A self-healing restoration of power grid based on two-stage adaptive decision-making strategy to enhance grid resilience

• Published in: International journal of electrical power & energy systems 2023-12, Vol.154, p.109435, Article 109435
• Main Authors: Aziz, Tarique, Waseem, Muhammad, Liu, Shengyuan, Ma, Yuanqian, Lin, Zhenzhi, Memon, Qamar-Ud-Din
• Format: Article
• Language: English
• Subjects: Adaptive decision making strategy; Grid resilience; Mixed integer linear program; Power system restoration
• ISSN: 0142-0615
• DOI: 10.1016/j.ijepes.2023.109435

•For grid resilience and real-time scenarios, a unique measure of capacity accessibility during severe events is developed. This metric is designed for real-time system adaptation.•By treating the three phases of PSR as a single, two-stage strategy and using the ADMS with two types of functions—ORP and ORO—at different times, optimal restoring and operating performance can be achieved.•The PSR process ensures global optimality and optimal operational performance.•Formulating the first stage ORP as a MILP problem with practical constraints to maximize restored load capacity for improving computational performance.•The interior point method solves AC optimal power flow for the second stage (ORO) by re-dispatching generators in a step-by-step manner for real-time operation. Large-scale power outages can affect the economy of the country and jeopardize the lives of millions of people, so there is a need for a fast, reliable, and self-healing tool to reduce these losses. Hence, the power system restoration performance can be improved by considering both restoring and operating performance, however, current restoration strategies only consider the restoring performance but ignore the operational performance which can enhance the power system resiliency. Given this background, challenging tasks such as optimal restoration planning (ORP) and optimal real-time operation (ORO) are planned to be tackled by developing an adaptive decision-making strategy (ADMS). In order to reduce the effects of emergency power outages on power systems, this study presents the ADMS, a two-stage self-healing restoration approach. Moreover, a novel metric of capacity accessibility (NMCA) is developed to determine the capacity adequacy status during severe events. The proposed ADMS has two steps, each of which is conducted at a certain time, and is referred to as ORP for the first stage and ORO functions for the second stage. The goal of the ORP optimization model is to maximize system-wide power capacity for electricity demand under extreme events by considering practical constraints, including non-black-start generators (NBSGs) constraints, network topology constraints, and power balance constraints. In order to reduce generation costs, the ORO optimization model adjusts the generated output power to enhance one or more operating performance metrics. The first stage problem is formulated using mixed-integer linear programming (MILP), and it is resolved using the branch-and-bound techni