PhD students

Marc Hohmann - Predictive optimal operational strategies for urban energy systems


Distributed energy systems require active control strategies in order to balance multiple energy streams and to ensure a reliable, economic and environmentally friendly operation. Multi-carrier energy systems offer additional degrees of freedoms compared to single carrier operational schemes managing only electricity, gas or heat. The objective is to increase energy efficiency, decrease carbon emissions and handle the uncertainty and intermittency of renewable energy sources. Active control strategies are therefore crucial to the transformation of energy systems, a central element of climate change mitigation measures.

The objective of this work is to develop improved central local energy management systems for urban districts. These must be able to include non-convex characteristics of energy conversion processes, decision-making for short- and long-term storage and avoid suboptimal results due to the self-interest of the energy system's agents. Using mixed-integer linear programming, energy conversion processes can be modeled with high accuracy. Aggregation methods can reduce the computation load when making long-term decisions. Mechanism design and locational marginal pricing can ensure that optimal operation modes are achieved in complex systems.

A prototype controller is implemented and tested on the NEST platform, located at Empa in Dübendorf, Switzerland. Distributed controller architectures are compared to the central control scheme proposed by this work.


Julien Marquant - Facilitating Multi-Scale Urban Energy Systems Modelling

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Network-level energy optimisation approaches can determine the optimal location of generation technologies within a region and the optimal layout of energy distribution networks to link them. This is a multi-scale problem as it must encompass decentralisation at the building, district, city or regional scale. However, computational limitations arise at larger spatial scales if full resolution is re-tained, and difficulties emerge in the quantification of different urban agglomeration levels when attempting to model network be-haviour at multiple spatial scales. Nevertheless, a tractable multi-scale optimisation of a large urban area is possible using a clustered, aggregated energy hub repre-sentation.

The main objective of this research is to model and optimise the interaction of complex energy net-works in order to understand the trade-off between centralised and decentralised energy systems at different urban scales. A method for multi-scale urban energy systems modelling with a hierarchical approach will be developed to facilitate this. An evaluation of the approximations necessary to make this modelling computationally feasible will then be conducted. Finally the process developed will be applied to a case study, and the outcomes analysed in the context of the Swiss Energiewende 2050.


Georgios Mavromatidis - Modelling of decentralized energy systems under uncertainty

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“Distributed Energy Systems (DES) are expected to be a core component of future urban energy systems incorporating a multitude of generation and storage technologies to supply the energy needs of urban districts. The motivation for this paradigm shift stems from global challenges like climate change, the high degrees of urbanization, cities’ high energy demand density and the potential for building integrated renewables.

The challenge to optimally design and operate DES relies heavily on modelling; however, as with any numerical modelling effort, models for the optimal DES design are irrevocably affected by uncertainty. Human behavior and the uncertain future global economic and energy outlook are only a subset of factors that can introduce uncertainty to a DES model.

Therefore, the aim of this research is to create an integrated modelling framework that will in a systematic way incorporate uncertainty into the design process of distributed urban energy systems. Embarking from a design model based on the energy hub concept, techniques from Uncertainty and Sensitivity Analysis are employed to investigate uncertainty’s impacts and identify its main drivers. Subsequently, techniques for Optimization under Uncertainty are used in order to select single optimal designs that will be robust in the face of uncertainty.”


Somil Miglani - Geospatial evaluation of standalone and district heating systems for residential buildings

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Future cities, urban areas and buildings are expected to undergo a transformation towards more sustainable energy systems. This transformation involves a move towards increased use of renewable energy resources, decentralized forms of energy production, energy efficient buildings, thermal networks etc. The aim is to achieve such a transformation optimally, considering economic and environmental constraints. The current energy systems in buildings, especially heating systems, are based on fossil fuels and must give way to more energy efficient and environmentally benign alternatives. Each building can either be connected to a district heating system or it can be individually heated through a fully decentralized standalone systems such as solar thermal for instance. Since the potential for decentralized energy sources exhibits a high degree of spatial and temporal variability, the optimal integration of these technologies in existing buildings remains an open question.

This research aims at investigating the technological tradeoff between renewable energy based standalone systems and a small scale district heating system taking the total costs and carbon emissions savings into account. More specifically, methods will be developed to evaluate optimal configurations of energy systems for single buildings and clusters of buildings representing spatially and temporally differentiated energy demand and supply patterns. Finally, this analysis of the above mentioned technological tradeoff is carried out on multiple case studies representing urban areas, diverse with regard to technical parameters that differentiate them in their spatial characteristics.


Boran Morvaj - Holistic optimisation of distributed multi energy systems for sustainable urban areas

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The aim of this PhD project is to develop a holistic optimisation model of distributed multi energy sys-tems to explore how existing urban areas can be best transformed into sustainable ones. Models are based on the energy hub concept. It combines models of distributed energy resources, electrical grids, decentralised district heating networks and building energy systems into one integrated optimisation model in order to find synergistic effects between them. The model is used to determine the optimal design and operation of distributed multi energy systems under different objectives such as the multi-objective minimisation of carbon emissions and costs, energy import independency and optimal power flow. Furthermore, the impact and benefits of decentralised district heating networks as well as large-scale integration of renewables in the existing electrical network are analysed. Finally, key influencing factors affecting the optimal solutions are identified.


Portia Murray - Integration of sustainable multi-hub systems from the building performance perspective

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Developing a method to assess the best combination of technologies for decentralized district heating systems to analyze district heating performance on the neighborhood scale. Storage technologies are of particular focus in this research, especially power-to-gas and battery storage technologies for storing excess renewable energy during off-peak demand. These methods are compared against more traditional storage and conversion technologies, such as thermal storage tanks, heat pumps and boilers. All technologies are incorporated into both a centralized and decentralized Energy-Hub model on the neighborhood scale for analysis.


Christoph Waibel - Hyper-Heuristic Framework for Multi-Objective Optimization of Urban Systems

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A major share of global resource and energy consumption can be associated with cities. Considering the ongoing trends of urbanization and population growth it becomes evident that their evolution is a crucial keystone in tackling global environmental and economic challenges. It has been shown that cities and buildings are far from the ”efficiency possibility frontier” and could achieve much higher utility with less energy and resource input. One reason for inefficient designs can be found in the inherent complexity of the design process, which requires the expertise of multiple disciplines. The traditional approach to cope with this is to separate responsibilities and to exchange information in sequential steps. However, generating, processing and exchanging information is a costly practice and every change in design requires re-evaluation of other related disciplines. Thus, only a low degree of reciprocity is realized.

Holistic optimization methods may overcome this issue, as they can inform the design process by exploring vast numbers of design solutions across multiple performance criteria simultaneously. One of the practical challenges lies in the selection of appropriate optimization algorithms best suited to specific problem formulations. Hyper-heuristics deals with this by introducing a higher-level method for automatically selecting and tuning a tailored heuristic from a set of algorithmic operators. This research focuses on the development and application of a hyper-heuristic framework for multi-objective urban design, including building morphology and urban energy systems. Questions to be addressed using the hyper-heuristic optimization framework include the range and degree of multi-energy network connectivity within and across neighborhoods, the degree of densification for optimal demand and renewable energy generation, and the use of building standards (Passivhaus, nZEB, active house) in the context of a connected multi-energy-grid. Hyper-heuristic methods have the potential to change the overall design approach and enable holistic design and planning, where reciprocities between different disciplines and scales can be captured, thus leading to more efficient urban systems. 


Danhong Wang - Renewable powered district heating networks

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Space heating accounts for around 70% of the final energy consumption in Swiss households. Therefore, as Switzerland looks towards its 2050 CO2 emission targets which require an 80% reduction in annual CO2 emissions per capita, there is a pressing need to increase the utilisation of energy efficient and renewable heating sources in the residential sector. It is claimed that district heating networks powered by local thermal energy sources like renewables (such as solar thermal energy, heat pumps, or waste heat) are considered a sustainable way to cover future heating and cooling demands in urban areas. However, existing types of district heating networks are not designed for decentralized renewable energy sources, and their integration becomes a challenging task. Existing networks are typically built in a branching configuration, whereas future renewable powered networks tend to be in ring topologies. Also, the efficiency of a thermal network is very much dependent on temperature levels of the thermal energy sources. These temperature levels can be easily controlled in networks that rely on centralized thermal energy generation sources like combined heat and power (CHP) or boiler units. However, temperature levels of non-dispatchable renewables cannot be controlled as easily as they are highly time variant. Also, the efficiency of a thermal network is strongly coupled to the supply and demand temperatures and flow rates of consumers connected to the network, and with the more frequent utilization of renewable energy sources it will become increasingly challenging to cover the temporal mismatch of demand and supply. Based on this background, a deeper knowledge is required in order to evaluate the potential of renewable energy in thermal networks. This phd project aims to deepen the knowledge by developing a holistic modelling framework to design and ideally operate renewable powered district heating networks (RePoDH). In this project a bi-level simulation approach is envisioned, which employs detailed dynamic modelling tools to evaluate the thermal performance and control of a network, and a simplified multi-energy modelling representation allowing to optimize the system design, for which dynamic tools are too complex, and computationally intensive. The two simulation approaches will be connected with a geographical information system, to evaluate potential network configurations using geo-referenced information. With the modelling framework we will assess how networks with a high share of renewable energy sources should be designed, in order to improve the operation of the network in terms of security and energy autarky. Moreover, we will evaluate what types of districts are suitable for RePoDH networks, and what types of networks should be used for which district in order to contribute to reaching future emission targets for our society.