Urban Energy SystemsOpen OpportunitiesThe global electric vehicle (EV) fleet is projected to reach 145 million units by 2030, posing new threats to the reliability of the power system. However, EVs can also play a key role as a source of demand-side flexibility to support the system in managing uncertainty resulting from the integration of renewable energy resources. The onsite coupling of photovoltaics (PVs), battery energy storage systems (BESS) and EV fleets with vehicle-to-grid (V2G) technology has shown promising performance in terms of demand-side flexibility provision. - Engineering and Technology
- Master Thesis
| Buildings are significant energy consumers, primarily due to the operation of heating, ventilation, and air conditioning (HVAC) systems. Effective control of such systems is crucial for enhancing overall energy efficiency. Typically, traditional rule-based controllers are used due to their affordability and interpretability. However, as complexity increases, these controllers suffer from non-optimal performance and limited scalability. Recent advancements in Deep Reinforcement Learning (DRL) provide a data-driven alternative, demonstrating promising control performance without the need for explicit system modeling. Despite these advantages, conventional DRL approaches often fail to account for specific operational constraints present in HVAC systems. One critical constraint is the requirement for smooth control actions with a limited number of on-off switches, as frequent switching can lead to faster deterioration of the controlled systems. Therefore, it is imperative to develop data-driven control strategies that not only optimize energy consumption but also adhere to these operational constraints. This study, part of the Euthermo Project, aims to develop safe reinforcement learning algorithms for building climate control. - Engineering and Technology
- Master Thesis
| Energy consumption in buildings is a critical concern, primarily driven by the operation of heating, ventilation, and air conditioning (HVAC) systems, lighting, and other appliances. Efficient control of these systems is paramount for achieving significant energy savings and reducing environmental impact. Traditional rulebased controllers, while cost-effective and easy to implement, often fail to provide optimal performance and lack scalability as system complexity grows. Recent advancements in Deep Reinforcement Learning (DRL) offer a powerful, data-driven alternative. DRL has shown promising results in optimizing control performance without the need for explicit system modeling. However, the complexity of managing multiple interdependent control variables within a building remains a challenge. For instance, the heating control of individual rooms can influence each other, and shading controls can affect both heating and cooling demands. - Engineering and Technology
- Master Thesis
| In recent years, the penetration of renewable energy resources in distribution grids has been steadily increasing, raising new issues such as voltage violations or line congestions. Due to their large thermal inertia, individual buildings can regulate their heating system to support distribution system operation.
In our previous work, we proposed a quantification of the flexibility potential of an electric heating system, using the concept of energy flexibility envelopes, and accounting for the impact of various uncertainties: the weather forecast, the building thermal model inaccuracy, and the uncertain inhabitants’ behavior. However, we considered that uncertainties are independent of the requested flexibility. Yet, in practice, the inhabitants’ behavior is correlated to flexibility requests as optimal control strategies. For example, a request to shift the consumption may increase the room temperature,
which in turn impacts the inhabitant behaviors, possibly reducing energy efficiency. - Building Science and Techniques, Electrical Engineering, Systems Theory and Control
- ETH Zurich (ETHZ), Master Thesis
| Prosumer communities leverage and share locally generated energy. In response to the Swiss Energy Strategy 2050 and potential gas supply vulnerabilities, transitioning from singular, gas/oil-reliant energy systems to multi-energy networks is imperative. Unlike conventional individual systems where devices are often oversized and underutilized due to being designed for demand peaks, a prosumer community can optimize device use, reducing both energy costs, investment overheads and ecological impact. Therefore, when properly designed and operated, prosumer communities mitigate the inefficiencies typical of traditional setups. However, achieving optimal design is challenging due to complex interactions among stakeholders. The economic and ecological aspects of prosumer communities need to be thoroughly investigated taking into account different scenarios, energy demands and building types. This project aims to investigate various case studies for prosumer communities in the Swiss scenario and explore the technological solutions that mostly benefit from energy sharing. - Mechanical and Industrial Engineering
- Semester Project
| Switzerland is committed to transitioning to a renewable energy system. The Swiss government has set a target of achieving net-zero carbon emissions by 2050. This will require a significant increase in the use of renewable energy sources. The Swiss power grid is also vulnerable to imbalances be-tween supply and demand. Demand flexibility can help to mitigate this risk and ensure the reliable operation of the power grid. Demand flexibility is the ability to shift or reduce energy use in response to changes in sup-ply or price. This is becoming increasingly important as the power grid transitions to renewable energy sources, such as solar and wind power, which are intermittent and less predictable. Demand flexibility can help to balance the grid and reduce the need for expensive and polluting backup power plants. Non-Intrusive Load Monitoring (NILM) and customer segmentation modeling are powerful tools that can be used to develop demand flexibility programs. NILM can be used to identify high-energy-consuming appliances and to track their energy usage over time. Customer segmentation modeling can be used to identify different groups of customers based on their energy consumption patterns. This information can then be used to develop targeted demand flexibility programs that are more likely to be effective for each group of customers. - Building not elsewhere classified, Building Science and Techniques, Neural Networks, Genetic Alogrithms and Fuzzy Logic, Signal Processing, Simulation and Modelling
- Master Thesis
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