Varsha N. Behrunani - Decentralized control of multi-energy systems
Multi-energy system perspectives in district and/or urban areas are core to carbon neutral societies. These systems incorporate electrical, thermal and chemical processes that follow individual demand and supply patterns under distinct time scales, i.e. from sub-second (electrical) to weekly/monthly (thermal) and seasonal (chemical) applications. Coordination of the related technologies in the domain of energy systems can only be achieved by optimally controlling the envisioned technology setup, under current or future stakeholder interests. This includes decentral control setups with restricted information propagation between stakeholders, working with measured data for modelling, controller synthesis and showcasing developments on operating real-life systems. In addition to sector specific technological limitations, the systemic coupling of energy carriers needs to account for the coupling of different time scales as well as different production and consumption patterns. A decentralized technological landscape facilitates faster computation and reduces the need for extending large-scale infrastructure, such as electrical grid reinforcements or international imports of energy. A decentral setup also results in more stakeholders being involved in the energetic supply chain that can benefit if they share information on their capabilities and intended production and consumption, such that they can be matched locally.
The goal of this project is to develop novel distributed control methods for operational decision making in multi objective optimization of joint thermal and electrical sectors under current and future stakeholder setups that will be fully developed, customized to the multi-energy setting and extended in two directions. Firstly, we address the coupling of multiple energy streams in urban areas during runtime and optimizing their operational behavior using data-driven control algorithms that can adapt to local specificities by the use of locally gathered measured data and improve system wide optimal behavior in terms of local preferences and privacy. Secondly, we investigate the coupling of agents in these energy grids and the balance between privacy of information and economic/environmental performance using a game theoretic approach for coordinating the decisions of multiple stakeholders. The ultimate goal of this project is to advance widespread acceptance of the coordinated operation of energy system technologies in districts and cities, a key enabling technology in the dynamic next generation multi-energy management systems.
Loris Di Natale
With the ratification of the Paris Agreement in 2015, many countries in Europe and in the world, including Switzerland, committed to ambitious greenhouse gas emission targets in 2050. In that context, electrification of the end-use services is required and developed around the globe.
In particular, the share of electrical vehicles (EVs) in the car market is growing faster than ever to replace classical Internal Combustion Engine (ICE) vehicles. Simultaneously, buildings are undergoing a similar transition to electricity, with heat pumps-based heating systems. Additionally, new constructions are now pushed to install on-site renewable generation capacity - typically through the installation of photovoltaic (PV) panels.
Taking all these changes into account, we can today use the growing share of EVs as an opportunity rather than a burden, due to the additional electricity demand required to charge them. Taking advantage of the introduction of EVs with bidirectional chargers and smart charging/discharging strategies, we can indeed simultaneously reduce the peak in the electricity demand and maximize the utilization of renewable energy production. The key is to consider EVs as energy storage systems as well as transportation means.
The following research question arises: What is the optimal use of electric vehicle batteries to maximize the utility from self-generated renewable energy and decrease the demand during peak-hours, thereby reducing the global energy costs of households? To answer it, in this project we aim to use state-of-the-art data-driven control techniques, like reinforcement learning, to tackle this problem.
Natalia Ewa Kobylinska - Low-carbon building design with value retention of materials and components across their multiple life cycles
The construction industry is one of the most significant carbon emitters and waste producers globally. At the same time, the building floor area is expected to rapidly expand in the next couple of decades, raising questions about the continuous demolition waste generation in densified cities on the one hand and demand for raw materials on the other hand. Direct reuse of construction materials and components can become a strategy for low-carbon building design. However, many obstacles to a substantial uptake of reused materials and components persist. Firstly, there is no scientific consensus on how to quantify the environmental impact of value retention processes in common methodologies like Life Cycle Assessment (LCA), so that different low-carbon building scenarios can be compared during the early design process. Secondly, the limited datasets on material availability in existing buildings, in a so-called urban mine, hinder an urban-scale supply-demand match between the decommissioned and the new construction. Finally, circular building design remains a marginalized approach with few stakeholders in the industry having enough knowledge and incentives to take it beyond theoretical concepts.
This research addresses the above-mentioned knowledge gaps and aims for developing a computational method for direct feedback on both embodied and operational impacts of design choices, concerning the reuse of building materials and components.
Professional expertise part: PhD Student; Circular Engineering; Architecture; Life Cycle Assessment; Machine Learning; Data management systems; Design systems;
Emmanouil Thrampoulidis - Large-scale building retrofit towards more effective energy policies and strategies
The building sector accounts for more than 40% of the total energy consumption and emissions in Europe. Building retrofit is of greatest importance to reduce the environmental footprint of the existing building stock. It may refer to two types of interventions: the first pertaining to interventions on the building envelope, for instance by enhancing the thermal insulation of a building’s walls, and the second one to building energy system replacements and renewable technologies integration. Even though building-specific solutions are important there is much more to gain if those are part of a coordinated large-scale retrofit plan. A more systematic and effective solution to derive energy policies, strategies and incentives is one of the benefits of such large-scale retrofit approaches.
Building retrofit is a complex process, which involves the use of highly heterogeneous building information (census data, 3D building data, weather data) and computationally intensive tools (multi-objective optimization, building simulation). Usually, due to the limited timeline and investment of the retrofit projects the building process is extensively simplified, for instance by just performing some steady state calculations. Moreover, most large-scale retrofit projects are based on archetypes and arbitrary generalize. Eventually, such approaches might lead to results that highly deviate from reality.
Therefore, the aim of this research is to exploit the principled generalization ability of machine learning to develop a large-scale data-driven retrofit approach with the use of both simulation and real building data. This approach can be more beneficial than the conventional ones in terms of: (i) generalization ability and adjustability, (ii) ease of application, (iii) retrofit selection time and (iv) computational cost. Last but not least, such a surrogate retrofit approach can contribute towards deriving more effective energy strategies and eventually accelerating the adoption of building retrofit measures.