University of Tabriz Tabriz Journal of Electrical Engineering 2008-7799 2025 12 22 Congestion management under conditions of uncertainty with the load shedding option and variance risk tolerance index. Congestion management under conditions of uncertainty with the load shedding option and variance risk tolerance index. 19940 10.22034/tjee.2025.65473.4955 FA Mohammad Masoud Mohammadi Tehran Jalal AleAhmad Nasr P.O.Box: 14115-111 Mohmoud-Reza Haghifam Tarbiat Modares University Hamid Reza Baghaee Tehran Jalal AleAhmad Nasr P.O.Box: 14115-111 Journal Article 2025 01 16 With the transformation of traditional distribution networks (DNs) into active DNs, the possibility of congestion has increased. Also, the presence of uncertainty in factors such as electric car charging or solar panels, causes the predictability of congestion in the DN to decrease and the occurrence of congestion can be expressed as a probability. These cases have increased the concern of distribution system operators for the occurrence of congestion in DNs and also, for managing the amount of congestion with minimum costs. Therefore, providing solutions for congestion management in the shortest time and cost is a priority for operators. In this article, a solution for congestion management based on load shedding while considering the uncertainties of electric vehicles and solar panels is presented. At first, to estimate the amount of depth and time of congestion, modeling of congestion is done through probabilistic power flow. The evaluation of the proposed method in the Modified IEEE 33-bus home DN, while proving its effectiveness, shows that by using that, the operator performs the load shedding by minimizing their costs through improved multi-interval optimal power flow, and the amount of possible congestion is reduced depending on the level of risk-taking of the operator. With the transformation of traditional distribution networks (DNs) into active DNs, the possibility of congestion has increased. Also, the presence of uncertainty in factors such as electric car charging or solar panels, causes the predictability of congestion in the DN to decrease and the occurrence of congestion can be expressed as a probability. These cases have increased the concern of distribution system operators for the occurrence of congestion in DNs and also, for managing the amount of congestion with minimum costs. Therefore, providing solutions for congestion management in the shortest time and cost is a priority for operators. In this article, a solution for congestion management based on load shedding while considering the uncertainties of electric vehicles and solar panels is presented. At first, to estimate the amount of depth and time of congestion, modeling of congestion is done through probabilistic power flow. The evaluation of the proposed method in the Modified IEEE 33-bus home DN, while proving its effectiveness, shows that by using that, the operator performs the load shedding by minimizing their costs through improved multi-interval optimal power flow, and the amount of possible congestion is reduced depending on the level of risk-taking of the operator.