Abstract: As fossil fuel stocks are being depleted, alternative sources of energy must be explored. Consequently, traditional thermal power plants must coexist with renewable resources, such as wind, solar, and hydro units, and all-day planning and operation techniques are necessary to safeguard nature while meeting the current demand. The fundamental components of contemporary power systems are the simultaneous decrease in generation costs and increase in the available transfer capacity (ATC) of current systems. Thermal units are linked to sources of renewable energy such as hydro, wind, and solar power, and are set up to run for 24 h. By contrast, new research reports that various chaotic maps are merged with various existing optimization methodologies to obtain better results than those without the inclusion of chaos. Chaos seems to increase the performance and convergence properties of existing optimization approaches. In this study, selfish animal tendencies, mathematically represented as selfish herd optimizers, were hybridized with chaotic phenomena and used to improve ATC and/or reduce generation costs, creating a multi-objective optimization problem. To evaluate the performance of the proposed hybridized optimization technique, an optimal power flow-based ATC was enforced under various hydro-thermal-solar-wind conditions, that is, the renewable energy source-thermal scheduling concept, on IEEE 9-bus, IEEE 39-bus, and Indian Northern Region Power Grid 246-bus test systems. The findings show that the proposed technique outperforms existing well-established optimization strategies.

Key words: Available transfer capability (ATC), biogeography-based optimization (BBO), chaotic map, chaotic selfish herd optimizer (CSHO), grey wolf optimizer (GWO), optimum power flow (OPF), power generation cost (PGC), renewable energy sources (RES), selfish herd optimizer (SHO)