Economic emission dispatch of power systems considering solar uncertainty with extended multi-objective differential evolution

Multi-objective economic emission dispatch (MOEED) is not only a hotspot issue for emission reduction, but also one of the fundamental problems for optimal operation of power systems. With the increasing scale of solar energy, the uncertainty of solar power brings intractable challenges to the power system operation. In this work, a MOEED model considering the uncertainty of solar power is proposed. Both scenarios of underestimation and overestimation of solar power are modeled and penalized in the form of operating cost. To solve this model effectively, a multi-objective method by extending our quadratic interpolation learning differential evolution (QILDE) is developed. The core is to redefine the quadratic interpolation from single-objective optimization to adapt to multi-objective problems. Besides, some multi-objective processing techniques including feasible so-lution technique, non-dominated sorting, summation-based sorting and diversified selection method, and fuzzy decision technique are integrated. Simulation results on 20 CEC 2020 multi-objective functions and a modified IEEE 30-bus system verify the superiority of the proposed method.