Improved Cross Entropy Method for Well-Being Evaluation of Composite Generation and Transmission Systems

Well-Being analysis is an approach that integrates deterministic criteria with probabilistic methods, and it plays a crucial role in the operational planning of power systems. However, assessing the Well-Being of composite generation and transmission systems presents a formidable challenge, characterized by significant computational burdens and sluggish processing speeds. To tackle this issue, we embarked on an effort to enhance the computational efficiency of Well-Being assessment by employing the cross-entropy method (CEM). Nonetheless, our experimental pursuits revealed that the conventional employment of CEM for Well-Being assessment can lead to protracted convergence of the marginal index. To overcome this limitation, we introduce an enhanced multi-objective cross-entropy method (MCEM) that integrates weight factors, thereby ensuring an accelerated convergence rate for both the risk and marginal indices. To validate the effectiveness and advancement of our proposed MCEM approach, we conduct a comprehensive comparative analysis using the IEEE RTS79 and MRTS79 test systems as case studies. We contrast our method with the conventional MCS and CEM approaches, conducting a thorough examination of the computational performance of MCEM. This comprehensive comparative study unequivocally confirms the efficacy and progressive nature of the MCEM framework presented in this paper.