Search

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]

The intermittent behavior of wind power generation results in a fast variation of both frequency and voltage magnitudes of isolated microgrids with high wind power penetration. In this paper, a hybrid energy microgrid with wind energy conversion system (WECS) and diesel synchronous generators are analyzed during wind gust conditions. Superconducting magnetic energy storage (SMES) is connected at the same node of the WECS to mitigate the intermittent wind power generation. A developed fuzzy logic controller has been applied to achieve active power sharing. The damping of voltage fluctuation depends on SMES reactive power, which is controlled by a voltage source converter. The studied microgrid comprises 33 nodes, two diesel SGs, and two WECSs of squirrel cage induction generator type at 30% penetration level. The results demonstrate the effectiveness of the proposed control strategy to mitigate the frequency fluctuations during the wind speed gusts by smoothing power output from WECSs/SMES system. Moreover, voltage control is achieved by injecting reactive power from the SMES system. All simulations are performed by MATLAB/Simulink package. [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]