Power Grid Network Research Articles

Finding influential nodes in networks using pinning control: Centrality measures confirmed with electrochemical oscillators.

The spatiotemporal organization of networks of dynamical units can break down resulting in diseases (e.g., in the brain) or large-scale malfunctions (e.g., power grid blackouts). Re-establishment of function then requires identification of the optimal intervention site from which the network behavior is most efficiently re-stabilized. Here, we consider one such scenario with a network of units with oscillatory dynamics, which can be suppressed by sufficiently strong coupling and stabilizing a single unit, i.e., pinning control. We analyze the stability of the network with hyperbolas in the control gain vs coupling strength state space and identify the most influential node (MIN) as the node that requires the weakest coupling to stabilize the network in the limit of very strong control gain. A computationally efficient method, based on the Moore-Penrose pseudoinverse of the network Laplacian matrix, was found to be efficient in identifying the MIN. In addition, we have found that in some networks, the MIN relocates when the control gain is changed, and thus, different nodes are the most influential ones for weakly and strongly coupled networks. A control theoretic measure is proposed to identify networks with unique or relocating MINs. We have identified real-world networks with relocating MINs, such as social and power grid networks. The results were confirmed in experiments with networks of chemical reactions, where oscillations in the networks were effectively suppressed through the pinning of a single reaction site determined by the computational method.

Improving protection of compensated transmission line using IoT enabled adaptive auto reclosing scheme

Abstract Protection of critical components in the power sector especially the transmission line system is very important to minimize loss of revenue, resources, and productivity. Improvement of power transfer capacity and voltage profile in smart the power grid is achieved by Series Compensation (SC). However, it poses many serious issues in the protection of compensated transmission lines. The majority of faults occurring in transmission lines are transient in nature for which service continuity can be regained by an auto-reclosing scheme. An efficient auto-reclosing scheme that can provide adaptive protection with reliable operation is required to prevent transient and permanent faults in the series compensated transmission line (SCTL). This work highlights the emulation of an Internet of Things (IoT) aided adaptive auto-reclosing scheme in association with an ESP32 controller that provides adaptive dead time control. The reclosing instance has been identified by means of a synchro-check relay. A real-time IoT-aided auto-reclosing scheme has been realized using the ESP32 controller that can generate a trip/reclosing signal instantaneously using the concepts of edge computing. The projected system has been also corroborated by simulating the modeled smart power grid network with wide variations in fault context in PSCAD. The validation of the set of rules is done in MATLAB software for the proposed auto-reclosing scheme. The extracted test outcomes of the simulation and its laboratory hardware implementation indicate an appreciable reduction in dead time which reflects the efficacy of the developed protection scheme.

Modeling two-stage failure mechanism of cascading in cyber-physical power systems

Interdependency in cyber-physical power systems enables efficient monitor and control, but also brings out many threats, leading to catastrophic blackouts. To address this problem, the propagation mechanism of cascading failure in systems is analyzed. In this paper, we propose the partial random coupling systems model, and detail the interactive mechanism between physical power grid and communication network. Then, the modified failure mechanism is present, including two-stage redistribution types of traffic loads in physical power grid, and stochastic routing strategies in communication network. In simulation, the impact of attack scenes and topology structure on systems robustness is studied. Compared with random attack, high degree/betweenness attack results in first-order transition of cascading failure at a critical point, and causes more serious damage on systems. Besides, we have proved the positive correlation between clustering coefficient and robustness. To random attack, the systems consisted of double-star network behave more robust than systems consisted of mesh network, and also indicate the misproportion of increase between average dependent degree and systems robustness.

Defense and Attack Game Strategies of Dual-Network Coupled CPPS with Communication Edge Failures

With the development of power technology and communication technology, the power grid and power communication network have become interdependent and closely coupled. The load shedding operation of the power grid is an important means to reduce the occurrence of chain faults and ensure the safe and stable operation of the power grid. Based on the transmission of load control services in the communication network, this paper establishes a model for a dual-network coupled cyber physical power system (CPPS). Considering communication edge faults, the associated load capacity of the communication edge and the expected load loss of the power grid are defined. On this basis, the paper proposes a complete information zero-sum game mechanism called “defense attack defense” for communication edge failures, which takes the expected loss of load from the power grid as the benefit. The paper studies the optimal attack and defense game strategies and provides the algorithm implementation process for the three stages of the game. Considering the bandwidth capacity of the communication edge, this paper uses the Dijkstra algorithm or k shortest paths (KSP) algorithm with the cost factor of the communication edge as the weight to plan the main and backup communication channels for multiple load control services. The simulation results show that the game mechanism proposed in this paper can effectively reduce the expected load loss from the power grid and improve the stability of the CPPS.

Analogue and Physical Reservoir Computing Using Water Waves: Applications in Power Engineering and Beyond

More than 3.5 billion people live in rural areas, where water and water energy resources play an important role in ensuring sustainable and productive rural economies. This article reviews and critically analyses the recent advances in the field of analogue and reservoir computing that have been driven by the unique physical properties and energy of water waves. It also demonstrates that analogue and physical reservoir computing, taken as an independent research field, holds the potential to bring artificial intelligence closer to people living outside large cities, thus enabling them to enjoy the benefits of novel technologies that are already in place in large cities but are not readily available or suitable for regional communities. In particular, although the physical reservoir computing systems discussed in the main text are universal in terms of processing input data and making forecasts, they can be used to design and optimise power grid networks and forecast energy consumption, both at local and global scales. Thus, this review article will be of interest to a broad readership interested in novel concepts of artificial intelligence and machine learning and their innovative practical applications in diverse areas of science and technology.

Perspectives on adaptive dynamical systems.

Adaptivity is a dynamical feature that is omnipresent in nature, socio-economics, and technology. For example, adaptive couplings appear in various real-world systems, such as the power grid, social, and neural networks, and they form the backbone of closed-loop control strategies and machine learning algorithms. In this article, we provide an interdisciplinary perspective on adaptive systems. We reflect on the notion and terminology of adaptivity in different disciplines and discuss which role adaptivity plays for various fields. We highlight common open challenges and give perspectives on future research directions, looking to inspire interdisciplinary approaches.

Research on the Performance of Text Mining and Processing in Power Grid Networks

This paper employs deep learning technique to perform the research of text mining for power grid networks, focusing on fundamental elements such as loss and activation functions. Through some analysis and formulas, we explain how these functions contribute to deep learning. We also introduce major deep learning training models, including CNN and RNN, and provide visual aids to aid understanding. To demonstrate the impact of various factors on deep learning training, we employ control variable experiments to analyze the influence of factors such as learning rate, batch size, and data noise on model training trends. While the influence of hyperparameters and data noise are covered in this paper, other factors such as CPU and memory frequency, as well as GPU performance, also play a crucial role in deep learning training. Therefore, continuous adjustments to various factors are necessary to achieve optimal training results for deep learning models in power grid networks.

From Delaunay triangulation to topological data analysis: generation of more realistic synthetic power grid networks

Abstract Assessing novel methods for increasing power system resilience against cyber-physical hazards requires real power grid data or high-quality synthetic data. However, for security reasons, even basic connection information for real power grid data are not publicly available. We develop a randomised model for generating realistic synthetic power networks based on the Delaunay triangulation and demonstrate that it captures important features of real power networks. To validate our model, we introduce a new metric for network similarity based on topological data analysis. We demonstrate the utility of our approach in application to IEEE test cases and European power networks. We identify the model parameters for two IEEE test cases and two European power grid networks and compare the properties of the generated networks with their corresponding benchmark networks.

Application of Internet of Things (IoT) in Nigerian Power Grid Management

The importance Internet of Things (IoT) in managing the Nigeria 330kV power grid cannot be overemphasized. The nation’s power sector has been deviled with problems ranging from low voltages to total system collapse. There have been efforts to reverse the negative trends. One of the efforts was the development of robust modern technologies to monitor and control the systems in real time. Another challenge of note is the lack of visibility with the grid controller, which can be compared to driving a car with blurred windscreen. The existing SCADA system has lower coverage and parades about 22% visibilities to control operators. Internet of things (IoT) is machine to machine connection via the internet for the purpose of controlling, monitoring and exchanging information. A lot can be achieved via IoT like controlling remote actuators, sensing of remote device’s parameters such as temperature, position, voltage, current, and power. It helps controllers at the master stations or control centers to make informed decisions for better grid stability. The paper explains the design and implementation of the IOT based solution to the problems. It describes its components and deployment in twenty-nine power stations in Nigeria. The paper discloses that the application of the homegrown solution has brought visibility and stability to the Nigerian power grid network.

Research on Intrusion Detection Technology of Computing Nodes in Digital Power Grid based on Artificial Intelligence

This paper aims to investigate an intrusion detection network for digital power grid networks, which consists of an edge server and two computational nodes that work collaboratively to detect any potential intrusion in the network. The primary objective of this study is to enhance the effectiveness of intrusion detection in the network. To achieve this objective, we first define the outage probability of the intrusion detection system under consideration. This is done to provide a measure of the probability that the system fails to detect an intrusion when it occurs. We then derive a closed-form expression for the outage probability to enable further analysis on the system behavior. Since the system resources, such as transmit power, are limited, we further design a transmit power allocation strategy to improve the system performance. This strategy seeks to optimize the allocation of transmit power across the different nodes of the intrusion detection network to maximize the likelihood of detecting intrusions while minimizing the resource usage. Finally, to evaluate the performance of the proposed system, we conduct simulations and provide results that demonstrate the accuracy of the closed-form expression and the effectiveness of the transmit power allocation strategy. These simulation results serve as evidence of the efficacy of the proposed approach in detecting intrusions in a resource-constrained network, especially for the digital power grid networks.

Performance Analysis and Research of Knowledge Sharing System for Power Grid Networks

Knowledge sharing is a critical aspect of machine learning and knowledge management, which also plays an important role in regulating the power grid networks. Hence, it is important to investigate the performance of knowledge sharing in the power grid networks. Motivated by this, we firstly investigate a typical power grid network with a knowledge sharing node, where the transmit power of users is constrained by the knowledge sharing node. We then measure the system performance by evaluating the system outage probability (OP), where the analytical expression of OP is derived in detail. Finally, we present some simulation and numerical results on the OP for the considered power grid networks with knowledge sharing, in order to verify the proposed studies on the OP. In particular, these results show that the knowledge sharing can help enhance the system OP performance efficiently.

Development of a novel grid-free district cooling system considering blockchain-based demand response management

District cooling system (DCS) enjoys improved cooling efficiency due to economies of scale cooling. However, most DCSs import electricity from the power grid, intensifying the electricity peak and grid investments. In addition, improving environmental sustainability is one of the major targets of DCSs. This study proposes a novel grid-free configuration to decouple the DCS from the power grid and natural gas networks. Meanwhile, an alternative operating strategy, waste cold energy recovery, ice storage, and cooling demand response are employed to promote the environmental sustainability of the DCS. Key results from this study have revealed that the proposed DCS does not need to import electricity from the power grid and facilitates a flexible plant location without the limitation of the natural gas network. Under the basic configuration, the daily primary energy saving rate (PESR) and energy cost saving rate (ECSR) can achieve 22.4% and 62.8%, respectively. The alternative operating strategy enhances the system’s part-load performance while the cold energy recovery unit facilitates extra cooling production. Further, the incorporation of the ice storage module improves the system’s average load rate and reduces the chillers' capacity. Finally, the blockchain-based decentralized network provides a trusted platform for implementing cooling demand response. Cooling demand response mitigates the cooling peak and total cooling consumption by precooling the space and adjusting the indoor set-point temperature in terms of the overall cooling load rate. Adopting these sustainable technologies yields the daily PESR and ECSR up to 35.2% and 68.9%, respectively.

Massively Parallel Modeling of Battery Energy Storage Systems for AC/DC Grid High-Performance Transient Simulation

Extensive integration of power electronics apparatuses complicates the modern power grid and consequently necessitates time-domain transients study for its planning and operation. In this work, a heterogeneous computing architecture utilizing the CPU and graphics processing unit (GPU) is proposed for the efficient study of interactions between a power grid network and massive utility-scale battery energy storage systems (BESSs). The device-level electromagnetic transient (EMT) simulation aiming at enhanced fidelity of the BESS is conducted simultaneously with electro-mechanical transient stability (TS) simulation which suffices system-level dynamic security assessment. Since the reservation of a large amount of energy storage units is computationally intensive for the CPU, the concurrent multi-streaming, multi-threading capability of GPU is exploited to achieve asynchronous sequential-parallel processing, so that the proposed EMT-TS co-simulation can flexibly harness all available computing resources. The multi-rate scheme is adopted for further computational burden alleviation in addition to achieving timely information exchange. It shows that the heterogeneous computation of an IEEE 118-bus system integrated with a substantial number of distributed batteries becomes feasible following the achievement of a remarkable speedup of over 200, and the device- as well as system-level accuracy are validated by MATLAB/Simulink^® and DSATools^TM/TSAT simulation, respectively.

GridNetOpt: Fast Full-Chip EM-Aware Power Grid Optimization Accelerated by Deep Neural Networks

This article presents a fast full-chip electromigration (EM) aware IR drop constrained optimization framework, named GridNetOpt, for on-chip power grid networks accelerated by deep neural networks (DNN). Compared to the existing linear programming-based methods, the new method employs more flexible conjugate gradient-based optimization to size the wire width of the power grids. To mitigate the high cost of sensitivity calculation of the adjoint network using full-chip IR drop analysis at every iteration step, the sensitivity is computed via a trained conditional generative adversarial network (CGAN). The new method exploits the differentiable characteristics of DNNs for fast sensitivity computation. The sensitivity, which is the node voltage with respect to wire resistance, will guide the search direction during the optimization process. In order to consider more accurate EM failure effects, the training data is obtained from the power grids under different wire widths and current loads analyzed by a state-of-the-art full-chip multi-physics-based coupled EM-IR drop analysis tool. This is in contrast with the existing linear programming-based methods, in which only immortal wires or wires with non-zero resistance can be dealt with. Numerical results on a number of synthesized power grid benchmarks from ARM Cortex-M0 processor designs show that the proposed GridNetOpt can lead to at least an order of magnitude speedup over the conjugate gradient-based method using the traditional adjoint network method. Compared to the previous localized power grid fixing work with GridNet, GridNetOpt leads to smaller area overhead for all the benchmarks we tested. It can also reduce IR drops for power grid circuits with immortal wires, which is not possible with the localized GridNet method.

Situation Awareness of Energy Internet of Things in Smart City Based on Digital Twin: From Digitization to Informatization

Rapid growth of diversity, uncertainty, and coupling effect of units in modern energy systems jointly challenge the traditional model-based situation awareness (SA) in energy internet of thing (EIoT). This work explores digital twin of EIoT (EIoT-DT), and then provides a novel data-driven SA paradigm, named DT-SA, as a promising alternative. Based on the combination of the latest data technologies and machine learning algorithms, DT-SA transfers those stubborn SA challenges to digital space, and then addresses them by building a domain-specific and data-friendly DT model upon massive data. The established model can be quantitatively tested via iterative virtual-real interaction, and thus be evaluated and updated through closed-loop feedback to improve its performance in the physical world. To this end, some engineering and scientific problems are raised: a) virtual-real interaction mechanism relevant to resource flow and data flow; b) unified modelling and analysis of heterogeneous spatial-temporal data; c) DT configuration and evolution; and d) domain-specific DT-SA characterization. To solve these problems, cloud-edge-terminal configuration, big data analytics (BDA), digital twin, and SA indicator systems are studied, respectively. Then, random matrix theory (RMT), overarching DT-SA framework are designed as a roadmap. Besides, some potential applications and under-going projects on the terminal, edge, or cloud are discussed, e.g., condition assessment of equipment, digital monitoring and diagnosis of power grid network, and EIoT construction in the smart city. Lastly, some perspectives and recommendations are proposed in conclusion for future researches. This research can be regarded as an efficient handbook for both energy engineering and data science, which may benefit enterprise digitization, smart city, etc.

Multi-parameter cooperative optimization and solution method for regional integrated energy system

As an energy system that uses the power grid and heat network to coordinate the energy interaction between subsystems in the region, the regional integrated energy system can further reduce the system's operating cost and improve the energy utilization rate. However, in the optimization of regional integrated energy systems, there is difficulty in balancing the accuracy of the results and the calculation time. Therefore, a nonlinear collaborative optimization model of a regional integrated energy system is established in this paper, which fully considers the electricity and heat sharing between energy stations. Subsequently, a three-layer-nested co-optimization method for a regional integrated energy system is proposed. The outer layer, middle layer, and inner layer optimize the rated capacity of the internal combustion engine, electric refrigeration ratio, and partial load rate of the internal combustion engine, respectively. Finally, the three-layer-nested co-optimization method is verified from the total system cost, energy utilization rate, and calculation time. The results show that compared with the exhaustive search method and genetic algorithm, the cost per unit area of the system is about 102.99 ¥/m2 (reduced by 1.00 ¥/m2), the energy utilization rate is 70.47% (increased by 2.43%), and the calculation time is about 50–100 h. The three-layer-nested co-optimization method significantly shortens the model's solution time while ensuring the optimization results' accuracy. Finally, this paper can provide a research idea for optimizing regional integrated energy system design.

A Static and Dynamic Analysis of Photovoltaic Penetration into MV Distribution Network

Photovoltaic (PV) systems are becoming increasingly prevalent worldwide, particularly in power distribution networks. However, their intermittency and integration into distribution networks can have adverse effects. This study investigates the impact of large-scale solar integration into a typical Malaysian power grid network, focusing on voltage stability, short circuits, and power loss under peak and no-load conditions. Using Digsilent Power Factory software, static and dynamic power flow analyses were performed on a network consisting of two 132/11 kV transformers, an 11 kV busbar, and 112 loads served through eight feeders. Solar PV of 100 kW was integrated into each node, and the maximum allowable solar grid connection level was determined. The static results show that there were no violations in no-load conditions at 100 kW PV penetration. However, during peak load, there were violations at 0% PV penetration, but by increasing the level of solar grid connection to 60% (60 kW), the voltage level moves up to the acceptable range. Under contingency conditions, the results show that the minimum level of solar penetration is 80% (80 kW). The highest power loss occurs during peak time and is observed at 0% PV penetration. Feeder 8, the lounge feeder with the highest number of loads, is identified as the main cause of power loss. According to the short circuit analysis in peak and no-load conditions, the system experiences the highest shorts during peak loads. On the other hand, we conducted a dynamic simulation with load characteristics and compared the results for different levels of PV penetration. The results from the dynamic simulations show that lower limit violations occur even at 100% PV penetration for a brief period in all case studies. This study identifies the maximum permissible PV penetration as 125 kW.

Transactive Control of Electric Railways Using Dynamic Market Mechanisms

Electricity demand of electric railways is a relatively unexplored source of flexibility in demand response applications in power systems. In this article, we propose a transactive control-based optimization framework for coordinating the power grid network and the train network. This is accomplished by coordinating dispatchable distributed energy resources (DERs) and demand profiles of trains using a two-step optimization. A railway-based dynamic market mechanism (rDMM) is proposed for the dispatch of DERs in the power network along the electric railway using an iterative negotiation process, generates the profiles of electricity prices, and constitutes the first step. The train dispatch attempts to minimize the operational costs of trains that ply along the railway, while subject to constraints on their acceleration profiles, route schedules, and the train dynamics, and generate demand profiles of trains and constitute the second step. The rDMM seeks to optimize the operational costs of the underlying DERs while ensuring power balance. Together, they form an overall framework that yields the desired transactions between the railway and power grid infrastructures. This overall optimization approach is validated using simulation studies of the Southbound Amtrak service along the Northeast Corridor (NEC) in USA, which shows a 25% reduction in energy costs when compared to standard trip optimization based on minimum work and a 75% reduction in energy costs when compared to the train cost calculated using a field dataset.

Emerging robust and data‐driven control methods for uncertain learning systems

Emerging robust and data‐driven control methods for uncertain learning systems

Design of advanced Aalborg inverter for extracting maximum power from renewable energy sources tied with autonomous grid system

AbstractOwing to increased energy demand, renewable energy sources have recently reached greater heights in the power industry. In this regard, integrating renewable energy with the power grid system has become essential in meeting consumer's energy demands. The novelty in this article is the design of an advanced Aalborg inverter with a reduced number of switches which results in enhanced system performance by reducing the switching losses. Integrating renewable energy sources with the power grid network has gained massive importance in supplying electric power to consumers. Integrating the power grid with renewable energy has become possible with the implementation of grid tie inverters. The only drawback in implementing renewable energy sources with the power grid is that the output of the resources varies from time to time in nature. Voltage Source Inverter‐based grid system requires two or three‐stage inverters for interfacing power units to transfer DC energy into the power grid network. To reduce the switching frequency across the system, many new excellent inverters have been designed to ensure the healthy operation of the system operating at a higher frequency. This article has been coined with the investigation of a novel buck and boosts converter‐based inverter operation named Aalborg inverter. The inverter operates at a higher frequency and minimum voltage drop across the inductor, where only one power stage has been implied to enhance the system's reliability and efficiency. This novel Aalborg inverter is applied and investigated for photovoltaic application.