Abstract
Stochastic nature of wind energy prevents the electrolyzer in wind-to-hydrogen (WindtH2) system to accomplish high capacity factor without the assistance of the battery energy storage system (BESS). Furthermore, design process focuses on the reliability of the system and its components to achieve low production cost. The goal of this investigation is to develop a decision making tool for solving constrained multi-objective optimization (CMO) problems to minimize the cost and power losses, and maximize the reliability of a small-scale WindtH2 system subjected to various constraints. The proposed algorithm, which combines a CMO model with a smoothing control strategy (SCS), solves the CMO problem to find Pareto-optimal solutions. Dynamic SCS smoothes the power provided to the electrolyzer by the assistance of the BESS. It favors the electrolyzer full-capacity operation for enhancing its capacity factor. Minimum/maximum electrolyzer power input and safe thresholds of battery and H2 tank state-of-charge are the main operational constraints taken into consideration by the CMO model. The four conflicting objectives considered in the optimization model are the Levelized Cost of H2 (LCOH), Total H2 Deficit (THD), Energy Dump Possibility (EDP) and a new indicator namely Electrolyzer Capacity Factor (ECF). Solution vector consists of seven decision variables including battery cycles spent and electrolyzer operation hours. The developed algorithm is applied to generate Pareto solutions of a case for industrial usage. Pareto results show: (1) The proposed algorithm with the associated indicators is useful for the design process; (2) The SCS significantly helps to improve the ECF; (3) Consistent relationships between the four objectives; (4) WindtH2 system consisting of wind turbine farm of 836.5 kW, electrolyzer of 276 kW, battery of 765 kWh and H2 Tank of 10.8 kg gives a LCOH of 27.01 $/kg, ECF of 20.5%, EDP of 8.19% and THD of 41.72%.
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