Operationally Perfect Solar Power Forecasts: A Scalable Strategy to Lowest-Cost Firm Solar Power Generation

2019 IEEE 46th Photovoltaic Specialists Conference (PVSC) Pub Date : 2019-06-16 DOI:10.1109/PVSC40753.2019.9198973

R. Perez, Marc J. R. Perez, Marco Pierro, J. Schlemmer, Sergery Kivalov, J. Dise, P. Keelin, M. Grammatico, A. Świerc, Jorge Ferreira, Andrew Foster, Morgan Putnam, T. Hoff

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引用次数: 14

Abstract

The SUNY solar irradiance forecast model is implemented in the SolarAnywhere platform. In this article, we evaluate its latest version and present a fully independent validation for climatically distinct individual US locations as well as one extended region. In addition to standard performance metrics such as mean absolute error or forecast skill, we apply a new operational metric that quantifies the lowest cost of operationally achieving perfect forecasts. This cost represents the amount of solar production curtailment and backup storage necessary to correct all over/under-prediction situations. This perfect forecast metric applies a recently developed algorithm to optimally transform intermittent renewable power generation into firm power generation with the optimal - least-cost – amount of curtailment and energy storage. We discuss how perfect forecast logistics can gradually evolve and scale up into firm solar power generation logistics, with the objective of cost-optimally displacing conventional [dispatchable] power generation.

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运行完美的太阳能发电预测:最低成本企业太阳能发电的可扩展策略

SUNY太阳辐照度预报模型在SolarAnywhere平台上实现。在本文中，我们对其最新版本进行了评估，并提出了一个完全独立的验证，适用于气候不同的美国个别地点以及一个扩展区域。除了标准的性能指标，如平均绝对误差或预测技能，我们还应用了一个新的操作指标，量化了实现完美预测的最低成本。这一成本代表了太阳能生产削减和备用存储的数量，这是纠正所有预测不足的情况所必需的。这个完美的预测指标应用了最近开发的一种算法，将间歇性可再生能源发电最优地转化为具有最优-最低成本-弃电和储能量的稳定发电。我们讨论了完美的预测物流如何逐步发展并扩大到稳定的太阳能发电物流，目标是以成本最优的方式取代传统的[可调度的]发电。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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2019 IEEE 46th Photovoltaic Specialists Conference (PVSC)

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