This paper develops a recent methodology based on social spider optimizer (SSO) to determine the optimal sizing of a hybrid renewable energy sources (RESs) integrated microgrid (MG). It comprises photovoltaic (PV), wind turbine (WT), battery, diesel generator (DG), and inverter. The cost of energy (COE) is proposed as fitness function. The objective of the proposed SSO is to determine three design variables which are number of PV panels, number of WT, and number of battery autonomy days such that COE is minimized. Additionally, an energy management strategy is presented. Loss of power supply probability (LPSP) is considered to confirm the reliability of operation. The selection of SSO is due to its simplicity in construction and requiring less controlling parameters. The proposed MG is installed in a remote area at Aljouf region in the northern of Kingdom of Saudi Arabia. Annual data of irradiance, wind speed, and temperature are recorded. The SSO results are compared to Harris Hawks optimizer (HHO), Grey Wolf Optimizer (GWO), Multi-Verse Optimizer (MVO), Antlion Optimizer (ALO), and Whale Optimization Algorithm (WOA). The results obtained show that the proposed approach provides the best optimal configuration of hybrid RESs compared to HHO, GWO, MVO, ALO and WOA with COE of 0.1349 $/kWh and LPSP of 0.01714. Moreover, sensitivity analysis with sizing different topologies of MG including PV/Battery/DG, WT/Battery/DG, and PV/WT/ Battery/DG is presented. The best COEs are obtained via SSO achieving 0.2180 $/kWh for the first topology and 0.2161$/kWh for the second architecture. Furthermore, sensitivity analysis is also presented to investigate the effect of design variables on COE. The experimental results confirm the superiority of the proposed approach in designing reliable and costless microgrid.

One of the worst negative phenomena faced by photovoltaic (PV) array is the operation under the shadow phenomenon, which significantly affects the generated power. Multiple local maximum power point (MPP) and unique global MPP are generated from the shaded array. Therefore, regular dispersion of the shadow falling on the PV array surface is a vital issue to extract the GMP via reconfiguration of the shaded modules in the array. This article proposes a recent approach based on Multi-objective grey wolf optimizer (MOGWO) to reconfigure the shaded PV array optimally. The main objective of the proposed MOGWO is providing the optimal structure for the switching matrix to minimize the row current difference and maximize the output power. The benefits of the proposed strategy is performing a dynamic reconfiguration process which closes to the reality. The proposed method is validated across $9 \times 9$ PV array with six shade patterns. MOGWO schemes results are compared with TCT and modified SuDoKu based on several statistical metrics. The comparison reveals the superiority of MOGWO in tackling the multi-peak issue in the P-V characteristics with harvesting the highest power levels.

This paper presents a recent metaheuristic optimization approach of multi-verse optimizer (MVO) to design load frequency control (LFC) based model predictive control (MPC) incorporated in large multi-interconnected system. The constructed system comprises six plants with renewable energy sources (RESs). MVO is employed to determine the optimal parameters of MPC-LFC to achieve the desired output of the interconnected system in case of load disturbances. The presented system comprises reheat thermal, hydro, photovoltaic (PV) model with maximum power point tracker (MPPT), wind turbine (WT), diesel generation (DG), and superconducting magnetic energy storage (SMES). The integral time absolute error (ITAE) of the frequencies and tie-line powers deviations is proposed as objective function. The effects of governor dead zone and generation rate constraint (GRC) of thermal plants are considered. The performance of the proposed MPC optimized via MVO is compared with the other designed via intelligent water drops (IWD) and genetic algorithm (GA). Additionally, the robustness of the proposed MPC-LFC based MVO with variation of the system parameters is presented. The obtained results confirmed the superiority and reliability of the proposed controller compared to the others.