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 application 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.
Planning for Distribution Resilience under Variable Generation: Prevention, Surviving and Recovery
Power grids based on traditional N-1 design criteria are no longer adequate because these designs do not withstand extreme weather events or cascading failures. Microgrid system has the capability of enhancing grid resilience through defensive or islanded operations in contingency. This paper presents a probabilistic framework for planning resilient distribution system via distributed wind and solar integration. We first define three aspects of resilient distribution system, namely prevention, survivability and recovery. Then we review the distributed generation planning models that comprehend
moment estimation, chance constraints and bi-directional power flow. We strive to achieve two objectives: 1) enhancing the grid survivability when distribution lines are damaged or disconnected in the aftermath of disaster attack; and 2) accelerating the recovery of damaged assets through pro-active maintenance and repair services. A simple 9-node network is provided to demonstrate the application of the proposed resilience planning framework
more »
« less
- Award ID(s):
- 1704933
- NSF-PAR ID:
- 10095312
- Date Published:
- Journal Name:
- 2018 IEEE Green Technologies Conference
- Page Range / eLocation ID:
- 49 to 56
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
More Like this
-
more » « less
ABSTRACT -
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.more » « less
-
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 efficiency and resource requirements.more » « less
-
Climate change is expected to intensify the effects of extreme weather events on power systems and increase the frequency of severe power outages. The large-scale integration of environment-dependent renewables during energy decarbonization could induce increased uncertainty in the supply–demand balance and climate vulnerability of power grids. This Perspective discusses the superimposed risks of climate change, extreme weather events and renewable energy integration, which collectively affect power system resilience. Insights drawn from large-scale spatiotemporal data on historical US power outages induced by tropical cyclones illustrate the vital role of grid inertia and system flexibility in maintaining the balance between supply and demand, thereby preventing catastrophic cascading failures. Alarmingly, the future projections under diverse emission pathways signal that climate hazards — especially tropical cyclones and heatwaves — are intensifying and can cause even greater impacts on the power grids. High-penetration renewable power systems under climate change may face escalating challenges, including more severe infrastructure damage, lower grid inertia and flexibility, and longer post-event recovery. Towards a net-zero future, this Perspective then explores approaches for harnessing the inherent potential of distributed renewables for climate resilience through forming microgrids, aligned with holistic technical solutions such as grid-forming inverters, distributed energy storage, cross-sector interoperability, distributed optimization and climate–energy integrated modelling.more » « less
-
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.more » « less
- Free Publicly Accessible Full Text
-
Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.1109/GreenTech.2018.00018
