نبذة مختصرة : ObjectivesTo address issues such as insufficient multi-dimensional coordination, incomplete quantification of environmental benefits, and the lack of dynamic feedback in evaluating the benefits of electric energy substitution in regional power grids, a dynamic evaluation method based on multi-source data fusion is proposed to enhance the decision-making accuracy of electric energy substitution projects.MethodsA three-dimensional evaluation index system covering “supply-side optimization, demand-side transformation, and environmental emission reduction synergy” is established, identifying 21 indicators including cross-regional consumption capacity, electric substitution penetration rate, and pollutant reduction, thereby forming a multi-level quantitative evaluation framework. A coupling weighting strategy based on the entropy weight method and an improved rank correlation method is developed to balance subjective and objective weight deviations through linear weighting. A Bayesian dynamic cloud model is proposed, optimizing the hyper-entropy parameters of the cloud model by incorporating a Bayesian feedback mechanism to achieve dynamic correction of evaluation results.ResultsThe proposed model can effectively quantify the comprehensive benefits of electric energy substitution, accurately identify shortcomings in cross-regional consumption and terminal substitution, and improve evaluation accuracy through the dynamic feedback mechanism, enhancing alignment with practical engineering.ConclusionsThis evaluation method overcomes the bottlenecks in traditional benefit evaluation, such as single-dimensional indicators, inaccurate weight allocation, and significant static deviations, providing a reference for improving the accuracy of electric energy substitution benefit evaluation.
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