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Load response potential evaluation for distribution networks: A hybrid decision-making model with intuitionistic normal cloud and unknown weight information

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  • Du, Puliang
  • Chen, Zhong
  • Gong, Xiaomin

Abstract

Load response potential evaluation of distribution networks is crucial in accelerating cooperative optimization of transmission and distribution networks, but the related research is comparatively rare. The purpose of this study is to establish a hybrid decision-making model considering unknown weight information and decision-makers’ psychological behavior to solve load response potential evaluation problems under uncertain environment. Firstly, a comprehensive evaluation attribute system with qualitative and quantitative nature is established. Secondly, intuitionistic normal clouds are utilized to depict linguistic evaluation, which avoids information distortion. Thirdly, a distance measure for intuitionistic normal clouds considering cloud droplets’ distribution characteristics is defined to compute grey relational degree of decision-making information with ideal information, thereby determining decision-makers’ dynamic weights. Furthermore, indifference threshold-based attribute ratio analysis is introduced to derive attribute weights. TODIM (an acronym in Portuguese for interactive and multicriteria decision-making) is extended to intuitionistic normal cloud environment to rank-order alternatives, which makes the framework more applicable for the actual evaluation. The case study shows that the proposed model provides more reasonable results, and the optimal and suboptimal alternatives are sensitive to the attenuation parameter, suggesting that loss avoidance behavior should deserve attention. This research has practical applied value for dispatchers in power grid dispatching management.

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  • Du, Puliang & Chen, Zhong & Gong, Xiaomin, 2020. "Load response potential evaluation for distribution networks: A hybrid decision-making model with intuitionistic normal cloud and unknown weight information," Energy, Elsevier, vol. 192(C).
    • Handle: RePEc:eee:energy:v:192:y:2020:i:c:s0360544219323680
    DOI: 10.1016/j.energy.2019.116673
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