A Multi-Dimensional Quality–Efficiency Evaluation Model for User-Side Flexibility Resources Based on SNA–Fuzzy-AHP–DEMATEL

Under the background of constructing a new-type power system, the large-scale grid integration of renewable energy sources such as wind power and photovoltaics has sharply increased the system’s demand for flexible regulation resources. User-side flexibility resources, owing to their wide distribution and rapid response, have become an important supplementary means of regulation. Addressing the core challenges of multi-dimensionality, fuzziness, and interrelationships in the quality–efficiency evaluation of user-side flexibility resources, this paper develops a multi-dimensional quality–efficiency evaluation model that integrates social network analysis (SNA), fuzzy analytic hierarchy process (Fuzzy-AHP), and decision-making trial and evaluation laboratory (DEMATEL) from a dual perspective of “quality” and “efficiency.” First, the connotation and constitutive dimensions of the quality–efficiency of user-side flexibility resources are defined, establishing a quality–efficiency identification framework comprising two main dimensions and seven sub-dimensions. Second, a co-occurrence network of indicators is constructed through SNA, and thresholds are determined based on network density, average degree, and connectivity. Redundant indicators are removed and the indicator system is optimized to form a three-level evaluation system consisting of 15 indicators. On this basis, Fuzzy-AHP is employed to handle the fuzziness of expert judgments and obtain subjective weights, while DEMATEL is used to quantify the causal influence relationships among indicators and obtain structural weights; the comprehensive weights are then derived through multiplicative combination. Finally, the fuzzy comprehensive evaluation method is applied to achieve quantitative scoring and grade determination of the quality–efficiency of evaluation objects. Case simulations based on five typical residential communities show that adjustable capacity (C1) ranks first, with a combined weight of 0.1589, while response success rate (C5) ranks second. The quality–efficiency scores of the five communities are ranked as C-C > C-E > C-A > C-B > C-D, among which the resource mode of C-C, dominated by commercial and industrial interruptible loads and ice-storage air conditioning, exhibits the optimal quality–efficiency, whereas the resource mode of C-D, dominated by dispersed electric heating equipment, lags significantly. Dimension-wise evaluation reveals differentiated patterns such as “emphasizing quality over efficiency” and “compensating quality with efficiency” across the communities. The analysis of parameter and input uncertainty shows that the model exhibits good robustness to reasonable fluctuations in rating values, membership function boundaries, fuzzy scales, expert judgments, and input indicator values; The analysis of generalization and computational feasibility shows that the model maintains stable ranking consistency and acceptable computation time even when the number of communities is expanded to 50 and the number of indicators is expanded to 30. The proposed model can provide credible quality–efficiency evaluation results in new-type power system scenarios lacking long-term operational statistical data, offering a scientific basis for power grid operators and load aggregators to optimize the allocation and scheduling decisions of user-side flexibility resources.