Analytical first-screening of distributed generation hosting capacity via extreme-value theory
Against the backdrop of large-scale interconnection requests for distributed generation (DG), there is a growing demand for rapid assessment of distribution network hosting capacity (DGHC). Conventional optimization and machine learning approaches often require high-resolution data and need substant...
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| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Elsevier
2025-10-01
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| Series: | International Journal of Electrical Power & Energy Systems |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S0142061525005113 |
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| Summary: | Against the backdrop of large-scale interconnection requests for distributed generation (DG), there is a growing demand for rapid assessment of distribution network hosting capacity (DGHC). Conventional optimization and machine learning approaches often require high-resolution data and need substantial computational costs, making them impractical for engineering timelines. This paper introduces a closed-form, sensitivity-based assessment framework that addresses these limitations. Grounded in extreme-value theory (EVT), the method employs a generalized extreme value (GEV) distribution to characterize the tail behavior of DG output, which is then embedded into a decoupled linearized power flow model to propagate uncertainty to nodal voltages and branch flows. By imposing risk thresholds on both voltage and thermal constraints, the nodal DGHC is analytically computed in a single step. Case studies on a 59-bus rural distribution network and the IEEE 123-node test system demonstrate that the proposed method offers a computational speed-up of three orders of magnitude over traditional AC-based optimization methods, and enables intuitive identification of dominant system bottlenecks. Furthermore, sensitivity analyses with violation probability, time scale, and load levels demonstrate that the proposed method exhibits stable conservatism and favorable scalability. The proposed method offers a practical, analytical solution for utilities to manage considerable interconnection requests and to support network reinforcement planning. |
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| ISSN: | 0142-0615 |