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1. A stochastic programming approach to enhance the resilience of infrastructure under weather‐related risk

   https://doi.org/10.1111/mice.12843  

   Zhang, Ning ; Alipour, Alice ( May 2022 , Computer-Aided Civil and Infrastructure Engineering)

   The presented methodology results in an optimal portfolio of resilience‐oriented resource allocation under weather‐related risks. The pre‐event mitigations improve the capacity of the transportation system to absorb shocks from future natural hazards, contributing to risk reduction. The post‐event recovery planning results in enhancing the system's ability to bounce back rapidly, promoting network resilience. Considering the complex nature of the problem due to uncertainty of hazards, and the impact of the pre‐event decisions on post‐event planning, this study formulates a nonlinear two‐stage stochastic programming (NTSSP) model, with the objective of minimizing the direct construction investment and indirect costs in both pre‐event mitigation and post‐event recovery stages. In the model, the first stage prioritizes a bridge group that will be retrofitted or repaired to improve the system's robustness and redundancy. The second stage elaborates the uncertain occurrence of a type of natural hazard with any potential intensity at any possible network location. The damaged state of the network is dependent on decisions made on first‐stage mitigation efforts. While there has been research addressing the optimization of pre‐event or post‐event efforts, the number of studies addressing two stages in the same framework is limited. Even such studies are limited in their applicationmore »due to the consideration of small networks with a limited number of assets. The NTSSP model addresses this gap and builds a large‐scale data‐driven simulation environment. To effectively solve the NTSSP model, a hybrid heuristic method of evolution strategy with high‐performance parallel computing is applied, through which the evolutionary process is accelerated, and the computing time is reduced as a result. The NTSSP model is implemented in a test‐bed transportation network in Iowa under flood hazards. The results show that the NTSSP model balances the economy and efficiency on risk mitigation within the budgetary investment while constantly providing a resilient system during the full two‐stage course.

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2. Co-Optimized Expansion Planning to Enhance Electrical System Resilience in Puerto Rico.

   Newlun, C. ; Figueroa, A. ; McCalley, J. ; Kimber, A. ; O'Neill-Carrillo, E. ( July 2019 , Proceedings of the 2019 North American Power Symposium)

   In September of 2017, the island of Puerto Rico (PR) was devastated by a category 5 hurricane, Hurricane Maria. The island experienced complete blackout, and full restoration of the electrical system took nearly 11 months to complete. Therefore, it is of high interest to re-develop the infrastructure at the generation, transmission, and distribution levels so that it is hurricane-resilient. This paper describes the methodologies behind developing a more resilient electric infrastructure using a co-optimized expansion planning (CEP) software. First, a model of the PR electric power system was developed to perform long term CEP studies. The CEP tool developed seeks the minimum total cost of the PR system in a 2018-2038 planning horizon while exploring various levels of expansion investment options. The CEP also models the system under extreme events (i.e., hurricanes) to allow for data-driven resilience enhancement decisions. Second, the paper summarizes infrastructure visions that contain resilience investment options; the visions differ in terms of invested amounts of distributed generation and centralized resource. Lastly, key findings from these visions are reported and the CEP model performance is discussed.
3. TRANSAX: A Blockchain-Based Decentralized Forward-Trading Energy Exchanged for Transactive Microgrids

   https://doi.org/10.1109/PADSW.2018.8645001  

   Laszka, Aron ; Eisele, Scott ; Dubey, Abhishek ; Karsai, Gabor ; Kvaternik, Karla ( December 2018 , 2018 IEEE 24th International Conference on Parallel and Distributed Systems (ICPADS))

   Power grids are undergoing major changes due to rapid growth in renewable energy and improvements in battery technology. Prompted by the increasing complexity of power systems, decentralized IoT solutions are emerging, which arrange local communities into transactive microgrids. The core functionality of these solutions is to provide mechanisms for matching producers with consumers while ensuring system safety. However, there are multiple challenges that these solutions still face: privacy, trust, and resilience. The privacy challenge arises because the time series of production and consumption data for each participant is sensitive and may be used to infer personal information. Trust is an issue because a producer or consumer can renege on the promised energy transfer. Providing resilience is challenging due to the possibility of failures in the infrastructure that is required to support these market based solutions. In this paper, we develop a rigorous solution for transactive microgrids that addresses all three challenges by providing an innovative combination of MILP solvers, smart contracts, and publish-subscribe middleware within a framework of a novel distributed application platform, called Resilient Information Architecture Platform for Smart Grid. Towards this purpose, we describe the key architectural concepts, including fault tolerance, and show the trade-off between market efficiencymore »and resource requirements.« less
4. False data injection attacks against contingency analysis in power grids: poster

   https://doi.org/10.1145/3317549.3326323  

   Rahman, M. Ashiqur ; Shahriar, Md Hasan ; Masum, Rahat ( January 2019 , ACM Conference on Security and Privacy in Wireless and Mobile Networks)

   The smart grid provides efficient and cost-effective management of the electric energy grid by allowing real-time monitoring, coordinating, and controlling the system using communication networks between physical components. This inherent complexity significantly increases the vulnerabilities and attack surface in the smart grid due to misconfigurations or the lack of security hardening. Therefore, it is important to ensure a secure and resilient operation of the smart grid by proactive identification of potential threats, impact assessment, and cost-efficient mitigation planning. This paper aims to achieve these goals through the development of an efficient security framework for the Energy Management System (EMS), a core smart grid component. In this paper, we present a framework that combines formal analytic with PowerWorld simulator which verifies the solution model to investigate the feasibility of false data injection attacks against contingency analysis in the power grid. We evaluate the impact of such attacks by running experiments using synthetic data on the standard IEEE test cases.
5. Evaluating Resilience of Grid Load Predictions under Stealthy Adversarial Attacks

   https://doi.org/10.1109/RWS47064.2019.8971816  

   Zhou, Xingyu ; Li, Yi ; Barreto, Carlos A. ; Li, Jiani ; Volgyesi, Peter ; Neema, Himanshu ; Koutsoukos, Xenofon ( November 2019 , 2019 Resilience Week (RWS))

   Recent advances in machine learning enable wider applications of prediction models in cyber-physical systems. Smart grids are increasingly using distributed sensor settings for distributed sensor fusion and information processing. Load forecasting systems use these sensors to predict future loads to incorporate into dynamic pricing of power and grid maintenance. However, these inference predictors are highly complex and thus vulnerable to adversarial attacks. Moreover, the adversarial attacks are synthetic norm-bounded modifications to a limited number of sensors that can greatly affect the accuracy of the overall predictor. It can be much cheaper and effective to incorporate elements of security and resilience at the earliest stages of design. In this paper, we demonstrate how to analyze the security and resilience of learning-based prediction models in power distribution networks by utilizing a domain-specific deep-learning and testing framework. This framework is developed using DeepForge and enables rapid design and analysis of attack scenarios against distributed smart meters in a power distribution network. It runs the attack simulations in the cloud backend. In addition to the predictor model, we have integrated an anomaly detector to detect adversarial attacks targeting the predictor. We formulate the stealthy adversarial attacks as an optimization problem to maximize prediction lossmore »while minimizing the required perturbations. Under the worst-case setting, where the attacker has full knowledge of both the predictor and the detector, an iterative attack method has been developed to solve for the adversarial perturbation. We demonstrate the framework capabilities using a GridLAB-D based power distribution network model and show how stealthy adversarial attacks can affect smart grid prediction systems even with a partial control of network.« less