Power system economic emission dispatch considering uncertainties of wind, solar, and small runoff hydropower via a hybrid multi-objective optimization algorithm - Xiong, Guojiang; Liu, Qinghua; Wang, Yang; Fu, Xiaofan (Expert systems with applications, 2025)
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.