Accurate power reserve estimation for a Photovoltaic Generator (PVG) is of paramount importance to combat frequency changes in a smart grid. Standalone PVG lacks inertia, or an internal power reserve due to power electronic converter grid-interface. Operating a PVG at deloaded percentage of its maximum power capacity mimics an internal power reserve, simulating the Automatic Generation Control (AGC) feature of synchronous machines. Thus, a deloaded PVG releases or absorbs the reserve according to the frequency variations for the grid stability. Moreover, an efficient switching between various reserves during grid operation is required. The common reserve estimation technique is to apply PVG manufacturer’s specification based deterministic approach. In this work, we compare the deterministic modeling results with a statistical learning model of Gaussian Process Regression (GPR). The GPR model is trained by dataset of PVG maximum power values evaluated by load line analysis in a simulation, according to the irradiance and historical temperature of Abbottabad, Pakistan. The trained model performance is compared with the deterministic model in a simulation, where the PVG is saturated to turn on a synchronous generator. Time difference of turning on the backup generator between GPR model and deterministic modeling validates the importance of accurate reserve estimation.

光伏发电机（PVG）的准确功率储备估算对于应对智能电网中的频率变化至关重要。独立的PVG缺乏惯性，或者由于电力电子转换器的电网接口而缺乏内部动力储备。以最大功率容量的卸载百分比运行PVG可以模拟内部功率储备，从而模拟同步电机的自动发电控制（AGC）功能。因此，卸载的PVG根据电网稳定性的频率变化释放或吸收储备。而且，需要在电网运行期间在各种储备之间进行有效的切换。常用的储量估算技术是应用基于PVG制造商规范的确定性方法。在这项工作中，我们将确定性建模结果与高斯过程回归（GPR）的统计学习模型进行比较。根据巴基斯坦阿伯塔巴德的辐照度和历史温度，通过模拟通过负荷线分析评估的PVG最大功率值数据集训练GPR模型。在仿真中将训练后的模型性能与确定性模型进行比较，在仿真中，PVG饱和以打开同步发电机。GPR模型和确定性模型之间打开备用发电机的时间差验证了准确的储量估算的重要性。在仿真中将训练后的模型性能与确定性模型进行比较，在仿真中，PVG饱和以打开同步发电机。GPR模型和确定性模型之间打开备用发电机的时间差验证了准确的储量估算的重要性。在仿真中将训练后的模型性能与确定性模型进行比较，在仿真中，PVG饱和以打开同步发电机。GPR模型和确定性模型之间打开备用发电机的时间差验证了准确的储量估算的重要性。