Open AccessOptimization of Generation Cost, Environmental Impact, and Reliability of a Microgrid Using Non-dominated Sorting Genetic Algorithm-II
Author(s) -
Ikramul Hasan Sohel,
Showrov Rahman
Publication year - 2020
Publication title -
international journal of sustainable development and planning
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.29
H-Index - 15
eISSN - 1743-761X
pISSN - 1743-7601
DOI - 10.18280/ijsdp.150814
Subject(s) - sorting , microgrid , reliability (semiconductor) , mathematical optimization , multi objective optimization , pareto principle , genetic algorithm , computer science , evolutionary algorithm , electricity generation , reliability engineering , key (lock) , constraint (computer aided design) , power (physics) , engineering , algorithm , mathematics , artificial intelligence , mechanical engineering , physics , control (management) , quantum mechanics , computer security
Over the past years, energy sectors have accomplished considerable progress in the transition from conventional fossil-based energy to low-carbon energy production, and microgrids are playing important roles in this sustainable energy transition. One of the key challenges for microgrids is to deliver power with the least possible cost and that too with such an approach that the environmental impact is the lowest and the overall system reliability is high enough. For this reason, generation cost, emission entities, and system reliability need to be efficiently optimized. Towards this goal, an online multi-objective technique has been employed to optimize cost, emission and system reliability taking these three factors in pairs at a time. The optimization model is designed using the non-dominated sorting genetic algorithm-II (NSGA-II), and the algorithm has been employed for several double objective scenarios considering reliability as an objective and later as a constraint. To evaluate the performance of the proposed approach, the simulation results are compared with the relative parameters from a different model that uses the strength pareto evolutionary algorithm (SPEA). The results show that the proposed technique satisfies the multi-objective optimization goals and provides good trade-offs between the conflicting objective functions while finding the optimal dispatch.