Application of polynomial regression and MILP for under-frequency load shedding scheme in islanded distribution system

• Main Authors: Sarwar, Sohail, Mokhlis, Hazlie, Othman, Mohamadariff, Shareef, Hussain, Wang, Li, Mansor, Nurulafiqah Nadzirah, Khairuddin, Anis Salwa Mohd, Mohamad, Hasmaini
• Format: Article
• Published: Elsevier 2022
• Subjects: TA Engineering (General). Civil engineering (General)
• Online Access: http://eprints.um.edu.my/33804/

Distributed Generation (DG) integration, especially based on renewable energy resources, has gained great attention by power utilities and frequently utilized in the electrical distribution systems. However, DG integration imposes some risks towards system stability which may lead to system blackouts. This mainly occurs when the grid is decoupled from a portion of the distribution system consisting DGs while the total load demand is greater than total DGs output power. In order to overcome this problem, load shedding technique can be adopted to stabilize the system frequency. However, existing load shedding techniques were unable to accurately estimate the power imbalance due the variation in system loading. This results in excessive/inadequate load shedding to stabilize the system frequency. Moreover, random selection of the loads without load prioritization might cause vital loads to be shed. Therefore, in this paper, a new load shedding strategy for islanded distribution system is proposed. Polynomial regression analysis estimates the power mismatch while MILP optimization estimates optimal load combination for shedding. Furthermore, load priority (i.e., vital, non-vital, and semi-vital) is also considered to avoid disconnecting vital loads. Efficiency of the proposed scheme is evaluated on three different test systems. Validation is performed by modelling the proposed load shedding on PSCAD/EMTDC software for dynamic analysis. From the results, it can be analyzed that the proposed technique is superior compared to other techniques proposed in the literature. (C) 2021 THE AUTHORS. Published by Elsevier BV on behalf of Faculty of Engineering, Alexandria University.