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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]

Recently, hybrid generation systems (HGSs) are considered to be the optimal solution for supplying remote areas with the required electrical power. HGSs contain two or more renewable energy sources (RESs), such as wind power generators (WPGs), photovoltaic (PV) generation systems and the energy of the tides. Due to the intermittent nature of RESs, power and voltage fluctuations have appeared accordingly at the connection point with the utility power grid. Hence, energy storage technologies can play an effective role in mitigating power and voltage fluctuations caused by hybrid PV–wind generation systems. This paper proposes a robust control strategy for superconducting magnetic energy storage (SMES) combined with HGS. Fuzzy logic control (FLC) is used to control the chopper circuit of SMES in order to charge/discharge the active power from/to HGS. The proposed FLC strategy considers the fluctuations due to PV and wind power generation systems and the random load power variation. SMES with the proposed FLC method can effectively mitigate the fluctuation of active and reactive powers transfer between HGS and grid, and it can also regulate the voltage profile of the common connection point of SMES with HGS. Moreover, the system line power loss is clearly reduced by using the SMES system. The complete power system including loads is built in MATLAB/Simulink platform. The simulation results indicate that the proposed FLC–SMES can smooth both power and voltage fluctuations due to uncertain climate changes of PV and WPG systems. [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]

• The applications of voltage stability indices (VSIs) in the various power system problems are discussed. • The several data for each VSI are presented. • Comparative analysis is presented for 49 types of VSIs. • Providing a rich source for researchers, students, and employers regarding VSIs. The increasing integration of renewable energy sources (RESs) into the power system, rapid load changes, and increasing power demand create problems for the stability of power systems. Therefore, the voltage stability index (VSI) becomes an important indicator of power system stability. This paper provides a comprehensive overview of most VSIs and a wealth of resources for researchers, students, and employers. For each VSI, list data such as name, abbreviation, calculation method, assumptions, the basic concept, steady-state, threshold, instability, pros, and cons are discussed in detail. Also, the paper describes the relationship between RESs, load changes, and VSI, and it investigates the stability issues of the power system. The significant outcome of this comprehensive review is to provide a good foundation for future work in this field and helps professionals to choose the best VSI that meets their needs for various applications. [Display omitted] [ABSTRACT FROM AUTHOR]

The unit commitment (UC) optimization issue is a vital issue in the operation and management of power systems. In recent years, the significant inroads of renewable energy (RE) resources, especially wind power and solar energy generation systems, into power systems have led to a huge increment in levels of uncertainty in power systems. Consequently, solution the UC is being more complicated. In this work, the UC problem solution is addressed using the Artificial Gorilla Troops Optimizer (GTO) for three cases including solving the UC at deterministic state, solving the UC under uncertainties of system and sources with and without RE sources. The uncertainty modelling of the load and RE sources (wind power and solar energy) are made through representing each uncertain variable with a suitable probability density function (PDF) and then the Monte Carlo Simulation (MCS) method is employed to generate a large number of scenarios then a scenario reduction technique known as backward reduction algorithm (BRA) is applied to establish a meaningful overall interpretation of the results. The results show that the overall cost per day is reduced from 0.2181% to 3.7528% at the deterministic state. In addition to that the overall cost reduction per day is 19.23% with integration of the RE resources. According to the results analysis, the main findings from this work are that the GTO is a powerful optimizer in addressing the deterministic UC problem with better cost and faster convergence curve and that RE resources help greatly in running cost saving. Also uncertainty consideration makes the system more reliable and realistic. [ABSTRACT FROM AUTHOR]