A Comprehensive Network Restoration Model for Active Distribution Network Considering Forecast Uncertainty

Guanghe Wang,Kai Sun,Lijun Wang,Xiang Lei,Yi Ding,Che Wang,Han Wu

doi:10.1109/access.2021.3109071

Abstract

The active distribution network management (ADNM) equipped by active distribution networks (ADNs) can enhance the resilience of the network after failure. This paper proposes a novel comprehensive post-event network restoration model and its chance-constrained variant for ADN that considering the dispatch of distributed generation (DG), energy storage system (ESS), demand response (DR), static var compensator (SVC), and network reconfiguration to fully investigate the potential of the ADNM in network restoration. In this paper, the line failure, bus failure, and the uncertainty from load and DG output forecast error are considered. Specifically, the line and bus failures are modeled by an enhanced fictitious network technique, while the uncertainty of load and DG output forecast is modeled by the chance-constrained optimization. The power flow is described by a linearized DistFlow model, and thus the deterministic and chance-constrained network restoration models are programmed by the mixed-integer linear programming (MILP). The proposed deterministic and chance-constrained restoration models are tested on the IEEE 33 bus system. Results demonstrate the effectiveness of the proposed deterministic and chance-constrained network restoration model.

Highlights

Recent extreme weather events pose unprecedented challenges to the power grid
In [17] the uncertainty of distributed generation (DG) output and load demand is handled by information gap decision theory, which is a kind of variation of robust optimization
This paper aims to bridge the gap in the network restoration problem with full active distribution network management (ADNM) schemes

Summary

INTRODUCTION

Recent extreme weather events pose unprecedented challenges to the power grid. Due to the limited back-up resources and fixed radial network structure, traditional distribution networks are extremely vulnerable to natural disasters (e.g., windstorm, earthquake, flood). The graphtheoretic based spanning-tree search method still requires a metaheuristic algorithm to handle Another formulation method in network restoration problem is mathematical programming. G. Wang et al.: Comprehensive Network Restoration Model for ADN Considering Forecast Uncertainty on the fictitious network technique, reference [6] combines renewable and thermal DGs, energy storage system (ESS), and micro-grid (MG) formulation to mitigate load shedding when contingency occurs. In [17] the uncertainty of DG output and load demand is handled by information gap decision theory, which is a kind of variation of robust optimization. The DG outputs, the ESS, microgrid formulation, and ADNM strategies (i.e., network configuration, SVC adjustment, demand response) are considered in a single network restoration model. 2) a chance-constrained variant for the proposed comprehensive network restoration framework is developed, which consider the uncertainty from load and DG output forecast.

DETERMINISTIC NETWORK RESTORATION MODEL

NETWORK RESTORATION RESULTS OF THE CHANCE-CONSTRAINED MODEL

CONCLUSION