Dynamic Economic Scheduling Strategy for a Stand-alone Microgrid System Containing Wind, PV Solar, Diesel Generator, Fuel Cell and Energy Storage:- A Case Study

2018 4th International Conference on Renewable Energy Technologies

Wang, Lili^1 ; Zhang, Linjuan^1 ; Xu, Changqing^1 ; Tesfaye Eseye, Abinet^2,3 ; Zhang, Jianhua^2 ; Zheng, Dehua^3

State Grid Henan Electric Power Company Economic and Technological Research Institute, Songshan South Road, Zhengzhou, Henan, China^1

North China Electric Power University, Changping District, Beijing

102206, China^2

Goldwind Science and Etechwin Electric Co. Ltd, BDA, Beijing

100176, China^3

关键词: Day-ahead scheduling;    Economic scheduling;    Energy productions;    Energy storage systems;    Genetic-algorithm optimizations;    Micro-grid systems;    Optimization techniques;    Stand-alone modes;   

Others  :  https://iopscience.iop.org/article/10.1088/1755-1315/168/1/012006/pdf
DOI  :  10.1088/1755-1315/168/1/012006

来源: IOP

【 摘 要 】

Efficient dynamic economic scheduling for a microgrid is essential to ensure optimal energy utilization and sustainability. In this paper, a day-ahead optimal dynamic scheduling for a stand-alone microgrid containing wind, PV solar, fuel cell, diesel generator and energy storage system is implemented. The primary objective of the dynamic economic scheduling is to minimize the energy production cost, maximize the energy storage system economic benefit and enhance the utilization of the renewables in the microgrid. The Genetic Algorithm (GA) optimization approach is proposed to solve the economic scheduling problem. Fluctuations of the load demands and renewables in the microgrid are considered and relevant predictions have been made to surmount these fluctuations. The proposed economic scheduling strategy has been tested on a case study microgrid in stand-alone mode (Goldwind Microgrid System, Beijing, China). Simulation results have demonstrated that the proposed approach can solve the day-ahead scheduling problem in a reasonably fast computation time. To validate and compare the performances of the proposed strategy, simulation results were also obtained using Pattern Search (PS) optimization technique. Comparisons of simulation results demonstrate the effectiveness of the proposed GA-based dynamic economic scheduling in attaining a minimum total cost of energy production within a short computation time.