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<p>The transition from passive to active distribution networks (DNs) has heightened the challenge of managing the intermittent and unpredictable nature of distributed generators (DGs). Optimal power flow (OPF) is a crucial tool in addressing this challenge, primarily aimed at minimizing active power loss. Traditional methods often simplify the problem by assuming balanced DNs or ignoring critical attributes of unbalanced DNs, leading to inaccuracies. This paper proposes a chance-constrained (CC) robust dynamic OPF model to manage the uncertainties in unbalanced DNs, considering uncertainties in the power injections of both the loads and DGs. The model uses convex relaxation with particular attention to computational efficiency and accuracy and CC optimization for handling injection uncertainties. The proposed approach is assessed using the IEEE-33 and IEEE-34 bus systems, demonstrating significant improvements in computational speed, achieving the results in approximately 0.021&#xa0;s, and robustness compared to existing methods. Numerical results highlight the model’s capability to reduce total active power losses by approximately 35.61%, showcasing its potential to enhance the operational performance of unbalanced DNs.</p>

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Chance-constrained robust dynamic OPF in mutually coupled distribution networks with uncertain injections

  • 期刊论文

  • 发表日期：

    2025年3月17日

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  • Sanat Kumar Paul,
  • Abheejeet Mohapatra,
  • Dulal Chandra Das

摘要

The transition from passive to active distribution networks (DNs) has heightened the challenge of managing the intermittent and unpredictable nature of distributed generators (DGs). Optimal power flow (OPF) is a crucial tool in addressing this challenge, primarily aimed at minimizing active power loss. Traditional methods often simplify the problem by assuming balanced DNs or ignoring critical attributes of unbalanced DNs, leading to inaccuracies. This paper proposes a chance-constrained (CC) robust dynamic OPF model to manage the uncertainties in unbalanced DNs, considering uncertainties in the power injections of both the loads and DGs. The model uses convex relaxation with particular attention to computational efficiency and accuracy and CC optimization for handling injection uncertainties. The proposed approach is assessed using the IEEE-33 and IEEE-34 bus systems, demonstrating significant improvements in computational speed, achieving the results in approximately 0.021 s, and robustness compared to existing methods. Numerical results highlight the model’s capability to reduce total active power losses by approximately 35.61%, showcasing its potential to enhance the operational performance of unbalanced DNs.