Work

Image recognition-based PV hosting capacity enhancement via distribution network retrofit

Year: 2025

Type: article

Abstract: The assessment and enhancement of photovoltaic (PV) hosting capacity using roof area analytics and classical optimization approach face serval challenges, like low accuracy and poor credibility. This paper proposes a novel image recognition-based hybrid optimization method to assess the allowable PV integration capacity and determine the cost-effective planning strategy for network retrofit. Specifically, it improves the geometry-aware YOLOv11 framework by a dual-phased regularization to precisely extract rooftop surface areas, thereby establishing spatially-constrained PV capacity boundaries. Then, by introducing transformer insulation aging formulas and security chance constraints, a probabilistic optimization model is developed to quantify the PV hosting capacity under varying risk tolerance levels. Besides, the infrastructure capacity expansion is formulated as integer decision variable and incorporated into this model to economically retrofit the distribution network for foreseeable PV-rich scenarios. Furthermore, to decrease computational complexity, the model is relaxed using second-order cone programming (SOCP) and decomposed into a master-subproblem structure via Benders Decomposition (BD). The simulation results demonstrate that this method enables precise identification and full exploitation of PV hosting capacity.

Source: Renewable Energy

DOI: 10.1016/j.renene.2025.124750

Authors: Zhengbo Li, Youbo Liu, Haolan Yang, Yue Xiang, Junyong Liu

Institution: Sichuan University

Language: English

Topic: Optimal Power Flow Distribution

Subfield: Electrical and Electronic Engineering

Field: Engineering

Domain: Physical Sciences

SDGs: Affordable and clean energy, Industry, innovation and infrastructure, Climate action, Sustainable cities and communities

Open Access status: hybrid

Estimated APC paid: $4,460