Optimizing Energy Scheduling and Power Flow in the Power System Using Coronavirus Algorithm

Addressing financial losses in power systems is essential, leading to the need for effective solutions. This study focuses on optimizing energy scheduling from various generation sources within the power system. To achieve this, an optimal power flow (OPF) problem has been formulated for IEEE 30 buses system considering constraints such as voltage stability, line capacity and power generation and solved in MATLAB using the coronavirus optimization algorithm (COVIDOA) and differential evolution algorithm (DE). The simulation results of static optimal power flow (SOPF) indicate that COVIDOA achieves a cost reduction of $576.459569/h, while DE achieves $578.125673/h. Additionally, COVIDOA and DE led to power loss reductions of 2.5906 MW and 2.6802 MW, respectively. Furthermore, COVIDOA and DE reduce carbon emissions by 0.2069 ton/h and 0.2081 ton/h, respectively, which shows the superior performance of COVIDO algorithm over DE. In dynamic optimal power flow (DOPF) over 24 hours under uncertainty with the presence of renewable energy and energy storage systems, the total cost minimization amounts to $11717.6471, with minimizing all below objectives as total power loss to 67.26100769 MW, total amount of emissions to 5.17373409 tons, and total voltage deviation to 11.14408357 pu when compared with the absence of renewable energy and energy Storage Systems. These results underscore the effectiveness of the proposed algorithm COVIDOA in dynamic optimal power flow (DOPF).