arXiv subjects

Luis Böttcher

Publications and source records attributed to Luis Böttcher.

5 recordsLinked to original sources

arXiv2024

End-to-End Reinforcement Learning of Curative Curtailment with Partial Measurement Availability

In the course of the energy transition, the expansion of generation and consumption will change, and many of these technologies, such as PV systems, electric cars and heat pumps, will influence the power flow, especially in the distribution grids. Scalable methods that can make decisions for each grid connection are needed to enable congestion-free grid operation in the distribution grids. This paper presents a novel end-to-end approach to resolving congestion in distribution grids with deep reinforcement learning. Our architecture learns to curtail power and set appropriate reactive power to determine a non-congested and, thus, feasible grid state. State-of-the-art methods such as the optimal power flow (OPF) demand high computational costs and detailed measurements of every bus in a grid. In contrast, the presented method enables decisions under sparse information with just some buses observable in the grid. Distribution grids are generally not yet fully digitized and observable, so this method can be used for decision-making on the majority of low-voltage grids. On a real low-voltage grid the approach resolves 100\% of violations in the voltage band and 98.8\% of asset overloads. The results show that decisions can also be made on real grids that guarantee sufficient quality for congestion-free grid operation.

cs.LG↗

arXiv2024

Analysing the interaction of expansion decisions by end customers and grid development in the context of a municipal energy system

In order to achieve greenhouse gas neutrality by 2045, the Climate Protection Act sets emission reduction targets for the years 2030 and 2040, as well as decreasing annual emission volumes for some sectors, including the building sector. Measures to decarbonize the building sector include energy retrofits and the expansion of renewable, decentralized power generators and low-CO2 heat generators. These measures thus change both the load and the generation of the future energy supply concept. Considering the interactions of the changed installed technologies on the building level and their influence on the electrical grid infrastructure is necessary. The grid operator will remedy the future congested grid states by grid expansion measures and pass on the costs to the connected grid users, which in turn could influence their behaviour and decisions. The aim of this work is a holistic analysis of the staggered interactions of generation expansion and grid expansion for a future decentralized energy supply concept conditioned by the expansion in the field of self-generation. To enable the analysis of the interactions, a multi-criteria optimization procedure for expansion and operation decisions at the building level is combined with an approach to determine grid expansion. As part of this work, the effect of an expansion of hosting capacity on the grid charges and thus the decision-making behaviour was investigated.

eess.SY↗

arXiv2023

Representation of Distribution Grid Expansion Costs in Power System Planning

The shift towards clean energy brings about notable transformations to the energy system. In order to optimally plan a future energy system, it is necessary to consider the influence of several sectors as well as the interaction of the transmission grid and distribution grid. The concept of Feasible Operation Region (FOR) is a detailed approach to representing the operational dependencies between the transmission and distribution grid. However, in previous planning procedures, only a simplified expansion of the distribution grids can be taken into account. With the method presented in this paper, a Feasible Planning Region (FPR) is developed, which represents the operational boundaries of the distribution grids for several expansion stages and thus represents an admissible solution space for the planning of distribution grids in systemic planning approaches. It hence enables a more detailed representation of the necessary distribution grid expansion for the integration of distributed technologies in an optimized energy system of the future. In this paper, we present the method by which the FPR is formed and its integration into an energy system planning formulation. In the results, the FPR is presented for different voltage levels, and its use in power system planning is demonstrated.

eess.SY↗

arXiv2023

Solving AC Power Flow with Graph Neural Networks under Realistic Constraints

In this paper, we propose a graph neural network architecture to solve the AC power flow problem under realistic constraints. To ensure a safe and resilient operation of distribution grids, AC power flow calculations are the means of choice to determine grid operating limits or analyze grid asset utilization in planning procedures. In our approach, we demonstrate the development of a framework that uses graph neural networks to learn the physical constraints of the power flow. We present our model architecture on which we perform unsupervised training to learn a general solution of the AC power flow formulation independent of the specific topologies and supply tasks used for training. Finally, we demonstrate, validate and discuss our results on medium voltage benchmark grids. In our approach, we focus on the physical and topological properties of distribution grids to provide scalable solutions for real grid topologies. Therefore, we take a data-driven approach, using large and diverse data sets consisting of realistic grid topologies, for the unsupervised training of the AC power flow graph neural network architecture and compare the results to a prior neural architecture and the Newton-Raphson method. Our approach shows a high increase in computation time and good accuracy compared to state-of-the-art solvers. It also out-performs that neural solver for power flow in terms of accuracy.

cs.LG↗

arXiv2022

Modelling Residential Supply Tasks Based on Digital Orthophotography Using Machine Learning

In order to achieve the climate targets, electrification of individual mobility is essential. However, grid integration of electrical vehicles poses challenges for the electrical distribution network due to high charging power and simultaneity. To investigate these challenges in research studies, the network-referenced supply task needs to be modeled. Previous research work utilizes data that is not always complete or sufficiently granular in space. This is why this paper presents a methodology which allows a holistic determination of residential supply tasks based on orthophotos. To do this, buildings are first identified from orthophotos, then residential building types are classified, and finally the electricity demand of each building is determined. In an exemplary case study, we validate the presented methodology and compare the results with another supply task methodology. The results show that the electricity demand deviates from the results of a reference method by an average 9%. Deviations result mainly from the parameterization of the selected residential building types. Thus, the presented methodology is able to model supply tasks similarly as other methods but more granular.

eess.SY↗