Optimization algorithms for energy storage integrated microgrid performance enhancement

Distributed energy resource (DER) in microgrid has emerged significant challenges in the existing centralized energy management systems. This is due to the stochastic energy sources integrated into microgrid and dynamic power demand that has brought difficulties in controlling the optimal output power. An inefficient and without optimally controlled DERs and charge/discharge of energy storage system results in high operating cost to consumers as well as decrease a lifetime of energy storage based microgrid. Therefore, to solve the issues, a dayahead optimized scheduling controller-based novel lightning search algorithm (LSA) technique is introduced to provide an optimum power delivery with minimum cost including optimum use of energy storage. The main objective of the proposed controller is to develop an optimized controller for the microgrid to minimize the operating cost of DER and optimal operation of charge/discharge of the energy storage system. The optimized controller's effectiveness is executed in a 14-bus test system based on a real load varying conditions recorded in Perlis, Malaysia for 24-hours' operation. The obtained results show that the performance of the optimized controller for energy storage-based microgrid successfully reduced the amount of power consumption which in turn saving the energy and cost of 62.5%. The proposed day-ahead optimized scheduling controller outperforms the backtracking search algorithm and particle swarm optimization techniques in terms of iteration (53.56) and time consumption (2915.2 min) which in turn validate the controller performance. Thus, the developed optimized controller can realize the effectiveness of energy storage integrated MG energy management with the optimum operation of DER units.

Automated summary

Challenges in managing distributed energy resources (DER) in microgrids have led to the introduction of a novel lightning search algorithm technique to optimize power delivery and reduce costs. Experimental results demonstrate that the optimized controller successfully reduced energy consumption by 62.5%, improving overall system performance.