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The superior access to renewable sources in modern power systems increases the fluctuations in system voltage and power. Additionally, the central dilemmas in using renewable energy sources (RESs) are the intermittent nature of and dependence on wind speed and solar irradiance for wind and photovoltaic (PV) systems, respectively. Therefore, utilizing a vigorous and effective energy storage system (ESS) with RESs is crucial to overcoming such challenges and dilemmas. This paper describes the impacts of using a battery storage system (BSS) and superconducting magnetic energy storage (SMES) system on a DC bus microgrid-integrated hybrid solar–wind system. The proposed method employs a combination of BSS and SMES to improve the microgrid stability during different events, such as wind variation, shadow, wind turbine (WT) connection, and sudden PV outage events. Distinct control approaches are proposed to control the system's different components in order to increase overall system stability and power exchange. Both the PV and wind systems are further equipped with unique maximum power point tracking (MPPT) controllers. Additionally, each of the ESSs is controlled using a proposed control method to supervise the interchange of the active power within the system and to keep the DC bus voltage constant during the different examined instabilities. Furthermore, to maintain the load voltage /frequency constant, the prime inverter is controlled using the proposed inverter control unit. The simulation results performed with Matlab/Simulink show that the hybrid BSS + SMES system successfully achieves the main targets, i.e., DC voltage, interchange power, and load voltage/frequency are improved and smoothed out. Moreover, a comparison among three case studies is presented, namely without using ESSs, using the BSS only, and once more using both BSS and SMES systems. The findings prove the efficacy of the proposed control method based on the hybrid BSS + SMES approach over BSS only in preserving the modern power system's stability and reliability during the variable events. [ABSTRACT FROM AUTHOR]

Owing to the stochastic behavior of renewable energy activity and the multiple design considerations, the advancement of hybrid renewable energy-based microgrid (HREMG) systems has become a complex task. This study proposes a design optimization algorithm for the long-term operation of an autonomous HREMG along with the optimal system capacities. The investigated energy system comprises photovoltaic panels, wind turbines, diesel generators, and batteries. It aims to energize a remote coastal community with a daily load demand of 400 kWh in Marsa Matruh, Egypt. Since most studies utilize commercial tools in the design optimization procedure, the African vultures optimization approach (AVOA) is developed to find the optimal energy alternative and determine the optimal component's capacity considering achieving the minimum energy cost and loss of power supply probability. Moreover, an adequate energy management strategy is suggested to coordinate the power flow within the energy system in which renewable energy sources are fully penetrated. To check the AVOA robustness and efficacy, its performance is compared with the HOMER Pro most popular commercial tool as well as with new metaheuristic algorithms, namely the grasshopper optimization algorithm (GOA) and Giza pyramid construction (GPC) under the same operating environment. The results revealed that the proposed AVOA achieved superior economic results toward the least net present cost ($346,614) and energy price (0.0947 $/kWh). Moreover, over 20 independent runs, the AVOA showed a better performance in terms of convergence and execution time compared to other tools/algorithms. The obtained findings could be a useful benchmark for researchers in the sizing problem of hybrid energy systems. [ABSTRACT FROM AUTHOR]