Optimal energy scheduling method for the North Cameroonian interconnected grid in response to load shedding

Abstract Renewable energy sources like sun and wind are intermittent, hence a hybrid system incorporating them is necessary. However, certain systems are more cost-effective and efficient than others, and they are not only more expensive but also far more harmful to the environment. To make up for the energy shortage in unconnected remote regions and urban areas with linked networks, renewable energies offer an alternative. This effort is focused on eliminating both load shedding and the pollution caused by conventional power plants that burn fossil fuels. In order to improve the interconnected Northern Cameroon grid, researchers looked into the possibility of using the permanently accessible sun and wind at the Waibé-Lokoro-Kalfou location in Cameroon. For the hot and humid climate of Waibé-Lokoro, Cameroon, based on four distributed generations, four combinations were established in the scenario. The goal was to maximize the net present value while minimising the energy expense. Electricity costs were found to drop from USD 0.097/kWh to USD 0.085/kWh under the PV-Wind-Grid-Battery scenario, saving a total of USD 0.54 million in net present cost. The particle swarm optimization method (PSO), genetic algorithmic algorithm (GA), cuckoo search approaches (CSA), as well as whale optimization algorithm (WOA) were utilized to calculate power losses as well as system size allocation. PSO was the only algorithm to converge quickly. The level of distortion caused by harmonics is measured experimentally to verify that power grid connectivity regulations are being followed. The standards for the IEEE 33-bus as well as IEEE 69-bus tests provide more precise voltage profiles for use in loss evaluation. Graphical Abstrac