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A review of behind-the-meter solar forecasting

Journal

RENEWABLE & SUSTAINABLE ENERGY REVIEWS
Volume 160, Issue -, Pages -

Publisher

PERGAMON-ELSEVIER SCIENCE LTD
DOI: 10.1016/j.rser.2022.112224

Keywords

Behind-the-meter; Solar forecasting; Solar photovoltaic

Funding

  1. U.S. Department of Energy (DOE) [DE-AC36-08GO28308]
  2. U.S. Department of Energys Office of Energy Efficiency and Renewable Energy under Solar Energy Technologies Office [33505]

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Solar photovoltaic systems integrated within the distribution grid face challenges in efficient network and market operations due to variability and uncertainty. Accurate forecasting methods play a key role in balancing supply and demand.
Solar photovoltaic systems largely integrated within the distribution grid are operated 'behind-the-meter' and power generation cannot be directly monitored by most utilities. The increasing penetration of behind the-meter solar photovoltaic systems can deter efficient network and market operations due to variability and uncertainty in net load, which is exacerbated by limited visibility and the difficulty in analyzing the hosting capacity. Risk introduced by behind-the-meter solar contributions may hinder reliable and secure grid operations due to biased system monitoring and forecasts. Accurate behind-the-meter estimations, together with capacity and specification forecasts, thus play a key role in balancing supply and demand and this article reviews the pertinent literature, identifying key characteristics and predictive methods for efficient behind-the meter solar photovoltaic generation. Forecasting is central to methods herein. The fundamental characteristics of behind-the-meter solar forecasting, including which methods are applicable for scenario-driven use cases, are driven by the metrics most useful for system-wide performance evaluation. To this aim, the literature is reviewed with a focus on forecasting applications for aggregate, regional behind-the-meter generation useful to bulk system and utility operations. As distinguished from net load forecasting, subtleties in these coincident tasks are explored before concluding with recommendations for current practice and future implementations.

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