Cross-entropy boosted CRO-SL for optimal power flow in smart grids

Abstract Optimal power flow (OPF) is a complex, highly nonlinear, NP-hard optimization problem, in which the goal to determine optimal operational parameters of power-related system (in many cases type smart or micro grid) guarantee an economic and effective dispatch. In recent years, number approaches based on metaheuristics algorithms have been proposed solve OPF problems. this paper, we propose use Cross-Entropy (CE) method as first step depth search operator assist population-based evolutionary methods framework problem. Specifically, new variant Coral Reefs Optimization with Substrate Layers algorithm boosted CE (CE+CRO-SL) presented work. We adopted IEEE 57-Bus System test scenario which, by default, has seven thermal generators for production grid. modified replacing three renewable source generators, order consider grid approach energy production. The performance CE+CRO-SL particular case study compared that well-known techniques such population’s CMA-ES EPSO (both CE). results obtained indicate showed superior than alternative terms efficiency accuracy. This justified its greater exploration capacity, since it internally operations coming from different heuristics, thus surpassing classic methods. Moreover, projection analysis, provides profit millions dollars per month all tested considering version system.