A trade-off between line hardening and dynamic line rating by a new convex optimization model for resilient micro-grid-oriented expansion planning of reconfigurable smart distribution networks incorporated with renewable energy sources

Abstract

Because of budget and right-of-way limitations, smart-grid technologies (SGTs) are widely incorporated in today’s distribution systems in order to satisfy the load demand growth and meet the network’s operational and reinforcement planning requirements. The main purpose of this paper is to propose a resilient expansion planning model based on a cost-effective comparison between dynamic line rating (DLR) and reinforcement of line conductors through low probability and high impact (LPHI) outages. Besides lines hardening and installing DLR-measuring devices, the planning options include optimal formation of radial reconfigurable micro-grids (MGs). The presented approach considers the total cost (including construction costs, operational costs, and CO₂ emission costs) and load shedding as the objective functions within a multi-objective optimization, and takes into account all the operational constraints and AC power flow equations. The developed model is constituted as a convex mixed-integer quadratic-constrained programming (MIQCP) which is implemented in GAMS and applied to the IEEE 24-bus system under different experiments. Furthermore, the Pareto optimization scenarios have been considered and the optimal solution is selected by the fuzzy-satisfying method. The simulation results demonstrate the efficacy of the conducted model. According to the optimal Pareto algorithm solution, the resiliency index is guaranteed to be more than 92% in the face of LPHI disasters. For practitioners, this work provides a decision-making toolkit to weigh DLR against conventional reinforcement, while policymakers can leverage the emissions-reliability trade-offs to design incentive programs. The proposed MG reconfiguration also offers a blueprint for outage response in disaster-prone regions.