A Scalable Solution for Running Ensemble Simulations for Photovoltaic Energy

TL;DR

This paper presents a scalable, high-performance workflow for ensemble simulation of photovoltaic energy production, utilizing Analog Ensemble for efficient weather forecast generation and optimizing panel configurations to reduce uncertainty.

Contribution

It introduces a scalable computational workflow for ensemble solar energy simulations using Analog Ensemble and demonstrates its deployment on a supercomputer for large-scale analysis.

Findings

Spring and summer have higher simulation uncertainty.

Optimizing panel configurations improves accuracy by over 12%.

Geographic location influences optimal panel setup.

Abstract

This chapter proposes and provides an in-depth discussion of a scalable solution for running ensemble simulation for solar energy production. Generating a forecast ensemble is computationally expensive. But with the help of Analog Ensemble, forecast ensembles can be generated with a single deterministic run of a weather forecast model. Weather ensembles are then used to simulate 11 10 KW photovoltaic solar power systems to study the simulation uncertainty under a wide range of panel configuration and weather conditions. This computational workflow has been deployed onto the NCAR supercomputer, Cheyenne, with more than 7,000 cores. Results show that, spring and summer are typically associated with a larger simulation uncertainty. Optimizing the panel configuration based on their individual performance under changing weather conditions can improve the simulation accuracy by more than 12%. This work also shows how panel configuration can be optimized based on geographic locations.