A Stochastic Planning Model for Improving Resilience of Distribution System Considering Master-Slave Distributed Generators and Network Reconfiguration

Mostafa Ghasemi,Mohammad Amin Gilani,Ahad Kazemi,Miadreza Shafie-Khah

doi:10.1109/access.2021.3083698

Abstract

The recent experiences of extreme weather events highlight the significance of boosting the resilience of distribution systems. In this situation, the resilience of distribution systems planning leads to an efficient solution for protecting the system from these events via line hardening and the installation of distributed generators (DGs). For this aim, this study presents a new two-stage stochastic mixed-integer linear programming model (SMILP) to hedge against natural disaster uncertainty. The first stage involves making investment decisions about line hardening and DG installation. Then, in the second stage, the dynamic microgrids are created according to a master-slave concept with the ability of integrating distributed generators to minimize the cost of loss of load in each uncertain outage scenario. In particular, this paper presents an approach to select the line damage scenarios for the SMILP. In addition, the operational strategies such as load control capability, microgrid formation and network reconfiguration are integrated into the distribution system plans for resilience improvement in both planning and emergency response steps. The simulation results for an IEEE 33-bus test system demonstrate the effectiveness of the proposed model in improving disaster-induced the resilience of distribution systems.

Highlights

TB KBMIωParameters B BM cdg Limited budget A sufficiently big number Capital cost ($) for installing a distributed generators (DGs)
Extreme weather conditions in recent years have resulted in long and widespread electricity service interruptions with enormous economic losses [1]
1 if distribution line l is hardened; 0 otherwise 1 if a back-up distributed generators (DGs) m is installed; 0 otherwise 1 if a back-up DG m is placedat bus b; 0 otherwise Binary variable indicating that bus b belongs to microgrid k 1 if line is active; 0 otherwise 1 if line in microgrid k is active; 0 otherwise Fictional flows on the distribution line l Binary variable for microgrid formation Fictional supply of master DG

Summary

TB KBMIω

Parameters B BM cdg Limited budget A sufficiently big number Capital cost ($) for installing a DG. Scheduled active loads Scheduled active load control Active power flow (kW) of lines Scheduled active power of DGs. Linearized active power generation of DGs Charging power of energy storage pesde,ckh,t ,ω ql ,t ,ω qDmG,b,,dke,pt ,s socEe,Sk ,t ,ω vb,k ,t ,ω zpl ,t ,ω zql ,t ,ω δb,k ,t ,ω PLj,Ck ,t ,ω. Discharging power of energy storage Reactive power flow (kvar) of lines Linearized reactive power generation of DGs SOC of energy storage Bus voltage magnitude Active power equality constraint slack variables Reactive power equality constraint slack variables Bus voltage angles Difference between scheduled and deployed load control Difference between scheduled and output power of DGs Linearization variable Status of energy storage Scheduled block for the load controls. 1 if distribution line l is hardened; 0 otherwise 1 if a back-up DG m is installed; 0 otherwise 1 if a back-up DG m is placedat bus b; 0 otherwise Binary variable indicating that bus b belongs to microgrid k 1 if line is active; 0 otherwise 1 if line in microgrid k is active; 0 otherwise Fictional flows on the distribution line l Binary variable for microgrid formation Fictional supply of master DG

INTRODUCTION

CONCLUSION