Robust Kernel Density Estimation Based Data-Driven Optimal Scheduling for Power Systems Considering Data Errors and Uncertainties of Renewable Energy
Author:
Affiliation:
1. School of Mechanical and Electrical Engineering, Zhoukou Normal University, Zhoukou, China
2. Nanning Branch of Guangxi Radio and Television Technology Center, Nanning, China
3. School of Mechanical Engineering, University of Leeds, Leeds, U.K.
Funder
Key Scientific Research Project of Colleges and Universities in Henan Province
Key Research and Development and Promotion Special Project of Henan Province
Zhoukou Normal University
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Link
http://xplorestaging.ieee.org/ielx8/6287639/10380310/10551751.pdf?arnumber=10551751
Reference29 articles.
1. Risk-constrained non-probabilistic scheduling of coordinated power-to-gas conversion facility and natural gas storage in power and gas based energy systems
2. Two-Stage Chance-Constrained Stochastic Thermal Unit Commitment for Optimal Provision of Virtual Inertia in Wind-Storage Systems
3. Stochastic frequency constrained unit commitment incorporating virtual inertial response from variable speed wind turbines
4. Stochastic Network-Constrained Unit Commitment to Determine Flexible Ramp Reserve for Handling Wind Power and Demand Uncertainties
5. Two‐stage chance‐constrained unit commitment based on optimal wind power consumption point considering battery energy storage
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