Coronavirus Herd Immunity Optimizer (CHIO) for Transmission Expansion Planning

Abstract

This paper presents a novel mathematical model to simultaneously tackle the economic dispatch (ED) problem considering valve point effect, load uncertainty, distributed generation (DG) uncertainty, incentive-based demand response, and plug-in electric vehicle into the transmission expansion planning (TEP) problem to minimize the total cost of the system. Monte–Carlo is employed to consider the uncertain characteristic of DGs and loads. Considering ED problem in solving TEP problem with uncertain aspects of DGs and loads, made the problem so complicated. So, to overcome this complicity, a new meta-heuristic coronavirus herd immunity optimizer (CHIO) algorithm is utilized. The presented methodology is verified on an IEEE 24-bus test system. Finally, to evaluate the CHIO algorithm efficiency, a comparison is made between the results obtained by CHIO and Branch and Bound (B&B) algorithm. Numerical results show the efficiency of the newly presented methodology in solving TEP and ED problems simultaneously.