Emended Harris Hawk Optimizer for Mixed Energy Generation Scheduling Problem

Abstract

This paper proposes an emended Harris hawk optimizer (EHHO) to solve the mixed energy generation scheduling problem with available resources from hydrothermal, pumped storage hydro, solar, and wind units while minimizing the operating cost, utilizing the available water, and satisfying the other operational constraints. Direct heuristics are applied to solve the constraints imposed on the generation scheduling problem. An optimistic binary optimizer is utilized to commit solar and wind units so that solar and wind units share a certain percentage of power demand to meet the uncertainty of renewable power. The hunting behavior of the hawks for the desired prey inspires the Harris hawk optimizer (HHO) because of its flexibility, sturdiness, and scalability features. Despite HHO giving premature convergence as it tends to trap into the local optima while solving complex non-linear optimization problems. Hence, the local search technique (LST) is employed with the HHO to overcome it. LST also empowers the exploitation phase of HHO. Two practical electric power test systems are successfully solved using the proposed EHHO. On implementing the proposed EHHO, with the penetration of pumped storage hydro units, savings in the cost of thermal units range from 3.90 to 11.44%. Further, with the penetration of wind and solar power, the savings in operating costs of thermal units have increased from 12.15 to 20.19%. The main idea and contribution of the proposed work is the thermal fuel cost reduction using renewable energy sources by utilizing EHHO. The convergence curves and whisker box plots demonstrate the EHHO’s robustness while solving the electric power test systems.