Improved Lagrangian relaxation generation decision-support in presence of electric vehicles

<p><span>Decision making strategies for resources available in macro/micro scales have long been a critical argument. Among existing methods to address such mixed-binary optimization model, Lagrangian relaxation (LR) found universal acceptance by many utilities, offering fast and accurate answer. This paper aims at retrofitting the solution way of LR algorithm dint meta-heuristic cuckoo search (CSA). When integrating CSA into mechanism, tighter duality gap is catered, representing more feasible solution. The key performance exhibits head start over other classical as gradient (GS) Newton Raphson (NR) when dealt with relative closure procedure. Further, electric vehicles (EV) its associated hard constraints are encompassed model imperiling proposed CSA-LR if encountered nonlinear fluctuation gap. Simulation results show that outperforms quality with/without EV compared conventional NR-LR method.</span></p>