A review of key strategies in realizing power system resilience

1 Introduction

Energy lies at the backbone of any advanced society.The world depends on reliable and affordable distribution of energy[1], [2]. The power systems are designed to resist stochastic component outage under the N-1 security principle. However, recently many natural disasters have brought unprecedented challenges to the power system,highlighting the situation that the power system is illprepared for extreme events of large scale and severity level, e.g., in 2008, a snow storm hit Southern China and caused over 129 line faults, leading to power loss for 14.66 million households; in the Great East Japan Earthquake in 2011, over 4 million households suffered from power outage for over seven to nine days; in 2012, Hurricane Sandy landed on the east coast of the United States and caused power outage for millions of people; in 2016, a tornado hit Jiangsu Province, China, which tripped over two 500kV transmission lines, four 220kV transmission lines, eight 110kV transmission lines, and caused power outage for 135000 households[3]. It has been noted that the power system is reliable but not necessarily resilient[4].It has become more apparent that further considerations beyond the traditional system reliability analysis are needed for keeping the lights on at all times.

The increasing frequency of these disruptions has made the research of power system resilience urgent. In 2009, the U.S. Department of Energy (DOE) claimed that resilience should be a characteristic of the smart grid. Two U.S. Presidential Policy Directives, PPD-8 and PPD-21,specifically addressed the national preparedness for critical infrastructure, and emphasized that the power system is uniquely critical due to its enabling functions across all other critical infrastructures.

Since its advent, resilience of critical infrastructure, in particular the power system, becomes the focus of utilities and researchers. There are abundant review papers on the concept, evaluation, and realization of resilience[5]-[7], and the application of smart grid technologies for better preparation against these disruptions has been investigated[8]-[11]. These papers lay solid foundations for future development. However, many of these papers are published when the concept of resilience was first introduced to the critical infrastructure. As there lacks a consensus of what resilience stands for, the focus of most of the early reviews is the appropriate definition of the concept. Readers can refer to [12] [13] for a review of resilience theory in general, and [3] [5] for a thorough examination of resilience theory and evaluation in the power system.

In recent years, the number of resilience application researches is steadily growing. As many aspects of the power system resilience are being realized with new technology, such as distributed energy resources (DER)integration, intentional islanding, microgrids, etc., it is urgent to carry out a systematic review of these new research efforts and directions. This paper summarizes the recent major research efforts and presents their contribution accordingly. The most-investigated problems are categorized according to their problem category, stage,hierarchy and model.

The rest of the paper is organized as follows： section 2 introduces the concept and aspects of resilience; section 3 summarizes the research that realizes resilience in different stages; and section 4 concludes the paper.

2 Understanding resilience

Though consensus on resilience definition is lacking, the essence of resilience definitions is generally the same, that is, it is an overarching concept that encompasses the system performance before, during and after disastrous events.Resilience therefore can be defined as “the ability of an entity to anticipate, resist, absorb, respond to, adapt to and recover from a disturbance” [12], as illustrated in Fig. 1.

Fig. 1 Illustrative process of a resilient power system through disruptions

For researchers and utility grids, it is becoming clear that it is not possible to resist all events at all time, and strategies beyond traditional reliability study is needed for keeping the lights on under extreme events. To realize resilience calls for various different capabilities and various subsystems. From the perspective of critical infrastructure,four domains, which emphasize situational awareness and decentralized decision-making, contribute to enhancing resilience. The overarching concept of resilience can best be understood through a scoring matrix proposed by [14]and shown in Table 1, where each cell within the matrix can be used to examine a limited aspect of capabilities and posture while the comprehensive overall structure provides for holistic treatment of inter-related systems.

Table 1 Capability and domain of resilience

Capability Domain Plan Absorb Recover Adapt Physical State and capability of equipment and personnel,network structure Event recognition and system performance to maintain function System changes to recover previous functionality Changes to improve system resilience

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Capability Domain Plan Absorb Recover Adapt Information Data preparation,presentation, analysis and storage Real time assessment of functionality, anticipation Data use to track recovery progress and anticipate recovery scenarios Creation and improvement of data storage and use protocols Cognitive System design and operation decision, with anticipation of adverse events Contingency protocols and proactive event management Recovery decisions-making and communication Design of new system configuration, objectives,and decision criteria Social Social network, social capital, institutional and cultural norms, and training Resourceful and accessible personnel and social institutions for event response Teamwork and knowledge sharing to enhance system recovery Addition of or changes to institutions, policies,training programs, and culture

Our previous work has dealt with the resilience evaluation[3]. The approaches to evaluate resilience can be generally classified as two different categories： qualitative and quantitative methods. These approaches are summarized in Fig. 2. In the qualitative evaluation, the different aspects and different resilience capabilities can be considered simultaneously. It can be seen that the quantitative resilience evaluation contains three categories：the simulation-based method, the analytic method, and the statistical analysis. Among them, the simulationbased method is most widely used because it can be easily combined with disaster scenarios and the disaster consequence can be readily calculated. The analytical method, on the other hand, exploits the probability of system failure in a certain situation. For systems that have accumulated past natural disaster event data, historic outage and restoration records can be used for data analysis.

Fig. 2 Evaluation methods of resilience

The evaluation of resilience is the basis to the researches of resilience enhancement. These days, the power system can achieve resilience with the aid of many edge-cutting technologies. These can be classified as planning measures and operational measures, as shown in Table 2. The planning can be carried out by hardening the system to a higher standard.Operational measures, especially smart grid technology,allow the operators of the system to access outage information within minutes of the disruption and take operative measures.Sometimes, due the limit of budget, the system planners should draw a balance between such two different types of measures. In the following sections, the specific techniquesthe researchers develop to harden the system or to efficiently restore from faults will be discusses.

Table 2 Planning an operational measures towards energy system resilience

Hardening measures Operational resilience strategies Short term√Reserve planning√Black-start capabilities installed√Repair crew member mobilization√Installation of DER or other onsite generation units√Coordination with adjacent networks, and repair crews√Accurate estimation of the weather location and severity√Demand side management√Fast topology reconfiguration√Microgrid island operation√Automated protection and control actions： load and generation rejection, system separation, etc.Long term√Tree trimming/vegetation management√Undergrounding the distribution/ transmission lines;√Upgrading poles and structures with stronger, more robust materials√Elevating substations and relocating facilities to areas less prone to flooding√Redundant transmission routes Redundant transmission routes by building additional transmission facilities√Monitoring： development of situation awareness; advanced visualization and information systems√Ensure communications functionality√Microgrids√Advanced control and protection schemes, such as system integrity protection schemes (SIPS)√Disaster assessment and priority setting√risk assessment and management for evaluating and preparing for the risk introduced by such events

3 Resilience improvement research overview

In this paper, reference [16]-[53] are selected and reviewed. They can be classified according to the following criteria, see Fig. 3. A detailed list of the paper is presented in Table 3.

Problem category： the problems addressed in these researches are generally falling into two categories, evaluation or decision-making. In the evaluation category,resilience is defined, analyzed and evaluated. The latter two categories deal with decision making problem throughout disruptions, which can be further classified as planning decisions and operational decisions.

Fig. 3 Research criteria

Hierarchy： the power system is a hierarchical system that contains the generation, transmission and distribution.Since disasters mostly affect the transmission system and distribution system, the researches have been carried out in these two levels, but the emphasis is leaning on the distribution system.

Model： modeling and simulation of energy infrastructures is constrained by physical constraints and topology constraints. The mathematical models used in the power system analysis are usually a) optimal power flow models and b) complex network models. A detailed comparison is in [15]. A multi-agent model can also be used to model the interaction between components in the power system.

Stage： bearing in mind the definition of resilience, it can be seen that the resilience usually encompasses three stages, which corresponds to the definition： before the disruption, during the disruption, and after the disruption.Most of the resilience researches are carried out in the corresponding stage, which is shown in Fig. 4.

Fig. 4 Resilience research at different stage

Table 3 Detailed list of the selected reference

Stage Reference number Problem category Herarcny Model Transmission Distribution Power flow Complex network Multi-agent[16-19,23]√√Before [20-22] √ √Planning During[24] Evaluation √ √[25] Evaluation √ √[26,27,29] Planning √ √[28] Planning √√[30] Operational √ √After-optimal topology reconfiguration and islanding[31] Operational √ √[32] Evaluation √ √[33-36] Operational √ √[37] Operational √√[38]Evaluation√√[39,40] Operational √ √[41] Planning √ √

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Stage Reference number Problem category Hierarcny Model Transmission Distribution Power flow Complex network Multi-agent After-optimal EMS[42-45,47-48] Operational √ √[46] √√After-resource allocation[49-52] Planning Operational √ √[53]

4 Power system resilience research analysis

In section, we review the researches according to their corresponding stage. We will see the problem formulation and application.

4.1 Before the disruption

In this stage, the system planners will envision damage scenarios and try to prepare the system for such scenarios.The problem is usually formulated as a two-stage problem.The first stage selects from a set of potential upgrades to the network. The second stage evaluates the network performance benefit of the upgrades with a set of damage scenarios. The second-stage uses different fault scenarios,e.g. in [16] the transmission system is hardened against stochastic fault scenarios. When the worst case attack scenario is considered for the second stage, the defenderattacker-defender (DAD) model can be used. In a tri-level defender-attacker-defender model, the defender (hardening planner) selects a network hardening plan in the first stage, the attacker (terrorist or natural disaster) disrupts the system with an interdiction budget, and the defender (system operator) responds by microgrid islanding operation or reconfiguration in order to minimize the shedded load.In this case, the third level is also considered, where the system operation can be corrected under attack. In [17]and [18] the optimal protection plan for the transmission system is proposed, while DC-optimal power flow model is used to model the corrective actions from the system operator. Distribution system can be hardened by similar methods. The DAD model is applied in the distribution system in [19]-[21]. [19] considered the maximum attack model and proposed optimal reconfiguration & DER-islanding as a defender strategy. [20] and [21] considered the occurrence of natural disasters and in the hardening phase, not only line hardening, but also DER placement,pole undergrounding and vegetation management are taken as resilience-improvement strategy.

Another direction of research enforces planning considering resilience requirement. [22] formulate an optimal electrical distribution grid design problem as a two-stage,stochastic mixed-integer program with damage scenarios from natural disasters modeled as a set of stochastic events.A two-stage stochastic program and heuristic solution of hardening strategy were proposed in [23], specifically for earthquake hazards, under the assumption that the repair times for similar types of components follow a uniform distribution, which simplifies the problem to a certain extent.

4.2 During the disruption

During the disruption, the research focus has shifted to the attacker’s side. While system planner and operators have resources to restore the system before and after the extreme events, there are researches that tackle the optimal attack problem. It is vital to learn the frequency and pattern of the disruptions, such as natural disasters or man-made attacks, because by studying how to attack, researchers can explore new defending measures of a power system. In the reliability evaluation, stochastic failures of the power system has been sampled by Monte-Carlo simulation in the power system. But most of the fault scenario considered in the reliability evaluation is N-1 fault. When considering resilience, the focus is those low probability events, which can cause serious damage to the power system. knowing the severity and characteristics of the attack is key to implement effective resilient preparation.

One branch of such research is to investigate the unique characteristics of the natural disaster, such as the influence of disasters on component failure, e.g. hurricane,earthquake, flood. [24] provides probabilistic models for hurricane occurrence and component failures and evaluate the power system risk in North America considering hurricanes. [25] proposed a simulation based resilience evaluation framework where hurricane occurrence is taken into consideration, component failures are sampled with using Monte Carlo simulation, and then the DC power flow model is used to simulate the system function.

Another branch is to find the worst-case fault scenario for the power system. Such research is meaningful for system planner to identify the most vulnerable components under attack. In addition, it is vital to have knowledge of such attacks if the planner is solving a robust planning or hardening problem. Some researchers have studied the power grid security problems under deliberate attacks by terrorists. [26] firstly formulized the terrorism attack problem in power systems, in which terrorists try to maximize the load shed. [27] proposed a two-level model to find the worst-case N-K failures in the power system. In the abovementioned DAD models, a similar attack scenario is assumed. According to complex network theory, [28] proposed different types of attacks to take down the most critical nodes. [29] proposed a new attack scenario, called the sequential attack, which assumes that substations/transmission lines can be removed sequentially, not synchronously.The sequential attack can identify more combinations of substation whose failures can cause large blackout size.

4.3 After the disruption

Fast restoration is the most salient feature of resilient power system, thus the majority of the research is in the field of post-disruption restoration stage. With the rapid spread of smart grid technology, the system operators are empowered with diverse strategies to deal with faults. We divide the current researches into three major subcategories as below. They are concerned with reconfiguration and DG islanding, optimal energy management algorithm, and resource allocation, respectively.

4.3.1 Optimal reconfiguration and DG islanding

One significant benefits of the smart active system is its capability to detect outages and remotely reroute electricity to undamaged circuits and feeders. In the transmission system, intentional islands can form to isolate fault and avoid cascading failure. [30] develops a new transmission network reconfiguration algorithm for restorative selfhealing based on the concept of “electrical betweeness”,considering the relative importance and restoration priority of non-black-start generators and important loads.Moreover, [31] proposed a splitting strategy by opening the transmission lines with minimum power exchanged, while guaranteeing that at least one black start unit within each island and assures sufficient generation capability to match the load consumption within each island. This strategy is incorporated into a Sequential Monte-Carlo risk assessment framework in [32], where the defensive islanding splits the system into stable and self-adequate islands in order to isolate vulnerable components, whose failure would trigger cascading events.

In the distribution system, power system reconfiguration has been widely adopted to reduce power loss, increase renewable energy integration, and improve power quality. In addition, combined with reconfiguration, DER islands also improve the resilience through mitigating the possible interruptions during natural disasters by forming microgrids. [33] provided the second-order-cone based distribution reconfiguration model for load restoration.[34] gave a framework where the DERs form connected DER islands when the main transformer of the distribution system is at fault. [35] established the post-disruption DER island formation algorithm based on a linearized DistFlow model for the distribution system and proposed a single-commodity flow method to guarantee the radial topology. [36] proposed a resilient microgrid formation method based on master-slave control algorithm. In[37] a distributed multi-agent coordination scheme was designed to achieve global information discovery via only local communications, which is suitable for resilient communication requirements after a natural disaster. [38]proposed an evaluation method based on complex system theory to compare and validate reconfiguration scheme for a system with multiple microgrids.

Noticing that most distribution networks are unbalanced,and single-phase network models may not represent them in enough detail, [39] proposed a three-phase microgrid formation scheme, with a quadratic objective function and mixed-integer linear constraints. [40] takes one step further by considering a consequential restoration problem, which can generate optimal restoration sequence step by step. To investigate the distribution resilience as a planning decision making problem, [41] proposed to install Soft Open Points considering it as a tool to reconfigure the distribution system during fault.

4.3.2 Optimal EMS

In this branch of study, the topology is not changed,but the system can use advanced energy management system (EMS) to optimize the operation of the system in the aftermath of disasters. The EMS can coordinate the multiple resilience resource, such as microgrids, storage,demand response, and electrical vehicles, to reach an optimal operation point.

The EMS algorithms are usually classified as centralized and decentralized. The centralized control scheme can collect global information and dispatch all the controllable resources in the grid. This kind of control is performed by a single central controller, which requires an extensive communication system between the central controller and controlled units. With the centralized EMS, [42] proposed a centralized control algorithm for microgrid, where the normal operation of the microgrid is coordinated with emergency operation to enable a feasible islanding, in the case of any disturbance event. In [43] a EMS based on model predictive control (MPC) approach is introduced for coordinated outage management of multi-microgrid systems by optimally minimizes load curtailments in emergency conditions, and addresses the uncertainties of outage duration. Concerning the restoration sequence, [44]proposed a multi-time step service restoration methodology to optimally generate a sequence of control actions for controllable switches, storage, and dispatchable DER to assist the system operator with decision making.

In the decentralized control scheme, each entity (e.g.a microgrid, a DER,) optimizes the operation on their own, while each entity only exchanges information with local entities. [45] proposed a decentralized EMS for a system with multiple microgrids. In order to implement decentralized control functions, intelligent agent technologies can be a good solution. An intelligent agent can exchange information with nearby agents and act autonomously. In [46] a multi-agent framework has been proposed to facilitate self-healing for a power system that incorporates microgrids.

Nowadays distribution system containing networked microgrids has drawn the attention of researchers. If the distribution system contains multiple microgrids, in the islanded mode, although microgrids cannot receive power from distribution network, sharing power through coordination with other microgrids allows them to sustain in an emergency situation. Therefore the coordinated EMS of networked microgrids is significant to increase resilience.An average consensus algorithm is used by [47] to allocate the required power amount among the normally-operating microgrids to support the on-emergency microgrids.[48]proposed nested EMS for a distribution system that contain multi-layer of microgrids, which uses the surplus/deficit information of lower level microgrids during optimizing its local resources and it reduces the operation cost of the network.

4.3.3 Repair resource allocation and dispatch

In the reliability evaluation, the faulted component is assumed to restore after a certain repair duration. When the fault is stochastic, the restoration resource is usually unlimited by assumption. However, if the disruptions are extreme and the outage area is wide, the limited restoration resource, such as mobile generators, spare components, and repair crew should be optimally dispatched.

Mobile emergency generators can be dispatched after the disaster to restore the loads. Among the lessons learned after the severe ice storm that affected eastern Canada and the northeastern US from 20 to 23 December 2013,the use of large-scale portable diesel generators comes out as recommended in the preparedness process[49]. In[49] the mobile emergency generator dispatch problem is formulated for a radial distribution system, while[50] account for both fixed and mobile DGs that can be optimally placed in the system, suitable for both radial and meshed topology.

The repair crew and vehicles are also resilience resources.Repair crews’ attributes are taken into consideration by resource, traveling time, repair time, and skill set constraints.In [51], to reduce the computational complexity of the model,the damaged components are clustered by an integer program considering their distances to depots and required repair resources. [52] established a dynamic resource allocation method considering wildfire, which is also applicable to other situations involving coupled dynamics of disaster and response. [53] provides an approach to deal with the trade-off between allocating limited resources to protect components and to construct redundant components, in order to decrease system destruction probability over a fixed time horizon.

5 Conclusion

Resilience of the Critical Infrastructure is no longer a new topic. Facing the complex operating environment,power system as the backbone of the modern world is also aiming at resilience enhancement. The concept of resilience encompass the three stages before, during and after external extreme event. This paper carried out an extensive review of existing researches regarding the strategies to achieve resilience, considering their problem category,stage, hierarchy and model. We hope this review can help governments, utilities, and researchers around the world to grasp the latest research directions of power system resilience.

Acknowledgements

This work is funded by Science and Technology Project of State Grid, China (5202011600UG) and the National Natural Science Foundation of China (51577147).