Assessing Distributed Photovoltaic Integration Capacity in Distribution Networks: A Graph-Neural Network-Driven Approach

Abstract

: With the carbon peaking and carbon neutrality targets, the penetration of distributed renewable energy such as photovoltaic (PV) systems in distribution networks is increasing. However, the rapid growth of rooftop PV causes operational problems such as transformer overloading and voltage violations. These issues challenge the distributed PV integration capability of distribution networks. At the same time, distribution networks have more complex load types, more flexible topologies, and frequently changing parameters. It is still difficult to achieve accurate and efficient assessment. To address this problem, this paper proposes a message-passing graph neural network (MPNN)-based method for assessing distributed photovoltaic integration capability in distribution networks. The proposed framework leverages historical operational data from multiple operating states. It applies the message-passing graph neural network to learn node topology features. It also uses a gated recurrent unit to capture temporal transitions across different topologies. An improved optimization algorithm is used to improve efficiency. The method considers practical constraints under complex and changing network conditions. It identifies bottlenecks of renewable integration and helps optimize resource allocation. It can improve the distributed PV integration capability of distribution networks. Case studies show that the method can achieve fast and accurate assessment of the maximum integration capability of distributed PV systems, which can provide support for the safe and economic operation of distribution networks.