Elsevier

Energy Policy

Volume 72, September 2014, Pages 249-256
Energy Policy

Short Communication
Metrics for energy resilience

https://doi.org/10.1016/j.enpol.2014.04.012Get rights and content

Highlights

  • Resilience is the ability of a system to recover from adversity.

  • There is a need for methods to quantify and measure system resilience.

  • We developed a matrix-based approach to generate energy resilience metrics.

  • These metrics can be used in energy planning, system design, and operations.

Abstract

Energy lies at the backbone of any advanced society and constitutes an essential prerequisite for economic growth, social order and national defense. However there is an Achilles heel to today׳s energy and technology relationship; namely a precarious intimacy between energy and the fiscal, social, and technical systems it supports. Recently, widespread and persistent disruptions in energy systems have highlighted the extent of this dependence and the vulnerability of increasingly optimized systems to changing conditions. Resilience is an emerging concept that offers to reconcile considerations of performance under dynamic environments and across multiple time frames by supplementing traditionally static system performance measures to consider behaviors under changing conditions and complex interactions among physical, information and human domains. This paper identifies metrics useful to implement guidance for energy-related planning, design, investment, and operation. Recommendations are presented using a matrix format to provide a structured and comprehensive framework of metrics relevant to a system׳s energy resilience. The study synthesizes previously proposed metrics and emergent resilience literature to provide a multi-dimensional model intended for use by leaders and practitioners as they transform our energy posture from one of stasis and reaction to one that is proactive and which fosters sustainable growth.

Introduction

The wealth and health of a nation are often measured through the extent and accessibility of its energy reserves; the capability of its energy distribution infrastructure; and the efficiency by which it leverages energy into economic output (National Infrastructure Advisory Council, 2013). Conversely, to the degree that these capabilities may be disrupted due to changing conditions, be they near or long-term, that nation becomes disproportionately vulnerable. High-profile events such as Deep Water Horizon, Hurricanes Katrina and Irene, and super-storm Sandy have encouraged the nation to re-examine the vulnerabilities of its energy systems; and to reevaluate how these systems are designed, configured, and managed to cope with frequent small variations, long-term trends and significant disruptive events.

The national power grid presents a case-in-point: The National Academy of Engineering has identified the US electrical power grid as the supreme engineering achievement of the 20th Century (National Academy of Engineering, 2014). It comprises the “largest interconnected machine on earth”, including 200,000 miles of high voltage transmission lines and 5.5 million miles of local distribution lines (National Academy of Sciences, 2013). More than 20 percent of all electrical infrastructure purchases on Earth are used just to keep the North American grid operating (Zolli and Healy, 2012). However, despite that massive investment, regulatory authorities have noted a disconcerting increase in the frequency and severity of electrical grid disruptions (Fig. 1). Moreover, although the increasing number of disruptions may be attributed primarily to changing environmental and climactic conditions, the grid׳s increasing technological complexity and operational “interconnectedness” have significantly exacerbated the severity, geographic distribution, and societal ramifications of those outages. For example, in August 2003, the heat-induced sagging of several local power lines in northern Ohio – a situation that might otherwise result in a temporary local power outage – resulted in a massive regional collapse of the power transmission and delivery system. Within eight minutes, the blackout affected over 50 million people in eight states and one Canadian province and ultimately resulted in a US financial impact of between $4 and $10B (US–Canada Power System Outage Task Force, 2004).

In response to the increasing trend in grid disruption frequency and severity, energy system owners and operators, regulatory authorities, and policy makers have mandated and initiated significant infrastructure improvements and operational changes. Predominantly, these actions sought to meet two main objectives: reinforce physical energy infrastructure and reduce recovery time. However, the uniqueness of energy attributes, the complexity of system and sub-system interactions, and the near-instantaneous time frames involved in modern system responses creates particular challenges for decision-makers. Prudent measures can protect against anticipated conditions such as high winds and earthquake; however, the current ad hoc approach to system hardening, which typically seeks to address past failure scenarios, does not necessarily assure protection from unexpected future scenarios. Moreover, the focus on optimizing performance and protecting the design condition fails to address energy system performance under varying conditions, emergency or otherwise, beyond a binary, all-or-nothing, approach. Subtle-yet-critical issues of energy quality (and tolerance), criticality, and efficiency are not adequately incorporated into the system design/preparation, response, and recovery processes.

Resilience offers an alternative to the current status quo (Linkov et al., 2014). Executive Order 13636 (2013) prescribes resilience as a risk management approach for critical infrastructure and Executive Order 13653 (2013) invokes the principle in the context of climate preparedness. In various contexts, resilience has been used to describe an individual׳s capacity to cope with adversity, a community׳s posture to weather disasters, or a species׳ adaptability in response to environmental change. Presidential Policy Directive 21 defines resilience as “the ability to prepare for and adapt to changing conditions and withstand and recover rapidly from disruptions. Resilience includes the ability to withstand and recover from deliberate attacks, accidents, or naturally occurring threats or incidents” (2013) (Presidential Policy Directive 2013). With respect to systems associated with essential social functions, a National Academy of Science (NAS) report identifies four basic resilience components: plan/prepare, absorb, recover, and adapt to anticipated and unanticipated conditions (2012). The Department of Homeland Security adds that “Having accurate information and analysis about risk is essential to achieving resilience. Resilient infrastructure assets, systems, and networks must also be robust, agile, and adaptable. Mitigation, response, and recovery activities contribute to strengthening critical infrastructure resilience” (2013) (Department of Homeland Security, 2013).

As the concept of resilience advances in prominence, discussion about effective resilience management and quantitative resilience metrics continues to grow. Thomas and Kerner (2010) characterize the need for energy resilience metrics, stressing examination of system response to change. General ideas have been advanced, for example the concept of a “secure energy premium” (US Army, 2009). To date, attempts to quantify such a value have been limited to actuarial risk estimates – estimating the probability and cost impacts of electrical power interruptions to a community or military installation. Recognizing the complexity and dynamic nature of resilience, Flynn and Burke (2012) consider system interactions such as resources, security, and policy at a national level. Folke et al. (2002) call for active adaptive management techniques that invoke the use of structured scenarios and monitoring to gauge overall system response, and to provide learning and adaptation opportunities. O’Brien and Hope (2010) emphasize interactions among physical and socio-economic domains, attributing resilience advantages to democratic systems involving distributed ownership and control compared to traditional centralized schema.

Responding to the lack of established resilience models and tools to implement the new generation of resilience policies and initiatives, Linkov et al. (2013a) set forth a taxonomy for metrics that accommodates both change and interactions among physical, information, and human domains. Further work (Linkov et al. 2013b) applied the taxonomy to cyber threats, which interact with energy resilience, but reflect substantial differences. Following the work of Linkov et al., 2013a, Linkov et al., 2013b, the purpose of the current work is to provide a framework for relevant metrics as a basis for development of coordinated energy-related solutions in the physical, information and human domains, with a stronger focus on adaptive management to foster learning and adaptation. We seek to inform models which in turn fill the gaps in energy-related design and resourcing processes, thus enabling leaders and investors to reconcile chronically disconnected considerations through the unifying lens of resilience.

Like most treatments of resilience, the NAS definition describes resilience in the context of changing conditions. Systems inevitably perform most effectively at a specific point or range of conditions; but in complex real-world systems, operational conditions almost always deviate from optimum design points. When the environment deviates from the design point, performance decreases. Small perturbations are much more frequent than dramatic ones. Even for typical stable systems, performance sensitivity to such incremental change impacts cumulative outcomes. As a generalized concept, a system that experiences a comparatively smaller decline under changing conditions displays increased resistance (Walker et al., 2004). Once displaced from its optimum point, Walker et al. describe latitude as the system׳s ability to restore performance. Factors influencing this behavior include sensitivity of the basic physical process to environmental conditions, diversity of processes or simple design margin (Fig. 2). Less frequent but more dramatic events bring the prospect of catastrophic disruption; under such circumstances, safety margins and dependencies become key. Substantial perturbations expose the importance of precariousness, or the margin from the system׳s current state to a performance threshold or “tipping point” in the rate of performance degradation.

Long-term change, either gradual or dramatic, calls for learning and adaptation to ensure performance under “new normal” conditions. Such fundamental ideas inform the identification of change-based metrics. As conditions change over time, a resilient system must be able to adapt inherent processes to optimize performance and maintain valued system outputs under the “new normal” conditions (Fig. 3). System attributes such as diversity, flexibility, and interoperability; rich information; knowledge/creativity; and innovative cultures foster such adaptive capacity. In fact, change is essential, and the process of learning and adaptation is key to survival in the real world (Taleb, 2012).

Finally, resilience must address the potential for substantial or dramatic changes. Given the complexity of modern infrastructure and systems, and the significant system disruptions posed by hostile acts (e.g., physical or cyber attack), it is reasonable to expect multimodal responses to change; with such responses exhibiting rate changes or even trend reversals (decreasing, then increasing performance) in key system pathways and parameters. Furthermore, the environmental conditions under which the system functions are often dynamic and notoriously difficult to accurately predict. All of these factors contribute to the apparently increasing incidence of unpredictable or “black swan” events (Fig. 4). In general, this category of change warrants explicit consideration to ensure the overall system can absorb, recover, and adapt from major disruptions, outside the realm of incremental adjustment. This principle complicates the challenge of timely recognition of true system conditions and status, and increases the importance of building sufficient flexibility to function under more extreme conditions.

In addition to characterizing change and system responses, it is worth examining alternative views of resilience as they relate to systems under consideration. Holling (1996) distinguishes between resilience metrics which focus on maintaining the underlying system functionality (such as energy) – a focus he terms engineering resilience – versus an “ecological” view that considers more holistic concepts emphasizing survival and adaptation of the overall system. Molyneaux et al. (2012) offers composite resilience indices, distinguishing between the scenarios of maintaining functionality under limited change versus the need for adaptive response to disruptive conditions. These diverse views can be useful in practical system analysis because resilience concepts inherently involve consideration of systems on hierarchal scales with complex interdependencies, with the inevitable focus on outcomes that transcend simply maintaining the status quo. While we seek to design robust systems and protect them from known threats, our ultimate goal nearly always lies in a larger good of survival, social order, or advancement.

Finally, we must acknowledge that which is intuitively obvious – there is no free lunch at the resilience buffet. Lietaer et al. (2010) demonstrated that increases in system diversity and interconnectedness – factors that generally enhance overall system resilience – involve tradeoffs with respect to the system׳s output efficiency (Fig. 5). However, closer inspection of Lietaer׳s curve reveals that the efficiency versus resilience curve is not bell-shaped. Rather, the optimal “window of viability” (i.e., the region where long-term system output sustainability is maximized) is skewed toward greater system resilience as opposed to greater system efficiency (Fig. 5). Viewed from this perspective, Lietaer׳s findings may have profound implications for the financing, design, and operation of energy infrastructure and other critical systems.

Section snippets

Materials and methods

Linkov et al. (2013a) described a framework for resilience metrics which aligns with the National Academy of Sciences (2012) definition of disaster resilience, while invoking multi-domain aspects captured in Network Centric Operations (NCO) doctrine (Alberts, 2002). The result is a matrix with metrics organized with respect to the four NAS-identified stages of change:

  • Plan/Prepare: Lay the foundation to keep services available and assets functioning during a disruptive event (malfunction or

Results

Because energy is a fundamental contributor to capabilities and considering the diversity of near and long-term change, the associated resilience metrics presented in Table 1 are oriented to substantially address constructive flexibility, learning, and adaptation rather than focusing on resistance to short-term adverse conditions.

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

Discussion

The proposed metrics are general and must be adapted by the user to the application at hand. No set of specific metrics will fit all situations. The elements of the matrix are intended to address the range of energy systems, attributes and meta-systems which manifest in the physical, information and human domains, as they significantly influence nearly any modern technology system. Recognizing the focus on change response and the complexity of energy interactions within infrastructure,

Conclusions and policy implications

Policy aimed at building a resilient posture requires consideration of physical systems, information, and individual and collective human behaviors. Resilience provides a framework through which to manage the complex interactions of energy among social, economic, and security considerations. Existing approaches that invoke distinct processes for cost minimization, critical infrastructure protection, and sustainability often produce internally competitive views of the system. These narrow and

Acknowledgements

Permission was granted by the US Army Corps of Engineers, Chief of Engineers to publish this material. The views expressed in this article are solely those of the authors and do not reflect the official policies or positions of the Department of Army, the Department of Defense, or any other department or agency of the US government.

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