Economic Dispatch with High Penetration of Wind Power Using Extreme Learning Machine Assisted Group Search Optimizer with Multiple Producers Considering Upside Potential and Downside Risk-Reference-Cited by-同舟云学术

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Economic Dispatch with High Penetration of Wind Power Using Extreme Learning Machine Assisted Group Search Optimizer with Multiple Producers Considering Upside Potential and Downside Risk

Published:2022 Issue:6 Volume:10 Page:1459-1471
ISSN:2196-5625
Container-title:Journal of Modern Power Systems and Clean Energy
language:en
Short-container-title:

Author:

Li Yuanzheng,Huang Jingjing,Liu Yun,Ni Zhixian,Shen Yu,Hu Wei,Wu Lei

Publisher

Journal of Modern Power Systems and Clean Energy

Subject

Energy Engineering and Power Technology,Renewable Energy, Sustainability and the Environment

Cited by 7 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. Data association load uncertainty and risk aversion in electricity markets with data center participation in the demand response;Energy Reports;2024-06

2. A Two-Stage Forecasting Approach for Day-Ahead Electricity Price Based on Improved Wavelet Neural Network With ELM Initialization;IEEE Transactions on Industry Applications;2024-05

3. A Data-Physical Fusion Method for Economic Dispatch Considering High Renewable Penetration and Security Constraints;2024

4. Multi-Resource Joint Power Dispatch with Coordinated Consideration of Deep Peak and Frequency Regulation Requirements;2023 IEEE 7th Conference on Energy Internet and Energy System Integration (EI2);2023-12-15

5. Dispatch of highly renewable energy power system considering its utilization via a data-driven Bayesian assisted optimization algorithm;Knowledge-Based Systems;2023-12

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