Hybrid kernel search and particle swarm optimization with Cauchy perturbation for economic emission load dispatch with valve point effect

Due to growing concerns over environmental protection, economic and environmentally responsible power dispatching has become a hot topic in the field of power system control. Multi-objective optimization minimizes fuel costs and pollution emissions without violating operational constraints. To solve this problem, the MOP is decomposed into individual objects via the weighted sum method, and Newton's method is used to tackle equality constraints iteratively. To this end, a hybrid algorithm (HKSOPSO-CP) based on kernel search optimization (KSO) and particle swarm optimization (PSO) with Cauchy perturbation is proposed in this paper. An experiment with 23 CEC benchmark functions shows that HKSOPSO-CP offers better performance compared with various popular algorithms proposed in recent years. When employed to solve the IEEE standard economic emission dispatch (EED) problems with 6, 10, 40, and 110 units, the proposed HKSOPSO-CP algorithm produces results indicating a better trade-off between the objectives relating to fuel costs and emissions compared to other algorithms that have recently been reported on in the literature.

Automated summary

Due to the growing concerns over environmental protection, the paper proposes a hybrid algorithm called HKSOPSO-CP, which combines kernel search optimization and particle swarm optimization with Cauchy perturbation, to achieve economic and environmentally responsible power dispatching. Experimental results with benchmark functions show that HKSOPSO-CP outperforms various popular algorithms. When applied to solve the IEEE standard economic emission dispatch problems, HKSOPSO-CP algorithm demonstrates a better trade-off between fuel costs and emissions compared to other algorithms reported in the literature.