Economic emission dispatch of power systems considering uncertainty of wind-solar-hydro with fractional order multi-objective differential evolution

Abstract

The integration of renewable energy brings significant uncertainty to the operation of power systems. Multi-objective economic emission dispatch (MOEED) becomes an important way to reduce operating costs and emissions under the current focus on environmental protection. This paper establishes a MOEED model that incorporates uncertainty of wind, solar, and run-of-river small hydro. The underestimated and overestimated prediction scenarios of renewable energy are transformed into operating costs by spinning reserve cost and penalty cost for modeling. To effectively solve the model, a multi-objective differential evolution called FOMODE is proposed by using the fractional order idea. A mutation operation FOMODE/rand/1 based on discrete fractional order is designed to accelerate the convergence. Besides, a parameters adaptive method is developed to automatically adjust the control parameters of FOMODE according to the population evolution. Furthermore, multi-objective machining techniques are integrated for performance enhancement of FOMODE. The superiority of FOMODE is first verified on twenty CEC’2020 multi-objective optimization problems and benchmarked against ten peer methods. FOMODE is then applied to the MOEED of a tailored IEEE 30-bus system. The results indicate that FOMODE ranks first in 8 of 16 evaluation metrics, and achieves superior solutions with better economic and emission benefits.