Optimal planning of energy storage technologies considering thirteen demand scenarios from the perspective of electricity Grid: A Three-Stage framework

Planning rational and profitable energy storage technologies (ESTs) for satisfying different electricity grid demands is the key to achieve large renewable energy penetration in management. The complexity related to the planning of ESTs lies in diversities of different ESTs properties, uniqueness and varieties of electricity grid demands and uncertainties of the decision-making environment. However, existing research cannot solve above problems simultaneously. To fill such gap, this paper focuses on the optimal planning of various ESTs considering thirteen demand scenarios in electricity grid through establishing a three stage multi criteria decision making framework under the uncertain environment. Firstly, critical features of ESTs in technology and application conditions and constrains (TCC, ACC) are identified and deeply analyzed integrating with the characteristics of thirteen ESTs demand scenarios by cluster analysis and correlation text. Following that, a three-stage planning framework is established in TCC, ACC and comprehensive aspect through a Lagrange optimized comprehensive subjective and objective weight determination model based interval fuzzy number (IVFN) and interval intuitionistic fuzzy numbers (IVIFN) - PROMETHEE-II model. The results show that the optimal planning vary with the demand scenarios from electricity grid. This research has important guiding significance for overall planning and application management of renewable energy and ESTs.

Automated summary

This research focuses on optimal planning of various ESTs considering thirteen demand scenarios in the electricity grid through a three-stage decision-making framework. The critical features of ESTs are identified and analyzed, and a three-stage planning framework is established based on Lagrange optimized weight determination model. Results show that optimal planning varies with demand scenarios in the electricity grid.