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Fast parallel quasi-static time series simulator for active distribution grid operation with pandapower
Abstract
The increasing penetration from intermittent renewable distributed energy resources in distribution grid brings along challenges in grid operation and planning. To evaluate the impact on the grid voltage profile, grid losses, and discrete actions from assets (e.g. transformer tap changes), quasi-static simulation is an appropriate method. Quasi-static time series and Monte-Carlo simulation requires a tremendous number of power flow calculations (PFCs), which can be significantly accelerated with a parallel High-Performance Computing (HPC)-PFC solver. In this paper, we propose a HPC-PFC-solver-based grid simulation (parallel simulation) approach for a multi-core CPU platform as well as a greedy method, which can prevent the errors caused by simultaneous parallel simulation. The performance of the proposed approach and the comparison is demonstrated with two use cases.
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Array programming provides a powerful, compact and expressive syntax for accessing, manipulating and operating on data in vectors, matrices and higher-dimensional arrays. NumPy is the primary array programming library for the Python language. It has an essential role in research analysis pipelines in fields as diverse as physics, chemistry, astronomy, geoscience, biology, psychology, materials science, engineering, finance and economics. For example, in astronomy, NumPy was an important part of the software stack used in the discovery of gravitational waves1 and in the first imaging of a black hole2. Here we review how a few fundamental array concepts lead to a simple and powerful programming paradigm for organizing, exploring and analysing scientific data. NumPy is the foundation upon which the scientific Python ecosystem is constructed. It is so pervasive that several projects, targeting audiences with specialized needs, have developed their own NumPy-like interfaces and array objects. Owing to its central position in the ecosystem, NumPy increasingly acts as an interoperability layer between such array computation libraries and, together with its application programming interface (API), provides a flexible framework to support the next decade of scientific and industrial analysis.
Publicly accessible, elaborated grid datasets, i.e., benchmark grids, are well suited to publish and compare methods or study results. Similarly, developing innovative tools and algorithms in the fields of grid planning and grid operation is based on grid datasets. Therefore, a general methodology to generate benchmark datasets and its voltage level dependent implementation is described in this paper. As a result, SimBench, a comprehensive dataset for the low, medium, high and extra-high voltage level, is presented. Besides grids that can be combined across several voltage levels, the dataset offers an added value by providing time series for a whole year as well as future scenarios. In this way, SimBench is applicable for many use cases and simplifies reproducing study results. As proof, different automated algorithms for grid planning are compared to show how to apply SimBench and make use of it as a simulation benchmark.
Quasi-static time-series (QSTS) analysis of distribution systems can provide critical information about the potential impacts of high penetrations of distributed and renewable resources, like solar photovoltaic systems. However, running high-resolution yearlong QSTS simulations of large distribution feeders can be prohibitively burdensome due to long computation times. Temporal parallelization of QSTS simulations is one possible solution to overcome this obstacle. QSTS simulations can be divided into multiple sections, e.g. into four equal parts of the year, and solved simultaneously with parallel computing. The challenge is that each time the simulation is divided, error is introduced. This paper presents various initialization methods for reducing the error associated with temporal parallelization of QSTS simulations and characterizes performance across multiple distribution circuits and several different computers with varying architectures.
To analyze large sets of grid states, e.g. when evaluating the impact from the uncertainties of the renewable generation with probabilistic Monte Carlo simulation or in stationary time series simulation, large number of power flow calculations have to be performed. For the application in real-time grid operation, grid planning and in further cases when computational time is critical, a novel approach on simultaneous parallelization of many Newton–Raphson power flow calculations on CPU and with GPU-acceleration is proposed. The result shows a speed-up of over x100 comparing to the open-source tool pandapower, when performing repetitive power flows of system with admittance matrix of the same sparsity pattern on both CPU and GPU. The speed-up relies on the algorithm improvement and highly optimized parallelization strategy, which can reduce the repetitive work and saturate the high hardware computational capability of modern CPUs and GPUs well. This is achieved with the proposed batched sparse matrix operation and batched linear solver based on LU-refactorization. The batched linear solver shows a large performance improvement comparing to the state-of-the-art linear system solver KLU library and a better saturation of the GPU performance with small problem scale. Finally, the method of integrating the proposed solver into pandapower is presented, thus the parallel power flow solver with outstanding performance can be easily applied in challenging real-life grid operation and innovative researches e.g. data-driven machine learning studies.
Pandapower is a Python based, BSD-licensed power system analysis tool aimed at automation of static and quasi-static analysis and optimization of power systems. It is a full fledged power system analysis tool that provides power flow, optimal power flow, state estimation, topological graph searches and short circuit calculations according to IEC 60909. The pandapower network model is based on electric elements, which are defined by nameplate parameters and internally processed with equivalent circuit models. The tabular data structure used to define networks is based on the Python library pandas, which allows comfortable handling of input and output parameters. The implementation in Python makes pandapower easy to use and allows comfortable extension with third-party libraries. pandapower has been successfully applied in several grid studies and validated with real grid data.
In recent years, the number of active grid components for voltage regulation in distribution grids has increased significantly. Besides voltage regulators (VRs), such as transformers with On Load Tap Changers (OLTCs), distributed generators can provide a certain voltage support by means of reactive power control (RPC). The different control entities, OLTC and RPC by photovoltaic (PV) systems, usually operate based on local measurements and control characteristics. Hence, unintended interactions between the control entities cannot be excluded in general. This study analyses the parallel operation of OLTC transformers with a voltage based control and PV systems with different RPC strategies (e.g. watt/power factor control PF(P), volt/var control (Q(V)) in a distribution system environment. The focus is on unintended interactions, such as an increase of OLTC switching operations by PV RPC. The contribution and novelty of this paper is to raise awareness for the likelihood of these unintended interactions and to provide a first methodology to assess the parallel operation of OLTC control and PV RPC in detail. The results show that the impact of PV RPC on the number of OLTC switching operations and the effectiveness in parallel operation can differ considerably between the applied PV RPC strategies.