The use of renewable energy techniques is becoming increasingly popular because of rising demand and the threat of negative carbon footprints. Wind power offers a great deal of untapped potential as an alternative source of energy. The rising demand for wind energy typically results in the generation of high-quality output electricity through grid integration. More sophisticated contemporary generators, power converters, energy management, and controllers have been recently developed to integrate wind turbines into the electricity system. However, a comprehensive review of the role of converters in the wind system’s power conversion, control, and application toward sustainable development is not thoroughly investigated. Thus, this paper proposes a comprehensive review of the impact of converters on wind energy conversion with its operation, control, and recent challenges. The converters’ impact on the integration and control of wind turbines was highlighted. Moreover, the conversion and implementation of the control of the wind energy power system have been analyzed in detail. Also, the recently advanced converters applications for wind energy conversion were presented. Finally, recommendations for future converters use in wind energy conversions were highlighted for efficient, stable, and sustainable wind power. This rigorous study will lead academic researchers and industry partners toward the development of optimal wind power technologies with improved efficiency, operation, and costs.

Electric vehicles (EVs) are getting increased attention due to their potential as reliable sources of transportation. In addition, EVs can be employed as a temporary energy storage system (ESS) in electrical power systems using the grid-to-vehicle (G2V), charging phase, and vehicle-to-grid (V2G), discharging phase, technologies. Renewable energy resources (RERs) are a clean and sustainable form of energy but are often intermittent in nature. Integrating ESS with RERs can mitigate the unpredictable fluctuations of the electrical power generated from RERs, but the investment cost of installing ESS is often high. In this paper, we investigate the financial benefit obtained from using EVs as a temporary ESS in a Microgrid (MG) integrated with a PV plant. The problem formulation considers the capital cost of the EV charging stations (EVCSs), feed-in tariff (FIT), and incentives given to the EVs’ owners. A planning algorithm is developed to find the required number of EVCSs needed to maximize the profit of the suggested system. The effectiveness of the proposed algorithm is illustrated using the Jordan University of Science and Technology (JUST) MG. Four different final state of charge (SOC) of EV scenarios are considered. Results show that the suggested system can be economically and technically feasible. Employing EVs, in G2V and V2G schemes, decreases the MG dependency on the grid and yields annual profit as well.

The Integration of renewable energy resources suffers from two fundamental issues: variability, and uncertainty of power output. These issues hinder the integration of renewable resources with the existing grid. This paper addresses these issues and proposes a new methodology to minimize the impact of intermittency by offering an alternative approach for energy storage. The concept of value storage is introduced as an alternative to energy storage to replace the typical large-scale battery energy storage system. The concept refers to the storage of excess renewable energy as products from industrial loads instead of the energy itself which enables a demand side management technique to be applied, namely, load shifting for some industrial plants. A hybrid Photovoltaic-wind turbine generator PV-WTG and storage system is proposed to penetrate the existing electric grid, with significant cost savings by displacing the conventional energy generation in a fossil fuel-rich location. A size optimization based on differential system cost is formulated and solved by an enhanced genetic algorithm technique. Uncertainty impact studies were done by incorporating multiple scenarios and comprehensive sensitivity analysis.

In the transportation sector, electric battery bus (EBB) deployment is considered to be a potential solution to reduce global warming because no greenhouse gas (GHG) emissions are directly produced by EBBs. In addition to the required charging infrastructure, estimating the energy consumption of buses has become a crucial precondition for the deployment and planning of electric bus fleets. Policy and decision-makers may not have the specific tools needed to estimate the energy consumption of a particular bus network. Therefore, many state-of-the-art studies have proposed models to determine the energy demand of electric buses. However, these studies have not critically reviewed, classified and discussed the challenges of the approaches that are applied to estimate EBBs’ energy demands. Thus, this manuscript provides a detailed review of the forecasting models used to estimate the energy consumption of EBBs. Furthermore, this work fills the gap by classifying the models for estimating EBBs’ energy consumption into small-town depot and big-city depot networks. In brief, this review explains and discusses the models and formulations of networks associated with well-to-wheel (WTW) assessment, which can determine the total energy demand of a bus network. This work also reviews a survey of the most recent optimization methods that could be applied to achieve the optimal pattern parameters of EBB fleet systems, such as the bus battery capacity, charger rated power and the total number of installed chargers in the charging station. This paper highlights the issues and challenges, such as the impact of external factors, replicating real-world data, big data analytics, validity index, and bus routes’ topography, with recommendations on each issue. Also, the paper proposes a generic framework based on optimization algorithms, namely, artificial neural network (ANN) and particle swarm optimization (PSO), which will be significant for future development in implementing new energy consumption estimation approaches. Finally, the main findings of this manuscript further our understanding of the determinants that contribute to managing the energy demand of EBBs networks.

Over the last few years, the number of grid-connected photovoltaic systems (GCPVS) has expanded substantially. The increase in GCPVS integration may lead to operational issues for the grid. Thus, modern GCPVS control mechanisms should be used to improve grid efficiency, reliability, and stability. In terms of frequency stability, conventional generating units usually have a governor control that regulates the primary load frequency in cases of imbalance situations. This control should be activated immediately to avoid a significant frequency variation. Recently, renewable distribution generators such as PV power plants (PVPPs) are steadily replacing conventional generators. However, these generators do not contribute to system inertia or frequency stability. This paper proposes a control strategy for a GCPVS with active power control (APC) to support the grid and frequency stability. The APC enables the PVPP to withstand grid disturbances and maintain frequency within a normal range. As a result, PVPP is forced to behave similar to traditional power plants to achieve frequency steadiness stability. Frequency stability can be achieved by reducing the active power output fed into the grid as the frequency increases. Additionally, to maintain power balance on both sides of the inverter, the PV system will produce the maximum amount of active power achievable based on the frequency deviation and the grid inverter’s rating by working in two modes: normal and APC (disturbance). In this study, a large-scale PVPP linked to the utility grid at the MV level was modeled in MATLAB/Simulink with a nominal rated peak output of 2000 kW. Analyses of the suggested PVPP’s dynamic response under various frequency disturbances were performed. In this context, the developed control reduced active power by 4%, 24%, and 44% when the frequency climbed to 50.3 Hz, 50.8 Hz, and 51.3 Hz, respectively, and so stabilized the frequency in the normal range, according to grid-code requirements. However, if the frequency exceeds 51.5 Hz or falls below 47.5 Hz, the PVPP disconnects from the grid for safety reasons. Additionally, the APC forced the PVPP to feed the grid with its full capacity generated (2000 kW) at normal frequency. In sum, the large-scale PVPP is connected to the electrical grid provided with APC capability has been built. The system’s capability to safely ride through frequency deviations during grid disturbances and resume initial conditions was achieved and improved. The simulation results show that the given APC is effective, dependable, and suitable for deployment in GCPVS.

The average wind speed in a given area has a significant impact on the amount of energy that can be harvested by wind turbines. The regions with the most attractive possibilities are typically those that are close to the seaside and have open terrain inland. There is also good potential in several mountainous locations. Despite these geographical restrictions on where wind energy projects can be located, there is enough topography in most of the world’s regions to use wind energy projects to meet a significant amount of the local electricity needs. This paper presents a new method of energy prediction of wind resources in several wind sites in Jordan, which can be used to decide whether a specific wind site is suitable for wind farm installation purposes. Three distribution models, Weibull, Gamma and Rayleigh, were employed to characterize the provided wind data. Different estimation methods were used to assign the parameters associated with each distribution model and the optimal parameters were estimated using whale optimization algorithms which reduce the error between the estimated and the measured wind speed probability. The distribution models’ performance was investigated using three statistical indicators. These indicators were: root mean square error (RMSE), coefficient of determination (R²), and mean absolute error (MAE). Finally, using the superlative distribution models, the wind energy for the chosen wind sites was estimated. This estimation was based on the calculation of the wind power density (E_D) and the total wind energy (E_T) of the wind regime. The results show that the total wind energy ranged from slightly under 100 kWh/m² to nearly 1250 kWh/m². In addition, the sites recording the highest estimated wind energy had the optimum average wind speed and the most symmetrical distribution pattern.