In recent years, the penetration of renewable power generations into the electrical grid has substantially increased. Continuous deployment of power electronic-based distributed generations and the reduction of traditional synchronous machines with their essential dynamics in modern power networks are very critical in this change. The use of power electronic inverters leads to the dissociation of sources and loads and lowering the power system inertia. Under power imbalance, this drop causes an elevated rate of change in frequency and frequency divergences, which has a notable impact on the system’s frequency stability. As a result, enhanced control techniques for grid-tied electronic converters are required to secure the power system’s stability and support. The virtual-synchronous generator (VSG) control is used to mimic the dynamics of a rotating synchronous generator and improve the power system’s stability. In this article, the problems of such low-inertia power systems, as well as the VSG technologies, are explored. This research also looks at different control orders and strategies for virtual-synchronous generators (VSG). In addition, the utilization of energy storage and critical matters in VSG and further research recommendations are explained.

Three-phase inverters are widely used in grid-connected renewable energy systems. This paper presents a new control methodology for grid-connected inverters using an adaptive fuzzy control (AFC) technique. The implementation of the proposed controller does not need prior knowledge of the system mathematical model. The capabilities of the fuzzy system in approximating the nonlinear functions of the grid-connected inverter system are exploited to design the controller. The proposed controller is capable to achieve the control objectives in the presence of both parametric and modelling uncertainties. The control objectives are to regulate the grid power factor and the dc output voltage of the photovoltaic systems. The closed-loop system stability and the updating laws of the controller parameters are determined via Lyapunov analysis. The proposed controller is simulated under different system disturbances, parameters, and modelling uncertainties to validate the effectiveness of the designed controller. For evaluation, the proposed controller is compared with conventional proportional-integral (PI) controller and Takagi–Sugeno–Kang-type probabilistic fuzzy neural network controller (TSKPFNN). The results demonstrated that the proposed AFC showed better performance in terms of response and reduced fluctuations compared to conventional PI controllers and TSKPFNN controllers.

For a decade, investments in solar photovoltaic (PV) systems have been increasing exponentially in the Middle East. Broadly speaking, these investments have been facing tremendous challenges due to the harsh weather in this particular part of the world. Dust accumulation is one the challenges that negatively affects the performance of solar PV systems. The overall goal of this paper is to thoroughly investigate the effect of dust accumulation on the energy yield of car park PV systems. With this aim in mind, the paper presents scientific values for further research and opens the horizon for attracting further investments in solar PV systems. This study is based on a real PV system in the Sultanate of Oman and considers different cleaning cycles for 16 months (from 29 July 2018 to 10 November 2019). Furthermore, four different PV groups were assessed, and the system was monitored under different cleaning frequencies. In general, it was found that dust accumulation has a significant impact; under 29-day, 32-day, 72-day, and 98-day cleaning cycles, the average percentages of energy loss due to soiling were 9.5%, 18.2%, 31.13%, and 45.6%, respectively. In addition, the dust effect has a seasonal variation. The study revealed that dust accumulation has a more negative impact during summer than during winter. During summer, the energy losses due to soiling were 8.7% higher than those during winter. The difference was attributed to different environmental conditions, with high humidity and low wind speed being the main factors that worsen the impact of dust during summer. Based on the findings of this research, a monthly cleaning program is highly recommended in the city of Muscat.

A novel optimization strategy is proposed to achieve a reliable hybrid plant of wind, solar, and battery (HWSPS). This strategy’s purpose is to reduce the power losses in a wind farm and at the same time reduce the fluctuations in the output of HWSPS generation. In addition, the proposed strategy is different from previous studies in that it does not involve a load demand profile. The process of defining the HWSPS capacity is carried out in two main stages. In the first stage, an optimal wind farm is determined using the genetic algorithm subject to site dimensions and spacing between the turbines, taking Jensen’s wake effect model into consideration to eliminate the power losses due to the wind turbines’ layout. In the second stage, a numerical iterative algorithm is deployed to get the optimal combination of photovoltaic and energy storage system sizes in the search space based on the wind reference power generated by the moving average. The reliability indices and cost are the basis for obtaining the optimal combination of photovoltaic and energy storage system according to a contribution factor with 100 different configurations. A case study in Thumrait in the Sultanate of Oman is used to verify the usefulness of the proposed optimal sizing approach.

In this paper, two aspects related to streetlight systems are considered. In the first part, the economic analysis of replacing existing HPS lamps with light-emitting diode (LED) and discrete LED lamps for street lighting is performed using actual data from Oman. The street lighting system inside Sultan Qaboos University is considered for the case study. The discounted payback period, which is calculated to study the practicability of implementing the system, is found to be 1.01 years, making the system financially appealing. Moreover, the estimated reduction of a carbon footprint shows that tonnes of CO₂ emissions are reduced, which makes it environmentally attractive. The second part of the paper considers optimal sizing of PV/battery system for a new streetlight system with LED lamps. The life cycle cost analysis was performed and the related cost of energy generated per kWh is estimated as 0.097 $/kWh which proves the economic viability of the system to be implemented in Oman besides minimizing the CO₂ emissions to zero.

Harnessing wind energy is one of the fastest-growing areas in the energy industry. However, wind power still faces challenges, such as output intermittency due to its nature and output reduction as a result of the wake effect. Moreover, the current practice uses the available renewable energy resources as a fuel-saver simply to reduce fossil-fuel consumption. This is related mainly to the inherently variable and non-dispatchable nature of renewable energy resources, which poses a threat to power system reliability and requires utilities to maintain power-balancing reserves to match the supply from renewable energy resources with the real-time demand levels. Thus, further efforts are needed to mitigate the risk that comes with integrating renewable resources into the electricity grid. Hence, an integrated strategy is being created to determine the optimal size of the hybrid wind-solar photovoltaic power systems (HWSPS) using heuristic optimization with a numerical iterative algorithm such that the output fluctuation is minimized. The research focuses on sizing the HWSPS to reduce the impact of renewable energy resource intermittency and generate the maximum output power to the grid at a constant level periodically based on the availability of the renewable energy resources. The process of determining HWSPS capacity is divided into two major steps. A genetic algorithm is used in the initial stage to identify the optimum wind farm. A numerical iterative algorithm is used in the second stage to determine the optimal combination of photovoltaic plant and battery sizes in the search space, based on the reference wind power generated by the moving average, Savitzky–Golay, Gaussian and locally weighted linear regression techniques. The proposed approach has been tested on an existing wind power project site in the southern part of the Sultanate of Oman using a real weather data. The considered land area dimensions are 2 × 2 km. The integrated tool resulted in 39 MW of wind farm, 5.305 MW of PV system, and 0.5219 MWh of BESS. Accordingly, the estimated cost of energy based on the HWSPS is 0.0165 EUR/kWh.