Multi-attribute decision-making method of pumped storage capacity planning considering wind power uncertainty

Abstract

Pumped storage technology plays a crucial role in achieving balance in power systems and enhancing the stability of energy systems. Scientific planning can help optimize the operation of power systems, promote the development of renewable energy, and conserve energy. This paper addresses the capacity planning problem of pumped storage stations in hybrid operation systems considering wind power uncertainty. A comprehensive decision-making method is proposed, based on the Delphi method, interval intuitionistic fuzzy theory, grey relational theory, entropy weight method, and prospect theory. This method comprehensively evaluates decision alternatives by considering various factors such as the uncertainty of qualitative indicators, the interrelationships between indicators, and the impacts of different risk environments. The test system in this paper is conducted on a hybrid wind-thermal-pump storage output model based on the IEEE 30-bus system. The optimal capacity planning scheme determined by the proposed method is 190 MW when decision-makers are risk-neutral or risk-averse, and 200 MW when they are risk-appetite. Subsequently, the case study verifies the impact of different load levels and proportions of wind power access on the optimal decision-making scheme, and compares it with decision-making method such as principal component analysis, technique for order preference by similarity to an ideal solution, and a combined method of Pythagorean fuzzy theory and cumulative prospect theory, validating feasibility and effectiveness.