Scheduling controller for microgrids energy management system using optimization algorithm in achieving cost saving and emission reduction. (15th June 2021) - Roslan, M.F.; Hannan, M.A.; Jern Ker, Pin; Begum, R.A.; Indra Mahlia, TM; Dong, Z.Y.
Highlights: Lightning search algorithm-based scheduling control for MG energy management system. Optimized controller to minimize cost and environmental emission in the MGs system. Effectiveness of the scheduling controller is executed based on uncertainty condition. Modelling and simulation is tested on modified version of the IEEE 14-bus test system. The proposed approach outperformed other techniques in solving optimization problems. Abstract: This study deals with the development of an optimal power scheduling controller for energy management of distributed energy resources in the microgrid system. The developed optimized controller is implemented using lightning search algorithm to overcome the uncertainties of microgrid energy management and to provide an optimum power delivery to loads with minimum cost. The primary objectives of the proposed optimized controller are to: (i) develop an optimized controller for microgrids energy management, (ii) minimize the total operating cost of the distributed energy resources units, (iii) reduce the environmental emission, and (iv) solve the complicated constraint optimization problems. The proposed optimization algorithm is implemented in the modified IEEE 14-bus test system to optimize the microgrid power management schedule. The optimized controller is executed based on the real load varying conditions recorded in Perlis, Malaysia. It is observed that the optimized controller successfully reduced the amount of power consumption from 971.65 MW to 364.3 MW which in turn saving cost of RM 265432.06. The proposed scheduling optimized controller performance is compared with the recent reported work of backtracking search algorithm optimization for validation. Result shows that the lightning search algorithm based MG controller produced a cost-effective system with 62.5% of cost saving and 61.98% of carbon dioxide emission reduction which is much higher compared to with MG and backtracking search algorithm based MG optimization, respectively. The effectiveness of the proposed approach outperformed other techniques in terms of minimum total operating cost of distributed energy resources and solving complicated constraints in optimization problems.