The　accurate　prediction　of　photovoltaic　power　generation　in　small　areas　has　become　a　technical　bottleneck　for　the　precise　control　and　operation　of　high-penetration　multi-zone　systems.　Currently,　photovoltaic　power　generation　prediction　methods　need　more　consideration　of　the　photovoltaic　cluster　effect　in　small　areas　of　the　station　area　and　ignore　the　inherent　causality　and　dynamic　correlation　of　input　variables.　To　solve　these　problems,　a　small　area　PV　prediction　method　based　on　a　hierarchical　digraph　and　dynamic　graph　convolutional　recurrent　network　(DGCRN)　is　proposed.　Firstly,　considering　the　unidirectional　relationship　between　output　data　and　numerical　weather　prediction　(NWP),　a　hierarchical　digraph　with　a　causal　relationship　is　generated.　Secondly,　a　dynamic　graph　is　generated　at　each　time　step　according　to　node　attributes,　which　is　organically　combined　with　a　pre-defined　static　graph　to　capture　the　dynamic　spatio-temporal　correlation　between　nodes.　Finally,　the　graph　structure　with　dynamic　spatiotemporal　correlation　is　used　for　model　training.　The　experimental　results　show　that　the　DGCRN　model　can　capture　the　causal　law　between　multiple　parameters,　extract　the　short-term　dynamic　characteristics　of　photovoltaic　power,　and　have　superior　performance　in　predicting　photovoltaic　power　in　a　small　area　with　multiple　nodes.　©　2024　Power　System　Technology　Press.　All　rights　reserved.