Service Restoration in DG-Integrated Distribution Networks Using an Exact Convex OPF Model

Abstract

The ever-increasing requirements for reliability and quality of supply suggest to enable the self-healing features of modern distribution networks. Within the context of Active Distribution Networks (ADNs), new, fast and efficient restoration strategies can be developed relying not only on switching devices to realize load transfer operation, but also on the contribution of Distributed Generators (DGs) while controlling their set points. In this paper, a global optimization method is proposed for the restoration problem in a DG-integrated distribution network. The objective is to restore a maximum of loads with a minimum number of switching operations. In order to check the technical constraints of the resulting configuration (ex., line current and bus voltage constraints), a recently published method for exact convex formulation of the OPF problem is incorporated making the restoration problem robust particularly in the case of high nodal injections. The proposed restoration model is formulated as a mixed-integer second-order cone programming (MISOCP) problem. Two test cases are used to quantify the DG effects on the quality of the restoration strategy and to demonstrate the feasibility of the identified solution in cases where general relaxation methods for OPF problem leads to inexact solutions.

Decision‐making method for critical load restoration by using MGs

“…To find the best restoration paths, Poudel and Dubey [28] evaluate the paths by a weighed sum of two objective functions (the amount of restored CLs and the unavailability of restoration path which resulted from the unavailability of DER units). As previously explained, the distribution network restoration problem is a mixed-integer and non-linear non-convex problem [29] and weighed sum methods (as used method in [28]) do not often find the best solution in non-convex problems [30]. Therefore, this may also be a reason why paper [28] has not reached to the best solution.…”

“…The mean re-energising times of CLs by the proposed method and the one given in [16] are 13.36 and 11.5 h, respectively. The supplying time variances of the restored CLs are also 63.0744 and 75.364 for the proposed method and method given in [16], Table 15 Obtained results by the proposed method and method in [28] DER-6 method in [28] 12, 19, 99 6, 5,3,9,10,12,16,17,18,19,57,58,59,60,99 10 9.72 proposed method 12,19,99 6, 5,3,9,10,12,16,17,18,19,57,58,59,60,99 10 9.72 DER-29 method in [28] 36,41 29,28,34,35,36,26,24,21,38,39,40 respectively, which clearly shows the more desirable performance of the proposed method.…”

“…To solve all objectives mentioned above for the CL's restoration, some papers such as [16,27,28] change the multiobjective functions to a single one by using weighing coefficients. However, restoration is a mixed-integer and non-linear non-convex problem [29] and weighed sum methods do not often find the best solution in non-convex problems [30]. To solve this drawback, this paper uses a multi-criteria decision-making method, named analytic hierarchical process (AHP) which preserves the multiobjective nature of the problem without assigning any weighing coefficient.…”