Integrated bidding strategy of distributed energy resources based on novel prediction and market model-Reference-Cited by-同舟云学术

Integrated bidding strategy of distributed energy resources based on novel prediction and market model

Published:2020-04 Issue:5 Volume:44 Page:4048-4062
ISSN:0363-907X
Container-title:International Journal of Energy Research
language:en
Short-container-title:Int J Energy Res

Author:

Lin Xinyue¹,Chen Cai¹,Gauzily Soleiman²^ORCID

Affiliation:

1. Financial Synergy Innovation of Science and Technology Center in Hebei Province/Science and Technology Finance Key Laboratory of Hebei ProvinceHebei Finance University Baoding China

2. Electrical and Energy DepartmentAnkara University Ankara Turkey

Publisher

Wiley

Subject

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

Link

https://onlinelibrary.wiley.com/doi/pdf/10.1002/er.5198

Reference17 articles.

1. Nonlinear Multi-Agent Optimal Load Frequency Control Based on Feedback Linearization of Wind Turbines

2. Wind power and market power in short-term electricity markets

3. A novel hybrid approach based on cuckoo search optimization algorithm for short-term wind speed forecasting

4. Optimal bidding strategy of wind power producers in pay-as-bid power markets

5. Integrated offering strategy for profit enhancement of distributed resources and demand response in microgrids considering system uncertainties

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. Forecast Research on Multidimensional Influencing Factors of Global Offshore Wind Power Investment Based on Random Forest and Elastic Net;Sustainability;2021-11-06

2. Planning and Scheduling Process for a Grid-Connected Microgrid Based on Renewable Energy Sources by a Novel Fuzzy Method;Complexity;2021-11-05

3. Optimisation and Management of Virtual Power Plants Energy Mix Trading Model;International Journal of Renewable Energy Development;2021-09-30

4. Distributed machine learning for energy trading in electric distribution system of the future;The Electricity Journal;2021-01

5. Effects of the COVID‐19 pandemic on the Brazilian electricity consumption patterns;International Journal of Energy Research;2020-09-07