In latest years, the need to reduce environmental effects and increase flexibility in the energy sector has led to increased penetration of renewable energy and sources and the shift from concentrated to decentralized generation. A fuel cell is an instrument that can produces the electricity by chemical reactions. Fuel cells are a capable technology for ultimate energy conversion and energy generation. We can see this system is integrated, where we find that the wind and photovoltaic energy system is complementary between them, because not all the days are sunny, windy or night, so we can see this system has higher reliability to provide permanent generation. At low load hours, PV and electrolysis units produce additional power. After being compressed, hydrogen is stored in tanks. The purpose of this article is to separate the Bahr AL-Najaf Area from the main power grid and make it an independent network. The PEM fuel cells were analyzed and designed, and it is were found that one layer is equal to 570.96 Watt at 0.61 volts and 1.04 A/Cm2.  The number of layers in one stack is designed to be equal to 13 layers, so that the total power of one stack is equal to 7422.48 Watt. That is, the number of stacks required to generate the required energy from the fuel cells is equal to 203 stk. This study provides an analysis of the hybrid system to envelop the electricity demand in the Bahr AL-Najaf region of 1.5 MW, the attained hybrid power generation system TNPC cost was about 9,573,208 USD, whereas the capital cost and energy cost (COE) were about 7,750,000 USD and 0.169 USD/kWh respectively.

We analyze the sensitivity of the optimal mixes to cost and variability associated with solar technologies and examine the role of Thermal Energy Storage (TES) combined to Concentrated Solar Power (CSP) together with time-space complementarity in reducing the adequacy risk—imposed by variable Renewable Energies (RE)—on the Moroccan electricity system. To do that, we model the optimal recommissioning of RE mixes including Photovoltaic (PV), wind energy and CSP without or with increasing levels of TES. Our objective is to maximize the RE production at a given cost, but also to limit the variance of the RE production stemming from meteorological fluctuations. This mean-variance analysis is a bi-objective optimization problem that is implemented in the E4clim modeling platform which allows us to use climate data to simulate hourly Capacity Factors (CFs) and demand profiles adjusted to observations. We adapt this software to Morocco and its four electrical zones for the year 2018, add new CSP and TES simulation modules, perform some load reduction diagnostics, and account for the different rental costs of the three RE technologies by adding a maximum-cost constraint. We find that the risk decreases with the addition of TES to CSP, the more so as storage is increased keeping the mean capacity factor fixed. On the other hand, due to the higher cost of CSP compared to PV and wind, the maximum-cost constraint prevents the increase of the RE penetration without reducing the share of CSP compared to PV and wind and letting the risk increase in return. Thus, if small level of risk and higher penetrations are targeted, investment must be increased to install more CSP with TES. We also show that regional diversification is key to reduce the risk and that technological diversification is relevant when installing both PV and CSP without storage, but less so as the surplus of energy available for TES is increased and the CSP profiles flatten. Finally, we find that, thanks to TES, CSP is more suited than PV and wind to meet peak loads. This can be measured by the capacity credit, but not by the variance-based risk, suggesting that the latter is only a crude representation of the adequacy risk.