Renewable energy sources (RESs) such as wind and solar are frequently hit by fluctuations due to, for example, insufficient wind or sunshine. Energy storage technologies (ESTs) mitigate the problem by storing excess energy generated and then making it accessible on demand. While there are various EST studies, the literature remains isolated and dated. The comparison of the characteristics of ESTs and their potential applications is also short. This paper fills this gap. Using selected criteria, it identifies key ESTs and provides an updated review of the literature on ESTs and their application potential to the renewable energy sector. The critical review shows a high potential application for Li-ion batteries and most fit to mitigate the fluctuation of RESs in utility grid integration sector. However, for Li-ion batteries to be fully adopted in the RESs utility grid integration, their cost needs to be reduced.

The risk of oscillation of grid-connected wind turbine generators (WTGs) is well known, making it all the more important to understand the characteristics of different WTGs and analyze their performance so that the problems’ causes are identified and resolved. While many studies have evaluated the performance of grid-connected WTGs, most lack clarity and precision in the modeling and simulation techniques used. Moreover, most of the literature focuses on a single mode of operation of WTGs to analyze their performances. Therefore, this paper updates the literature by considering the different operating conditions for WTGs. Using MATLAB/SIMULINK it expands the evaluation to the full range of vulnerabilities of WTGs: from the wind turbine to grid connection. A network representing grid-connected squirrel-cage induction generator (SCIG) and doubly-fed induction generator (DFIG) wind turbines are selected for simulation. The performances of SCIG and DFIG wind turbines are evaluated in terms of their energy generation capacity during constant rated wind speed, variable wind speed, and ability of fault-ride through during dynamic system transient operating conditions. The simulation results show the performance of DFIG is better than SCIG in terms of its energy generation capacity during variable wind speed conditions and active and reactive power control capability during steady-state and transient operating conditions. As a result, DFIG wind turbine is more suitable for large-scale wind power plants connected to weak utility grid applications than SCIG.

Wind energy is an increasingly important renewable resource in today’s global energy landscape. However, it faces challenges due to the unpredictable nature of wind speeds, resulting in intermittent power generation. This intermittency can disrupt power grid stability when integrating doubly fed induction generators (DFIGs). To address this challenge, we propose integrating a Li-ion battery energy storage system (BESS) with the direct current (DC) link of grid-connected DFIGs to mitigate power fluctuations caused by variable wind speed conditions. Our approach entails meticulous battery modeling, sizing, and control methods, all tailored to match the required output power of DFIG wind turbines. To demonstrate how well our Li-ion battery solution works, we have developed a MATLAB/Simulink R2022a version model. This model enables us to compare situations with and without the Li-ion battery in various operating conditions, including steady-state and dynamic transient scenarios. We also designed a buck–boost bidirectional DC-DC converter controlled by a proportional integral controller for battery charging and discharging. The battery actively monitors the DC-link voltage of the DFIG wind turbine and dynamically adjusts its stored energy in response to the voltage level. Thus, DFIG wind turbines consistently generate 1.5 MW of active power, operating with a highly efficient power factor of 1.0, indicating there is no reactive power produced. Our simulation results confirm that Li-ion batteries effectively mitigate power fluctuations in grid-connected DFIG wind turbines. As a result, Li-ion batteries enhance grid power stability and quality by absorbing or releasing power to compensate for variations in wind energy production.

The system under consideration in this paper consists of a photovoltaic (PV) array, described as having a 10 kWp capacity, battery storage, and connection to the grid via a university grid network. It is stated that the system meets a local load of 4–5 kVA. The system is in Ethiopia, and the authors give details of the location and solar resource to provide information to assess its performance. However, the performance assessment will be specific to the details of the installation and the operational rules, including the variable nature of the load profile, charging and discharging the battery storage, and importing from and exporting to the university grid. The nearby load is mostly supplied from PV and grid sources, and hence the battery installed is found to be idle, showing that the PV together with storage battery system was not utilized in an efficient and optimized way. This in turn resulted in inefficient utilization of sources, increased dependency of the load on the grid, and hence unnecessary operational expenses. Therefore, to alleviate these problems, this paper proposes a means for techno-economic optimization and performance analysis of an existing photovoltaic grid-connected system (PVGCS) by using collected data from a plant data logger for one year (2018) with a model-based Matlab/Simulink simulation and a hybrid optimization model for electric renewables (HOMER) software. According to the simulation result, the PVGCS with 5 kWp PV array optimized system was recommended, which provides a net present cost (NPC) of 5770 (€/kWh), and a cost of energy (COE) of 0.087 (€/kWh) compared to an existing 10 kWp PV system, which results in a NPC value of 6047 (€/kWh) and COE of 0.098 (€/kWh). Therefore, the resulting 5 kWp PV system connected with a storage battery was found to be more efficient and techno-economically viable as compared to the existing 10 kWp PVGCS plant.