The lack of accessible and reliable electrical energy in Cameroon has become a pervasive obstacle to the nation’s progress, with energy availability, quality, and cost identified as key hindrances to development over the past 15 years. Conventional solutions that rely on combustion engines and electrochemical storage systems have proven to be cost-prohibitive, limited in power output, and constrained in capacity. The dependence on traditional diesel generators has perpetuated maintenance challenges and a continuous demand for fuel supply, while the accompanying noise and pollution have restricted their use in residential areas. Recognizing the imperative of reducing dependence on fossil fuels and curbing greenhouse gas emissions, the need for clean and sustainable energy sources has emerged as a critical concern for the advancement of civilization. Against this backdrop, this research endeavors to identify the most cost-effective and efficient blend of renewable energy sources capable of meeting the power requirements of three small communities on Manoka Island, a district of Douala, Cameroon. Through a comprehensive technical, environmental, and economic analysis, this study addresses the substantial energy needs of 334 households, with an average daily power consumption of 1082.90 kWh and a peak electrical load of 183.99 kW. Leveraging the Hybrid Optimization Model for Electric Renewables (HOMER) program, this investigation assesses the feasibility of implementing Hybrid Renewable Energy Systems (HRES) to meet the region’s energy demands. The research highlights the most optimal scenario integrating solar panels, wind turbines, battery cells, fuel cell generators, biogas, and an electrolyzer within an off-grid HRES system. Notably, the study demonstrated an absence of idle load, resulting in remarkably low unit energy costs of $0.1981 and a compelling net present value of $2,209,741. The cost-effective arrangement featured 201 batteries, yielding a project profit of $57,387, with an impressive Internal Rate of Return (IRR) of 9.09%, Return on Investment (ROI) of 6.19%, and a payback period of 8.76 years over a 25-year term. In essence, the insights gleaned from this exploration of hybrid energy systems represent a pioneering case study in sustainable electricity provision. This research significantly contributes to the knowledge base on renewable energy within the nation, underscoring its tremendous potential for sustainable development and energy security.

Monitoring energy quality events is crucial for maintaining the stability and reliability of power grids. This paper presents a novel system integrating Discrete Wavelet Transform (DWT) and Extreme Learning Machine (ELM) for detecting and classifying power quality disturbances. The DWT performs multi-resolution analysis to decompose signals into time-frequency components, capturing various disturbances such as sags, swells, and harmonics. The ELM classifier, trained on these decomposed signals, achieves an impressive classification accuracy of 99.69%, significantly outperforming conventional methods like STFT with SVM (97.22%) and FFT with ANN (99.30%). The system was validated on a Xilinx Zynq-7000 SoC FPGA, demonstrating real-time processing capabilities with a latency of 1.5 milliseconds and a power consumption of 1.8 W. These findings highlight the effectiveness of the proposed method for real-time, accurate, and energy-efficient power quality monitoring.

Here, a new approach is proposed for solving the optimal power flow (OPF) problem in transmission networks using a Gradient Bald Eagle Search Algorithm (GBES) with a Local Escaping Operator (LEO). The method takes into account uncertainty of the renewable energy sources (wind energy and photovoltaic systems) and Vehicle‐to‐Grid (V2G) in the stochastic OPF problem. To improve the efficiency of the proposed technique and enhance its local exploitation capability, the LEO method's selection features are utilized. Monte Carlo methods are employed to estimate the generation costs of the renewable sources and PEVs and study their feasibility. The uncertainty of the renewable sources and PEVs is represented by Weibull, lognormal, and normal probability distribution functions (PDFs). The GBES approach is experimentally compared with well‐known meta‐heuristics using twenty‐three different test functions, and the results indicate its superiority over BES and other recently developed algorithms. Furthermore, the proposed method's effectiveness is evaluated using IEEE 30‐bus test system under various scenarios, and the simulation results demonstrate that it can effectively address OPF issues considering renewable energy sources and V2G, providing superior optimal solutions compared to other algorithms.

An effective optimization technique, called gradient jellyfish search optimizer (GJSO), is introduced here to address the optimal reactive power dispatch (ORPD) issue in electric networks. The ORPD problem is a complex non‐linear optimization issue involving integrated variables, aimed at achieving safe and cost‐effective operation of the system by determining optimal values for generator voltage, tap changers of transformers, and reactive power compensation. The performance of the original JSO technique is enhanced by integrating the local escaping operator into the GJSO approach. The effectiveness of the GJSO methodology is evaluated via comparison with two existing methodologies: the original jellyfish search optimizer and the equilibrium optimizer. Jellyfish search optimizer is a meta‐heuristic optimization algorithm inspired by the movement of jellyfish in the water, while equilibrium optimizer draws inspiration from game theory and equilibrium concepts. Simulations were conducted using typical IEEE‐30 bus and IEEE‐57 bus systems to validate the performance of the GJSO methodology. Two versions of the objective function are examined: minimizing line power loss and minimizing total voltage deviations at the buses. The simulation results demonstrated that the GJSO algorithm exhibited superior performance in terms of accuracy and stability compared to the standard jellyfish search optimizer and equilibrium optimizer algorithms.

Distributed generators have grown in significance for the technical and financial operation of electric power systems in recent years. The integration of these generators into the electrical distribution network (EDN) has experienced rapid growth, primarily driven by advancements in renewable energy sources (RESs), particularly photovoltaic distributed generators (PVDGs). With the increasing implementation of solar energy as a RES, designing the optimal integration of PVDGs into medium voltage direct current (MVDC) networks is crucial to comprehensively analyzing technical and economic factors. To address this issue, a new multiple objective function (MOF) is proposed, which combines various techno‐economic parameters such as total active power loss (APL), voltage deviation (VD), and the investment cost of PVDGs (IC _PVDG ) installed in the test system. The objective is to minimize the MOF simultaneously to achieve the optimal incorporation of PVDGs. This study aims to solve the allocation problems related to the location and size of PVDG units in the modified MVDC test systems IEEE 27 and 33‐bus. Simulation results demonstrate the superior accuracy and effectiveness of the equilibrium optimization algorithm (EOA) in achieving the minimum MOF. The utilization of multiple PVDG units reduces overall active power losses and improves voltage profiles across all buses.

Environment conservation is a matter subject affecting both developing and developed countries. Long-lasting energy can be achieved by attenuating Greenhouse gas emissions. All over the world, Hybrid Renewable Energy Sources (HRES) appear as a vital element when it comes to cover the rapid growth of the energy demand. Moreover, renewable energy (RE) is an unaffordable response to the fight against unpredicted events such as the diseases, industries development, reliability of energy sources, added to the various directives related to produce sustainable electricity. Henceforth, it is crucial to optimize the various energy sources for satisfying the electrical demand. In particular, this paper aims to value the hybridization of optimization techniques to achieve a robust optimization. Photovoltaic (PV), and Battery Storage Systems (BSS) constitute the various RE sources in this work. The main goal was to simultaneously minimize the Deficit of power supply probability (DPSP) and maximize the BSS capacities. Particle Swarm Optimization (PSO), Grey Wolf Optimizer (GWO), and hybridization of both techniques are employed to proceed with the optimization. This study reveals the performance of hybrid optimization used for several configurations of loads. Indeed, the results show that the autonomous day of the BSS can reach 03 days, while the DPSP can decrease towards 1%. In this way, the HRES built up is more ecofriendly and autonomous. Furthermore, the proposed idea provides improved reliability and robustness of our system under various types of loads due to different climate scenario. The statistical analysis also carried on shows a good stability while doing hybridization of both techniques and a better efficiency in comparison to single techniques.