A Clustering-based Microgrid Planning for Resilient Restoration in Power Distribution System
Primary author: Hongda Ren
Co-author(s): Noel Schulz
Faculty sponsor: Dr. Noel Schulz
Primary college/unit: Voiland College of Engineering and Architecture
With frequent natural disasters, like hurricanes, storms, and earthquakes, it is critical to improve the power distribution systems’ resiliency to deal with them. One potential solution is the utilization of to increase the probability of critical loads restoration. Critical loads restoration refers to swiftly restore electricity supply to important loads, like hospitals, emergency lights services, central control room, and communication network, after the electric power outage from the local utility.
A weighted load-impedance density-based clustering method utilizes network topology and DERs to form multiple microgrids to restore critical loads based on load density and priority. The method has two objectives 1) to find restoration network and maximize its availability by DER location selection; 2) to identify areas with high load density with low impedance distance connections, to ensure high priority loads to recover.
The proposed method is tested in IEEE 37-node feeder with three DERs to restore eight critical loads. The results show:
1.the proposed method effectively identifies the optimal restoration paths to form microgrids.
2.Multiple solutions are provided when parameter adjustment while other methods only offer one solution. Compared to apply fewer large size DERs, using more small sizes DERs to form more microgrids have better performance in critical loads restoration and power loss reduction.
3.Compared with the result of benchmark optimization, the result of proposed method is close to the optimal solution in term of power loss. The power loss of the method is only 10% of the original case.