Smart Distribution Networks Resilience Quantification
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.
2. Resilience in Smart Grids
2.1. From Reliability to Resilience
2.2. Resilience and QoS in ICT Networks
3. Taxonomy of Resilience Evaluation Methods
4. Resilience Quantification Objectives
Anticipation phase (phase I): Represents the time period before the event occurrence, when performance is at its nominal level. Monitoring information, impact projections, and historical data when available are used for prediction studies, and all possible defensive measures are implemented. This serves particularly in the case of multi-hazard management where risks and vulnerabilities to each event are investigated. For single hazard resilience analysis which is the most relevant in the case of HILP event, this phase is not considered and a post-event resilience study is adopted. However, this also refers to the period of normal operation where reliability and risk management for recurrent failures can be conducted, which participates in system resilience, because a resilient system needs to be first as reliable and low-risk as possible. In addition, security measures for protecting the system and preparing it to withstand malicious behaviors are implemented at this stage .
Mitigation phase (phase II): Once an extreme event hits the network, reliance is on system robustness, reactivity, and absorption to minimize the effect on services and infrastructure. Adding to some preparation policies that could be anticipated, many dynamic actions can be implemented to reduce the aftermath, like distribution automation actions, load shedding, and monitoring actions in power distribution networks or customer prioritization in telecom networks. These actions can withstand performance degradation that is in place, or serve to coordinate between entities in order to achieve an accurate assessment of consequences and prepare next crisis management steps.
Recovery phase (phase III): Unlike short-timed low impact incidents where maintenance actions are achieved relatively fast, in major events, recovery actions can require anywhere between several weeks to months . The main reason is that, given the safety of emergency crews and logistic constraints, restoration is conducted carefully and waits for the reduction in hazard intensity, or more generally identification of restoration windows. Priority is first given to service restoration where all alternative (even temporary) ways to provide services are explored and deployed allowing to regain an intermediate level of performance. Complete recovery will take more time and effort as it involves mostly infrastructure catering which turns out to be very challenging.
Learning phase (phase IV): This phase is less considered than the two previous phases in quantitative resilience frameworks, generally with the argument that resilience is best examined in face of exogenous threats . The post-recovery phase should still be looked at closely in order to draw conclusions about damages experienced by the network and how various implemented policies helped to alleviate consequences. Data collection through field surveys and supervisory management tools enable improvement in system performance and enhancement in preparation for upcoming extreme events backing the vision for a sustainable network.
Proactive evaluation: The procedure in this case is to drive pre-event studies with the goal of obtaining resilience indicators before contingency happens. The outbuilding is in prediction data, recommendations of experts, supervision alerts, and historical records. However, for HILP anomalies, little information is available, then designing preventive measures appeals for simulation tools, emulation, and analytical models which help to make projections for the impact that will be borne by the network in face of uncertain events.
Reactive evaluation: Quantification is carried out as the event happens, meaning that resilience metrics are computed on-the-fly, and policies adopted to cope with severe hazards are taken from the inherent reaction capacity of the system without support from pre-event recommendations. Metrics are calculated as the event goes for the two broad phases of robustness and recovery. In such real-time setup, information that can be gathered is realistic and narrows down failure modes space. However, the flexibility margin can be very tight because the HILP event hits the network by surprise while no anticipative actions are in place. There are no good or bad choices between proactive and reactive evaluation, they are both suitable for resilience analysis and can be complementary. The goal is to find a balanced fit for a given use case .
Deductive evaluation: When resilience metrics are computed at the end of a HILP disturbance, they mainly serve to draw conclusions about how the system handled an external event . Results of this are intended to point out axes of improvement for future reference in similar extreme situations, and can also be considered as performance evolvement baseline. Further, the output of such post-recovery evaluation can be fed to the pre-event phase for hazards in the future, closing a kind of a cycle with the evaluations presented above.
The entry is from 10.3390/en14102888
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