A machine-learning approach to speed-up simulation towards the design of optimum operating profiles of power plants-Reference-Cited by-同舟云学术

A machine-learning approach to speed-up simulation towards the design of optimum operating profiles of power plants

Published:2019 Issue: Volume: Page:
ISSN:
Container-title:Proceedings of the 8th International Conference on Informatics, Environment, Energy and Applications - IEEA '19
language:
Short-container-title:

Author:

Rosado-Tamariz Erik¹,Zuniga-Garcia Miguel A.¹,Santamaria-Bonfil G.²,Batres Rafael¹

Affiliation:

1. Tecnologico de Monterrey, Mexico

2. Instituto Nacional de Electricidad y Energias Limpias-CONACYT, Mexico

Funder

Consejo Nacional de Ciencia y Tecnologia

Publisher

ACM Press

Reference23 articles.

1. Alizadeh, M. I., Moghaddam, M. P., Amjady, N., Siano, P., & Sheikh-El-Eslami, M. K. (2016). Flexibility in future power systems with high renewable penetration: A review. Renewable and Sustainable Energy Reviews, 57, 1186--1193.

2. Ulbig, A., & Andersson, G. (2015). Analyzing operational flexibility of electric power systems. International Journal of Electrical Power & Energy Systems, 72, 155--164.

3. Nemati, M., Braun, M., & Tenbohlen, S. (2018). Optimization of unit commitment and economic dispatch in microgrids based on genetic algorithm and mixed integer linear programming. Applied Energy, 210, 944--963.

4. Saravanan, B., Das, S., Sikri, S., & Kothari, D. P. (2013). A solution to the unit commitment problem---a review. Frontiers in Energy, 7(2), 223--236.

5. Liu, Z., & Karimi, I. A. (2017). Simulation and optimization of a combined cycle gas turbine power plant for part-load operation. Chemical Engineering Research and Design.

Cited by 1 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. Enhanced dynamic simulation approach towards the efficient mining thermal energy supply with improved operational flexibility;International Journal of Energy Research;2020-10-21