Stochastic multi-criteria decision-making for scheduling of wind–photovoltaic–hydropower systems

Author:

Liu Weifeng1,Zhang Yu2,Xing Xigang1,Guo Xuning1,Ma Rui1,Li Jieyu3,Li Yunling1

Affiliation:

1. a General Institute of Water Resources and Hydropower Planning and Design, Beijing 100120, China

2. b Nanjing Hydraulic Research Institute, Nanjing 210029, China

3. c Yellow River Institute of Hydraulic Research, YRCC, Zhengzhou 450003, China

Abstract

ABSTRACT The decision-making process of wind–photovoltaic–hydropower systems involves knowledge from many fields. Influenced by the knowledge level of the decision-maker and the attribute information of the scheme set, there exists a certain uncertainty in the indicator weights. In view of this, this paper proposes a stochastic multi-criteria decision-making framework for scheduling of wind–photovoltaic–hydropower systems, which overcomes the difficulty of uncertainty in indicator weights or even completely unknown information about indicator weights at the time of decision-making. The Stochastic Multi-criteria Acceptability Analysis (SMAA) theory and the VIKOR model are introduced, and the proposed SMAA–VIKOR model makes the indicator weight space explicit. The study shows that the proposed SMAA–VIKOR model can overcome the obstacle of decision-makers’ lack of information on indicator weights. The ranking acceptability indicators calculated by the model show a more obvious trend of advantages and disadvantages, which gives full confidence to the decision-making group to formulate a plan to be implemented. It breaks through the bottleneck of group decision-making, which is difficult to make effective decisions due to the condition of incomplete information, and enriches the library of stochastic multi-criteria decision-making methods for the scientific formulation of scheduling schemes of wind–photovoltaic–hydropower systems under uncertainty conditions.

Funder

National Key Research and Development Program of China

National Natural Science Foundation of China

China Postdoctoral Science Foundation Funded Project

Publisher

IWA Publishing

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