Optimal scheduling of microgrid with distributed power based on water cycle algorithm
Energies , Volume 11 - Issue 9
Microgrid, taking advantage of distributed power generation technology, plays an important role in maximizing the utilization of renewable energy. Based on the problems of the energy crisis, environmental contamination and the high operating cost of the microgrid, the microgrid model can effectively ease energy pressure.We can dispatch the output of each part in the microgrid to obtain the optimal economy. Since many traditional optimization algorithms have limitations of local optimization, multiple iterations, and slow convergence speed, this paper proposes a method that applies the Water Cycle Algorithm (WCA) to optimize the dispatch of the microgrid to minimize the operating cost. The mathematical model of each distributed power is established. The interactive power between the microgrid and large grid is also considered. The lowest generation cost considering environmental benefits is taken as the objective function. Water cycle algorithm is implemented to obtain the optimal solution under various constraints. Some optimization algorithms such as Genetic Algorithm (GA), Interior Search Algorithm (ISA), and Differential Search Algorithm (DSA) were used for results evaluation. By comparing the results obtained from four different algorithms, a case study shows the WCA possesses the advancements of better convergence performance, faster calculation and higher precision compared to the other algorithms. The results demonstrate that the WCA applied to determine the optimal scheduling of the microgrid can achieve a better result than some other algorithms with an acceptable accuracy and efficiency.
|Distributed generation, DSA, GA, ISA, Microgrid, Optimal scheduling, WCA|
|Organisation||Department of Systems and Computer Engineering|
Yang, X. (Xiaohui), Long, J. (Jiating), Liu, P. (Peiyun), Zhang, X. (Xiaolong), & Liu, P. (2018). Optimal scheduling of microgrid with distributed power based on water cycle algorithm. Energies, 11(9). doi:10.3390/en11092381