Critical Load Restoration using Distributed Energy Resources for Resilient Power Distribution System

Abstract: Extreme weather events have a significant impact on the aging and outdated power distribution infrastructures. These high-impact low-probability (HILP) events often result in extended outages and loss of critical services, thus, severely affecting customers' safety. This calls for the need to ensure resilience in distribution networks by quickly restoring the critical services during a disaster. This paper presents an advanced feeder restoration method to restore critical loads using distributed energy resources (DERs). A resilient restoration approach is proposed that jointly maximizes the amount of restored critical loads and optimizes the restoration times by optimally allocating grid's available DER resources. The restoration problem is modeled as a mixed-integer linear program with the objective of maximizing the resilience to post-restoration failures while simultaneously satisfying grid's critical connectivity and operational constraints and ensuring a radial operation for a given open-loop feeder configuration. Simulations are performed to demonstrate the effectiveness of the proposed approach using IEEE 123-node feeder with 5 DERs supplying 11 critical loads and IEEE 906-bus feeder with 3 DERs supplying 17 critical loads. The impacts of DER availability and fuel reserve on restored networks are assessed and it is shown that the proposed approach is successfully able to restore a maximum number of critical loads using available DERs.

• The paper introduces a MILP optimization method to restore critical loads with DERs in disrupted power networks.
• It validates the approach through simulations on IEEE 123-node and 906-bus systems, demonstrating improved resilience under varying damage scenarios.
• The study benchmarks its method against existing models, highlighting practical strategies for effective disaster recovery.

Critical Load Restoration Using Distributed Energy Resources for Resilient Power Distribution System

The paper "Critical Load Restoration using Distributed Energy Resources for Resilient Power Distribution System" (1912.04535) addresses the resilience challenges in power distribution networks posed by extreme weather events. It proposes a method for restoring critical loads using Distributed Energy Resources (DERs) during major disruptions. The following sections detail the paper's methodology, the optimization problem formulation, the simulation results, and the implications for real-world application.

Restoration Problem Formulation

The paper proposes a mixed-integer linear programming (MILP) approach to solve the critical load restoration problem. The model frames the power distribution network as a graph with nodes representing buses and edges representing distribution lines. DERs are managed to form restored subtree networks (RSNs) that provide power to critical loads based on their availability, capacity, and resilience to further disruptions.

Objective Function

The primary objective is to maximize the resilience of restored networks by minimizing an effective restoration unavailability metric ($U_R$), which incorporates both path reliability and DER availability. The MILP formulation is designed to optimize the allocation of DER resources while ensuring radial network operation and meeting operational constraints such as voltage limits and capacity bounds.

Constraints

1. Connectivity and Topology Constraints: Ensure the network remains connected and radial by incorporating nodal assignment variables that dictate how nodes and DERs form RSNs.
2. Power Flow Constraints: Utilize a linearized version of the DistFlow model to maintain accurate power distribution throughout the network.
3. Operational Constraints: Include DER capacity and restoration duration constraints to optimize the equitable allocation of energy across critical loads.

Simulation and Results

The methodology is evaluated using standard IEEE 123-node and 906-bus test feeders, with scenarios representing both minor and major network damages.

Figure 1: IEEE 123-node distribution system with simulated locations and parameters of DERs and Critical Loads.

Minor Damage Assessment

For cases of minor damages, the paper demonstrates the restoration framework's capability in forming reliable RSNs and highlights the impact of DER availability on restoration strategies. It shows the different restoration paths and biases in restoration times when availability and energy reserve considerations are adjusted.

Major Damage Assessment

The proposed approach shows efficacy in scenarios with multiple faults by maximizing the number of critical loads restored, despite significant distribution network damage. The study showcases optimization under real-world constraints like limited DER availabilities and load demands.

Figure 2: IEEE 906-bus test feeder with simulated locations of DERs and Critical Loads.

Comparison with Existing Methods

Performance is benchmarked against heuristic and MILP-based methods in literature, such as those by [gao2016resilience] and [chen2016resilient]. The proposed MILP model uniquely integrates tie-switch configurations giving it an edge in situations with numerous network faults.

Implications and Future Research

The paper is pivotal in bridging conceptual approaches to practical solutions for disaster-affected distribution systems. The incorporation of DERs significantly factors into future grid designs that prioritize resilience and quick restoration of services. However, actual deployment raises considerations for the investment in necessary infrastructure for remote operation capabilities.

Furthermore, the model opens pathways for future research into dynamic control strategies and real-time optimization under uncertainty, as well as integration with evolving smart grid technologies.

Conclusion

This paper contributes a methodical solution to enhancing power distribution network resilience through adaptive DER use and optimal restoration strategies. The experiments validate the model's applicability to real-world scenarios, particularly in high-stakes disaster recovery, underscoring its potential to transform responses to grid failures in vulnerable infrastructures.