The spatiotemporal distribution prediction method for distributed photovoltaic installed capacity based on power supply measurement data

Abstract

Abstract With the anticipated expansion of distributed power grid integration in the foreseeable future, the consideration of distributed power’s impact on power balance becomes paramount in distribution network planning. In this research, we presented a novel approach for predicting the spatial and temporal distribution of distribution network planning areas, with a specific focus on estimating the installed capacity of distributed photovoltaic (PV) systems. Our method leveraged the saturated capacity of distributed PV, requiring minimal data inputs. By establishing a quantitative model that elucidated the relationship between installed distributed PV capacity and land area, we generated PV installed capacity evolution curves for various types of land. Subsequently, we derived the development coefficient of distributed PV installed capacity. By combining this coefficient with the current status of installed distributed PV capacity in the target area’s land parcels, we forecasted the spatial and temporal distribution of future distributed PV capacity within the region. The proposed prediction model held significant implications for the planning of new distribution networks. Additionally, this study predicted the installed distributed PV capacity for distinct land use types, including residential, commercial, and industrial land, using a regional power supply unit as a representative example. We employed the installed PV capacity unit to forecast the electricity loss rate and energy saving rate within the planning area. By validating the model and method through exemplary test results, we demonstrated the model’s feasibility and accuracy. Furthermore, our model effectively predicted the impact of distributed PV integration on overall load forecasting, thereby offering the power grid more precise load forecasting capabilities.