Fuzzy multi-criteria decision-making method-based operational assessment of Chinese electricity markets

The evaluation of the electricity market is crucial for fostering market construction and development.An accurate assessment of the electricity market reveals developmental trends,identifies operational issues,and contributes to stable and healthy market growth.This study investigated the characteristics of electricity markets in different provinces and synthesized a comprehensive set of evaluation indicators to assess market effectiveness.The evaluation framework,comprising nine indicators organized into two tiers,was constructed based on three aspects:market design,market efficiency,and developmental coordination.Furthermore,a novel fuzzy multi-criteria decision-making evaluation model for electricity market performance was developed based on the Fuzzy-BWM and fuzzy COPRAS methodologies.This model aimed to ensure both accuracy and comprehensiveness in market operation assessment.Subsequently,empirical analyses were conducted on four typical provincial-level electricity markets in China.The results indicate that Guangdong’s electricity market performed best because of its effective balance of stakeholder interests and adherence to contractual integrity principles.Zhejiang and Shandong ranked second and third,respectively,whereas Sichuan exhibited the poorest market performance.Sichuan’s electricity market must be improved in terms of market design,such that market players can obtain a fairly competitive environment.The sensitivity analysis of the constructed indicators verified the effectiveness of the evaluation model proposed in this study.Finally,policy recommendations were proposed to facilitate the sustainable development of China’s electricity markets with the objective of transforming them into efficient and secure markets adaptable to the evolution of novel power systems.

Multi-objective optimization of multi-cell conical structures under dynamic loads

In this paper,crashworthiness performance of multi-cell conical tubes with new sectional configuration design(i.e.square,hexagonal,octagonal,decagon and circular)has been evaluated under axial and three different oblique loads.The same weight conical tubes were comparatively studied using an experimentally validated finite element model generated in LS-DYNA.Complex proportional assessment(COPRAS)method was then employed to select the most efficient tube using two conflicting criteria,namely peak collapse force(PCF)and energy absorption(EA).From the COPRAS calculations,the multi-cell conical tube with decagonal cross-section(MCDT)showed the best crashworthiness performance.Furthermore,the effects of possible number of inside ribs on the crashworthiness of the decagonal conical tubes were also evaluated,and the results displayed that the tubes performed better as the number of ribs increased.Finally,parameters(the cone angle,θ,and ratio of the internal tube size to the external one,S)of MCDT were optimized by adopting artificial neural networks(ANN)and genetic algorithm(GA)techniques.Based on the multi-objective optimization results,the optimum dimension parameters were found to beθ=7.9o,S=0.46 andθ=8o,S=0.74 from the minimum distance selection(MDS)and COPRAS methods,respectively.

Estimation of the effectiveness of multi-criteria decision analysis and machine learning approaches for agricultural land capability in Gangarampur Subdivision, Eastern India

Land suitability analysis(LSA)is an evaluation method that measures the degree to which land is suitable for certain land use.The primary aims of this study are to identify potentially viable agricultural land in the Gangarampur subdivision(West Bengal)using Multiple Criteria Decision Making(MCDM)and machine learning procedures and to evaluate the efficacy of the employed methodologies.The Analytic Hierarchy Process(AHP)model was used to assign relative weights to the fifteen various criteria in this suitability analysis,and then the Fuzzy Complex Proportional Assessment(FCOPRAS)model was applied using the AHP’s normalised pairwise comparison matrix,whereas the Waikato Environment for Knowledge Analysis(Weka)Software was used to apply machine learning algorithms to the field data.The Random Forest(RF)model,on the other hand,is a better fit for the locational study of soil potential.According to the RF findings,areas of 14.67 per cent(15368.46 ha)are excellent(ZONE Ⅴ)for growing crops,approximately 22.30 per cent(23367.9 ha)are highly suitable(ZONE Ⅳ),and 23.63 per cent(24762.12 ha)are moderately suitable(ZONE Ⅲ)for cultivation,respectively.The numbers for FCOPRAS are roughly 15.39%(16130.52 ha),22.54%(23620.65 ha),and 19.79%(20733.26 ha).The Receiver Operating Characteristic(ROC)curve and accuracy measurements of the results indicate the high accuracy of the applied models,with Random Forest and FCOPRAS being the most popular and effective techniques.This study will make an important contribution to evaluations of soil fertility and site suitability.This will help local government officials,academics,and farmers scientifically use the land.