Ocean wave energy is one of the most abundant energy sources in the world. There is a wide variety of wave energy conversion systems that have been designed and developed, resulting from the different ways of ocean wave energy absorption and also depending on the location characteristics. This paper reviews and analyses the concepts of hydraulic power take-off (PTO) system used in various types of wave energy conversion systems so that it can be a useful reference to researchers, engineers and inventors. This paper also reviews the control mechanisms of the hydraulic PTO system in order to optimise the energy harvested from the ocean waves. Finally, the benefits and challenges of the hydraulic PTO system are discussed in this paper.

This study is concerned with the application of two major kinds of optimisation algorithms on the hydraulic power take-off (HPTO) model for the wave energy converters (WECs). In general, the HPTO unit’s performance depends on the configuration of its parameters such as hydraulic cylinder size, hydraulic accumulator capacity and pre-charge pressure and hydraulic motor displacement. Conventionally, the optimal parameters of the HPTO unit need to be manually estimated by repeating setting the parameters’ values during the simulation process. However, such an estimation method can easily be exposed to human error and would subsequently result in an inaccurate selection of HPTO parameters for WECs. Therefore, an effective approach of using the non-evolutionary Non-Linear Programming by Quadratic Lagrangian (NLPQL) and evolutionary Genetic Algorithm (GA) algorithms for determining the optimal HPTO parameters was explored in the present study. A simulation–optimisation of the HPTO model was performed in the MATLAB/Simulink environment. A complete WECs model was built using Simscape Fluids toolbox in MATLAB/Simulink. The actual specifications of hydraulic components from the manufacturer were used during the simulation study. The simulation results showed that the performance of optimal HPTO units optimised by NLPQL and GA approaches have significantly improved up to 96% and 97%, respectively, in regular wave conditions. The results also showed that both optimal HPTO units were capable of generating electricity up to 62% and 77%, respectively, of their rated capacity in irregular wave circumstances.

This paper presents accurate control parameters estimation of the hydraulic Power Take-Off (PTO) model for the wave energy conversion system to maximise energy production. In general, the performance of the hydraulic PTO system depends on the parameters setting of hydraulic PTO system components such as hydraulic motor displacement setting, pre-charge of the hydraulic accumulator, and et cetera. Conventionally, it requires to manually obtain the optimal parameters of a hydraulic PTO system by repeating the simulation process. However, this estimation method exposed to human error and would easily be resulting in a non-optimal selection of hydraulic PTO parameters for the wave energy conversion system. Therefore, an easy and accurate approach of using the GA optimisation method for determining hydraulic PTO parameters was introduced in the present study. This approach is simple and more accurate compared to the conventional optimisation method. The hydraulic PTO model was developed in SIEMENS/Amesim environment using available components in the library. The specifications of the actual hydraulic PTO system components from the manufacturer were used during the simulation set-up. The complete hydraulic PTO system was optimised using a special genetic algorithm (GA) optimisation tools in the SIEMENS/Amesim software. The simulation results showed that GA was effective to determine the optimal configuration parameters of hydraulic PTO system. From the results, the optimal configuration parameters of hydraulic PTO system were successfully reduced about 38%. Consequently, the maximum force applied to the WEC devices was reduced up to 34%. This force reduction is important since it will enable the WECS to be operated during a smaller wave condition.

<span lang="EN-MY">This paper presents an accurate Lithium-ion battery model representation in Matlab/Simulink. The Tremblay's battery model was used as a BES model platform, where the determination of the model parameters was obtained based on heuristic optimization approach. This approach is simple but more accurate compared to the conventional method. In the classical method, it requires the user to manually select the battery model parameters from relevant points on the manufacturer discharge curves. However, this way of battery parameters extraction normally exposed to the human error and would easily result in an inaccurate selection of battery parameters for the BES simulation studies. Therefore, an easy and accurate approach using heuristic optimization for determining battery model parameters was introduced. The simulation studies utilized three different optimization algorithms for comparison purposes, i.e. 1) Particle Swarm Optimization (PSO), 2) Gravitational Search Algorithm (GSA), and 3) Genetic Algorithm (GA). The performance of BES model discharge accuracy with respect to the test data from three different algorithms was compared and the results showed that the GA approach gives the best results in terms of accuracy and execution time. </span><span lang="EN-MY">Finally, the validated results of GA-optimized battery model showed the accuracy of 98% compared to the conventional approach.</span>

The high penetration of fluctuated photovoltaic (PV) output power into utility grid system will affect the operation of interconnected grids. The unnecessary output power fluctuation of PV system is contributed by unpredictable nature and inconsistency of solar irradiance and temperature. This paper presents a control scheme to mitigate the output power fluctuations from PV system and dispatch out the constant power on an hourly basis to the utility grid. In this regards, battery energy storage (BES) system is used to eliminate the output power fluctuation. Control scheme is proposed to maintain parameters of BES within required operating constraints. The effectiveness of the proposed control scheme is tested using historical PV system input data obtained from a site in Malaysia. The simulation results show that the proposed control scheme of BES system can properly manage the output power fluctuations of the PV sources by dispatching the output on hourly basis to the utility grid while meeting all required operating constraints.