Intelligent Algorithmic Multi-Objective Optimization for Renewable Energy System Generation and Integration Problems: A Review

Integrated renewable energy is now becoming an option for sustainable growth of humanity. Because it provides the uninterrupted energy supply to small, and micro grids, as well as it penetrates the larger conventional energy grid to decrease the emissions and active losses. Combining renewable sources to conventional grid results in an integrated renewable energy system. Sometimes for smaller loads hybrid renewable energy systems (HRES) can be used as an alternative. This whole integration of different renewable energy systems (RES) which can be grid connected or off grid, requires optimization of various factors like total levelized cost of energy, total CO2 emission (life cycle), total percentage of grid penetration etc. Hence, these kinds of problems include a large data regarding, energy resources, their annual availability, use pattern of the energy. Therefore to solve these complex problems one has to use intelligent computer algorithms, because of less calculation time and better accuracy than any other means. This paper highlights an updated literature regarding algorithmic multiobjective optimization for generation and integration side problems of renewable energy resources to satisfy electrical and in some cases thermal demand also. It will be helpful to the researchers working in the field of multiobjective optimization and integrated RES.