(61f) Applying a Comprehensive View of Resilience to Power Distribution Network Optimization

A key challenge which complicates the design and operation of the power grid is the need for resilience, which can be defined as “the ability to prepare and plan for, absorb, recover from, or more successfully adapt to actual or potential adverse events” (National Research Council, 2012). Resilience has been particularly emphasized in the power systems community in recent years due to the increasing frequency of disruptions such as extreme weather events (Bhusal et al., 2020; Wang et al., 2016).

Prior works on enhancing the resilience of power systems have primarily limited their focus to one type of stressor, corresponding to one type of disruptive effect on the system, at a time. Additionally, these works have considered a relatively small set of resilience-enhancing investments and actions that are tailored to the specific disruption or stressor that they consider. For instance, several prior works have proposed optimally investing in line hardening and automatic line switches to enhance the resilience of distribution networks to line faults caused by hurricanes (Ma et al., 2018; Zare‐Bahramabadi et al., 2018). While this approach is suitable for the specific disruption considered in the study, it does not enable the system to be resilient to other types of potential stressors, such as widespread generating unit failures like the ones occurred during the 2021 winter blackout in Texas (Busby et al., 2021). Another feature of prior works on power system resilience is that they typically only consider one aspect or concept of resilience. In most cases, resilience as robustness, which is the ability to manage stressors with limited-to-no impact on normal activities (Woods, 2015), is investigated and enhanced. Some other works focus on resilience as rebound, where resources are actively managed to restore system capabilities after a stressful event (Thiébaux et al., 2013). Another aspect of resilience considered in the literature is resilience as extensibility, which is the ability to extend system performance or capabilities to respond to surprise events that challenge current activities (Munoz-Delgado et al., 2021).

In this work, we propose a decision-support tool that enables distribution system operators to optimally employ a variety of resilience-enhancing investments and activities to respond to a variety of uncertain stressors in a risk-averse manner. In particular, we develop a scenario-based stochastic programming model that incorporates line faults, generating unit failures, and demand uncertainty caused by short-term forecasting uncertainty and long-term load growth uncertainty. In this model, line hardening projects, mobile battery storage systems, and mobile ammonia-based energy storage systems are available for investment and coordinated use with line switching procedures. By incorporating these various disruptions and resilience-enhancing measures, the model enables the simultaneous consideration of resilience as robustness, rebound, and extensibility. Moreover, the Conditional Value at Risk (CVaR) is incorporated as a risk measure to penalize high-impact scenarios (Garcia et al., 2022). Computational case studies are performed to investigate the optimal choice of resilience-enhancing investments when multiple types of potential stressors are considered and explore the synergy and coordinated use of these investments. The large-scale problem instances considered in the case study are solved using a tailored decomposition algorithm that takes advantage of the problem’s structure as a two-stage stochastic mixed-integer linear program with mixed-integer recourse.

References

Bhusal, N., Abdelmalak, M., Kamruzzaman, M., & Benidris, M. (2020). Power System Resilience: Current Practices, Challenges, and Future Directions. IEEE Access, 8, 18064–18086. https://doi.org/10.1109/ACCESS.2020.2968586

Busby, J. W., Baker, K., Bazilian, M. D., Gilbert, A. Q., Grubert, E., Rai, V., Rhodes, J. D., Shidore, S., Smith, C. A., & Webber, M. E. (2021). Cascading risks: Understanding the 2021 winter blackout in Texas. Energy Research & Social Science, 77, 102106. https://doi.org/10.1016/J.ERSS.2021.102106

Garcia, M., Austgen, B., Pierre, B., Hasenbein, J., & Kutanoglu, E. (2022). Risk-Averse Investment Optimization for Power System Resilience to Winter Storms. 2022 IEEE/PES Transmission and Distribution Conference and Exposition (T&D), 2022-April, 1–5. https://doi.org/10.1109/TD43745.2022.9816875

Ma, S., Su, L., Wang, Z., Qiu, F., & Guo, G. (2018). Resilience enhancement of distribution grids against extreme weather events. IEEE Transactions on Power Systems, 33(5), 4842–4853. https://doi.org/10.1109/TPWRS.2018.2822295

Munoz-Delgado, G., Contreras, J., Arroyo, J. M., Sanchez de la Nieta, A., & Gibescu, M. (2021). Integrated Transmission and Distribution System Expansion Planning Under Uncertainty. IEEE Transactions on Smart Grid, 12(5), 4113–4125. https://doi.org/10.1109/TSG.2021.3071385

National Research Council. (2012). Disaster Resilience. In Disaster Resilience: A National Imperative. National Academies Press. https://doi.org/10.17226/13457

Thiébaux, S., Coffrin, C., Hijazi, H., & Slaney, J. (2013). Planning with MIP for supply restoration in power distribution systems. IJCAI International Joint Conference on Artificial Intelligence, 2900–2907.

Wang, Y., Chen, C., Wang, J., & Baldick, R. (2016). Research on Resilience of Power Systems Under Natural Disasters—A Review. IEEE Transactions on Power Systems, 31(2), 1604–1613. https://doi.org/10.1109/TPWRS.2015.2429656

Woods, D. D. (2015). Four concepts for resilience and the implications for the future of resilience engineering. Reliability Engineering and System Safety, 141, 5–9. https://doi.org/10.1016/j.ress.2015.03.018

Zare‐Bahramabadi, M., Abbaspour, A., Fotuhi‐Firuzabad, M., & Moeini‐Aghtaie, M. (2018). Resilience‐based framework for switch placement problem in power distribution systems. IET Generation, Transmission & Distribution, 12(5), 1223–1230. https://doi.org/10.1049/iet-gtd.2017.0970