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Georgios Mavromatidis received his M.Eng degree in Mechanical Engineering, specializing in the production and usage of energy, from Aristotle University of Thessaloniki (Greece) in 2010. Following his graduation, he spent a year participating in research projects of the Laboratory of Heat Transfer and Environmental Engineering, in Aristotle University of Thessaloniki. In 2011 he embarked on the MSc in Sustainable Energy Futures, at Imperial College London. His MSc project was titled “Diagnostic tools of energy performance for supermarkets using Artificial Neural Networks” and was part of Imperial College’s partnership with Sainsbury’s Supermarkets Ltd. From September 2012 until March 2013, he was a research assistant at the Centre for Process Systems Engineering of the Chemical Engineering Dept. of Imperial College, participating in projects dealing with energy efficiency and the design of zero carbon supermarkets, as well as the development of an agentbased model of urban energy usage. In July 2013, he started his PhD project at ETH Zurich (Chair of Building Physics, Department of Architecture), supervised by Prof. Jan Carmeliet.
PhD topic: Design of distributed urban energy systems under uncertainty
Global challenges like climate change dictate a paradigm shift in the structure of the future energy system that will incorporate renewable energy sources and other forms of distributed energy systems (DES). Urban environments are ideal for such systems due to high degrees of urbanization, high energy demand density and the potential for building integrated renewables.
However, modelling for the optimal design of distributed urban energy systems is irrevocably affected by uncertainty. The intermittent nature of renewables, the stochastic nature of human behaviour and the unknown climate, economic and energy policy outlook render many model parameters uncertain. As a result, when modelling is performed in a deterministic manner, the successful design outcome of an urban energy system depends heavily on the choice of the model parameters as any deviation can render the design suboptimal.
Therefore, the goal of this PhD is to build upon the existing framework of energy hub modelling for the optimal design of distributed urban energy systems by incorporating uncertainty into the design process.
Figure 1. Conceptual illustration of an energy hub supplying energy to a set of buildings in an urban neighbourhood. Additionally, a sample of the uncertainties that are considered in this PhD research are also depicted
To incorporate uncertainty, this PhD research has developed a 3step approach that is described as follows:
1. Uncertainty characterisation: The first step of this PhD research is the uncertainty characterisation of the energy hub model parameters. Firstly, the subset of the model parameters that should be considered as uncertain is identified and, secondly, a mathematical representation to each parameter's uncertainty is assigned.
2. Uncertainty & Sensitivity Analysis (UA&SA): In this step, the impacts of uncertainty on the optimal energy system design are explored. Initially, UA is performed by randomly sampling from the uncertain input parameters and running the model in a Monte Carlo fashion. This analysis allows us to investigate the impact of uncertainty on the design outcome in terms of both the optimal objective value of the energy system (energy or cost) as well as the optimal system configuration. Subsequently, Global SA techniques are employed that allow the identification of the uncertain input parameters that are mostly responsible for these variations as well as the parameters whose uncertainty can be safely ignored as they do not influence the design outcome.
3. Optimization under Uncertainty: Even though UA&SA allow the investigation of uncertainty's impacts, they cannot point towards a single design decision for the envisioned energy system. This is the task of Optimization under Uncertainty techniques that aim to identify "how to act now, before uncertainty is resolved". Techniques like Stochastic Programming (SP) and Robust Optimization (RO) seek to identify the optimal energy system configuration "here and now" that will minimize the expected and the worstcase cost/emissions, respectively, for any realisation of the uncertain parameters.
Publications
Journal papers
1.
 Wu, R., Mavromatidis, G., Orehounig, K., Carmeliet, J. (2017) Multiobjective Optimisation of Energy Systems and Building Envelope Retrofit in a Residential Community. Applied Energy, 190, 634649 DOI
 2.
 Mavromatidis, G., Orehounig, K., Richner, P., Carmeliet, J. (2016) A strategy for reducing CO2 emissions from buildings with the Kaya identity  A Swiss energy system analysis & a case study. Energy Policy, 88, 343354 DOI
 3.
 Allegrini, J., Orehounig, K., Mavromatidis, G., Ruesch, F., Dorer, V., Evins, R. (2015) A review of modelling approaches and tools for the simulation of districtscale energy systems. Renewable and Sustainable Energy Reviews, 52, 13911404 DOI
 4.
 Mavromatidis, G., Orehounig, K., Carmeliet, J. (2015) Evaluation of photovoltaic integration potential in a village. Solar Energy, 121, 152168 DOI
 5.
 Orehounig, K., Mavromatidis, G., Evins, R., Dorer, V., Carmeliet, J. (2014) Towards an energy sustainable community: an energy system analysis for a village in Switzerland. Energy and Buildings, 84, 277286 DOI
 6.
 Mavromatidis, G., Acha, S., Shah, N. (2013) Diagnostic tools of energy performance for supermarkets using Artificial Neural Network algorithms. Energy and Buildings, 62, 304314. DOI

Conference proceedings
1.
 Wu, R., Mavromatidis, G., Orehounig, K. (2016) Reliability Optimisation of a District MultiEnergy System. In: 19. StatusSeminar, ETH Zurich, Switzerland, September 8th – 9th 2016. Link
 2.
 Mavromatidis, G., Orehounig, K., Carmeliet, J. (2016) Uncertainty and sensitivity analysis for the optimal design of distributed urban energy systems. In: Sustainable Built Environment Conference, SBE16, Zurich, Switzerland, June 13th – 17th 2016. Link
 3.
 Wu, R., Mavromatidis, G., Orehounig, K., Carmeliet, J. (2016) Optimal Energy System Transformation of a Neighbourhood. In: Sustainable Built Environment Conference, SBE16, Zurich, Switzerland, June 13th – 17th 2016.
 4.
 Mavromatidis, G., Orehounig, K., Carmeliet, J. (2015) Evaluation Of Solar Energy Integration Potential In A Neighborhood. In: 14th International Conference of the International Building Performance Simulation Association (IBPSA), BS 2015, Hyderabad, India, December 7th – 9th 2015. Link
 5.
 Mavromatidis, G., Orehounig, K., Carmeliet, J. (2015) Climate change impact on the design of urban energy systems. In: International Conference Future Buildings & Districts, Sustainability from Nano to Urban Scale, CISBAT 2015, Lausanne, Switzerland, September 9th – 11th 2015. DOI
 6.
 Orehounig, K., Mavromatidis, G., Derome, D., Carmeliet, J. (2015) Photovoltaic panels as a main component of energy sustainable communities: comparative energy analysis of a village under Swiss and South African climatic loads. In: Third Southern African Solar Energy Conference, SASEC 2015, Skukuza, Kruger National Park, South Africa, May 11th – 13th 2015. Link
 7.
 Mavromatidis, G., Evins, R., Orehounig, K., Dorer, V., Carmeliet, J. (2014) Multiobjective optimization to simultaneously address energy hub layout, sizing and scheduling using a linear formulation. In: Engineering Optimization (ENGOPT) 2014, Lisbon, Portugal, September 8th – 11th 2014. Link
 8.
 Orehounig, K., Mavromatidis, G., Evins, R., Dorer, V., Carmeliet, J. (2014) Predicting energy consumption of a neighborhood using building performance simulation. In: Building Simulation and Optimization conference (BSO), London, UK, June 23rd – 24th 2014.
 9.
 Acha, S., Mavromatidis, G., Caritte, V., Shah, N., (2013) Technoeconomical Technology Assessment for Operational Zero Carbon Supermarkets. In: Proceedings of 26th International Conference on Efficiency, Cost, Optimization, Simulation and Environmental Impact of Energy Systems (ECOS 2013), Guilin, China, July 16th – 19th 2013.
 10.
 Acha, S., Mavromatidis, G., Caritte, V., Shah, N., (2013) Effective Lowcost Energy Saving Strategies in Supermarkets: An UK Case Study. In: Proceedings of 26th International Conference on Efficiency, Cost, Optimization, Simulation and Environmental Impact of Energy Systems (ECOS 2013), Guilin, China, July 16th – 19th 2013.
 11.
 Mavromatidis, G., Papadopoulos A. M. (2011) Thermodynamic analysis of a solar operated domestic absorption refrigerator. In: Proceedings of the National Conference on Architecture, Energy and Environment in buildings and cities (ARENEP 2011) (A.M. Papadopoulos, ed), Athens, Greece, May 3rd4th May 2013 (in Greek).

Reports
1.
 Wagner, M., Weyell, C., Christiaanse, K., Mikoleit, A., Carmeliet, J., Orehounig, K., Mavromatidis, G., Hellweg, S., Frömelt, A., Steubing, B., Schlüter, A., Geyer, P., Cisar, S., Schlegel, M., Zaugg, H. and Gruber, S. (2015) Zernez Energia 2020  Aktionsplan. ETHZürich. DOI
 2.
 Wagner, M., Weyell, C., Christiaanse, K., Mikoleit, A., Carmeliet, J., Orehounig, K., Mavromatidis, G., Hellweg, S., Frömelt, A., Steubing, B., Schlüter, A., Geyer, P., Cisar, S., Schlegel, M., Zaugg, H. and Gruber, S. (2015) Zernez Energia 2020  Leitfaden. ETHZürich. DOI

