High Impact Low Probability Research Articles

Resiliency-oriented dynamic expansion planning of MG-based distribution networks

High-Impact Low-Probability (HILP) events are the ever-increasing threats against distribution networks (DNs). Boosting DN resilience can be achieved by such measures as redundancy increment by substation allocation, feeder routing from less affected area, design of reconfigurable multi-MicroGrid based (MGs) DN and deployment of different distributed generation sources (DGs). This paper proposes an approach for designing resilient DNs by posing the problem as a dynamic modeling of bi-level resilient DN expansion planning (RDEP) program. In the upper level of the RDEP, distribution company (DisCo) identifies the optimal eco-reliable planning and operation of different assets, while in the lower level, DisCo determines the optimal operation point of the expanded DN against the specified HILP. The uncertainties of renewable energy resources, electric load and its market price and ZIP modeling, well incorporate into the problem. To evaluate the effectiveness of RDEP, computer simulation is done on a large scale 138-bus DN and the results are discussed.

Post-event electric taxis dispatch for enhancing resilience of distribution systems

With the frequent occurrences of high-impact-low-probability (HILP) events such as typhoons, lightning strikes, and cyber attacks, people have the increasingly need of raising distribution network’s (DN) resilience. Deploying backup sources into the system can effectively improve the resilience of DNs. With the development of transportation electrification, the number of electric taxis (ETs) in urban transit is now growing. Besides, Vehicle-to-Grid (V2G) technology has also developed vigorously. Therefore, ETs can be dispatched to charging stations under unified command to feed power back to the grid when HILP events happen. To this end, we establish an optimization model for the post-event recovery of the DN using ETs. In this model, we combine the energy provided by V2G stations with the existing distributed generators and energy storage in the grid to restore the outage areas jointly. Topology reconfiguration technique and power flow balance of DN have also been fully considered in the modeling process. The whole grid is divided into several microgrids and powered by different sources. The multi-source multi-region power restoration method improves the stability of the DN restoration. It is easier to control the voltage and power quality. The method we proposed is tested on the modified IEEE 33-bus system. The simulation results verify the effectiveness and efficiency of our work.

Developing an innovating optimization framework for enhancing the long-term energy system resilience against climate change disruptive events

The energy system is one of the major sub-sectors of the economy in every society in which both supply with high reliability and compatibility of the energy sector with the environment is a necessity in the process of economic reconstruction and sustainable development. The energy system has been more susceptible to various disruptive events in recent years. Thus, designing a resilient energy system capable of resisting and recovering rapidly from high-impact low-probability (HILP) disruptive events is essential. This paper focuses on developing a two-stage long-term optimization framework for evaluating the energy system resilience as a crucial Critical Infrastructure (CI) system against HILP caused by climate change. The results show the share of electricity generation shift from coal and hydropower to PV in different scenarios based on the maximization of energy system resiliency. The faster occurring this shift, the greater vulnerability of the system. Furthermore, the sensitivity analysis of the optimum solution shows that the vulnerability of energy systems against HILP events depends on planner flexibility.

A Risk-Averse Approach for Distribution Grid Expansion Planning

Recent episodes of natural disasters have challenged the resilience of power grids. Adequate distribution grid planning that properly captures the risk aversion of the utility system planner is a key factor to increase the flexibility of distribution networks to circumvent these events. In this paper, we propose a methodology to determine the optimal portfolio of investments in lines and storage devices in order to minimize a convex combination between expected value and CVaR of operational costs, including energy not served, while taking into account the multistage nature of the energy storage management within this context. While the expected value of energy not served has been traditionally employed to tackle routine failures, we also minimize the CVaR of energy not served to address high-impact, low-probability (HILP) events. We illustrate the performance of the proposed methodology with a 54-Bus system test case.

Impact of geomagnetic disturbances on power transformers: risk assessment of extreme events and data availability

Certain rare events can have a drastic impact on power systems. Such events are generally known as high-impact low-probability (HILP) events. It is challenging to predict the occurrence of a HILP event mainly due to lack of data or sparsity and scarcity of data points. Yet, it is essential to implement an evidence-driven asset management strategy. In this paper, event tree analysis is used to assess the risk of power transformer failure due to a geomagnetically induced currents (GIC). Those currents are caused by geomagnetic disturbances in Earth’s magnetic field due to solar activity. To assess the impact on power transformers, an understanding of the mechanism and sequence of sub-events that lead to failure is required to be able to construct an event tree. Based on the constructed event tree, mitigation actions can be derived. GIC blockers or reducers can be used. However, that would require extensive installation and maintenance efforts, and the impact on system reliability has to be studied. Also, such technology is still in its infancy and needs extensive validation. A suggested alternative is to combine early warning data from solar observatories with a load management plan to keep transformers below their rated operation point such that a DC offset due to GIC would not cause magnetic core saturation and overheating. Load management and the risk of early warning false positives can incur a negative effect on reliability. Nevertheless, the risk assessment performed in this paper show that incorporating load management in asset planning is a viable measure that would offset the probability of catastrophic failure.

Resilience of electric utilities during the COVID-19 pandemic in the framework of the CIGRE definition of Power System Resilience

Resilience is a vital concept in engineering, business, and natural sciences, and is a measure of the ability of an entity to withstand High Impact Low Probability (HILP) events. During the COVID-19 pandemic, which started in late 2019/early 2020, power system utilities around the globe have responded in effective and efficient ways to enhance the resilience of their organisations, both in terms of real-time operations and prudent management of its infrastructure, in order to continue their mandate in providing reliable supply to meet customer demands. This paper presents the CIGRE definition for power system resilience, established by the C4.47 Working Group in 2018, and demonstrates the application of resilience-oriented thinking within the electrical sector. The response and recovery efforts are described, with respect to the key actionable measures integral to the power system resilience definition, taken before, during and after the COVID-19 pandemic. A practical conceptual framework is also presented for thinking about resilience in terms of three key components of resilience strategies: organisational, infrastructure and operational resilience. The paper also discusses the different strategies adopted in response to COVID-19, based on the C4.47 members’ experiences during the pandemic. Finally, a case study is presented, which proves the effectiveness of a set of response measures, using graph theory and the characteristics of the staff-asset interactions.

Multi-stage and resilience-based distribution network expansion planning against hurricanes based on vulnerability and resiliency metrics

Distribution networks should be expanded to supply reliable and cost-effective services for new and existing customers to respond to the rising demand. On the other hand, there is an increasing awareness of resilience in the face of high impact low probability events. Therefore, this paper presents a multistage, dynamic, and resilient distribution network expansion planning framework to expand a distribution network resiliently. The proposed model is constructed as a six-step structure. The first step develops a network expansion topology. The hurricane occurrence model is executed in the second one that introduces a novel vulnerability index to recognize the most vulnerable facilities. Resilient planning based on the resilience resources such as distributed generations, hardening actions, and distributed automation systems is performed as the third step to reinforce the network preventive capabilities. In the fourth step, a resilient operation is considered to decrease unserved loads and restore the distribution system rapidly. Resiliency evaluation based on technical, financial, and social welfare metrics is employed as the fifth step. Finally, the non-dominated sorting genetic algorithm II optimization method operators are implemented in the sixth step to optimize the problem. Numerical simulations are performed on a 20 nodes distribution system and a real network. The results demonstrate the effectiveness of the proposed method.

From Security to Resilience: Technical and Regulatory Options to Manage Extreme Events in Low-Carbon Grids

Extreme events are increasingly affecting power systems worldwide, calling for new and effective ways to deal with these so-called high-impact, lowprobability (HILP) events. The situation is exacerbated by a transition to low-carbon grids. These systems are dominated by inverter-based resources, including different types of variable renewable energy (VRE) sources and distributed energy resources (DERs), and are characterized by higher operational uncertainty and a risk profile that is correspondingly difficult to assess.

Resilient electrical distribution grid planning against seismic waves using distributed energy resources and sectionalizers: An Indian's urban grid case study

The urban and suburban advancement has increased the consumption of energy per capita gradually. Electrical distribution companies have expanded their network to cater to the demand, making the system more vulnerable to low probability high impact events. The extreme events occurred in last five year had shown us complete to partial blackout, which calls for resiliency enhancement. The presented study concentrates on the Dehradun, India, which listed as the most earthquake-prone region by Union Nations Development Programme. Hence, this paper proposes three main strategies to make a resilient distribution grid against an earthquake; (i) Hardening approach at grid side and demand side against earthquake disaster (ii) Monte-Carlo methodology for earthquake hazard model (iii) K-means algorithm with clustering quality index to identify the multiple vulnerable zones. Grid side hardening is performed using the optimal unit placement of the Solar PV plant (SPP) with storage and parallel cables with sectionalizers. For demand-side hardening, electric vehicles are considered storage after an event, duly taking their future growth rate into account. Furthermore, a Mixed Integer non-linear problem is formulated to identify the optimal sizing and sitting of SPP with storage. The proposed methodology is developed and applied to the distribution system (156-bus) of Dehradun, India.

Resilience Quantification of Smart Distribution Networks—A Bird’s Eye View Perspective

The introduction of pervasive telecommunication devices, in the scope of smart grids (SGs), has accentuated interest in the distribution network, which integrates a huge portion of new grid applications. High impact low probability (HILP) events, such as natural hazards, manmade errors, and cyber-attacks, as well as the inherent fragility of the distribution grid have propelled the development of effective resilience tools and methods for the power distribution network (PDN) to avoid catastrophic infrastructural and economical losses. Multiple resilience evaluation frameworks are proposed in the literature in order to assist distribution system operators (DSOs) in managing their networks when faced with exogenous threats. We conduct detailed analysis of existing quantitative resilience studies in both electric and telecommunication domains of a PDN, focusing on event type, metrics, temporal phases, uncertainty, and critical load. Our work adopts the standpoint of a DSO, whose target is to identify feasible resilience assessment frameworks, which apply to pre-defined requirements in terms of resilience evaluation objectives (planning, reactive response, or simple assessment), time of evaluation, and available enhancement strategies. Finally, results and observations on selected works are presented, followed by discussion of identified challenges and opportunities.

A multi-objective resilience-economic stochastic scheduling method for microgrid

In this paper, the applicability of Microgrids (MGs) is reviewed for power system resilience against low-probability high-impact (LPHI) events. Financial issues have been always one of the major priorities in the scheduling of MGs. Although these systems can feed their loads in islanding mode, resilient operation of them under critical situations caused by LPHI events is a challenging problem. Improving resilience increases MG costs, therefore it is necessary to establish a trade-off between resilience and economic metrics. Therefore, the main purpose of this paper is to develop a novel multi-objective resilience-economic stochastic MG scheduling model. The proposed bi-level resilience-oriented stochastic scheduling integrates the economic perspective along with resilience function simultaneously using a multi-objective mixed-integer linear programming approach. The considered MG resilience function includes various metrics such as the ability to withstand, quick recovery, and the technical criteria in the face of low-probability high-impact events. The proposed method is tested on the modified IEEE 33-bus power system with a set of distributed energy resources, energy storage systems, and electric vehicle parking lots. The results outlined that, although, the integration of the resilience metrics in the MG scheduling problem has increased the operation cost of the MG approximately 25%, but improved the MG resilience more than 70%. On the other hand, the proper management of the independent distributed energy resources enhanced the resilience of the MG approximately 16% while decreased the operation cost of the MG by at least 28%.

Uncertainty-Aware Deployment of Mobile Energy Storage Systems for Distribution Grid Resilience

With the spatial flexibility exchange across the network, mobile energy storage systems (MESSs) offer promising opportunities to elevate power distribution system resilience against emergencies. Despite the remarkable growth in integration of renewable energy sources (RESs) in power distribution systems (PDSs), most recovery and restoration strategies do not unlock the full potential in such resources due to their inherent uncertainty and stochasticity. This paper develops a novel restoration mechanism in PDSs for routing and scheduling of MESSs integrated with stochastic RESs to achieve agile system response and recovery in facing the aftermath of high-impact low-probability (HILP) incidents. The proposed integrated model is presented as a non-convex non-linear stochastic optimization formulation with joint probabilistic constraints (JPCs). The problem is equivalently reformulated to a tractable mixed-integer linear programming (MILP) model that can be solved by commercial off-the-shelf solvers. Case studies on the IEEE 33-node and 123-node test systems demonstrate the effectiveness and scalability of the proposed framework in boosting the system resilience. This is achieved via effective routing and scheduling of MESSs jointly managed with dynamic network reconfiguration in presence of stochastic RESs.

Dynamic analysis for fire-induced domino effects in chemical process industries

Domino effects are typically high-impact low-probability (HILP) accidents, which pose a serious threat to chemical process industries. Previous researches on domino effects in chemical industries focus more on static analysis at the spatial scale. From the perspective of the spatial and temporal characteristics of the accident, this study proposed a model to analyze dynamic evolution process of domino effects by using matrix calculation coupled with Monte Carlo simulation, and the dynamic propagation of pool fire accidents is considered as the evolution of domino effects. The algorithm of the model for dynamic domino probabilities considering the synergistic effects of multiple escalation vectors from different units can be used to analyze the complex scenarios of domino effects with high-level and multiple primary accident units. Moreover, the model can be applied in chemical areas with a large number of installations due to the greatly improved calculation efficiency. The proposed model is tested and validated using earlier studied dynamic Bayesian network method, and the application of the model is demonstrated on a complex multi-unit system. The results show that domino effects have strong temporal correlation, and the scenario with multiple primary accident units is much more serious than that with only one primary accident unit, which provide important support for the implementation of emergency response. The study highlights that the proposed model serves as an important tool to evaluate strategies for prevention and control of domino effects.

Planning for resilience in power distribution networks: A multi‐objective decision support

Power grid response against high-impact low-probability (HILP) events could be enhanced by (a) hardening mechanisms to boost its structural resilience and (b) corrective recovery and mitigation analytics to improve its operational resilience. Planning for structural resilience and attempts to find the optimal location of the Tie switches in radially operated power distribution networks that enable harnessing the network topology for maximised resilience against HILP disasters are focussed. This goal is achieved through a novel resilience-oriented multi-objective decision making platform, which employs a k-PEM based probabilistic power flow (PPF) algorithm. The proposed framework offers a decision making analytic embedded with the fuzzy satisfying method (FSM) that characterises the system resilience features, such as robustness, restoration agility, load criticality, and recovered capacity, to assess different network reconfiguration options and select the optimal solution for implementation. The aforementioned resilience features are formulated in nodal level and then aggregated over the entire system to characterise the system-level objective functions. The performance of the suggested framework is analysed on the IEEE 33-Bus test system under a designated HILP event, and the applicability on larger networks has been verified on the IEEE 69-bus test system. The results demonstrate the efficacy and applicability of the proposed framework in boosting the network resilience against future extremes.

Reliability of Supply and the Impact of Weather Exposure and Protection System Failures

Extreme weather is known to cause failure bunching in electrical transmission systems. However, protection systems can also contribute to the worsening of the system state through various failure modes—spontaneous, missing or unwanted operation. The latter two types of failures only occur when an initial failure has happened, and thus are more likely to happen when the probability of failure of transmission lines is high, such as in an extreme weather scenario. This causes an exacerbation of failure bunching effects, increasing the risk of blackouts, or High Impact Low Probability (HILP) events. This paper describes a method to model transmission line failure rates, considering both protection system reliability and extreme weather exposure. A case study is presented using the IEEE 24 bus Reliability Test System (RTS) test system. The case study, using both an approximate method as well as a time-series approach to calculate reliability indices, demonstrates both a compact generalization of including protection system failures in reliability analysis, as well as the interaction between weather exposure and protection system failures and its impact on power system reliability indices. The results show that the inclusion of protection system failures can have a large impact on the estimated occurrence of higher order contingencies for adjacent lines, especially for lines with correlated weather exposure.

Resilience‐oriented service restoration modelling interdependent critical loads in distribution systems with integrated distributed generators

The exponential increase in the frequency and intensity of high impact low probability weather-related events have pivoted the paradigm of research pertaining to power systems towards resilience. Power system is considered as a critical infrastructure, directly linked to the nation's economy, security and health. Therefore, recent researchers have proposed several techniques to enhance the resilience of power systems. In those techniques, critical loads have been considered independent in nature and different metrics have been proposed to evaluate the resilience of the network. To enhance the resiliency, this paper incorporated distributed generators in the distribution network and critical loads are modelled interdependently. Furthermore, a novel resilience metric is proposed in this paper to evaluate the resilience of a distribution system. The proposed model is formulated as a mixed integer second-order cone programming problem and the efficacy of the proposed model is evaluated on IEEE 33- and 69-bus systems. The competence of the proposed resilience metric is evaluated after comparison with existing resilience metrics.

Probabilistic–proactive distribution network scheduling against a hurricane as a high impact–low probability event considering chaos theory

High impact–low probability (HILP) incidents, such as hurricanes, usually and gravely damage electric distribution networks. A resilient distribution network must have an ability to recover itself with a fast restoration methodology against the effects of HILP events. Due to improve the electric distribution network resiliency against HILP incidents, this paper suggests a probabilistic–proactive distribution network operation model based upon the chaos theory (P-PDNOMC(. Here, a resilience index based upon operational cost and load shedding cost is employed to decrease the effects of a hurricane as an HILP incident. P-PDNOMC is formulated as a framework that consists of a novel hurricane modelling and operation scheduling in normal and emergency conditions with uncertainties considering. It should be noted that a prediction approach based on the chaos theory and the least-squares support vector machine (LS-SVM) is also designed to consider the provisional and spatial behaviour of the hurricane. Furthermore, this paper proposes a novel optimization framework for damaged lines determination based upon the P-PDNOM C (DLsP-PDNOM C) by considering multi-zone and multi-period damaged equipment budget constraints. Here, the load shedding cost is applied to present the efficiency of the proposed model. The numerical results indicate the resiliency enhancement of the electric distribution network in the face of HILP incidents.

Artificial intelligence for resilience enhancement of power distribution systems

The threat of high impact low probability (HILP) events on power distribution system is substantial but quite unpredictable. Enhancing the resilience of power distribution grids against such events requires solving combinatorial planning and operational problems in stochastic spaces, as well as classifying system conditions based on high-dimensional input data. Since traditional mathematical solutions struggle with both uncertainty and the curse of dimensionality, data-driven techniques based on artificial intelligence (AI) are gaining momentum for solving those problems. This paper reviews AI capabilities for decision making in uncertain and high-dimensional spaces in general, and their particular application in resilient enhancement problems such as damage detection and estimation, cyber-physical anomaly detection, stochastic operation, and cyber security enhancement. Efficient data structures and AI approaches are suggested for each problem, which depend on the type of input signals, search-based or game-based structure of the problem, as well as the uncertainty sources involved. In particular, potential applications of supervised and unsupervised deep learning combined with Monte Carlo Tree Search and ε-greedy search is explored to find near optimal operational decisions that help enhance the resilience of power distribution systems.

The zero option and NATO’s dual-track decision: Rethinking the paradox

ABSTRACT An insufficiently understood paradox surrounded the NATO dual-track decision. This paradox was at the core of an almost classical high impact-low probability scenario which became reality with the INF Treaty of 1987, bringing NATO closer to a crisis of to be or not to be. NATO governments (except France) formally decided in 1979 that new long-range missiles in Europe were necessary but lacked political willpower to exclude the zero option, the possibility that NATO’s missile deployments may be obviated through arms control. The article analyses why this was the case, clarifies why this mattered, and draws policy implications from this crucial episode.

THE ROLE OF SUPPORTING COMPONENTS AS A MULTIPLE FORCE IN INDONESIAN TOTAL DEFENSE SYSTEM

This study discusses the role of the Supporting Components in national defense as a multiplier force in the Indonesian Universal Defense System. The method in this study uses the Estimative Methodology method, namely predictive analysis which can accurately predict several key variables to be presented through several levels of analysis (levels of analysis) to customers which can be used as a basis for strategic decision making. The results of the study show that the role of the Supporting Components has strong relevance to the prediction of changes in the Universal Defense System in the long term by generating new defense strategies as a result of High Impact Low Probability, while threats are the variables with the largest changes (dominant) so that they will affect the Defense System in the long term