Multi-Objective Short-Term Hydro-Thermal Scheduling Using Meta-Heuristic Approaches

Every day humans face new challenges to survive in this world. It is a big challenge to utilize hydro and thermal generating unit properly. Researchers are trying to explore new techniques to improve scheduling of generating units. Environmental matter is a big issue to modern society. This chapter suggests a well-organized and effective approach using concept of grey wolf optimization (GWO) to deal with nonlinear, multi-objective, short-term, hydro-thermal scheduling (MOHTS) problem. Moreover, authors have incorporated oppositional based learning (OBL) to enhance characteristics of GWO to achieve solution more consistently and accurately. To explore authenticity of our proposed algorithms, GWO and OGWO (oppositional based GWO) are applied to multi-chain cascade of 4-hydro and 3-thermal test system. Effective constraints like valve-point loading, water discharge, water storage, etc., are considered here. Statistical comparisons with other enlisted heuristic methods are done. The projected methods solve MOHTS problem quickly and efficiently. Multi-Objective ShortTerm Hydro-Thermal Scheduling Using MetaHeuristic Approaches

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