Combined Economic Emission Dispatch using a Novel Quantum Behaved-BAT Algorithm for a Hybrid Microgrid

Ensuring an affordable and reliable electrical power supply is imperative for efficient and effective operation of academic institutions, given that pivotal functions within the institution, including pedagogical delivery, scholarly pursuits, and research endeavors, heavily rely on energy consumption. The penetration of Renewable Energy (RE) resources into the energy mix has become indispensable due to escalating fuel costs, diminishing reserves of fossil fuels, and heightened environmental apprehensions. Various Economic Dispatch (ED) methodologies have been devised to address the challenge of ensuring uninterrupted and sustainable power provision at minimized expenditures. This paper introduces a novel optimization algorithm designed specifically to tackle the Combined Economic Emission Dispatch (CEED) issue, using a university campus as a focal point for case study analysis. The case study model comprises six conventional and two RE generators. The optimization problem is solved utilizing the novel quantum-behaved BAT algorithm (QBBA) as proposed. This algorithm is an enhancement over the traditional BAT algorithm, specifically targeting the joint optimization CEED solution. When tested, the QBBA performs better in terms of optimizing the CEED objective function than just optimizing individual objectives of economic and emission dispatch. The QBBA has proven to be an effective tool for optimizing CEED problems. The findings from this study are valuable for stakeholders and policymakers aiming to develop decision-making policies based on optimized solutions to energy and emission challenges.