2015. During his PhD, his research activities were focused on dynamic modelling of thermal systems and buildings and he collaborated with several international partners, e.g. Polytechnic University of Valencia (Spain), Royal Institute of Technology (Sweden), Ural Federal University (Russia), etc.
In June 2015, Mattia moved to Queen's University Belfast where he conducted research activities on numerical modelling of thermodynamic cycles, heat transfer and thermal system components – in collaboration with the University of Liege (Belgium), ASME and several industrial partners.
Mattia joined the School of Mechanical and Materials Engineering, University College Dublin, in July 2017 as Senior Researcher in the framework of the ESIPP program. His current research is mainly focused on energy modelling of building and heating/cooling system, renewable energies and waste heat recovery and thermo-economic analysis of investments.
Speech Title: Energy flexibility in buildings
The greater penetration of variable renewable energy sources makes the issue of matching power supply and demand a more critical and complex problem. While traditionally, the responsibility of managing imbalances was the responsibility of the supply side, for economic, environmental and physical constraint reasons, demand side management (DSM) is being increasingly required and implemented. Large buildings are well suited for providing DSM due to their inherent thermal inertia and use of Heating, Ventilation and Air Conditioning (HVAC) systems which are usually connected to Building Automation Systems that allow control measures to be implemented. A large building would utilise a range of demand response technologies including but not limited to: building passive thermal mass, active thermal storage systems, onsite generation and shifting non-essential loads. The question then arises whether the flexibility should be calculated for each of these technologies separately or whether a particular combination should be used. In this context, dynamic interaction between building and heating/cooling system is paramount and dynamic models able to reproduce these phenomena could lead to significant energy and cost savings. Moreover, reliable reduced-order models for forecasting heat loads and market price with low computational costs would allow the development of smart real-time control systems to be implemented to optimise the energy consumption in buildings.