The negative environmental impact and the rapidly declining reserve of fossil fuel-based energy sources for electricity generation is a big challenge to finding sustainable alternatives. This scenario is complicated by the ever-increasing world population growth demanding a higher standard of living. A fuel cell system is able to generate electricity and water with higher energy efficiency while producing near-zero emissions. A common fuel cell stack displays a nonlinear power characteristic as a result of internal limitations and operating parameters such as temperature, hydrogen and oxygen partial pressures and humidity levels, leading to a reduced overall system performance. It is therefore important to extract as much power as possible from the stack, thus hindering excessive fuel use. This study considers and compares two Maximum Power Point Tracking (MPPT) approaches; one based on the Mamdani Fuzzy Inference System and the other on the Particle Swarm Optimisation (PSO) algorithm to maintain the output power of a fuel cell stack extremely close to its maximum. To ensure that, the power converter interfaced to the fuel cell unit must be able to continuously self-modify its parameters, hence changing its voltage and current depending upon the Maximum Power Point position. While various methods exist for Maximum Power Point tracker design, this paper analyses the response characteristics of a Mamdani Fuzzy Inference Engine and the Particle Swarm Optimisation technique. The investigation was conducted on a 53 kW Proton Exchange Membrane Fuel Cell interfaced to a DC-to-DC boost converter supplying 1.2 kV from a 625 V input DC voltage. The modelling was accomplished using a Matlab/Simulink environment. The results showed that the MPPT controller based on the PSO algorithm presented better tracking efficiency as compared to the Mamdani controller. Furthermore, the rise time of the PSO controller was slightly shorter than the Mamdani controller and the overshoot of the PSO controller was 2% lower than that of the Mamdani controller.

Due to the intermittent power output of renewable energy sources, energy storage has become unavoidable and necessary in grid connected renewable energy systems. This is applicable in grid connected renewable energy integration, transmission, distribution, micro-grid and ancillary services like voltage regulation, frequency regulation and spinning reserves. In this paper, different energy storage systems are studied and presented, with regards to technology advancement, individual benefits, challenges, and applications, considering their influence on the total power system network. Consideration is given to generation, transmission, distribution and applicability. Different energy storage system (ESS) technologies are reviewed and explored together with benefits of ancillary services on energy storage system on grid connected renewable energy sources. The challenges of high penetration level of renewable energy sources on energy storage application in power systems are investigated considering the technical and economic benefits together with challenges. However, the advancement and growth of potential energy storage system is estimated and investigated accordingly.