Martin Braun

Tanja M. Kneiske, Martin Braun

Due to the 2012 change in the renewable energy act the feed-in tariffs and as result the number of newly installed photovoltaic systems decreased dramatically in Germany. Therefore, there was the need, particularly, in the residential sector to develop new business ideas for photovoltaic systems. In context of this development, combined photovoltaic and heat-power systems were analyzed, which provide not only electricity but also heat throughout one entire year. Flexibilities are provided in form of thermal and electrical storage systems leading to many possible setting options requiring an elaborated control management. In this paper, a new optimized control algorithm is proposed that in contrast to standard strategies can operate optimal even under incorrect weather and load forecasts. A predictive controller based on a mixed integer optimization problem and an additional so called secondary, rule-based controller is combined. The controller is based on several input data. Depending on the choice of these data the PV-CHP hybrid system can be used for different control strategies, like maximum self-consumption, minimum CO2 emission or minimum operational costs. The main focus in this paper is to study if the flexibility potentials of the combined thermal and electrical storage systems can ensure a market oriented or a grid-friendly behavior. This is studied in five control options. As a result we found that the control algorithm is stable and able to adapt to the different conditions. In conclusion, it was discovered that the storage systems play a crucial role in terms of forecast differences and parameter changes. Storage systems are as expected the key-element for the flexibility of PV-CHP hybrid systems.

Zhenqi Wang, Sebastian Wende-von Berg, Martin Braun

Nowadays, the PQ flexibility from the distributed energy resources (DERs) in the high voltage (HV) grids plays a more critical and significant role in grid congestion management in TSO grids. This work proposed a multi-stage deep reinforcement learning approach to estimate the PQ flexibility (PQ area) at the TSO-DSO interfaces and identifies the DER PQ setpoints for each operating point in a way, that DERs in the meshed HV grid can be coordinated to offer flexibility for the transmission grid. In the estimation process, we consider the steady-state grid limits and the robustness in the resulting voltage profile against uncertainties and the N-1 security criterion regarding thermal line loading, essential for real-life grid operational planning applications. Using deep reinforcement learning (DRL) for PQ flexibility estimation is the first of its kind. Furthermore, our approach of considering N-1 security criterion for meshed grids and robustness against uncertainty directly in the optimization tasks offers a new perspective besides the common relaxation schema in finding a solution with mathematical optimal power flow (OPF). Finally, significant improvements in the computational efficiency in estimation PQ area are the highlights of the proposed method.

Danyal Maheshwari, Gerhard Hellstern, Martin Zaefferer et al.

This study explores quantum and classical hybrid architectures for financial time-series fore casting, focusing on Quantum Long Short-Term Memory (QLSTM) networks and Quantum Reservoir Computing (QRC), using univariate and multivariate lag structures on real financial data. We assess how lag embeddings affect predictive accuracy and robustness. Data are en coded into quantum states via amplitude encoding, enabling efficient representation of normalized lagged observations under realistic qubit constraints. The recurrent dynamics of QLSTM and the reservoir of QRC are implemented as parameterized quantum circuits, while classical optimizers train the readout and, where applicable, variational circuit parameters. We benchmark quantum models against classical LSTM and reservoir computing using common error like metrics. Our results show that, with suitable lag selection and amplitude encoding, quantum-enhanced archi tectures match classical baselines in univariate settings and can modestly outperform them in multivariate regimes with correlated inputs, where expressive encodings are most beneficial.

Florian Schaefer, Jan-Hendrik Menke, Martin Braun

Fast approximations of power flow results are beneficial in power system planning and live operation. In planning, millions of power flow calculations are necessary if multiple years, different control strategies or contingency policies are to be considered. In live operation, grid operators must assess if grid states comply with contingency requirements in a short time. In this paper, we compare regression and classification methods to either predict multi-variable results, e.g. bus voltage magnitudes and line loadings, or binary classifications of time steps to identify critical loading situations. We test the methods on three realistic power systems based on time series in 15 min and 5 min resolution of one year. We compare different machine learning models, such as multilayer perceptrons (MLPs), decision trees, k-nearest neighbours, gradient boosting, and evaluate the required training time and prediction times as well as the prediction errors. We additionally determine the amount of training data needed for each method and show results, including the approximation of untrained curtailment of generation. Regarding the compared methods, we identified the MLPs as most suitable for the task. The MLP-based models can predict critical situations with an accuracy of 97-98 % and a very low number of false negative predictions of 0.0-0.64 %.

Alexander Scheidler, Leon Thurner, Martin Braun

Network integration studies try to assess the impact of future developments, such as the increase of Renewable Energy Sources or the introduction of Smart Grid Technologies, on large-scale network areas. Goals can be to support strategic alignment in the regulatory framework or to adapt the network planning principles of Distribution System Operators. This study outlines an approach for the automated distribution system planning that can calculate network reconfiguration, reinforcement and extension plans in a fully automated fashion. This allows the estimation of the expected cost in massive probabilistic simulations of large numbers of real networks and constitutes a core component of a framework for large-scale network integration studies. Exemplary case study results are presented that were performed in cooperation with different major distribution system operators. The case studies cover the estimation of expected network reinforcement costs, technical and economical assessment of smart grid technologies and structural network optimisation.