A Novel Renewable Energy Management System for Optimal Dispatching of Hybrid Wind and Marine Current Turbines

  • Muhammad Bashar Anwar

Student thesis: Master's Thesis


Wind is the most dominant source of renewable energy across the world, however, the unpredictable and intermittent nature of wind power raises serious issues concerning stability, security and reliability problems for the power system. Grid integration of wind energy, thus, is a major challenge as grid codes require electricity generation units to schedule their dispatch ahead of the trading period and to limit the power fluctuations. Unlike the random nature of wind, marine currents are highly predictable and consequently marine currents (MCTs) are increasingly gaining scholarly attention as a viable source of renewable energy. Taking in to consideration the slow, cyclic and highly predictable nature of marine current speeds, this thesis proposes a novel strategy for mitigating the effects of wind intermittency by developing hybrid off-shore wind and sea flow marine current turbines. In order to accurately forecast wind speeds, a prediction model based on bootstrapped Artificial Neural Networks (ANNs) has been developed and validated. Marine current speeds have been mathematically modeled using the Harmonic Analysis Method (HAM). Subsequently, a Renewable Energy Management System (REMS) with a novel dispatching strategy using minimum energy storage has been designed and implemented. This strategy is based on the UK electricity market regulations and the predicted wind and current speeds. The results demonstrate the effectiveness of the proposed method which ensures secure dispatch and significant cost saving potential. Additionally, a new frequency control strategy specifically designed for Marine Current Turbines (MCTs) has been proposed and developed. This strategy is based on the combination of inertial response and a deloaded margin of 10%. The presented technique is verified at varying load levels and different marine speeds. The simulation results demonstrate the capability of the proposed frequency control strategy in satisfying the grid code requirements and improving frequency regulation and transient response.
Date of AwardMay 2015
Original languageAmerican English
SupervisorMohamed El Moursi (Supervisor)


  • Renewable energy
  • Wind energy
  • wind power
  • power systems
  • marine currents
  • current turbines
  • artificial neural networks
  • renewable energy management system.

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