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Determining the optimal strategy in self-healing smart distribution network restoration with reliability cost predication method
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Seyed Hossein Hassanzadeh Fard   , Amin Samanfar * , Mehdi Nikzad , Mohsen Rashidi |
| Department of Electrical Engineering, Khorramabad Branch, Islamic Azad University, Khorramabad, Iran |
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Abstract: (66 Views) |
Today's distribution networks have made significant progress in terms of dimensions and components and are one of the important pillars of the economy of each country. Governments often look for ways to minimize network damage in critical and fault situations, limiting the scope of faults to maintain maximum service continuity. After a failure occurs, during the restoration period, the operator or network administrator`s right decision, can reduce the dimensions of the failure and prevent further damage. As future networks move towards self-healing, decision-making functions must be empowered so that by maintaining the technical constraints of the network, they can create sustainable emergencies with timely interventions. To do this, the customer damage function factor can be used, which is presented as a number for each type of customer with the unit of $/kWh. So, the feeders can be valued, but due to the probable nature of the cost in outages, this factor is not complete alone. This article uses the customer damage probability function, which is calculated separately for each feeder and can be applied to the entire network. The cost prediction method for the sample feeder with different kinds of load has been simulated, using the Monte Carlo method as well as the UGF method in MATLAB software. The results show despite having the same input and achieving the same results for both methods, the response speed in the UGF method is much higher. |
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| Keywords: Self-healing distribution networks, smart restoration, Predicting the cost of reliability |
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Full-Text [PDF 1274 kb]
(39 Downloads)
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Type of Study: Research |
Subject:
Special
Received: 2024/07/21 | Accepted: 2024/10/1 | Published: 2024/11/30
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