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Renewable energy-based smart grids are famous nowadays due to their high intellectual properties. The world is starting new inventions in renewable energy-based electrical power generation systems to reduce global warming. However, a single renewable energy source cannot maintain a proper energy management system and reliability of power towards loads. Hence, integrating two or more systems is very important and can form a smart grid with an appropriate energy management system. Effective energy management system for a 4-wire 1-MW smart grid system is proposed in this paper. The system is composed of three solar plants and three wind farms with a battery bank. The battery energy management system can operate the complete system as a smart grid with the proper design of the controllers. The maximum power points of PV plants are tracked using a hybrid algorithm that merges the merits of Modified Invasive Weed Optimization and Perturb and Observe (P&O). Thus, the maximum power is obtained under partial shading conditions. The P&O algorithm is also developed to track the maximum power of wind farms. All the loads and generation units are connected in a ring-configuration distribution with a centralized battery energy management system. The loads are selected to be unbalanced, nonlinear and reactive to simulate practical cases. TS-Fuzzy based common inverter controller is implemented to maintain acceptable power quality, which is linked to the battery. The proposed inverter controller can work as a reactive power compensator, active power filter, voltage regulator under unbalanced load, and power balancing device between generation and load. Extensive Hardware-in-Loop (HIL) results are presented to validate the effectiveness of the proposed system.

The study investigates the implementation of novel Neuro-Fuzzy controllers to maintain the power quality for standalone Photovoltaic (PV)-Electrolyzer-Fuel Cell- Battery based power generation systems. Standalone solar based power generation systems are widely becoming popular particularly in the remote areas with no connectivity to a grid. There are challenges to maintain the power output to meet the demand in these standalone systems because of the random nature of solar irradiances, variation of load and no irradiance during night time. The battery bank is required to store the excess energy for use later when required. The battery bank is integrated into a standalone system through a bidirectional DC to DC converter. However, large batteries are costly and require maintenance. Hence, electrolyzer and fuel cell are also integrated with respective DC to DC converters to make a cost effective operation. Small size battery bank is used to stabilize dc-link voltage during transients due to slow dynamics of electrolyzer and fuel cells. The maximum power point tracking (MPPT) device and perturbed and observed algorithm is used for the PV system to operate at maximum utilization. In this paper Neuro-Fuzzy controller based novel controllers are implemented to inverter and DC to DC converters for supplying quality power to both three phase and single phase loads at AC load bus. The presented results are examined through hardware-in-loop on the platform of OPAL-RT to investigate the proposed controllers in all the possible scenarios of a 1 MW standalone system. Results show that the proposed controllers improve the power quality and eliminate the frequency oscillations from the system voltage.

The world is looking for utilization of renewable energy sources to reduce global warming as well as the consumption of fossil fuels. In this scenario, solar and wind energy are widely used at many places worldwide. However, both solar irradiance and wind speed are depending on nature. Hence, an energy storage device must be required to operate at their best utilization level by converting them to electricity. One of the best energy storage devices for medium power range is the battery. However, batteries require high maintenance and suffering from self-discharge as well as storage capacity will be decreased day by day. Hence, storing hydrogen can be an economically feasible solution instead of using batteries for high power range. Usually an aqua electrolyzer can easily convert water to hydrogen and oxygen through electricity. However, due to slow dynamics of heat transfer from electricity, the generation of hydrogen cannot meet the fast response like electrical devices. Therefore, a novel controlling technique is required to increase the production quality of hydrogen during random changes in both solar irradiance and wind speed. In order to achieve the best utilization, both photovoltaic panels and wind turbines are operated at their maximum power point levels. In this research boost converters are used to operate as maximum power point tracking devices. The whale optimization technique is integrated to respective controllers of all the converters to achieve stable production of hydrogen during rapid changes in irradiance and wind speed. The Whale Optimization Algorithm (WOA) technique is compared with Genetic Algorithm (GA), Particle Swarm Optimization (PSO) and Grey Wolf Optimization (GWO) to show the benefits of tracking response of the system on improving production of hydrogen from hybrid renewable energy sources based Microgrid. Hardware – in the – Loop (HIL) is developed to analyze the results with the help of OPAL-RT modules. Along with HIL results, a Real Time Digital Simulator (RTDS) based results are also presented to evaluate the performance of the proposed method.

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