Chaotic simplex moth swarm algorithm for optimization of alternative energy integrated power grid including static synchronous series compensator

Abstract

An optimal power flow (OPF) model is developed in this paper, which includes three highly volatile energy sources: wind energy, photovoltaic energy, and electric vehicle as a vehicle to grid energy source. An interpretable probabilistic approach is implemented to estimate the power generation from the mentioned energy sources using different probability distribution functions. A single static synchronous series compensator is optimally configured and positioned in the combined grid power system to achieve balanced operation. This significant uncertainty imposed OPF model is solved by blending (a) chaotic mapping technique, and (b) Nelder–mead simplex method with conventional moth swarm algorithm (MSA) and the modified version of MSA is called chaotic simplex MSA (CSMSA). Proposed CSMSA's performance is evaluated analytically using extensive simulation approaches that include six different power‐related simulation instances considering IEEE 30 and 118‐bus test grid systems. The strength of the proposed CSMSA is examined by comparing its performance to five outstanding metaheuristic techniques.

Special issue on: AI/data‐based modeling, control, and operation of smart grid power systems

“…A multiloop frequency controller augmented with fractional‐order calculus with in‐depth qualitative analysis for smart grid has been presented 1,2 . The challenging area in EV charging stations with PV array and distribution network, 3 optimal power flow with renewables integrated smart grid, 4 security assessment of grid‐connected society during grid failure or blackouts, 5 resilient power quality control framework for an autonomous microgrid, 6 and detection of false data injection attacks in smart grid 7 have been addressed to cope with challenging issues in smart grid operation/control continuum.…”