Abstract
Countries throughout the world are aggressively encouraging the development of renewable energy. Renewable energy power generation presents distributed characteristics, which increases the difficulty of power coordination and optimization. Compared to the thermal power stations, distributed energy storage system (DESS) can help reduce the aforementioned difficulty without environmental pollution and energy consumption, it has good social and environmental benefits. However, due to the high operating cost of DESS, its investment is cold. Therefore, the investment decision-making of DESS must be considered from the perspective of sustainability, but there is still a blank in this aspect. This problem belongs to the typical research category of decision theory, it can be solved by constructing a decision framework composed of decision index system and decision-making model. So the decision index system of DESS project plan was established based on the sustainability theory and the real DESS operational scenario; for the decision model, the probabilistic linguistic term set (PLTS) is taken as the expression of decision data of DESS, the fuzzy measure and VIKOR were used to reflect the importance of criteria and integrate the decision data respectively. The decision framework proposed in this paper has the following advantages: the proposed decision index system content 3 attributes, 7 criteria and 21 sub-criteria, can provide scientific guidance for DESS project plan decision; second, it can not only deal with uncertainty effectively but also is convenient for experts to express their own preferences; third, it can solve the problem that the independence assumption of criteria is difficult to meet in reality; forth, it reflect the overall utility and local disadvantages of the plan of DESS at the same time. Through comparative analysis and sensitivity analysis, the proposed decision framework can provide more scientific decision-making results of DESS project plan for energy enterprises, and the results have better robustness.
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