Scholarlink

<p>Distribution network reconfiguration (DNR) is a critical task for ensuring the reliability, economic operation, and resilience of distribution systems, particularly under the influence of uncertain and fluctuating distributed generation (DG). Existing DNR models often fail to adequately address the stochastic nature of DG output, leading to suboptimal network performance. In this paper, we propose a novel distributionally robust DNR (DR-DNR) model to address the problem of DG output uncertainty. To make full use of DG, the distributionally robust-based conditional value-at-risk (CVaR) of discarded power is introduced into the objective function of minimizing network loss. To balance the robustness and economic efficiency, distributionally robust chance constraints are integrated into the proposed model. We transform the proposed model into a mixed-integer second-order cone programming (MISOCP) problem through the duality principle and approximation of CVaR. Numerical experiments on three test systems validate the effectiveness of the proposed approach. The results demonstrate that the DR-DNR model improves reliability by 44.8% compared to deterministic models and achieves lower network losses and substation output compared to robust models, while enhancing DG utilization. These findings highlight the model’s potential for practical applications in modern distribution systems with high DG penetration.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

A CVaR-based distributionally robust approach for distribution networks reconfiguration with distributed generation

  • 期刊论文

  • 发表日期：

    2025年2月18日

查看全文
  • Linfeng Yang,
  • Jinjiao Yin,
  • Xiangyu Liu,
  • Haiyan Zheng

摘要

Distribution network reconfiguration (DNR) is a critical task for ensuring the reliability, economic operation, and resilience of distribution systems, particularly under the influence of uncertain and fluctuating distributed generation (DG). Existing DNR models often fail to adequately address the stochastic nature of DG output, leading to suboptimal network performance. In this paper, we propose a novel distributionally robust DNR (DR-DNR) model to address the problem of DG output uncertainty. To make full use of DG, the distributionally robust-based conditional value-at-risk (CVaR) of discarded power is introduced into the objective function of minimizing network loss. To balance the robustness and economic efficiency, distributionally robust chance constraints are integrated into the proposed model. We transform the proposed model into a mixed-integer second-order cone programming (MISOCP) problem through the duality principle and approximation of CVaR. Numerical experiments on three test systems validate the effectiveness of the proposed approach. The results demonstrate that the DR-DNR model improves reliability by 44.8% compared to deterministic models and achieves lower network losses and substation output compared to robust models, while enhancing DG utilization. These findings highlight the model’s potential for practical applications in modern distribution systems with high DG penetration.