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Decreasing fossil energy resources and increasing greenhouse gas emissions increase the need for clean and renewable energy sources day by day. One of the sources of renewable energy is wind power, which has been constantly evolving in recent years. Wind power plants are sometimes unresponsive during peak hours, so a backup storage system seems essential for these power plants. In this study, a hybrid system is presented for connection to wind power plants consisting of fuel cell and hydrogen production, to provide reliable power and valuable by-products. In this paper, a case study is conducted for the desired system in the Shahryar County. The techno-economic optimization of the above system indicates that in the best design (consisting of three wind turbines), the annual production capacity of the hybrid system will be 1795 MWh, of which 12 % is the share of fuel cells. The results show that the hybrid system increases the capacity factor of the wind power plant by 2.8 %. The calculated cost of energy (COE) and the net present cost (NPC) for the hybrid system would amount to $ 0.77 and $ 5 235.066, respectively.

The present study sought to address the scheduling of the grid-connected hybrid energy resources under uncertainty of renewable sources, and load in the residential sector. After introducing hybrid resources, scheduling model was implemented through a power management algorithm in an attempt to optimize resource cost, emissions, and energy not supplied (ENS). The stated problem consists of two decision-making layers with different weight coefficients based on the prioritization of each objective function. The proposed algorithm is selected for energy optimal management based on technical constraints of the dispatchable and non-dispatchable resources, uncertainty parameters and day ahead real time pricing (RTP). Furthermore, the impact of demand response programs (DRP) on the given algorithm was investigated using load shedding and load shifting techniques. Finally, the results obtained led to the optimization of the functions in all decision-making layers with different modes of operation.