SolarBoost通过分网格建模提升分布式光伏预测精度。
SolarBoost: Distributed Photovoltaic Power Forecasting Amid Time-varying Grid Capacity
- 将电网拆分为小区域，用统一输出函数乘容量预测
- 在多地部署验证，显著降低电力损失
- 适合关注电网调度与分布式能源管理的从业者
本文提出SolarBoost，一种针对分布式光伏（DPV）系统功率预测的新方法。现有集中式光伏（CPV）方法依赖系统均匀性，难以适用于存在电网数据缺失、装机容量动态变化、地理差异和组件多样性的DPV系统。SolarBoost通过将聚合功率建模为多个小电网输出之和，每个电网输出由单位输出函数乘以其容量构成，实现统一输出函数与动态容量的解耦，提升预测准确性。针对损失函数计算瓶颈，提出了基于上界近似的高效算法。理论分析与实验均证明了电网级建模的优势。该方法已在我国多个城市部署，显著降低潜在电力损失，为电网运行提供重要参考。代码已开源：https://github.com/DAMO-DI-ML/SolarBoost。
原文摘要 · Abstract (English)
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.
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