Resilience Of Distribution Systems Research Articles

Rolling Optimization of Mobile Energy Storage Fleets for Resilient Service Restoration

Mobile energy storage systems (MESSs) provide promising solutions to enhance distribution system resilience in terms of mobility and flexibility. This paper proposes a rolling integrated service restoration strategy to minimize the total system cost by coordinating the scheduling of MESS fleets, resource dispatching of microgrids and network reconfiguration of distribution systems. The integrated strategy takes into account damage and repair to both the roads in transportation networks and the branches in distribution systems. The uncertainties in load consumption and the status of roads and branches are modeled as scenario trees using Monte Carlo simulation method. The operation strategy of MESSs is modeled by a stochastic multi-layer time-space network technique. A rolling optimization framework is adopted to dynamically update system damage, and the coordinated scheduling at each time interval over the prediction horizon is formulated as a two-stage stochastic mixed-integer linear program with temporal-spatial and operation constraints. The proposed model is verified on two integrated test systems, one is with Sioux Falls transportation network and four 33-bus distribution systems, and the other is the Singapore transportation network-based test system connecting six 33-bus distribution systems. The results demonstrate the effectiveness of MESS mobility to enhance distribution system resilience due to the coordination of mobile and stationary resources.

Distributionally Robust Distribution Network Configuration Under Random Contingency

Topology design is a critical task for the reliability, economic operation, and resilience of distribution systems. This paper proposes a distributionally robust optimization (DRO) model for designing the topology of a new distribution system facing random contingencies (e.g., imposed by natural disasters). The proposed DRO model optimally configures the network topology and integrates distributed generation to effectively meet the loads. Moreover, we take into account the uncertainty of contingency. Using the moment information of distribution line failures, we construct an ambiguity set of the contingency probability distribution, and minimize the expected amount of load shedding with regard to the worst-case distribution within the ambiguity set. As compared with a classical robust optimization model, the DRO model explicitly considers the contingency uncertainty and so provides a less conservative configuration, yielding a better out-of-sample performance. We recast the proposed model to facilitate the column-and-constraint generation algorithm. We demonstrate the out-of-sample performance of the proposed approach in numerical case studies.

Sustainable and Resilient Distribution Systems With Networked Microgrids [Point of View

Grid modernization calls for increasing requirements of electric grid operation with enhanced sustainability and resilience [1] . In particular, distribution grids serve as a critical venue to bridge bulk upstream transmission and generation systems and a large number of downstream end users on the customer side, playing a significant role in modern electric grids for multiple purposes (e.g., renewable energy integration, power flow distribution, and end-user power quality enhancement) [2] . Under a normal grid operation condition, the increasing penetration level of renewable energy sources imposes new challenges on conventional distribution grid infrastructure (e.g., protection malfunction [3] and voltage violation [4] ); on the other hand, in an extreme grid operation scenario, it is urgently needed to restore grid services after severe power outages, such as those caused by natural disasters [5] . In particular, for critical infrastructures, an efficient grid service restoration strategy should be implemented to avoid further damage over an extended period of time.

ResilientDB

Recent developments in blockchain technology have inspired innovative new designs in resilient distributed and database systems. At their core, these blockchain applications typically use Byzantine fault-tolerant consensus protocols to maintain a common state across all replicas, even if some replicas are faulty or malicious. Unfortunately, existing consensus protocols are not designed to deal with geo-scale deployments in which many replicas spread across a geographically large area participate in consensus. To address this, we present the Geo-Scale Byzantine Fault-Tolerant consensus protocol (GeoBFT). GeoBFT is designed for excellent scalability by using a topological-aware grouping of replicas in local clusters, by introducing parallelization of consensus at the local level, and by minimizing communication between clusters. To validate our vision of high-performance geo-scale resilient distributed systems, we implement GeoBFT in our efficient ResilientDB permissioned blockchain fabric. We show that GeoBFT is not only sound and provides great scalability, but also outperforms state-of-the-art consensus protocols by a factor of six in geo-scale deployments.

A resilience assessment of an interdependent multi-energy system with microgrids

ABSTRACT Multi-energy systems (MES) depend on various energy sources to supply reliable and constant energy for our daily activities. The interdependencies between various energy sources could cause the MES to become vulnerable to unexpected failures. To enhance the resilience of a power distribution system against failures, incorporating microgrids has been deemed as one of the most effective strategies. Studies on the microgrid’s impact on the interdependent MES is thus far still limited. In this paper, the impact of adding microgrids on the resilience of an interdependent gas-power network is assessed. Graph and network theories are employed to model the interdependent gas-power network, and resilience assessments are performed to compare the effectiveness of different addition scenarios of microgrids to the network. The findings from this study will provide insights toward developing more advanced microgrid management systems as an approach to realize resilience in MES.

Approaches for optimal planning of the energy storage units in distribution network and their impacts on system resiliency — A review

In the recent decade, a significant increase in the penetration level of renewable energy sources (RESs) into the distribution grid is evident due to the world's shift towards clean energy and to increase the reliability or inboard manner resiliency of electrical distribution system. RES based microgrids are the most favorable option available, especially to enhance resiliency. However, the integration of RES over the distribution grid would hamper the grid stability due to its stochastic nature under normal conditions. During extreme weather conditions, RES behavior is completely uncertain. Hence there is a need to eliminate the adverse effects caused by the RES and make the distribution grid more reliable and stable under normal and resilient conditions. To address these issues, many researchers proposed several methods to place energy storage units (ESUs) and microgrids (RES integrated), which can support critical loads at an optimal location in the distribution system during normal and extreme conditions, respectively. The aim of this article is to consolidate and review the research towards various approaches to formulate the problem (optimal location, allocation, and operation of ESU and microgrids to face regular and extreme weather condition) and tools to solve it for enhanced system flexibility and resiliency. Based on the review, a generalized methodology has been designed to adapt the inputs and address both conditions. At the end of the review, future aspects for ESU to strengthen resistance and resiliency of its own are presented, which can be helpful to further improve the reliability and resiliency of the distribution system.

Minimax-Regret Robust Co-Optimization for Enhancing the Resilience of Integrated Power Distribution and Natural Gas Systems

Natural gas system that supplies flexible and distributed energy resources can play a significant role in enhancing the resilience of the power distribution system. In this paper, a minimax-regret robust resilience-constrained unit commitment model is proposed for enhancing the resilience of the integrated power distribution and natural gas system (IDGS). First, a tri-level robust cooperation optimization problem is formulated based on the minimax-regret robust criterion to enhance the distribution system resilience against worst N-k contingencies. The multi-stage extreme weather model is considered to obtain the spatial dynamics of extreme weather. Then, the proposed robust model is reformulated as a mixed-integer convex programming model by relaxing nonconvex power and natural gas flow equations into second-order conic (SOC) constraints. The SOC-based column-and-constraint generation algorithm is employed to solve the proposed two-stage robust optimization problem. The effectiveness of proposed robust optimization model is validated using several case studies applied to the 33-bus-6-node and 123-bus-20-node IDGSs.

Optimal Life-Cycle Resilience Enhancement of Aging Power Distribution Systems: A MINLP-Based Preventive Maintenance Planning

Power distribution systems in the US are commonly supported by wood utility poles. These assets require regular maintenance to enhance the reliability of power delivery to support many dependent functions of the society. Limitations in budget, however, warrant efficient allocation of limited resources based on optimal preventive maintenance plans. A few studies have developed risk-based metrics to support risk-informed decision making in preventive maintenance planning for power distribution systems. However, integration of risk-based metrics and optimization for enhancing the life-cycle resilience of distribution systems has not been explored. To address this gap, this paper proposes a mixed-integer nonlinear programming (MINLP) model to maximize the life-cycle resilience of aging power distribution systems subject to multi-occurrences of hurricane events using an optimal risk-based maintenance planning. For this purpose, a risk-based index called the Expected Outages is proposed and integrated into the optimization problem to minimize the total expected number of power outages in the entire planning horizon. Various uncertainties in the performance of poles under stochastic occurrences of hazards are taken into account through advanced fragility models and an efficient recursive formulation that models the uncertainty of precedent pole failures. The proposed approach is applied to a large, realistic power distribution system for long-term maintenance planning given a total budget limit and different levels of periodic budget constraints. The resulting optimization problems are solved through the branch and bound algorithm. Results indicate that applying the presented methodology leads to a significant enhancement of the life-cycle resilience of distribution systems compared to the commonly implemented strength-based maintenance strategy set by National Electric Safety Code.

Using Microgrids for Critical Load Restoration in Distribution Systems

Abstract This paper proposes a restoration strategy using microgrids to restore power to critical loads to enhance distribution system resilience after an extreme event. The restoration problem is posed as an optimization problem to maximize the number of critical loads restored after the extreme event has resulted into multiple faults. After the fault, the restoration strategy alters the topological structure of the distribution network by removing the faulted lines and applies minimum spanning tree algorithm to find a possible topology that will be used for the restoration. The service time to the restored critical loads is incorporated as one of the constraints to ensure continuous supply for the duration of the outage. The problem is modeled as a mixed-integer linear program (MILP) and solved using the CPLEX solver on the MATLAB platform. The effectiveness of the proposed restoration strategy is validated using the modified IEEE 33-bus test system under multiple fault scenarios that represent damage resulted from the extreme event.

Microgrid Control, Storage, and Communication Strategies to Enhance Resiliency for Survival of Critical Load

Adequately, as far as the global system is concerned with the variation in the climatic condition, the frequency of disaster is rising, resulting in various damages to the power grid. To cope with the power network problems due to disasters such as grid outages, frequency, and voltage deviation, the network should be incorporated with numerous distributed generations such as solar and wind. During the disaster condition, these distributed fossil generations form Microgrids (MGs) by disconnecting itself from the grid and maintain power flow to the local region. Besides, the negative impacts on the environment, such as carbon emission has reduced by using Renewable Energy Sources (RES). Apart from reducing carbon emission (as in the case of fossil fuel generation plants), RES based microgrid also useful for the resilient distribution system. However, high penetration of distributed generation, for a resilient system that can survive at least its critical loads during extreme disaster conditions, requires robust architecture and communication between the devices of a microgrid. This article presents the latest review of the various classification of microgrid architecture along with the technical characteristics of energy storage devices, various communication channels and discover the gaps to form a bridge between microgrid in normal and abnormal (during a climatic disaster) conditions. In addition, alteration in control techniques for Alternative Current and Direct Current microgrid for robust MGs is presented systematically. Furthermore, the latest developments with sectionalizer placement to provide the steps to achieve near-real-time data and necessary actions required to take during or before the actual disaster are also presented in a systematic manner.

A Review Paper on the Role of FACTS and Resilient AC Distribution Systems in the Development of an Intelligent Power System

A Review Paper on the Role of FACTS and Resilient AC Distribution Systems in the Development of an Intelligent Power System

Markov Decision Process-Based Resilience Enhancement for Distribution Systems: An Approximate Dynamic Programming Approach

Because failures in distribution systems caused by extreme weather events directly result in consumers' outages, this paper proposes a state-based decision-making model with the objective of mitigating loss of load to improve the distribution system resilience throughout the unfolding events. The sequentially uncertain system states, e.g., feeder line on/off states, driven by the unfolding events are modeled as Markov states, and the probabilities from one Markov state to another Markov state throughout the unfolding events are determined by the component failure caused by the unfolding events. A recursive optimization model based on Markov decision processes (MDP) is developed to make state-based actions, i.e., system reconfiguration, at each decision time. To overcome the curse of dimensionality caused by enormous states and actions, an approximate dynamic programming (ADP) approach based on post-decision states and iteration is used to solve the proposed MDP-based model. IEEE 33-bus system and IEEE 123-bus system are used to validate the proposed model.

Improving the Resilience of Postdisaster Water Distribution Systems Using Dynamic Optimization Framework

Improving the resilience of water distribution systems (WDSs) to handle natural disasters (e.g., earthquakes) is a critical step toward sustainable urban water management. This requires the water utility to be able to respond quickly to such disaster events, and in an organized manner, to prioritize the use of available resources to restore service rapidly while minimizing the negative impacts. Many methods have been developed to evaluate the WDS resilience, but few efforts are made so far to improve the resilience of a postdisaster WDS through identifying optimal sequencing of recovery actions. To address this gap, the authors propose a new dynamic optimization framework in this study in which the resilience of a postdisaster WDS is evaluated using six different metrics. A tailored genetic algorithm is developed to solve the complex optimization problem driven by these metrics. The proposed framework is demonstrated using a real-world WDS with 6,064 pipes. Results obtained show that the proposed framework successfully identifies near-optimal sequencing of recovery actions for this complex WDS. The gained insights, conditional on the specific attributes of the case study, include the following: (1) the near-optimal sequencing of a recovery strategy heavily depends on the damage properties of the WDS; (2) replacements of damaged elements tend to be scheduled at the intermediate-late stages of the recovery process due to their long operation time; and (3) interventions to damaged pipe elements near critical facilities (e.g., hospitals) should not be necessarily the first priority to recover due to complex hydraulic interactions within the WDS.

Enhancing Distribution System Resilience With Proactive Islanding and RCS-Based Fast Fault Isolation and Service Restoration

The resilience of power systems against extreme events has become a growing concern in recent years. In view of the fact that the resilience of a distribution system depends on its system function throughout the entire multi-stage recovery process after extreme events, and to comprehensively improve the abilities of distribution systems in resisting and rapidly recovering from extreme events, it is necessary to consider this multi-stage recovery process. In this paper, a novel resilience enhancement method is proposed, where proactive islanding and RCS (remote-controlled switch)-based fast fault isolation and service restoration are comprehensively considered to improve the abilities of distribution systems in resisting and rapidly recovering from extreme events. The multi-stage recovery process is modeled in detail, including the topological and operational constraints of each stage in the multi-stage recovery process, as well as the coupling relationship among the stages. The model is formulated as a mixed-integer linear programming problem and applies to scenarios where the network topology varies. Resilience metrics based on the proposed method are introduced to evaluate the resilience level. Case studies on the IEEE 33-bus system and the real-world 94-bus system show that the proposed method is effective at resilience enhancement.

Distribution system resilience enhancement by microgrid formation considering distributed energy resources

Due to increasing in natural disasters in the recent years, the issue of distribution network resilience has become highly important. Microgrids with different types of distributed energy resources have the capabilities to improve distribution network resilience under extreme events. In this paper, we present a mixed-integer linear program to restore prioritized loads while satisfying topology and operational constraints. In the presented model, we study dynamic microgrid formation and optimal management of various smart grid technologies such as distributed generations, demand response programs, wind turbines and energy storage units. We also investigate the significant impact of renewable energy resources, loads and their uncertainty on distribution system resilience. In addition, we determine the required emergency budgets of operation to restore a distribution system from extreme events. We also intend to examine the effects of demand response programs on improving the distribution system performance in the recovery period. Finally, we verify the effectiveness of the presented model on a modified IEEE 33-node test system and a real distribution system.

A novel planning‐attack‐reconfiguration method for enhancing resilience of distribution systems considering the whole process of resiliency

A novel planning‐attack‐reconfiguration method for enhancing resilience of distribution systems considering the whole process of resiliency

An efficient interactive framework for improving resilience of power-water distribution systems with multiple privately-owned microgrids

Resilience improvement of power distribution networks against natural disasters is an important problem. Water network similar to other important infrastructures depends on power networks. In this paper, resilience improvement is defined as increasing the users’ accessibility to water and power after natural disasters. Microgrids with appropriate operation can provide energy to restore disconnected loads in distribution networks. In the proposed interactive framework, a stochastic energy management program for microgrids is designed that not only determines the amount of energy can be delivered to distribution systems, but also considers the reliability of local loads during emergency conditions. Each microgrid provides a list of bid-quantity energy blocks to the distribution system operator (DSO) during the emergency period. Then, the DSO chooses the best plan to restore disconnected loads considering inaccessibility values to power and water and also the damage of power and water distribution networks. Demand response actions in microgrids are also considered as effective tools for the energy management program, and their impact on the distribution system resilience is investigated. The proposed model is tested on the modified IEEE 33-bus distribution system with multiple microgrids, and the effectiveness of the proposed method is validated accordingly.

Energy resilience through self-organization during widespread power outages

ABSTRACT Resilience of power systems is highly impacted by factors such as increasing severity and frequency of weather events, but also smart grid advances that introduce major operational changes in power systems. Rapidly adapting to these changing circumstances and harnessing the potential of technological advances is the key to ensuring that power systems stay operational during disturbances, thereby improving resilience. This paper addresses this challenge by presenting an approach for improving resilience through local energy resource sharing across multiple distribution systems. The approach brings together the physical and the ICT layer of power systems through a self-organization approach that automatically alters the physical grid topology and forms local energy groups in order to mitigate the effects of widespread outages. Thereby, supply and demand are locally matched, and demand met is maximized during an outage. The results demonstrate that using the proposed approach, operational resilience of impacted distribution systems is improved.

Risk-Based Probabilistic Quantification of Power Distribution System Operational Resilience

It is of growing concern to ensure the resilience in electricity infrastructure systems to extreme weather events with the help of appropriate hardening measures and new operational procedures. An effective mitigation strategy requires a quantitative metric for resilience that can not only model the impacts of the unseen catastrophic events for complex electric power distribution networks, but also evaluate the potential improvements offered by different planning measures. In this article, we propose probabilistic metrics to quantify the operational resilience of the electric power distribution systems to high-impact low-probability (HILP) events. Specifically, we define two risk-based measures: value-at-risk ( $VaR_\alpha$ ) and conditional value-at-risk ( $CVaR_\alpha$ ) that measure resilience as the maximum loss of energy and conditional expectation of a loss of energy, respectively, for the events beyond a prespecified risk threshold, $\alpha$ . Next, we present a simulation-based framework to evaluate the proposed resilience metrics for different weather scenarios with the help of modified IEEE 37-bus and IEEE 123-bus system. The simulation approach is also extended to evaluate the impacts of different planning measures on the proposed resilience metrics.

An exact microgrid formation model for load restoration in resilient distribution system

The increase in extreme weather affects the distribution system, leading to system collapse and interruption power supply to loads. Microgrid formation and operation in island mode is a resilient solution when major faults happen in the distribution system. This paper proposes a novel microgrid formation model considering power loss and voltage constraints in the power balance and operation feasibility. The introduction of binary variables to represent the reconfigured network and the adoption of non-linear equations to represent the exact power flow will lead the microgrid formation model to be a mixed integer nonlinear program (MINLP). This paper proposes an iteration based linear approximation of the MINLP, the exactness of which can be guaranteed by the convergence of the iteration process. Numerical results based on IEEE 33 and PE&G 69 bus standard distribution systems demonstrate the effectiveness of the developed method.