Comparison of Random Sampling and Heuristic Optimization-Based Methods for Determining the Flexibility Potential at Vertical System Interconnections

Gerster, Johannes; Sarstedt, Marcel; Veith, Eric Msp; Lehnhoff, Sebastian; Hofmann, Lutz

doi:10.1109/isgteurope52324.2021.9640108

Cited by 6 publications

(2 citation statements)

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“…Next, we simulated the power grid using pandapower [33] for one year of simulated time. We follow [34] and plotted active against reactive power at the slack bus to get an estimate of the distribution of all of the simulation data and to be able to detect possible convolution problems. This can be seen in Figure 3.…”

Section: Challenges Of Power Grid Samplingmentioning

confidence: 99%

“…To not suppress the randomness of the sampling, we defined a threshold t of 0.85 and ignored all correlations that were lower. Each sample s is initially generated with the Dirichlet distribution, which was used by Gerster et al [34] with good results. For each sample s i in s, all subsequent entries s j with i < j, are adapted depending on their partial correlation C ij :…”

Section: Samplingmentioning

confidence: 99%

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Sampling Strategies for Static Powergrid Models

Balduin¹,

Veith²,

Lehnhoff³

2022

Preprint

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Machine learning and computational intelligence technologies gain more and more popularity as possible solution for issues related to the power grid. One of these issues, the power flow calculation, is an iterative method to compute the voltage magnitudes of the power grid's buses from power values. Machine learning and, especially, artificial neural networks were successfully used as surrogates for the power flow calculation. Artificial neural networks highly rely on the quality and size of the training data, but this aspect of the process is apparently often neglected in the works we found. However, since the availability of high quality historical data for power grids is limited, we propose the Correlation Sampling algorithm. We show that this approach is able to cover a larger area of the sampling space compared to different random sampling algorithms from the literature and a copula-based approach, while at the same time inter-dependencies of the inputs are taken into account, which, from the other algorithms, only the copula-based approach does.The knowledge about the current state of the power grid is, usually, limited to information about the power generation or consumption of the participants of the grid, either through prognosis or by estimations via default load profiles. However, a stable grid operation requires a certain frequency level (50 Hz in Europe) and certain voltage levels. Since only the power values are known, voltage information needs to be calculated, which is done with Power Flow (PF) analysis. The PF analysis is performed many times during the operation of power grids and the results can be used, e. g., for market analysis or short-term operational planning.Since the PF analysis often requires to perform matrix inversion, a task with a high computational burden, there are many approaches to reduce this computation time. Active research for improvements of the more traditional methods can be found, e. g., in [1,2,3,4]. On the other side, the advancement and application of Machine Learning (ML) models for energy systems has also increased in the past two decades. Mosavi et al. [5] reviewed a broad range of such applications, but PF analysis is barely mentioned, possibly due to their selection of relevant papers. [6] gave an overview about works regarding the closely related Optimal Power Flow (OPF) problem. OPF, however, is only a specific use case for PF. Nevertheless, ML for PF analysis is a field of active research and, especially, Artificial Neural Networks (ANNs) are often used with great performance, e. g., in [7], [8], and [9]. However, ANNs require a large amount of data, especially when the ANN becomes a Deep Neural Network (DNN). In our recent works in [10] and [11], we build different ML models, including a DNN, as a surrogate model for a Low Voltage (LV) power grid to avoid the costly PF analysis. One issue we identified concerns the availability and generation

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Section: Challenges Of Power Grid Samplingmentioning

confidence: 99%

Section: Samplingmentioning

confidence: 99%

Sampling Strategies for Static Powergrid Models

Balduin¹,

Veith²,

Lehnhoff³

2022

Preprint

Self Cite

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Monetarization of the Feasible Operation Region of Active Distribution Grids Based on a Cost-Optimal Flexibility Disaggregation

Sarstedt

Hofmann²

2022

IEEE Access

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Hierarchical grid control strategies are an appropriate design concept for the coordination of future transmission system operator (TSO) and distribution system operator (DSO) interactions. Hierarchical approaches are based on the aggregation of decentralized ancillary service potentials, represented by converter-coupled, communicable active and reactive power flexibility providing units (FPU, e.g. wind turbines) at vertical TSO/DSO system interfaces. The resulting PQ-polygon made available by the DSO for a potential request of ancillary service flexibilities by the TSO is called feasible operation region (FOR). A monetarization of the FOR is necessary for the implementation as operational degree of freedom within TSO grid control. In the context of a local DSO and global TSO market, this article presents an approach for the monetarization of the FOR by a cost structure using metadata from population based aggregation methods. At the local DSO market free bids for the active and reactive power flexibilities by the FPUs stakeholders are assumed. Within the aggregation method multiple FPU flexibility polygons at a single bus are aggregated for a reduction of the search space dimensions. Thereby, the main contribution of the proposed method is the cost-optimal disaggregation of a flexibility demand to the single FPUs within the aggregated FPU by a mixed integer linear program. The approach can be simply adapted for the coordination of DSO/DSO-interactions regarding hierarchical multi-level grid control strategies.

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