Placement and sizing of EESS bundled with uncertainty modeling by two-stage stochastic search based on improved shark smell optimization algorithm in micro-grids. (November 2021)

• Record Type: Journal Article
• Authors: Tian, Ye-fei; Liao, Rui-jin; Farkoush, Saeid Gholami
• Is Part Of: Energy reports. Volume 7(2021)
• Page Start: 4792
• Page End: 4808
• Publication Date: 2021-11
• Subjects: Optimal placement and size of storages -- Renewable resources -- Optimization -- Shark smell optimization -- Microgrid; Power resources -- Periodicals; Energy industries -- Periodicals; Power resources; Periodicals; Electronic journals; 621.04205
• DOI: 10.1016/j.egyr.2021.07.019
• Languages: English
• ISSNs: 2352-4847

Abstract

Abstract: Microgrids are low voltage small-scale grids which are used for supplying the thermal and electric loads in small areas or locations without access to the main grid. A microgrids can be a proper solution for power generation considering the environmental and economic problems of these locations. In this paper, the optimum size and placement of electric energy storage is presented considering the uncertainties associated with renewable energy resources and fault in the microgrid lines as well as impact of responsive loads in a microgrid. The studied hybrid system is composed of wind turbines and fuel cells. Basically, the wind energy is not certainly accessible and the newly suggested method is based on a two-stage stochastic search (TSSS) model for modeling the impact of uncertainty in the power generated by the wind generation resources by considering the wind energy prediction error as the main stochastic parameter in the planning. The standard probability distribution function is used for showing the error variation. With respect to continuity of this function, the prediction error probability function is discretized using a new method and a certain number of scenarios with specific probability are extracted. Finally, the studied problem is converted to an optimization problem and the developed shark smell optimization (SSO) algorithm is used to solve it. Numerous case studies show and approve the performance of the proposed model.