Deriving Optimal Operation Rule for Reservoir System Using Enhanced Optimization Algorithms

Recently, there has been a growing interest in employing optimization techniques to ascertain the most efficient operation of reservoirs. This involves their application to various facets of the reservoir operating system, particularly in determining optimal rule curves. This study delves into the exploration of different algorithms, including Artificial Bee Colony (ABC), Particle Swarm Optimization (PSO), Genetic Algorithm (GA), Firefly Algorithm (FA), Invasive Weed Optimization (IWO), Teaching Learning-Based Optimization (TLBO), and Harmony Search (HS). Each algorithm was integrated into a reservoir simulation model, focusing on finding optimal rule curves for the Mujib reservoir in Jordan from 2004 to 2019. The primary objective was to evaluate the long-term impact of water shortages and excess releases on the Mujib reservoir. Furthermore, the study aimed to determine the effects of water demand management by reducing it by 10%, 20%, and 30%. The results revealed that the used algorithms effectively mitigated water shortages and excess releases compared to the current operational strategy. Notably, the Teaching Learning-Based Optimization (TLBO) algorithm yielded the most favorable outcomes, reducing the frequency and average of water shortages to 55.09% and 56.26%, respectively. Additionally, it curtailed the frequency and average of excess releases to 63.16% and 73.31%, respectively.