http://hdl.handle.net/2268.2/22266 Thu, 05 Mar 2026 19:29:44 GMT 2026-03-05T19:29:44Z http://hdl.handle.net/2268.2/24778 Title: Simulation framework for distribution planning under uncertainty using OpenDSS Abstract: Distribution System Operators are moving from predictable, one–way delivery to grids with high variability, driven by distributed energy resources and ongoing electrification of uses. This thesis builds a reproducible simulation-based methodology to identify how a representative medium-voltage feeder behaves under in- creasing photovoltaic (PV) penetration and higher electrical vehicle (EV) presence. Simple, deployable battery energy storage systems (BESS) and demand shaping are solutions that are explored to preserve voltage quality and reduce technical losses. First, this thesis frames the shift from deterministic and traditional "worst case" specifications to data- aware, time-series planning. Then, two archetypal networks are defined, a "within limits" case and an intentionally overloaded variant. The quasi-static time-series setup in OpenDSS/Python is then described in detail, as well as the line voltage band of 0.95–1.05 chosen for planning decisions and the Monte-Carlo PV siting to produce realistic, non-uniform scenarios. Thereafter, the model is used to first quantify the effects of photovoltaic panels alone. In the within-limits network, average daily losses reduce along the increase in PV penetration, to reach a drop of ≈ 28% at 70% of PV penetration. All the while daily voltage maxima remain below 1.05 pu. In the overloaded feeder, losses still lessen remarkably (≈ 30% at 80 % of PV penetration) but several buses still exhibit daily minima below 0.95 pu across scenarios. The Monte-Carlo scenario spread peaks at intermediate penetrations (≈40–60%), showing why single-snapshot planning can be misleading. To further improve the state of the overloaded network, batteries were then integrated to the model. A fully local algorithm that charges when the local voltage exceeds 1.00 pu and discharge below 0.95 pu is implemented, while respecting the power limits of the batteries. This implementation allows to reduce the percentage of time the furthest bus spends in undervoltage from 15% to 2%. Furthermore, it yields additional loss cuts of around 24%. Using batteries with a lesser charge power shows a trade-off: it allows for late evening and night support as energy remains but slightly increases average losses due to longer operation. The last feature to be added to the model is the effect of EV on the grid. With realistic residential, workplace and commercial usage, unmanaged evening charging shifts the daily voltage minima downwards relative to the no-EV case, even at high PV penetration. Two load management solutions were then presented: the Time Of Use method and the Peak Shaving method. To conclude, this thesis has demonstrated that, in this realistic network, photovoltaic panels are ab- solutely beneficial to reduce losses but do not guarantee acceptable voltages on stressed networks. Local and voltage driven storage control is an effective and rapidly deployable lever to avoid this issue. EV charging must be managed to avoid minor under-voltage, but network reinforcement might be ultimately necessary. Sun, 07 Sep 2025 22:00:00 GMT http://hdl.handle.net/2268.2/24778 2025-09-07T22:00:00Z http://hdl.handle.net/2268.2/23284 Title: Unbalanced Low Voltage Distribution Network Reinforcement Abstract: The growing electrification of transportation and heating systems is leading to a significant increase in electricity demand, resulting in increasingly congested and unbalanced low-voltage distribution networks. The objective of this work is to implement an innovative network reinforcement method that considers the phase connections of households in order to reduce reinforcement costs. To this end, the effects of phase imbalance in distribution networks are first examined, revealing that assuming a balanced system can obscure critical issues such as voltage violations or line overloads. Subsequently, a residential load profile generator incorporating electric vehicle charging profiles is developed and validated on real consumption data. The generated profiles lead to an underestimation of the average household consumption by 6% in the case without electric vehicles and by 0.5% in the case with electric vehicles, following the calibration of the model parameters. A network reinforcement model is then developed, taking into account the results of power flow analyses under different household-to-phase connection configurations, with the goal of min- imizing both investment and operational costs. The algorithm is tested on a benchmark network comprising 69 households equipped with EVs and solved using genetic algorithm. Results indi- cate that phase reassignment enables a reduction of the total annualized cost of operations and investments by 16% in a network without PV panels, and by 20% in a specific scenario including PV panels. A final scenario considering residential storage systems used as peak shaving devices was explored. The results indicate that, at current storage system costs, such residential devices are not economically viable. However, it is also demonstrated that, at lower costs, the storage strategy developed in this work could significantly reduce investment needs for new line reinforcements. Sun, 29 Jun 2025 22:00:00 GMT http://hdl.handle.net/2268.2/23284 2025-06-29T22:00:00Z http://hdl.handle.net/2268.2/22453 Title: Master's thesis and internship : Development and Improvement of the power system module within the European Integrated Assessment Model MEDEAS Abstract: This master's thesis is situated within the broader framework of global climate change and the European Green Deal, which sets the ambitious goal of achieving net-zero greenhouse gas (GHG) emissions in Europe by 2050. In particular, this research focuses on the Integrated Assessment Model (IAM) MEDEAS. This model aims to address the challenges of the energy transition within the European Union (EU) by providing comprehensive assessments of the potential impacts and mitigation strategies associated with various policy measures. The master's thesis aims to propose a new version of MEDEAS which incorporates a machine learning-based surrogate model (SM) to improve the predictive potential of the IAM, particularly in simulating the European electrical power grid's curtailment and load shedding dynamics. This surrogate model was developed in previous works and is an efficient and flexible tool mirroring Dispa-SET unit commitment and economic dispatch model. The other key advancements include the integration of additional data from PyPSA-EUR, enabling both the integration of the SM and new investment assessments of renewable energy sources (RES), grid reinforcement, and storage installations. Additionally, new feedback mechanisms inspired by PID control theory simulate instantaneous societal responses aimed at reducing energy curtailment and load shedding. A comparative analysis against the previous MEDEAS version and a practical case study demonstrate the enhanced model's utility in exploring new energy scenarios and providing meaningful insights for policymakers. Thu, 23 Jan 2025 23:00:00 GMT http://hdl.handle.net/2268.2/22453 2025-01-23T23:00:00Z