Microgrids being an important entity in the distribution system, and to get their full advantages by incorporating maximum distributed generation, standalone hybrid renewable energy systems (HRESs), being environmentally-safe and economically-efficient, are considered as the promising solution to electrify remote areas where the grid power is not available. In this work, a techno-economic investigation with an optimal design of HRES is presented to fulfill the domestic electricity need for a residential area of the Sherani district in the Province of Baluchistan, Pakistan. Nine case studies based on PV/wind/diesel/battery are analyzed based on net present cost (NPC), cost of energy (COE), and emission to decide the feasible solution. HOMER tool is utilized to accomplish modeling and simulation for economic analysis and optimal sizing. Simulation results demonstrated that HRES with PV-wind-battery is the most viable option for the specified area, and the optimal sizing of components are also obtained with $ 28,620 NPC and 0.311 $/kWh COE which shows 81.65% reduction in cost and 100% preserving in toxic emission while fulfilling 100% energy demand with 67.3% of excess energy. Furthermore, MATLAB/Simulink modeling for the optimally designed system is built for technical analysis while its effectiveness is proved by keeping dc and ac buses voltage constant, safe operating range of battery state of charge (SOC) with active power balance between HRES components, as well as efficient ac voltage quality, regardless of generation disturbances and load fluctuations. The output signal has total harmonic distortion (THD) of 0.30% as compared to 5.44% with the conventional control scheme. The novelty lies in the sequential application of both HOMER and MATLAB simulations of the proposed HRES model and validation of the proposition for the studied area; by using and implementing model predictive control (MPC) of a reconfigurable inverter.

An effective maximum power point tracking (MPPT) technique plays a crucial role in improving the efficiency and performance of grid-connected renewable energy sources (RESs). This paper uses the African Vulture Optimization Algorithm (AVOA), a metaheuristic technique inspired by nature, to tune the proportional–integral (PI)-based MPPT controllers for hybrid RESs of solar photovoltaic (PV) and wind systems, as well as the PI controllers in a storage system that are used to smooth the output fluctuations of those RESs in a hybrid system. The performance of the AVOA is compared with that of the widely used the particle swarm optimization (PSO) technique, which is commonly acknowledged as the foundation of swarm intelligence. As a result, this technique is introduced in this study to draw a comparison. It is observed that the proposed algorithm outperformed the PSO algorithm in terms of the tracking speed, robustness, and best convergence to the minimum value. A MATLAB/Simulink model was built, and optimization and simulation for the proposed system were carried out to verify the introduced algorithms. In conclusion, the optimization and simulation results showed that the AVOA is a promising method for solving a variety of engineering problems.

In this study, optimal decision-making process in photovoltaic (PV) system location selection in Saudi Arabia is described. First, to identify the criteria that influence the decision of selecting a suitable location for the PV system, the geographical information system (GIS)-based multi-criteria decision making (MCDM) approach is used. Next, to assess the weights of the criteria that present different aspects of the investigated locations, four major criteria and 11 sub-criteria are proposed, and analytic hierarchy process (AHP) is applied to develop comparison decision matrix. Finally, the order preference by similarity to ideal solution (TOPSIS) technique evaluates and classifies 17 cities (such as Riyadh, Jeddah) in Saudi Arabia. The result shows that Tabuk city in the northern of Saudi Arabia is the best location. Among the 17 cities, the performance score of seven cities is above or equal 80%, and Tabuk city has the highest score with 87%. This analytical approach could contribute as an early planning to locate suitable sites for the selection of PV system region in Saudi Arabia.

The integration of photovoltaic (PV) into electric power systems has been widely explored and adopted to address the problems associated with the depletion of fossil fuels and the release of greenhouse gases. PV panels convert sunlight into electricity, minimizing the reliance on fossil fuels and mitigating environmental pollution. It is crucial to optimally utilize the PV power in the system; hence maximum power point tracking (MPPT) algorithms have been developed to ensure optimal performance of grid-connected PV systems at the maximum power point (MPP) despite changes in weather conditions. Moreover, deep reinforcement learning (DRL) developments provide a promising approach for optimizing grid-connected PV systems, replacing the conventional proportional-integral-derivative (PID) controllers. However, there is limited research evaluating the efficiency of these systems using DRL techniques. This paper proposes a new dandelion optimizer (DO)-based DRL for MPPT of grid-connected photovoltaic systems and evaluates the proposed method for a 100-MW PV plant connected to a 33-kV distribution system. The proposed DRL technique uses proximal policy optimization (PPO) and deep deterministic policy gradient (DDPG) algorithms for continuous states and discrete or continuous action spaces to adjust the PV-measured voltage based on a reference one produced via DO-PPO and DO-DDPG methods. To test the effectiveness and practicality of the introduced methods, simulations were conducted using actual input data of a 100 MW PV plant connected to a 33-kV distribution system for typical days in summer and winter seasons using MATLAB/Simulink software. The proposed implemented methods were evaluated by comparing their simulation results with other techniques: DO-PID, particle swarm optimization (PSO), and incremental conductance (InC-PI). The findings revealed that the efficiencies of the DC-DC boost and the voltage source converters using the introduced methods were 84.25%-85.90%, and 78.33%-81.10% on a summer day while they were 92.77%-95% and 86.70%-89.50% on a winter day, respectively, which proves that these methods were efficient and effective, indicating their promising potential for future applications.

Feed-in tariff (FIT) is the most commonly used strategy worldwide for promoting renewable energy. The FIT strategy mainly consists of three key elements—certain admission to the grid, long-term contracts (10 to 20 years), and reimbursement levels that are founded on the prices of renewable energy production. The most common renewable energy in the Kingdom of Saudi Arabia (KSA) is solar energy, and it can be incorporated into the main grid through a favorable feed-in tariff that will attract investment. This paper aims to review the FIT rates in Germany and the United States, then the design of FIT in these countries to study the results, which helps to determine the most appropriate FIT in the KSA for different regions with regard to investment costs, household electricity consumption, compatibility with the existing grid, period required for return on assets, and long-term benefits. This study will also explain the importance of interest rates for residential investors and the challenge created by the recent tariff increase to 0.18 SAR/kWh. Saudi Arabia has the advantage of being able to use this information to assess the best approach to the economic and environmental impacts of FIT.