An Intensified Sparrow Search Algorithm for Combined Economic Emission Dispatch including Renewables and Electric Vehicles

Security, reliability, economics and environments are the four pillars of power system operation and control.Combined economic emission dispatch (CEED) is one such problem which can able to accommodate all these aspects.Though CEED is mainly to minimize operating cost and emission costs, it has been redefined in recent times due to the emerging trends, namely, renewable energy sources (RES) and electric vehicles (EVs).In this paper, the problem of CEED is augmented in this context by considering spinning reserve (SR) due to the fluctuation in network loading conditions, and the penetration of EV load.Furthermore, the differences in power of photovoltaic (PV) and wind turbine (WT) systems are covered.These modifications transform the CEED into a non-convex, multi-objective, multi-variable optimization problem with several constraints.The proposed solution is a novel metaheuristic intensified sparrow search algorithm (ISSA) designed to enhance search features.ISSA incorporates a neighbour search technique and saltation learning mechanism for the fundamental SSA.Simulations are performed for various scenarios on a standard 3-bus power system.At first, the solution for conventional CEED is determined using ISSA and compare it with other recent meta-heuristics.Augmented CEED is performed in the second stage, taking into account hourly variability in loading conditions on the system, RES, and EVs, along with SR requirements for stability and generation loss.With RES and EVs, the SR is estimated equal to 875.371 MWh, up from 787.182 MWh without them, indicating improvement in system.Furthermore, the penetration of RES reduces net power consumption by 1975.6 MW, leading to a reduction in fuel costs of $17,649.112 per hour.Also, it resulted in an effective solution for global warming by reducing emission costs by $537.161every hour.