This study presents a Particle Swarm Optimization (PSO) framework for the techno-economic design of photovoltaic–battery energy storage system–hydrogen (PV–BESS–H₂) residential systems targeting green hydrogen production. The methodology integrates National Renewable Energy Laboratory Annual Technology Baseline (NREL ATB) Advanced 2035 cost projections with time-varying electricity tariffs to optimize PV capacity, battery energy storage system (BESS) sizing, electrolyzer power, and hydrogen storage volume. The optimal configuration achieved a levelized cost of hydrogen (LCOH) of 4.09 USD/kg through strategic energy management, combining self-consumption maximization, price arbitrage via BESS charge/discharge cycles, and electrolyzer load balancing. The PV–BESS–H₂ system demonstrated superior performance with high self-consumption rates and efficient PV-to-H₂ conversion pathways, validated through comprehensive Sankey energy flow analysis and sensitivity studies on key techno-economic parameters. Results highlight the critical role of BESS in enabling competitive green hydrogen production at the residential scale under future cost scenarios.

In this paper, we present an economic optimization and sizing study of a hybrid solar-wind system integrated with energy storage and grid connection for green hydrogen production. The proposed model used nonlinear constrained optimization to determine the optimal capacities of photovoltaic panels, wind turbines, storage devices, and electrolyzers to maximize the net present value (NPV) of the investment. The system operation was simulated over a multi-year horizon accounting for intermittent renewable generation profiles, electricity market prices, and operational constraints. The optimization yielded an optimal configuration with 85.95 kW of solar PV, 59.87 kW of wind power, 64.18 kW/100 kWh of battery storage, and 100 kW of electrolyzer capacity, achieving a cumulative hydrogen production of 318,545 kg over 20 years. The system achieved a NPV of 524,720 USD with a Levelized Cost of Hydrogen of 3.35 USD/kg. Sensitivity analyses revealed that NPV varied from approximately 60,000 USD to 180,000 USD as the discount rate increased from 2% to 16% and showed a strong positive correlation with hydrogen selling prices. The results demonstrated the techno-economic feasibility of hybrid renewable systems for sustainable hydrogen production, highlighting the trade-offs between capital expenditure and operational revenues.

This paper proposes an improved biogeography-based optimization (BBO) algorithm to effectively solve the economic and environmental dispatch (EED) problem in power systems. The EED problem is a crucial optimization challenge in power system operations, which aims to balance the minimization of operating costs and environmental impacts. Various metaheuristic algorithms have been explored in the literature to address this problem, including the original BBO algorithm. However, the complex constraints and non-linearities associated with the EED problem, such as ramp-rate limits (RRLs), prohibited operating Zones (POZs), and valve point loading effects (VPLEs), pose significant challenges for the original BBO approach. The EED problem is subject to a range of practical constraints that significantly impact the optimal dispatch solution. Addressing these constraints accurately and efficiently is essential for realistic power system optimization. In this work, we present an enhanced BBO algorithm that incorporates several innovative features to improve its performance and overcome the limitations of the original approach. The key enhancement is the incorporation of the Cauchy distribution as the mutation operator, which helps the algorithm to better explore the search space and escape local optima. Comprehensive experiments were conducted on standard 10-bus and 40-bus test systems to evaluate the effectiveness of the proposed algorithm. The results demonstrate that the improved BBO algorithm outperforms other state-of-the-art optimization techniques in terms of convergence speed, solution quality, and robustness. Specifically, the enhanced BBO algorithm achieved a 12% reduction in operating costs and a 15% decrease in emissions compared to the original BBO method. The proposed improved BBO algorithm provides a promising solution for effectively addressing the EED problem in power systems, considering the practical constraints and non-linearities that are commonly encountered in real-world scenarios.