Tri-Metaheuristic Optimization for Renewable Energy-Based Potable Water Production

Abstract

Integrating hybrid renewable energy sources with potable water production systems faces critical challenges due to solar and wind intermittency, resulting in suboptimal energy utilization (typically $\lt 75 \%$ efficiency), system unreliability, and high operational costs exceeding $\mathbf{2. 5 0} \boldsymbol{/} \mathbf{m}^{\mathbf{3}}$. Current optimization strategies rely on static design-phase models or single metaheuristic algorithms that fail to dynamically adapt to fluctuating environmental conditions and inadequately manage complex multi-source energy interactions. This paper addresses these limitations by proposing a novel tri-metaheuristic optimization framework that synergistically integrates three complementary bio-inspired algorithms: the Dolphin Echolocation Algorithm (DEA) for solar irradiance prediction and photovoltaic optimization, the African Wild Dog Algorithm (AWDA) for coordinated wind pattern optimization and wake effect mitigation, and the Honeybee Colony Algorithm (HCA) for intelligent energy storage management and dynamic resource allocation. Unlike conventional approaches, this framework incorporates adaptive learning modules enabling real-time system reconfiguration based on predictive analytics. Comprehensive field validation across three geographically diverse pilot installations (Rajasthan-India, Atacama-Chile, Western Australia) over 12-18 months demonstrates that the proposed framework achieves =94.7 ± 1.2 %= energy conversion efficiency, $847 \pm 24 \mathrm{~L} / \mathrm{kWh}$ specific water production rate, 96.8% system availability, and $0.78 / \mathrm{m}^{3}$ levelized cost.