SolarBoost: Distributed Photovoltaic Power Forecasting Amid Time-varying Grid Capacity

TL;DR

SolarBoost introduces a distributed photovoltaic power forecasting method that effectively handles time-varying capacities and geographic variability, outperforming centralized models and aiding grid management.

Contribution

The paper proposes a novel distributed modeling approach for PV forecasting that decouples unit output from capacity, addressing data gaps and variability in DPV systems.

Findings

SolarBoost achieves higher forecasting accuracy than existing methods.

The approach reduces potential grid losses significantly.

Validated across multiple Chinese cities with real deployment.

Abstract

This paper presents SolarBoost, a novel approach for forecasting power output in distributed photovoltaic (DPV) systems. While existing centralized photovoltaic (CPV) methods are able to precisely model output dependencies due to uniformity, it is difficult to apply such techniques to DPV systems, as DPVs face challenges such as missing grid-level data, temporal shifts in installed capacity, geographic variability, and panel diversity. SolarBoost overcomes these challenges by modeling aggregated power output as a composite of output from small grids, where each grid output is modeled using a unit output function multiplied by its capacity. This approach decouples the homogeneous unit output function from dynamic capacity for accurate prediction. Efficient algorithms over an upper-bound approximation are proposed to overcome computational bottlenecks in loss functions. We demonstrate the superiority of grid-level modeling via theoretical analysis and experiments. SolarBoost has been validated through deployment across various cities in China, significantly reducing potential losses and provides valuable insights for the operation of power grids. The code for this work is available at https://github.com/DAMO-DI-ML/SolarBoost.