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In this introductory chapter, we lay the groundwork for the rest of the book by providing a more detailed picture of the expected purpose, shape, and architecture of future grid systems. We structure the chapter in terms of six questions that we believe are central to this discussion: Why do we need computational grids? What types of applications will grids be used for? Who will use grids? How will grids be used? What is involved in building a grid? And, what problems must be solved to make grids commonplace? We provide an overview of each of these issues here, referring to subsequent chapters for more detailed discussion.
Modern electrical power grids represent complex cyber-physical systems requiring specialized cybersecurity frameworks beyond traditional IT security models. Existing threat intelligence standards such as STIX 2.1 and MITRE ATT\&CK lack coverage for grid-specific assets, operational technology relationships, and cyber-physical interdependencies essential for power system security. We present Grid-STIX, a domain-specific extension of STIX 2.1 for electrical grid cybersecurity applications. Grid-STIX employs a modular architecture encompassing physical assets, operational technology components, cyber-physical relationships, and security policies that capture modern power systems including distributed energy resources, advanced metering infrastructure, and nuclear energy facilities. The framework provides threat modeling capabilities through systematic representation of attack patterns, supply chain risks, and cross-domain impact analysis while maintaining STIX 2.1 compliance. Grid-STIX includes modules for nuclear safeguards and non-proliferation verification, enabling cybersecurity modeling across conventional and nuclear energy sectors. The ontology supports Zero Trust enforceme
We propose a profitable trading strategy for the cryptocurrency market based on grid trading. Starting with an analysis of the expected value of the traditional grid strategy, we show that under simple assumptions, its expected return is essentially zero. We then introduce a novel Dynamic Grid-based Trading (DGT) strategy that adapts to market conditions by dynamically resetting grid positions. Our backtesting results using minute-level data from Bitcoin and Ethereum between January 2021 and July 2024 demonstrate that the DGT strategy significantly outperforms both the traditional grid and buy-and-hold strategies in terms of internal rate of return and risk control.
In recent years, the frequency and intensity of grid-ignited wildfires have increased significantly, leading to an elevated level of risk exposure to public safety and financial repercussions for electric utilities threatening their solvency. It is, therefore, imperative for electric utilities to accurately assess the financial impact of potential wildfires ignited by their power infrastructure. This is a critical step toward developing risk-informed strategies to mitigate grid-ignited wildfires from both operational and financial perspectives. This paper proposes and develops an integrated model to evaluate the damage costs associated with potential grid-ignited wildfires to allow assessing financial risk with greater precision than existing literature. The proposed model is tailored to assess the financial risk associated with grid-ignited wildfires, including environmental damages, destroyed structures, and damage to the power grid assets. We quantify the risk associated with each power line, thereby identifying areas that require immediate preemptive actions. To visually represent the risk levels associated with the transmission grid topology, we implement a color-coded risk he
Wildfires ignited by the power lines have become increasingly common over the past decade. Enhancing the operational and financial resilience of power grids against wildfires involves a multifaceted approach. Key proactive measures include meticulous vegetation management, strategic grid hardening such as infrastructure undergrounding, preemptive de-energization, and disaster risk financing, among others. Each measure should be tailored to prioritize efforts in mitigating the consequences of wildfires. This paper proposes a transmission line risk assessment method for grid-ignited wildfires, identifying the transmission lines that could potentially lead to damage to the natural and built environment and to other transmission lines if igniting a wildfire. Grid, meteorological, and topological datasets are combined to enable a comprehensive analysis. Numerical analysis on the standard IEEE 30-bus system demonstrates the effectiveness of the proposed method.
The power grid is the foundation of modern society, however extreme weather events have increasingly caused widespread outages. Enhancing grid resilience is therefore critical to maintaining secure and reliable operations. In disaster relief and restoration, vehicle-to-grid (V2G) technology allows electric vehicles (EVs) to serve as mobile energy resources by discharging to support critical loads or regulating grid frequency as needed. Effective V2G operation requires coordinated charging and discharging of many EVs through optimization. Similarly, in grid restoration, EVs must be strategically routed to affected areas, forming the mobile charging station placement (CSP) problem, which presents another complex optimization challenge. This work reviews state-of-the-art optimization methods for V2G and mobile CSP applications, outlines their limitations, and explores how quantum computing (QC) could overcome current computational bottlenecks. A QC-focused perspective is presented on enhancing grid resilience and accelerating restoration as extreme weather events grow more frequent and severe.
Modern power systems face new operational hurdles due to the increasing adoption of inverter-coupled distributed energy resources, which impact system stability and control. Central to these challenges is the dynamic nature of grid impedance. To address this, a novel real-time estimation algorithm based on the Discrete Fourier Transform is proposed. This algorithm is embedded within an Advanced Angle Estimation Kalman Filter framework that employs a Linear Quadratic Regulator for current control (AAEKF-LQR). The impedance data directly informs and refines the controller's phase angle estimation. Simulation analyses demonstrate robust collaboration between the estimator and controller, sustaining system stability under weak grid conditions. The technique proves capable of delivering swift and accurate impedance updates during grid variations, which is crucial for maintaining stable inverter operation
Grid computing has made substantial advances during the last decade. Grid middleware such as Globus has contributed greatly in making this possible. There are, however, significant barriers to the adoption of Grid computing in other fields, most notably day-to-day user computing environments. We will demonstrate in this paper that this is primarily due to the limitations of the existing Grid middleware which does not take into account the needs of everyday scientific and business users. In this paper we will formally advocate a Grid Operating System and propose an architecture to migrate Grid computing into a Grid operating system which we believe would help remove most of the technical barriers to the adoption of Grid computing and make it relevant to the day-to-day user. We believe this proposed transition to a Grid operating system will drive more pervasive Grid computing research and application development and deployment in future.
The Gamma-Ray Integrated Detectors (GRID) are a space science mission that employs compact gamma-ray detectors mounted on NanoSats in low Earth orbit (LEO) to monitor the transient gamma-ray sky. Owing to the unpredictability of the time and location of gamma-ray bursts (GRBs), obtaining the photon responses of gamma-ray detectors at various incident angles is important for the scientific analysis of GRB data captured by GRID detectors. For this purpose, a dedicated Monte Carlo simulation framework has been developed for GRID detectors. By simulating each GRID detector and the NanoSat carrying it, the spectral energy response, detection efficiency, and other angular responses of each detector for photons with different incident angles and energies can be obtained within this framework. The accuracy of these simulations has been corroborated through on-ground calibration, and the derived angular responses have been successfully applied to the data analysis of recorded GRBs.
Visual generation has witnessed remarkable progress in single-image tasks, yet extending these capabilities to temporal sequences remains challenging. Current approaches either build specialized video models from scratch with enormous computational costs or add separate motion modules to image generators, both requiring learning temporal dynamics anew. We observe that modern image generation models possess underutilized potential in handling structured layouts with implicit temporal understanding. Building on this insight, we introduce GRID, which reformulates temporal sequences as grid layouts, enabling holistic processing of visual sequences while leveraging existing model capabilities. Through a parallel flow-matching training strategy with coarse-to-fine scheduling, our approach achieves up to 67 faster inference speeds while using <1/1000 of the computational resources compared to specialized models. Extensive experiments demonstrate that GRID not only excels in temporal tasks from Text-to-Video to 3D Editing but also preserves strong performance in image generation, establishing itself as an efficient and versatile omni-solution for visual generation.
The grid-connected electric vehicles (EVs) serve as a promising regulating resource in the distribution grid with Vehicle-to-Grid (V2G) facilities. In the day-ahead stage, electric vehicle batteries (EVBs) need to be precisely dispatched and controlled to ensure high efficiency and prevent degradation. This article focuses on considering a refined battery model, i.e. the electrochemical model (EM), in the optimal dispatch of the local energy system with high penetration of EVs which replenish energy through V2G-equipped charge station and battery swapping station (BSS). In this paper, to utilize the EM efficiently, recursive EVB constraints and a corresponding matrix-based state update method are proposed based on EM power characterization. The charging EV state distribution is profiled and a multi-layer BSS model along with binary aggregation is proposed, in order to overcome the computation complexity of combining the refined battery constraints with the mixed integer optimization. Finally, a local energy system scenario is investigated for evaluation. The efficiency and effectiveness of EM consideration are assessed from the perspective of both the system and battery.
The progressive displacing of conventional generation in favour of renewable energy sources requires restoring an adequate capacity of regulating power to ensure reliable operation of power systems. Battery Energy Storage Systems (BESSs) are considered to be promising assets to restore suitable frequency regulation capacity levels. BESSs are typically connected to the grid with power-converters, able to operate in either grid-forming or grid-following modes. This paper quantitatively assesses the impact on the local distribution grid of BESSs providing frequency regulation to bulk power systems. Specific metrics are proposed to compare the performance of grid-forming and grid-following control. Experimental results are obtained taking advantage of a 720 kVA/500 kWh BESS connected to the 20 kV distribution grid of the EPFL campus. The quantitative evaluation based on suitably proposed metrics confirms the superior performance of the grid-forming strategy, compared to the grid-following one.
A knot is a closed loop in space without self-intersection. Two knots are equivalent if there is a self homeomorphism of space bringing one onto the other. An arc presentation is an embedding of a knot in the union of finitely many half planes with a common boundary line such that each half plane contains a simple arc of the knot. The minimal number of such half planes among all arc presentations of a given knot is called the arc index of the knot. A knot is usually presented as a planar diagram with finitely many crossings of two strands where one of the strands goes over the other. A grid diagram is a planar diagram which is a non-simple rectilinear polygon such that vertical edges always cross over horizontal edges at all crossings. It is easily seen that an arc presentation gives rise to a grid diagram and vice versa. It is known that the arc index of an alternating knot is two plus its minimal crossing number. There are 4878 prime alternating knots with minimal crossing number 13. We obtained minimal arc presentations of them in the form of grid diagrams having 15 vertical segments. This is a continuation of the works on prime alternating knots of 11 crossings and 12 crossings
The current generation of Grid infrastructures designed for production activity is strongly computing oriented and tuned on the needs of applications that requires intensive computations. Problems arise when trying to use such Grids to satisfy the sharing of data-oriented and service-oriented resources as happens in the IVOA community. We have designed, developed and implemented a Grid query element to access data source from an existing production Grid environment. We also enhanced the Grid middleware model (collective resources and sites) to manage Data Sources extending the Grid semantic. The query element and the modified grid Information System are able to connect the Grid environment to Virtual Observatory resources. A specialized query element is designed to work as Virtual Observatory resource in the Grid so than an Astronomer can access Virtual Observatory data using the IVOA standards.
Grid and peer-to-peer (P2P) networks are two ideal technologies for file sharing. A P2P grid is a special case of grid networks in which P2P communications are used for communication between nodes and trust management. Use of this technology allows creation of a network with greater distribution and scalability. Semantic grids have appeared as an expansion of grid networks in which rich resource metadata are revealed and clearly handled. In a semantic P2P grid, nodes are clustered into different groups based on the semantic similarities between their services. This paper proposes a reputation model for trust management in a semantic P2P Grid. We use fuzzy theory, in a trust overlay network named FR TRUST that models the network structure and the storage of reputation information. In fact we present a reputation collection and computation system for semantic P2P Grids. The system uses fuzzy theory to compute a peer trust level, which can be either: Low, Medium, or High. Our experimental results demonstrate that FR TRUST combines low (and therefore desirable) a good computational complexity with high ranking accuracy.
Big data storage management is one of the most challenging issues for Grid computing environments, since large amount of data intensive applications frequently involve a high degree of data access locality. Grid applications typically deal with large amounts of data. In traditional approaches high-performance computing consists dedicated servers that are used to data storage and data replication. In this paper we present a new mechanism for distributed and big data storage and resource discovery services. Here we proposed an architecture named Dynamic and Scalable Storage Management (DSSM) architecture in grid environments. This allows in grid computing not only sharing the computational cycles, but also share the storage space. The storage can be transparently accessed from any grid machine, allowing easy data sharing among grid users and applications. The concept of virtual ids that, allows the creation of virtual spaces has been introduced and used. The DSSM divides all Grid Oriented Storage devices (nodes) into multiple geographically distributed domains and to facilitate the locality and simplify the intra-domain storage management. Grid service based storage resources are ado
GRID-launcher-1.0 was built within the VO-Tech framework, as a software interface between the UK-ASTROGRID and a generic GRID infrastructures in order to allow any ASTROGRID user to launch on the GRID computing intensive tasks from the ASTROGRID Workbench or Desktop. Even though of general application, so far the Grid-Launcher has been tested on a few selected softwares (VONeural-MLP, VONeural-SVM, Sextractor and SWARP) and on the SCOPE-GRID.
In the present paper, we introduce a new method for the automated generation of residential distribution grid models based on novel building load estimation methods and a two-stage optimization for the generation of the 20 kV and 400 V grid topologies. Using the introduced load estimation methods, various open or proprietary data sources can be utilized to estimate the load of residential buildings. These data sources include available building footprints from OpenStreetMap, 3D building data from OSM Buildings, and the number of electricity meters per address provided by the respective distribution system operator (DSO). For the evaluation of the introduced methods, we compare the resulting grid models by utilizing different available data sources for a specific suburban residential area and the real grid topology provided by the DSO. This evaluation yields two key findings: First, the automated 20 kV network generation methodology works well when compared to the real network. Second, the utilization of public 3D building data for load estimation significantly increases the resulting model accuracy compared to 2D data and enables results similar to models based on DSO-supplied mete
Legendre-Gauss-Lobatto (LGL) grids play a pivotal role in nodal spectral methods for the numerical solution of partial differential equations. They not only provide efficient high-order quadrature rules, but give also rise to norm equivalences that could eventually lead to efficient preconditioning techniques in high-order methods. Unfortunately, a serious obstruction to fully exploiting the potential of such concepts is the fact that LGL grids of different degree are not nested. This affects, on the one hand, the choice and analysis of suitable auxiliary spaces, when applying the auxiliary space method as a principal preconditioning paradigm, and, on the other hand, the efficient solution of the auxiliary problems. As a central remedy, we consider certain nested hierarchies of dyadic grids of locally comparable mesh size, that are in a certain sense properly associated with the LGL grids. Their actual suitability requires a subtle analysis of such grids which, in turn, relies on a number of refined properties of LGL grids. The central objective of this paper is to derive just these properties. This requires first revisiting properties of close relatives to LGL grids which are subs
WISDOM is an international initiative to enable a virtual screening pipeline on a grid infrastructure. Its first attempt was to deploy large scale in silico docking on a public grid infrastructure. Protein-ligand docking is about computing the binding energy of a protein target to a library of potential drugs using a scoring algorithm. Previous deployments were either limited to one cluster, to grids of clusters in the tightly protected environment of a pharmaceutical laboratory or to pervasive grids. The first large scale docking experiment ran on the EGEE grid production service from 11 July 2005 to 19 August 2005 against targets relevant to research on malaria and saw over 41 million compounds docked for the equivalent of 80 years of CPU time. Up to 1,700 computers were simultaneously used in 15 countries around the world. Issues related to the deployment and the monitoring of the in silico docking experiment as well as experience with grid operation and services are reported in the paper. The main problem encountered for such a large scale deployment was the grid infrastructure stability. Although the overall success rate was above 80%, a lot of monitoring and supervision was s