Resilient Networks

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https://hdl.handle.net/10125/79224

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Now showing 1 - 7 of 7
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    Stability Considerations for a Synchronous Interconnection of the North American Eastern and Western Electric Grids
    ( 2022-01-04) Overbye, Thomas ; Shetye, Komal ; Wert, Jess ; Li, Hanyue ; Cathey, Casey ; Scribner, Harvey
    This paper presents some of the stability considerations for an ac interconnection of the North American Eastern and Western electric grids. Except for a brief time around 1970, the North American Eastern and Western grids have operated asynchronously, with only small power transfers possible through a few back-to-back HVDC ties. This paper provides results from a study showing that an ac interconnection may be possible with only modest changes to the existing transmission grid. The paper’s main focus is on the dynamic aspects of such an interconnection. The paper also shows how newer visualization techniques can be leveraged to show the results of larger-scale, long duration dynamic simulations. Results are given for a 110,000-bus model of the actual North American electric grid and an 82,000-bus synthetic grid.
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    PyProD: A Machine Learning-Friendly Platform for Protection Analytics in Distribution Systems
    ( 2022-01-04) Wu, Dongqi ; Kalathil, Dileep ; Begovic, Miroslav M. ; Xie, Le
    This paper introduces PyProD, a Python-based machine learning (ML)-compatible test-bed for evaluating the efficacy of protection schemes in electric distribution grids. This testbed is designed to bridge the gap between conventional power distribution grid analysis and growing capability of ML-based decision making algorithms, in particular in the context of protection system design and configuration. PyProD is shown to be capable of facilitating efficient design and evaluation of ML-based decision making algorithms for protection devices in the future electric distribution grid, in which many distributed energy resources and pro-sumers permeate the system.
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    Joint Planning of Natural Gas and Electric Power Transmission with Spatially Correlated Failures
    ( 2022-01-04) Blumsack, Seth ; Su, Wenjing
    We develop and illustrate a method for the joint planning of natural gas and electric power systems that are subject to spatially correlated failures of the kind that would be expected to occur in the case of extreme weather events. Our approach utilizes a two-stage stochastic planning and operations framework for a jointly planned and operated gas and electric power transmission system. Computational tractability is achieved through convex relaxations of the natural gas flow equations and the use of a machine learning algorithm to reduce the set of possible contingencies. We illustrate the method using a small test system used previously in the literature to evaluate computational performance of joint gas-grid models. We find that planning for geographically correlated failures rather than just random failures reduces the level of unserved energy relative to planning for random (spatially uncorrelated failures). Planning for geographically correlated failures, however, does not eliminate the susceptability of the joint gas-grid system to spatially uncorrelated failures.
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    How to trade electricity flexibility using artificial intelligence - An integrated algorithmic framework
    ( 2022-01-04) Hanny, Lisa ; Körner, Marc-Fabian ; Leinauer, Christina ; Michaelis, Anne ; Strueker, Jens ; Weibelzahl, Martin ; Weissflog, Jan
    In course of the energy transition, the growing share of Renewable Energy Sources (RES) makes electricity generation more decentralized and intermittent. This increases the relevance of exploiting flexibility potentials that help balancing intermittent RES supply and demand and, thus, contribute to overall system resilience. Digital technologies, in the form of automated trading algorithms, may considerably contribute to flexibility exploitation, as they enable faster and more accurate market interactions. In this paper, we develop an integrated algorithmic framework that finds an optimal trading strategy for flexibility on multiple markets. Hence, our work supports the trading of flexibility in a multi-market environment that results in enhanced market integration and harmonization of economically traded and physically delivered electricity, which finally promotes resilience in highly complex electricity systems.
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    Efficient Representation for Electric Vehicle Charging Station Operations using Reinforcement Learning
    ( 2022-01-04) Kwon, Kyung-Bin ; Zhu, Hao
    Effectively operating an electric vehicle charging station (EVCS) is crucial for enabling the rapid transition of electrified transportation. By utilizing the flexibility of EV charging needs, the EVCS can reduce the total electricity cost for meeting the EV demand. To solve this problem using reinforcement learning (RL), the dimension of state/action spaces unfortunately grows with the number of EVs, which becomes very large and time-varying. This dimensionality issue affects the efficiency and convergence performance of generic RL algorithms. To this end, we advocate to develop aggregation schemes for state/action according to the emergency of EV charging, or its laxity. A least-laxity first (LLF) rule is used to consider only the total charging power of the EVCS, while ensuring the feasibility of individual EV schedules. In addition, we propose an equivalent state aggregation that can guarantee to attain the same optimal policy. Using the proposed aggregation scheme, the policy gradient method is applied to find the best parameters of a linear Gaussian policy. Numerical tests have demonstrated the performance improvement of the proposed representation approaches in increasing the total reward and policy efficiency over existing approximation-based method.
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    Data-Driven Power System Optimal Decision Making Strategy under Wildfire Events
    ( 2022-01-04) Hong, Wanshi ; Wang, Bin ; Yao, Mengqi ; Callaway, Duncan ; Dale, Larry ; Huang, Can
    Wildfire activities are increasing in the western United States in recent years, causing escalating threats to power systems. This paper developed an optimal and data-driven decision-making framework that improves power system resilience under wildfire risks. An optimal load shedding plan is formulated based on optimal power flow analysis. To avoid power system cascading failure caused by wildfire, we added additional transmission line flow constraints based on the identification of power lines with high ignition risk. Finally, a data-driven method is developed, leveraging multiple machine learning techniques, to model the complex correlations between input wildfire scenarios and the output power management strategy with significantly reduced computational complexities. The proposed data-driven decision-making framework can reduce the safety impacts on the electricity consumers, improve power system resilience under wildfire events.
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