Trading performance evaluation for traditional power generation group based on an integrated matter-element extension cloud model

• Main Authors: Jun Dong, Dongran Liu, Yao Liu, Huijuan Huo, Xihao Dou, Aruhan Bao
• Format: Article
• Language: English
• Published: Elsevier 2021-11-01
• Series: Energy Reports
• Subjects: Electricity trading performance; Traditional power generation groups (TPGGs); Fuzzy delphi; Analytic network process (ANP); Matter-element extension cloud model
• Online Access: http://www.sciencedirect.com/science/article/pii/S2352484721003206

China has embarked on a new round of power market reforms since 2015. Traditional power generation groups (TPGGs), as important practitioners of the electricity market, have taken various actions to adapt to the new market environment during recent years. Trading performance, the most manifestation of enterprise operation is evaluated in this paper, to help TPGGs explore transition strategies to improve business and achieve a sustainable development. Considering multiple factors influencing TPGG trading performance, a hybrid framework based on Multiple-Criteria Decision-Making (MCDM) under fuzzy rules is proposed. Meanwhile, to deal with the ambiguous language in the evaluation process, the fuzzy Delphi method is combined with triangular fuzzy numbers (TFNs). Thus, twelve key indicators are selected, and the integrated evaluation index system is established. Then, a novel method combining Analytic Network Process (ANP) and Matter-Element Extension Cloud model (MEECM) is applied to evaluate the performance, which cover both fuzziness and randomness of the problem. In addition, an optimized cloud entropy algorithm considering the “3En” and “50% relevance” is implemented in MEECM. At further, through empirical analysis of five power generation companies in China, the proposed evaluation framework is verified. The results show that company A is the best, and more attention should be paid to the criteria related to market and support technology research (Z1), as well as trading capability and behavior (Z2). Finally, a series of sensitivity analyses have been conducted to examine the validity and effectiveness of the established evaluation framework and results. Meanwhile, some suggestions are put forward to improve trading performance for TPGGs. The study improves traditional cloud model and MCDM techniques. Therefore, it can help TPGGs track their course of actions during market reforms, and then take steps to enhance their management capabilities. On the other hand, it provides an effective means for market management agencies to evaluate market conditions through physical performance, to achieve a sustainable development of the power industry.