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A review of behind-the-meter solar forecasting

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  • Erdener, Burcin Cakir
  • Feng, Cong
  • Doubleday, Kate
  • Florita, Anthony
  • Hodge, Bri-Mathias

Abstract

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.

Suggested Citation

  • Erdener, Burcin Cakir & Feng, Cong & Doubleday, Kate & Florita, Anthony & Hodge, Bri-Mathias, 2022. "A review of behind-the-meter solar forecasting," Renewable and Sustainable Energy Reviews, Elsevier, vol. 160(C).
  • Handle: RePEc:eee:rensus:v:160:y:2022:i:c:s1364032122001472
    DOI: 10.1016/j.rser.2022.112224
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    2. Mao, Hongzhi & Chen, Xie & Luo, Yongqiang & Deng, Jie & Tian, Zhiyong & Yu, Jinghua & Xiao, Yimin & Fan, Jianhua, 2023. "Advances and prospects on estimating solar photovoltaic installation capacity and potential based on satellite and aerial images," Renewable and Sustainable Energy Reviews, Elsevier, vol. 179(C).
    3. Liu, Chao Charles & Chen, Hongkun & Shi, Jing & Chen, Lei, 2022. "Self-supervised learning method for consumer-level behind-the-meter PV estimation," Applied Energy, Elsevier, vol. 326(C).
    4. Wen, Haoran & Du, Yang & Chen, Xiaoyang & Lim, Eng Gee & Wen, Huiqing & Yan, Ke, 2023. "A regional solar forecasting approach using generative adversarial networks with solar irradiance maps," Renewable Energy, Elsevier, vol. 216(C).
    5. Gandhi, Oktoviano & Zhang, Wenjie & Kumar, Dhivya Sampath & Rodríguez-Gallegos, Carlos D. & Yagli, Gokhan Mert & Yang, Dazhi & Reindl, Thomas & Srinivasan, Dipti, 2024. "The value of solar forecasts and the cost of their errors: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 189(PB).

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