Power system economic emission dispatch considering uncertainties of wind, solar, and small runoff hydropower via a hybrid multi-objective optimization algorithm

Economic emission dispatch (EED) plays a key role for the power system operation. With the integration of renewable energy sources (RESs), their uncertainties pose great challenges to the EED. This work establishes an EED model of thermal generators, wind, solar, and small runoff hydropower and cons...

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Bibliographic Details
Published in:Expert systems with applications Vol. 278; p. 127375
Main Authors: Xiong, Guojiang, Liu, Qinghua, Wang, Yang, Fu, Xiaofan
Format: Journal Article
Language:English
Published: Elsevier Ltd 10.06.2025
Subjects:
Archimedes optimization algorithm
Artificial gorilla troops optimizer
Economic emission dispatch
Renewable energy
Teaching–learning-based optimization
Uncertainty
Uncertainty
Archimedes optimization algorithm
Economic emission dispatch
Artificial gorilla troops optimizer
Renewable energy
Teaching–learning-based optimization
ISSN:0957-4174
Online Access:Get full text
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Summary:Economic emission dispatch (EED) plays a key role for the power system operation. With the integration of renewable energy sources (RESs), their uncertainties pose great challenges to the EED. This work establishes an EED model of thermal generators, wind, solar, and small runoff hydropower and considers varied practical factors and constraints. Overestimation and underestimation models are used to describe the uncertainties. Meanwhile, to solve this model effectively, a hybrid multi-objective method MOAGT by combining Archimedes optimization algorithm, artificial gorilla troops optimizer, and teaching–learning-based optimization is presented via both parallelization and serialization. Some techniques including chaotic opposition initialization, Morlet wavelet mutation, memory retention, and modified compromise solution selection are utilized to raise the performance. The validity of the proposed model with MOAGT is demonstrated on two systems. Results compared with peer algorithms and the CPLEX solver show that MOAGT achieves better solutions in less costs and lower emissions, which can make the systems run in a more economical and low-carbon state under the premise of meeting the system reliability. Besides, the established EED model has good portability that supports the transition to a cleaner energy system. Furthermore, MOAGT can be seen as an effective alternate to the EED problem with RESs.
ISSN:0957-4174
DOI:10.1016/j.eswa.2025.127375