Sustainable and reliable energy management for urban hybrid energy systems: A case study in Islamabad Pakistan on hydrogen and battery integration using transient search optimization algorithm
Shoaib Ahmed, Yongyi Huang, Mitsunaga Kinjo, Tomonobu Senjyu, Dongran Song, M.H. Elkholy
Abstract
• Conducts a detailed techno-economic analysis to determine the best hybrid microgrid. • TS algorithm outperforms MFO and RSA, ensuring faster and more accurate optimization. • Intelligent EMS dynamically manages power flow, optimizing costs and grid stability. • Provides real-world insights on financial, technical, and policy challenges in deployment. • Optimized hybrid setup cuts costs by 13.2 %, proving economic feasibility for urban grids. Islamabad, Pakistan faces rising electricity demand, frequent power shortages, and increasing dependence on imported fossil fuels. These challenges create an urgent need for sustainable and reliable energy solutions. This study presents a hybrid microgrid system that includes PV panels, wind turbines (WTs), battery energy storage systems (BESSs), and hydrogen fuel cells (FCs), managed by an intelligent energy management system (EMS). To optimize system performance, the study evaluates three metaheuristic algorithms: Moth Flame Optimization (MFO), Reptile Search Algorithm (RSA), and Transient Search (TS). The TS algorithm shows the best performance in terms of convergence and solution quality. A comparative techno-economic evaluation of three hybrid energy system configurations over a 25-year period indicates that the fully integrated PV, wind, battery, and FC system incurs a total cost of $225.53 billion. The PV, wind, and battery configuration results in the lowest total cost at $195.64 billion, while the PV, wind, and FC setup costs $208.80 billion. Among the three, the battery-only backup system emerges as the most economically viable option, achieving a 13.2 % cost reduction compared to the fully integrated system and a 6.3 % reduction relative to the PV, wind, and FC configuration. These outcomes demonstrate the value of advanced optimization methods in enhancing the efficiency and cost-effectiveness of microgrid systems and offer strategic guidance for energy planners and decision-makers. This study confirms that the proposed EMS, supported by the TS algorithm, improves both the operational performance and cost-efficiency of hybrid renewable energy systems (HRESs). It provides practical guidance for developing smart, low-cost, and scalable microgrid solutions in fast-growing urban areas.