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Integrating hybrid energy storage system (HESS) consisting of battery and supercapacitor provides balance over the power generation and load demand, ensuring the system's stability under transient conditions. Along with battery storage, to control its rate of charging/discharging a rate limiter (RTL) is usually employed. However, the impact of the RTL on battery storage efficiency and system performance has not been explored in the existing literature. This paper investigates the performance of a grid‐tied photovoltaic (PV) system with HESS under dynamic scenarios with different RTL values applied to the battery storage. The paper explores the simulation analysis considering transient, linear, and steady‐state conditions with varying PV generation and load demand. The simulation study shows that a lower RTL value may slow down the battery response, leading to inefficient utilization of battery storage. Alternatively, a very high RTL value will not appropriately regulate the battery current, which may lead to frequent charging and discharging and deterioration of battery life. For the proposed system, RTL with a value of 100 A/s seems appropriate, providing 1.29 Wh of energy through battery storage with an energy efficiency of 91.81% for the simulated time. The DC voltage is efficiently maintained at 400 V with a ripple factor of 0.45%. [ABSTRACT FROM AUTHOR]

Hybrid renewable energy systems (HRES) are gaining high interest in supplying electric energy for remote communities. Energy storage systems (ESS) are utilized by green autonomous HRESs to accommodate the variability of renewable resources such as wind and solar energy systems. The lack of any traditional energy source is adding a great reliability challenge which should be compensated using expensive ESS. This challenge can be avoided by using a pumped hydro energy storage system (PHES) in harmony with batteries. The PHES is an excellent option to be used in NEOM city due to the perfect topographical characteristic of this site. The minimum cost of energy and the highest reliability is used as an objective for sizing the proposed entire green HRES. Using smart grid principles (SGP) and demand‐side management (DSM) in the design and operation stages will minimize system size and cost, which can result in a significant reduction in consumer bills. As a result, this paper introduces an innovative DSM based on a dynamic tariff. The suggested DSM technique was developed utilizing a unique fuzzy logic that takes into account the present and day‐ahead ESS situations to intelligently determine the ideal tariff for the lowest cost and maximum reliability of the HRES. This paper introduces a modified grey wolf optimization (MGWO) technique to shorten convergence time while preserving the best accuracy. The suggested MGWO is assessed against 10 swarm optimization techniques. The payback period of the project is 7 years. The findings acquired from this unique program demonstrated its superiority, with conversion times reduced by 22% to 80% when compared to previous optimization procedures. Furthermore, as compared to the flat rate pricing tariff, the usage of the dynamic tariff lowered the LCOE by 53%. [ABSTRACT FROM AUTHOR]

In this paper, a novel liquefied air energy storage (LAES) system driven by wind energy and natural gas, integrated with a two‐stage supercritical carbon dioxide cycle is proposed and investigated. Three different sensible thermal energy storage (TES) systems are considered in this study to store the heat produced in the compressions stage and use it later to improve the performance. The proposed system is analyzed in terms of energy, exergy, exergo‐economic, and environmental impacts. A detailed economical and technical investigation is carried out to determine system hotspots, exergy destruction rates, and the performance of the system and subsystems. Finally, by considering ten design variables, the nondominated sorting genetic algorithm‐II (NSGA‐II) is employed to evaluate the optimal design solutions for double objective functions, including cost per unit of exergy and overall system exergy efficiency. Results indicate that for a 50 MW of discharging power, 80.75 MW of charging electricity and 1.22 kg/s of fuel are required. Additionally, the product cost per unit of exergy and the levelized costs are calculated as 32.02 $/GJ and 0.133 $/kWh, respectively. The results of the optimization study also show that for the optimal Pareto solution, the energy efficiency would be 52.2% while the exergy efficiency is 45.26%. [ABSTRACT FROM AUTHOR]