Application of Soft Computing in Hybrid Renewable Energy Network

The energy crisis is a major concern globally. At present, there exist only two major means of electricity generation methods. One of them is the burning of fossil fuels which is a massive threat to the planet due to its detrimental nature of producing harmful emissions. The other is exploiting renewable energy sources. The energy that is derived from persistently replenishable and omnipresent natural sources, is termed as renewable energy. At 10,000 times the rate at which the current population on Earth is consuming energy, the solar energy that reaches the Earth’s surface has tremendous potential to be intercepted favourably. Additionally, renewable energy devices do not live up to the expectations of efficiency conversion rate, which is barely 60% for wind turbines, resulting in the need for optimization ideas and inventions. One such innovative concept is Hybrid Energy Systems (HES). This circuitry theoretically involves the combinational use of two or more renewable energy sources, such as solar and wind, and an electricity storage device to improve the efficiency, support the temperamental changes of weather conditions that determine the throughput and endure a consistent flow of electricity for commercial use.

Therefore, to overcome the complications associated with HES, such as optimization, classification, forecasting, networking, and supply control, soft computing techniques have been introduced. Keeping the human mind as its role model, soft computing is basically an artificially intelligent technique based on natural selection, that virtually provides practical yet approximate solutions to complex problems. These problems are otherwise impossible to be solved by hard computing techniques because of their uncompromising nature. Soft computing is adaptive, learns from experiments and apply human-like fuzzy logic to convoluted objectives.

Hydrogen hybrid systems aim to store the converted chemical energy from the solar source, in the form of hydrogen, and then reverse it back into either thermal or electrical energy for substantial use. These systems might involve the use of technologies such as electrolysers, fuel cells, photovoltaic cells, hydrogen storage, piping, electrical and electronic control systems as a whole. The research required for implementing this technology is huge. It is vital to acquaint experts and scholars for conducting surveys, on-site visits, and advanced studies in the presence of adequate equipment facility. Issues such as loss management of electricity, power forecasting, relevant dataset building, analysis and prediction in SCADA systems, power transaction accuracy calculation, location analysis, identification of suitable criteria and practical possibility of deployment locations require serious discussion for successful implementation of this technology in India.

India has an enormous potential for renewable source development. Start-ups in India play a critical role in the commercialization of renewable sources including wind, solar and geothermal power. Uravu Labs in Bengaluru have created a ‘water from air’ system that works on 100% renewable resources which is not just limited to the conventional methods but also utilizing waste heat from industries and biomass. Many others are also actively working on battery-driven electric cars, combinational cooking devices, e-bikes, and sodium-ion batteries from agricultural waste.

To commercialize these systems on a larger scale it is vital for soft computing strategies to provide solutions such that an optimum design can be achieved that reduces the Net Present Cost (NPC), levelized cost of energy (LCE), investment costs, increasing space efficiency, unit sizing, power supply losses or multi-dimensional upgradation. Software such as HYBRID2, TRANSYS and GAMS, among others, are more or less based on soft computing techniques and are widely used to compare and contrast various constraints involved.

Soft computing approaches can be broadly divided into AI approach, multi-objective design and analytical approach. AI approach can further be classified into ANNs, Generic Algorithms, Particle Swarm Optimization (PSO) among others. Neural networks nearly imitate the human brain functioning with the help of inter-connected nodes with weights. ANNs have a 2% more point-efficiency in comparison to conventional methods. A proposed multi-objective quantum particle swarm optimization approach mainly focuses on the curtailment of the wind’s uncontrollable instability. Generic algorithm is generally independent of constraints associated with the problem and PSO is primarily a type of stochastic evolutionary algorithm used for optimization.

Furthermore, multi-objective design is a combination of various objectives that follows the integrate-and-optimize components principle. On the contrary, the analytical approach analyses the component arrangement first. Other approaches have their own unique way of dealing with optimization. These approaches can be named as probabilistic, experimental, iterative and graphical approach.

All in all, it is imperative for start-ups, the government, researchers, environmentalists, and green-enterprise developers to devote their expert eco-teams to study various approaches and their models in order to create a vision map for the nation as we move towards the era where the consumption and reliance on fossil fuels is no longer an option. We must strive to explore green alternatives and consciously adopt renewable technology in our households for a sustainable future.

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