Showing papers by "Hydro-Québec published in 2021"

Definition and Classification of Power System Stability – Revisited & Extended

Abstract: Since the publication of the original paper on power system stability definitions in 2004, the dynamic behavior of power systems has gradually changed due to the increasing penetration of converter interfaced generation technologies, loads, and transmission devices. In recognition of this change, a Task Force was established in 2016 to re-examine and extend, where appropriate, the classic definitions and classifications of the basic stability terms to incorporate the effects of fast-response power electronic devices. This paper based on an IEEE PES report summarizes the major results of the work of the Task Force and presents extended definitions and classification of power system stability.

Roles of Dynamic State Estimation in Power System Modeling, Monitoring and Operation

Abstract: Power system dynamic state estimation (DSE) remains an active research area. This is driven by the absence of accurate models, the increasing availability of fast-sampled, time-synchronized measurements, and the advances in the capability, scalability, and affordability of computing and communications. This paper discusses the advantages of DSE as compared to static state estimation, and the implementation differences between the two, including the measurement configuration, modeling framework and support software features. The important roles of DSE are discussed from modeling, monitoring and operation aspects for today's synchronous machine dominated systems and the future power electronics-interfaced generation systems. Several examples are presented to demonstrate the benefits of DSE on enhancing the operational robustness and resilience of 21st century power system through time critical applications. Future research directions are identified and discussed, paving the way for developing the next generation of energy management systems and novel system monitoring, control and protection tools to achieve better reliability and resiliency.

Phosphate Polyanion Materials as High-Voltage Lithium-Ion Battery Cathode: A Review

Abstract: Followed by decades of successful efforts in developing cathode materials for high specific capacity lithiumion batteries, currently the attention is on developing a high voltage battery (>5 V vs Li/Li+ ) with an aim to increase the energy density for their many fold advantages over conventional <4 V batteries. Among the various cathode materials, phosphate polyanion materials (LiMPO4, where M is a single metal or a combination of metals) showed promising candidacy given their high electrochemical potential (4.8−5 V vs Li/Li+), long cycle stability, low cost, and achieved specific capacity (∼165 mAh·g−1) near to its theoretical limit (170 mAh·g−1). In this review, factors affecting the electrochemical potential of the cathode materials are reviewed and discussed. Techniques to improve the electrical and ionic conductivities of phosphate polyanion cathodes, namely, surface coating, particle size reduction, doping, and morphology engineering, are also discussed. A processing−property correlation in phosphate polyanion materials is also undertaken to understand relative merits and drawbacks of diverse processing techniques to deliver a material with targeted functionality. Strategies required for high-voltage phosphate polyanion cathode materials are envisioned, which are expected to deliver lithium-ion battery cathodes with higher working potential and gravimetric specific capacity

Pillar-beam structures prevent layered cathode materials from destructive phase transitions.

Abstract: Energy storage with high energy density and low cost has been the subject of a decades-long pursuit. Sodium-ion batteries are well expected because they utilize abundant resources. However, the lack of competent cathodes with both large capacities and long cycle lives prevents the commercialization of sodium-ion batteries. Conventional cathodes with hexagonal-P2-type structures suffer from structural degradations when the sodium content falls below 33%, or when the integral anions participate in gas evolution reactions. Here, we show a “pillar-beam” structure for sodium-ion battery cathodes where a few inert potassium ions uphold the layer-structured framework, while the working sodium ions could diffuse freely. The thus-created unorthodox orthogonal-P2 K0.4[Ni0.2Mn0.8]O2 cathode delivers a capacity of 194 mAh/g at 0.1 C, a rate capacity of 84% at 1 C, and an 86% capacity retention after 500 cycles at 1 C. The addition of the potassium ions boosts simultaneously the energy density and the cycle life. The specific capacity of P2-type sodium-ion battery cathode is limited because full extraction of Na ions leads to structural degradation. Here authors report pillar-beam structured material to overcome this issue by using K pillar ions to uphold the transition metal layers upon extraction of Na ions.

Energy storage in metal cobaltite electrodes: Opportunities & challenges in magnesium cobalt oxide

Abstract: Ternary metal cobaltites (TMCs) offering high charge storability, multiple oxidation states, and improved electrical conductivity are widely explored as electrodes for energy storage devices. Among them, magnesium cobalt oxide or magnesium cobaltite (MgCo2O4) could be a cheaper analogue due to the abundance of magnesium; however, limited by materials stability in certain electrolytic conditions, formation of insulating layers hindering the charge response under charge/discharge conditions. While certain morphologies of MgCo2O4 delivered capacities higher than the theoretical capacity in lithium ion storage mode, it delivered only a quarter of the theoretical value in a supercapattery storage mode. This review critically analyzes the progress in MgCo2O4 as a charge storing electrode for lithium-ion battery, supercapattery (battery-type electrode), and magnesium rechargeable battery cathodes. A detailed account of the research undertaken is presented herewith and identifies opportunities and research gaps, which are expected to serve as a guide for future research in ternary metal cobaltites.

Using environmental DNA for biomonitoring of freshwater fish communities: Comparison with established gillnet surveys in a boreal hydroelectric impoundment

Abstract: This is an open access article under the terms of the Creative Commons Attribution-NonCommercial License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited and is not used for commercial purposes. © 2020 The Authors. Environmental DNA published by John Wiley & Sons Ltd 1Département de Biologie, Institut de Biologie Intégrative et des Systèmes (IBIS), Université Laval, Québec, QC, Canada 2Englobe Corp, Études Environnementales et Relations avec les Communautés, Montréal, QC, Canada 3Direction Environnement Place-Dupuis, Hydro-Québec Environnement Naturel et Humain, Montréal, QC, Canada

Improving the accuracy of electricity carbon footprint: Estimation of hydroelectric reservoir greenhouse gas emissions

Abstract: Hydropower is usually considered as a low-carbon electricity source, as it does not lead to direct greenhouse gas (GHG) emissions, unlike producing electricity from fossil fuels. However, the flooding of lands following the construction of the dam generally leads to an increase in biogenic GHG emissions due to the degradation of biomass found in the newly created reservoir. The life cycle assessment (LCA) methodology is widely used to calculate and compare the carbon footprint of different electricity production pathways, while considering all life cycle stages. Net biogenic GHG emissions from hydropower reservoirs have been poorly considered in LCA because of the scarcity of data. These emissions are complex to quantify as several mechanisms are involved, and extrapolating observations from one reservoir to another is risky as emissions vary greatly depending on different parameters, such as climate, geographic location, age of impoundment, and watershed properties. The objective of this article is to compare different approaches to estimate hydropower reservoir emissions in LCA, to select the most appropriate one, and to apply it to the calculation of the carbon footprint of electricity distributed in the Canadian province of Quebec. Net biogenic GHG emissions of all hydropower reservoirs in the province (with 2.5 and 97.5% confidence intervals), as estimated using the G-res model, are 16.5 (14.7–18.6) gCO2∙kWh−1 and 0.29 (0.23–0.35) gCH4∙kWh−1. Combined to ecoinvent data for other life cycle emissions, the carbon footprint of electricity distributed in the province in 2017 is 34.5 gCO2eq∙kWh−1.

A Deep Learning-Based Cyberattack Detection System for Transmission Protective Relays

Abstract: The digitalization of power systems over the past decade has made the cybersecurity of substations a top priority for regulatory agencies and utilities. Proprietary communication protocols are being increasingly replaced by standardized and interoperable protocols providing utility operators with remote access and control capabilities at the expense of growing cyberattack risks. In particular, the potential of supply chain cyberattacks is on the rise in industrial control systems. In this environment, there is a pressing need for the development of cyberattack detection systems for substations and in particular protective relays, a critical component of substation operation. This article presents a deep learning-based cyberattack detection system for transmission line protective relays. The proposed cyberattack detection system is first trained with current and voltage measurements representing various types of faults on the transmission lines. The cyberattack detection system is then employed to detect current and voltage measurements that are maliciously injected by an attacker to trigger the transmission line protective relays. The proposed cyberattack detection system is evaluated under a variety of cyberattack scenarios. The results demonstrate that a universal architecture can be designed for the deep learning-based cyberattack detection systems in substations.

Automatic Vulnerability Detection in Embedded Devices and Firmware: Survey and Layered Taxonomies

Abstract: In the era of the internet of things (IoT), software-enabled inter-connected devices are of paramount importance. The embedded systems are very frequently used in both security and privacy-sensitive applications. However, the underlying software (a.k.a. firmware) very often suffers from a wide range of security vulnerabilities, mainly due to their outdated systems or reusing existing vulnerable libraries; which is evident by the surprising rise in the number of attacks against embedded systems. Therefore, to protect those embedded systems, detecting the presence of vulnerabilities in the large pool of embedded devices and their firmware plays a vital role. To this end, there exist several approaches to identify and trigger potential vulnerabilities within deployed embedded systems firmware. In this survey, we provide a comprehensive review of the state-of-the-art proposals, which detect vulnerabilities in embedded systems and firmware images by employing various analysis techniques, including static analysis, dynamic analysis, symbolic execution, and hybrid approaches. Furthermore, we perform both quantitative and qualitative comparisons among the surveyed approaches. Moreover, we devise taxonomies based on the applications of those approaches, the features used in the literature, and the type of the analysis. Finally, we identify the unresolved challenges and discuss possible future directions in this field of research.

Two decades of blackbox optimization applications

Abstract: This article reviews blackbox optimization applications of direct search optimization methods over the past twenty years. Emphasis is placed on the Mesh Adaptive Direct Search ( Mads ) derivative-free optimization algorithm. The main focus is on applications in three specific fields: energy, materials science, and computational engineering design. Nevertheless, other applications in science and engineering, including patents, are also considered. The breadth of applications demonstrates the versatility of Mads and highlights the evolution of its accompanying software NOMAD as a standard tool for blackbox optimization.

Cause and Mitigation of Lithium-Ion Battery Failure-A Review.

Abstract: Lithium-ion batteries (LiBs) are seen as a viable option to meet the rising demand for energy storage. To meet this requirement, substantial research is being accomplished in battery materials as well as operational safety. LiBs are delicate and may fail if not handled properly. The failure modes and mechanisms for any system can be derived using different methodologies like failure mode effects analysis (FMEA) and failure mode methods effects analysis (FMMEA). FMMEA is used in this paper as it helps to identify the reliability of a system at the component level focusing on the physics causing the observed failures and should thus be superior to the more data-driven FMEA approach. Mitigation strategies in LiBs to overcome the failure modes can be categorized as intrinsic safety, additional protection devices, and fire inhibition and ventilation. Intrinsic safety involves modifications of materials in anode, cathode, and electrolyte. Additives added to the electrolyte enhance the properties assisting in the improvement of solid-electrolyte interphase and stability. Protection devices include vents, circuit breakers, fuses, current interrupt devices, and positive temperature coefficient devices. Battery thermal management is also a protection method to maintain the temperature below the threshold level, it includes air, liquid, and phase change material-based cooling. Fire identification at the preliminary stage and introducing fire suppressive additives is very critical. This review paper provides a brief overview of advancements in battery chemistries, relevant modes, methods, and mechanisms of potential failures, and finally the required mitigation strategies to overcome these failures.

Nanoboxes with a porous MnO core and amorphous TiO2 shell as a mediator for lithium–sulfur batteries

Abstract: Nanoboxes with a porous MnO core and amorphous TiO2 shell have been synthesized via a wet-chemistry method for the first time. The uniqueness of this material is the core–shell nanostructure composed of a crystalline porous core of MnO and amorphous shell of TiO2. Reduced graphene oxide (RGO) supported MnO–TiO2 core–shell nanocubes with different Ti : Mn molar ratios demonstrated excellent performance as high-capacity cathode materials for lithium–sulfur (Li–S) batteries. These porous core–shell nanocomposite structures with trapped S crystals also offer a good model to investigate the morphological and structural effects of nanocomposites and their influence on the capacity and stability of Li–S batteries.

Identification and Application of Machine Learning Algorithms for Transformer Dissolved Gas Analysis

Abstract: Power transformers represent one of the most abundant and expensive components in the electric power industry. Dissolved gas analysis (DGA) of transformer is the most widely accepted diagnostic tool across the globe to understand insulation incipient failures. Nevertheless, DGA fault gas interpretation is a remarkable challenge for transformer owners and utility engineers. Several computational techniques have been adopted for DGA fault classification along with offline methods. However, limited data availability, high ambiguity in DGA interpretation, suitability, and model accuracy are critical challenges in the DGA fault classification using computational techniques. In this work, highly diverse and large DGA data samples of in-service transformer fleets from five different utilities have been used to develop an efficient fault classification methodology. A total of 4580 DGA samples and IEC TC 10 database are used for training and testing, respectively, for various machine learning algorithms. Discussions on performance indicators and evaluation of several algorithms to verify the most suitable class algorithms are also the focus of this work. Furthermore, a best-performing model is identified based on various performance indicators. The hyperparameters of the best model are further tuned to achieve a most precise fault classification. It is inferred that non-parametric methods and non-linear SVM are best suitable for transformer DGA fault classification. Importantly, the rankings in the present study suggest that transformer DGA fault prediction is better with ensemble learning methods.

A Novel Wide-Area Control Strategy for Damping of Critical Frequency Oscillations via Modulation of Active Power Injections

Abstract: This article proposes a novel wide-area control strategy for modulating the active power injections to damp the critical frequency oscillations in power systems, this includes the inter-area oscillations and the transient frequency swing. The proposed method pursues an efficient utilization of the limited power reserve of existing distributed energy resources (DERs) to mitigate these oscillations. This is accomplished by decoupling the damping control actions at different sites using the oscillation signals of the concerned mode as the power commands. A theoretical basis for this decoupled modulating control is provided. Technically, the desired sole modal oscillation signals are filtered out by linearly combining the system-wide frequencies, which is determined by the linear quadratic regulator based sparsity-promoting (LQRSP) technique. With the proposed strategy, the modulation of each active power injection can be effectively engineered considering the response limit and steady-state output capability of the supporting device. The method is validated based on a two-area test system and is further demonstrated based on the New England 39-bus test system.

Metal–organic framework-based materials: advances, exploits, and challenges in promoting post Li-ion battery technologies

Abstract: After exclusive research for three decades on metal–organic frameworks (MOFs), can there be anything unexplored, unmapped, or unexplained? Synthetic processes, fundamental characteristics, and their suitability for various applications have previously been broadly highlighted elsewhere It is time, however, to focus on their prospect of application in the field of post-lithium batteries Considering the perpetual rise in the demand for safer rechargeable batteries and an urgent need to refrain from Li-based batteries, which is attributed to the limited supply of lithium, a serious consideration regarding the implementation of post-lithium rechargeable batteries at a commercial level is needed Even though post-lithium batteries seem to be an effective solution to refrain from the excessive use of a limited reserves of lithium, several concerns are still needed to be addressed before they can be recognized for practical applications MOFs can prove to be advantageous in providing aid for the design of electrode materials with better stability and conductivity for metal-ion batteries, act as catalysts for improving the reaction kinetics in metal–air batteries, and serve as hosts for sulfur encapsulation in metal–sulfur batteries Currently available reviews focus mainly on the use of MOFs and MOF-based materials for Li-based rechargeable batteries This survey aims to highlight the problems and their possible solutions in cutting-edge post-lithium batteries implementing MOFs and MOF-based materials, together with highlighting the remarkable works that have been carried out to understand the various design aspects of electrode materials so as to direct future research in this regime

Growth Mechanism of Micro/Nano Metal Dendrites and Cumulative Strategies for Countering Its Impacts in Metal Ion Batteries: A Review.

Abstract: Metal-ion batteries are capable of delivering high energy density with a longer lifespan. However, they are subject to several issues limiting their utilization. One critical impediment is the budding and extension of solid protuberances on the anodic surface, which hinders the cell functionalities. These protuberances expand continuously during the cyclic processes, extending through the separator sheath and leading to electrical shorting. The progression of a protrusion relies on a number of in situ and ex situ factors that can be evaluated theoretically through modeling or via laboratory experimentation. However, it is essential to identify the dynamics and mechanism of protrusion outgrowth. This review article explores recent advances in alleviating metal dendrites in battery systems, specifically alkali metals. In detail, we address the challenges associated with battery breakdown, including the underlying mechanism of dendrite generation and swelling. We discuss the feasible solutions to mitigate the dendrites, as well as their pros and cons, highlighting future research directions. It is of great importance to analyze dendrite suppression within a pragmatic framework with synergy in order to discover a unique solution to ensure the viability of present (Li) and future-generation batteries (Na and K) for commercial use.

Evaluation of Simulation Methods for Analysis of Geomagnetic Disturbance System Impacts

Abstract: Geomagnetic Disturbance (GMD) impacts a power system by causing the circulation of quasi-dc Geomagnetically-induced Currents (GICs) in transmission lines and high-voltage transformer windings leading to transformer saturation, increased reactive power losses, and voltage regulation problems. Utility planners use various analysis methods and simulation tools to analyze GMD system impacts. These techniques can be broadly categorized into load-flow-based (LF), transient stability type (TS), and electromagnetic transient type (EMT) methods. These methods are based on different modeling assumptions and solution techniques. There is a need for evaluation of these assumptions and cross-examination of results to ensure their accuracy. Such an evaluation is essential to ensure that planners performing required GMD vulnerability assessments have confidence in the results. This paper evaluates the LF, TS, and EMT methods through cross-examination of their results. The objective is to identify their limitations, assess the consistency of their results, and provide assumptions on their use for analysis of GMD system impacts. The study is conducted in consistence with the North American Electric Reliability Corporation (NERC) GMD guidelines and standards.

Effect of pressure on the properties of a NASICON Li1.3Al0.3Ti1.7(PO4)3 nanofiber solid electrolyte

Abstract: We report the effect of pressure on a membrane made of dense electrospun NASICON-like Li1.3Al0.3Ti1.7(PO4)3 (LATP). The properties and performance of the pressed LATP nanofibers were investigated and compared with those of pristine LATP nanofibers. While the applied pressure affects the purity and homogeneity of LATP, it is beneficial for ionic transport across the solid electrolyte. The presence of impurity phases as well as the decrease of porosity results in a two order of magnitude higher ionic conductivity at room temperature (3 × 10−5 S cm−1) which is promising to replace bulk NASICON materials in energy storage devices.

A Survey on FOPID Controllers for LFO Damping in Power Systems Using Synchronous Generators, FACTS Devices and Inverter-Based Power Plants

Abstract: In recent decades, various types of control techniques have been proposed for use in power systems. Among them, the use of a proportional–integral–derivative (PID) controller is widely recognized as an effective technique. The generalized type of this controller is the fractional-order PID (FOPID) controller. This type of controller provides a wider range of stability area due to the fractional orders of integrals and derivatives. These types of controllers have been significantly considered as a new approach in power engineering that can enhance the operation and stability of power systems. This paper represents a comprehensive overview of the FOPID controller and its applications in modern power systems for enhancing low-frequency oscillation (LFO) damping. In addition, the performance of this type of controller has been evaluated in a benchmark test system. It can be a driver for the development of FOPID controller applications in modern power systems. Investigation of different pieces of research shows that FOPID controllers, as robust controllers, can play an efficient role in modern power systems.

Analytically Validated SSCI Assessment Technique for Wind Parks in Series Compensated Grids

Abstract: This work highlights limitations in existing screening approaches to assess low-frequency interactions during interconnection studies of grid-connected inverters (GCIs), such as wind power plants It then proposes a new screening methodology capable of addressing these limitations for realistic representations of power system components and GCIs It demonstrates that the asymmetric input admittance characteristics of GCIs, whose consideration is required for proper stability assessment, can be accurately extracted with the proposed adequate scanning method implemented in an electromagnetic transient (EMT) software package This is verified through rigorous validation against analytical formulations developed for field validated type-III and type-IV wind turbine models It shows that the technique is capable of correctly assessing stability in all presented cases All results are validated against detailed EMT simulations