Markus Schläpfer

Markus Schläpfer, Tom Kessler, Wolfgang Kröger

The ongoing evolution of the electric power systems brings about the need to cope with increasingly complex interactions of technical components and relevant actors. In order to integrate a more comprehensive spectrum of different aspects into a probabilistic reliability assessment and to include time-dependent effects, this paper proposes an object-oriented hybrid approach combining agent-based modeling techniques with classical methods such as Monte Carlo simulation. Objects represent both technical components such as generators and transmission lines and non-technical components such as grid operators. The approach allows the calculation of conventional reliability indices and the estimation of blackout frequencies. Furthermore, the influence of the time needed to remove line overloads on the overall system reliability can be assessed. The applicability of the approach is demonstrated by performing simulations on the IEEE Reliability Test System 1996 and on a model of the Swiss high-voltage grid.

Seanglidet Yean, Jiazu Zhou, Bu-Sung Lee et al.

The mobility patterns of people in cities evolve alongside changes in land use and population. This makes it crucial for urban planners to simulate and analyze human mobility patterns for purposes such as transportation optimization and sustainable urban development. Existing generative models borrowed from machine learning rely heavily on historical trajectories and often overlook evolving factors like changes in population density and land use. Mechanistic approaches incorporate population density and facility distribution but assume static scenarios, limiting their utility for future projections where historical data for calibration is unavailable. This study introduces a novel, data-driven approach for generating origin-destination mobility flows tailored to simulated urban scenarios. Our method leverages adaptive factors such as dynamic region sizes and land use archetypes, and it utilizes conditional generative adversarial networks (cGANs) to blend historical data with these adaptive parameters. The approach facilitates rapid mobility flow generation with adjustable spatial granularity based on regions of interest, without requiring extensive calibration data or complex behavior modeling. The promising performance of our approach is demonstrated by its application to mobile phone data from Singapore, and by its comparison with existing methods.

Paul D. H. Hines, Seth Blumsack, Markus Schläpfer

While many large infrastructure networks, such as power, water, and natural gas systems, have similar physical properties governing flows, these systems tend to have distinctly different sizes and topological structures. This paper seeks to understand how these different size-scales and topological features can emerge from relatively simple design principles. Specifically, we seek to describe the conditions under which it is optimal to build decentralized network infrastructures, such as a microgrid, rather than centralized ones, such as a large high-voltage power system. While our method is simple it is useful in explaining why sometimes, but not always, it is economical to build large, interconnected networks and in other cases it is preferable to use smaller, distributed systems. The results indicate that there is not a single set of infrastructure cost conditions under which optimally-designed networks will have highly centralized architectures. Instead, as costs increase we find that average network sizes increase gradually according to a power-law. When we consider the reliability costs, however, we do observe a transition point at which optimally designed networks become more centralized with larger geographic scope. As the losses associated with node and edge failures become more costly, this transition becomes more sudden.

Eduardo Alejandro Martinez-Cesena, Pierluigi Mancarella, Mamadou Ndiaye et al.

Detailed knowledge of the energy needs at relatively high spatial and temporal resolution is crucial for the electricity infrastructure planning of a region. However, such information is typically limited by the scarcity of data on human activities, in particular in developing countries where electrification of rural areas is sought. The analysis of society-wide mobile phone records has recently proven to offer unprecedented insights into the spatio-temporal distribution of people, but this information has never been used to support electrification planning strategies anywhere and for rural areas in developing countries in particular. The aim of this project is the assessment of the contribution of mobile phone data for the development of bottom-up energy demand models, in order to enhance energy planning studies and existing electrification practices. More specifically, this work introduces a framework that combines mobile phone data analysis, socioeconomic and geo-referenced data analysis, and state-of-the-art energy infrastructure engineering techniques to assess the techno-economic feasibility of different centralized and decentralized electrification options for rural areas in a developing country. Specific electrification options considered include extensions of the existing medium voltage (MV) grid, diesel engine-based community-level Microgrids, and individual household-level solar photovoltaic (PV) systems. The framework and relevant methodology are demonstrated throughout the paper using the case of Senegal and the mobile phone data made available for the 'D4D-Senegal' innovation challenge. The results are extremely encouraging and highlight the potential of mobile phone data to support more efficient and economically attractive electrification plans.

Markus Schläpfer, Pierluigi Mancarella

Increasing shares of fluctuating renewable energy sources induce higher and higher power flow variability at the transmission level. The question arises as to what extent existing networks can absorb additional fluctuating power injection without exceeding thermal limits. At the same time, the resulting power flow characteristics call for revisiting classical approaches to line temperature prediction. This paper presents a probabilistic modeling and simulation methodology for estimating the occurrence of critical line temperatures in the presence of fluctuating power flows. Cumbersome integration of the dynamic thermal equations at each Monte Carlo simulation trial is sped up by a specific algorithm that makes use of a variance reduction technique adapted from the telecommunications field. The substantial reduction in computational time allows estimations closer to real time, relevant to short-term operational assessments. A case study performed on a single line model provides fundamental insights into the probability of hitting critical line temperatures under given power flow fluctuations. A transmission system application shows how the proposed method can be used for a fast yet accurate operational assessment.