Quantifying the value of probabilistic forecasting for power system operation planning-Reference-Cited by-同舟云学术

Quantifying the value of probabilistic forecasting for power system operation planning

Published:2023-08 Issue: Volume:343 Page:121254
ISSN:0306-2619
Container-title:Applied Energy
language:en
Short-container-title:Applied Energy

Author:

Wang Qin,Tuohy Aidan,Ortega-Vazquez Miguel,Bello Mobolaji,Ela Erik,Kirk-Davidoff Daniel,Hobbs William B.,Ault David J.,Philbrick Russ

Funder

Office of Energy Efficiency and Renewable Energy

Electric Power Research Institute

U.S. Department of Energy

Solar Energy Technologies Office

Publisher

Elsevier BV

Subject

Management, Monitoring, Policy and Law,Mechanical Engineering,General Energy,Building and Construction

Reference44 articles.

1. International Renewable Energy Agency (IRENA), Global energy transformation: a roadmap to 2050; 2019.

2. U.S. Energy Information Administration. Preliminary Monthly Electric Generator Inventory; April 26, 2022.

3. U.S. Department of Energy. Solar futures study; September 2021.

4. Hybrid weather prediction: a blend of machine learning and numerical modeling;Arcomano,2020

5. Machine learning based multi-physical-model blending for enhancing renewable energy forecast - improvement via situation dependent error correction;Lu,2015

Cited by 5 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. A hierarchical framework for minimising emissions in hybrid gas-renewable energy systems under forecast uncertainty;Applied Energy;2024-11

2. Robust Scheduling of Spinning Reserve Adopting Risk Assessment for Power Systems Engaging Building Energy Flexibility;2024

3. A Hierarchical Framework for Minimising Emissions in Hybrid Gas-Renewable Energy Systems Under Forecast Uncertainty;2024

4. Optimal risk-driven operation of renewable-penetrated distribution network during natural-disasters: A resiliency-oriented analysis;Sustainable Cities and Society;2023-12

5. On the Added Value of State-of-the-Art Probabilistic Forecasting Methods Applied to the Optimal Scheduling of a PV Power Plant with Batteries;Energies;2023-09-11