Article citationsMore>>

Shadmani, A., Nikoo, M.R., Gandomi, A.H., Wang, R. and Golparvar, B. (2023) A Review of Machine Learning and Deep Learning Applications in Wave Energy Forecasting and WEC Optimization. Energy Strategy Reviews, 49, Article 101180.
https://doi.org/10.1016/j.esr.2023.101180

has been cited by the following article:

• TITLE: Mathematical Modeling of Wave Energy Exploitation for the Stabilization and Securing of the Power Grid in Douala, Cameroon

  AUTHORS: Batambock Samuel, Biboum Alain Christian, Nyatte Nyatte Jean, Kikmo Wilba Christophe, Nsouandele Jean Luc

  KEYWORDS: Wave Energy, Mathematical Modeling, Grid Integration, Dynamic Stability, Marine Renewable Energy, Energy Resilience, Douala

  JOURNAL NAME: Journal of Power and Energy Engineering, Vol.14 No.5, May 15, 2026

  ABSTRACT: Douala, Cameroon’s main economic hub, faces chronic electricity instability due to rapidly increasing demand and climate-driven disruptions. This study investigates the potential of wave energy, a largely untapped marine renewable resource in the Gulf of Guinea, to enhance urban energy resilience. We develop a comprehensive mathematical framework that couples nonlinear hydrodynamic wave propagation, wave energy converter dynamics, and adaptive grid integration control. Solving the governing differential equations, we quantify exploitable wave power and optimize its injection into the city’s electrical network. Extensive numerical simulations and parametric analyses identify operational regimes that maintain a reliable electricity supply despite wave intermittency. The results demonstrate that controlled wave energy integration can substantially stabilize Douala’s grid and provide a scalable strategy for sustainable marine renewable exploitation in Central Africa.