Robust Power Grid Research Articles

A quantum particle swarm optimization-based optimal LQR-PID controller for load frequency control of an isolated power system

One of the characteristics of a robust power grid is minimal variations in its frequency to load change or loss in generating unit(s). From the perspective of optimal control theory, the issue of load frequency control in the context of the interconnected functioning of power systems is investigated in this work, and a novel load frequency controller is proposed for a single area isolated power network. This novelty incorporates all the primary characteristics of the solutions that are based on a mixture of optimal controller designs by establishing a linear quadratic regulator optimized with quantum particle swarm optimization to design a proportional integral derivative (PID) controller unlike the conventional PID controller designs that are based on a combined Ziegler-Nichols and root locus (ZN-RL) method and manual tuning. The simulation results of the proposed controller using MATLAB show its efficacy in not only ensuring that there is no steady-state error in terms of the system frequency with load changes but also in achieving smoother transients. Following these landmark achievements, a transfer function model of the resulting power grid is constructed. The outcome of the model reveals that the system transients have been improved while keeping the intended steady-state characteristics. Furthermore, it is observed that the proposed load frequency controller has the best performance when compared with the combined ZN-RL method and the manual PID designs. This, therefore, demonstrates the superiority of the proposed design for load frequency control in power systems.

Finding critical nodes in a complex network from information diffusion and Matthew effect aggregation

The identification of critical nodes is a crucial aspect in studying the spread of diseases, vaccination strategies, power grid robustness, advertisement placement, and rumor control. Consequently, this topic has become of immense interest in recent times. In the last decade, numerous methods have been proposed for identifying critical nodes, but each method has its own strengths and weaknesses, which can be attributed to the complex nature of networks and different scenarios. Therefore, it is unlikely that a single method can be applicable to all networks. To address the need for improved critical node identification in propagation scenarios, we propose a new approach called IDME (Information Diffusion and Matthew Effect aggregation). This approach is inspired by the real-world phenomenon of information diffusion and the Matthew effect. IDME simulates the dissemination of information in the real world and obtains information from multilayer neighbors, which is then aggregated using the Matthew effect. By considering its own information as well as that of its multilayer neighbors, IDME can more accurately identify critical nodes in networks while maintaining low time complexity. Experimental results on numerous real-world networks demonstrate that the IDME approach is effective in detecting critical nodes in networks and outperforms representative algorithms on most networks.

Design and Implementation of an Integrated Control Scheme for GaN-Based Multiple Power Converters

In response to the rapid changes in the international energy environment, developing renewable energy (RE)-based distributed generation (DG) and various smart micro-grid systems is crucial for creating a robust electric power grid and new energy industries. In this aspect, there is an urgent need to develop hybrid power systems suitable for coexistent AC and DC power grids, integrated by high-performance wide ban gap (WBG) semiconductor-based power conversion interfaces and advanced operating and control strategies. Due to the intrinsic feature of variation in RE-based power generation, the design and integration of energy storage devices, real-time regulation of power flow, and intelligent energy control schemes are key technologies for further promoting DG systems and micro-grids. This paper investigates an integrated control scheme for multiple GaN-based power converters in a small- to medium-capacity, grid-connected, and RE-based power system. This is the first time that a complete design case demonstrating three GaN-based power converters with different control functions integrated with a single digital signal processor (DSP) chip to achieve a reliable, flexible, cost effective, and multifunctional power interface for renewable power generation systems is presented. The system studied includes a photovoltaic (PV) generation unit, a battery energy storage unit, a grid-connected single-phase inverter, and a power grid. Based on system operation condition and the state of charge (SOC) of the energy storage unit, two typical operating modes and advanced power control functions are developed with a fully digital and coordinated control scheme. Hardware of the GaN-based power converters and digital controllers are designed and implemented. The feasibility and effectiveness of the designed controllers and overall performance of the proposed control scheme are verified with results from simulation and experimental tests on a 1-kVA small-scale hardware system.

Analysis of IR Drop for Robust Power Grid of Semiconductor Chip Design: A Review

Modern semiconductor industry has a major focus on die size reduction and favours the usage of multiple metal layers to increase gross margins. High congestion and voltage or IR drop challenges result from designs with higher and lower core utilisations. Owing to this issue, one of the major difficulties in large silicon-on-chip (SoC) design is the implementation of a power grid design. It’s essential to correctly analyze IR drops to guarantee the reliability of the power grid. This paper presents a comprehensive review approach for the analysis of IR drop for robust power grid design in semiconductor chips. The necessity of the vector-based dynamic analysis and the drawbacks of the vector-less analysis have been addressed. The various techniques used to mitigate IR drop effects, including power grid modeling techniques, use of decoupling capacitors, and voltage drop analysis are explored. The review concludes by identifying the most promising techniques for robust power grid design in semiconductor chips and providing recommendations for future research. The simulations are carried out using ANSYS RedHawk, and the analysis findings are achieved using FinFET technology.

Application Prospect Analysis of Molten Salt Energy Storage Technology

Molten salt (MS) energy storage technology is one of the key topics of today's research. According to studies, MS energy storage technology is critical to integrating renewable energy and is vital to sustaining a robust and trustworthy contemporary power grid. The research on the benefits and use of MS energy storage still has several limitations, though. This essay provides an organized and thorough overview of MS energy storage. The use and growth possibilities of MS energy storage technology in the sectors of solar power, wind power, and nuclear power are investigated on the basis of an examination of the properties of this technology. This study demonstrates the critical role that molten salt energy storage technology plays in lowering power fluctuations, enhancing the adaptability of power networks, and storing and distributing energy produced by intermittent renewable sources like wind and solar energy. It protects the environment and performs well economically. This technology represents the direction in which energy storage technology will grow in the future and is deserving of widespread use.

An improved spatial upscaling method for producing day‐ahead power forecasts for wind farm clusters

Large-scale day-ahead wind power forecasting (WPF) for wind farm clusters (WFCs) can enable dispatching agencies to formulate scientifically sound power generation plans and enhance the robustness of power grids. Most available WPF methods for WFCs only involve mathematical models and rarely consider spatial correlation factors. This necessitates further improvements to forecasting systems. In this study, to increase the day-ahead WPF accuracy for WFCs, fractal transform theory is introduced to optimize the process of WPF for WFCs through spatial upscaling and establish a day-ahead WPF model for WFCs based on an improved spatial upscaling method. First, a WFC is partitioned into subclusters. Then, using fractal transform theory, an affine relation is established between the local output of each subcluster and the overall output of the WFC. Finally, a day-ahead power forecast is produced for the WFC through spatial upscaling of the forecast for the subcluster with the highest grey relational grade with the output of the WFC. The applicability of the proposed forecasting model is examined using historical measured data for a large WFC in north-eastern China. The case study results show that the proposed forecasting model outperforms the summation method and the statistical upscaling method in terms of forecasting accuracy.

Electric load forecasting under False Data Injection Attacks using deep learning

Precise electric load forecasting at different time horizons is an essential aspect for electricity producers and consumers who participate in energy markets in order to maximize their economic efficiency. Moreover, accurate prediction of the electric load contributes toward robust and resilient power grids due to the error minimization of generators scheduling schemes. The accuracy of the existing electric load forecasting methods relies on data quality due to noisy real-world environments, and data integrity due to malicious cyber-attacks. This paper proposes a cyber-secure deep learning framework that accurately predicts electric load in power grids for a time horizon spanning from an hour to a week. The proposed deep learning framework systematically integrates Autoencoder (AE), Convolutional Neural Network (CNN), and Long Short-Term Memory (LSTM) models (AE-CLSTM). The feasibility of the proposed solution is validated by using realistic grid data acquired from the distribution network of Tabriz, Iran. Compared to other load forecasting methods, the proposed method shows the highest accuracy in both a normal case with real-world noise and a stealthy False Data Injection Attack (FDIA). The proposed load forecasting method is practical and suitable for mitigating noise in real-world data and integrity attacks.

Mixed strategy for power grid resilience enhancement under cyberattack

ABSTRACT Power infrastructure networks continue to be at risk under natural and anthropogenic hazard events. Cyberattacks targeting power grids aim at magnifying the impacts through damage propagation to other dependent infrastructure network. In this respect, the current study focuses on the “draw-down” phase of power infrastructure network resilience considering different centrality measures to evaluate the robustness of power grids against cyberattacks. The study considers two network representations for the grid based on the network’s topological/connectivity (i.e., unweighted network) and the network’s power flow data (i.e., weighted network). Therefore, the study utilizes the evaluated measures to improve network robustness under cyberattacks, through considering (or not) the proposed mixed strategy. Based on the analyses, network-level vulnerability is quantified considering five different scenarios through evaluating two key performance metrics—Topology and Functionality indices. Nonetheless, applying the proposed mixed strategy to limit the attacker’s access to the network hubs would boost the overall grid robustness.

Robustness of the Chinese power grid to cascading failures under attack and defense strategies

The power grid system is one of the most important and complex infrastructure systems in our society. Catastrophic blackouts caused by cascading failures, however, still occur and lead to devastating effects. Here, for the first time, we investigate the robustness of the Chinese power grid (CPG) system under various attack and defense scenarios by considering cascading failures. The simulation results illustrate that the effect of the loading level of the power grid network has a large influence on the cascading process. When the initial load of the edge is smaller (α ≤ 1.6), the ascending order strategy of the edge collective influence is the most effective attack strategy for triggering cascading failures over the CPG network. While when the initial load of the edge is larger (α≥1.6), the high load strategy appears to outperform the other strategies. To protect the national power grid from cascading failure, two approaches can be adopted: one approach is to improve the network load capacity, and the other approach is to protect the critical edges. The efforts needed to improve the cascading robustness of the CPG network within these two approaches are quantified. Our results can guide protective measures to avoid large cascading failures and are useful for the design of a robust national power grid network.

Guest Editorial Stability and Robustness of Power Grids With High Penetration of Power Electronics

This Special Issue of the IEEE Journal on Emerging and Selected Topics in Circuits and Systems (JETCAS) is dedicated to demonstrating the latest research progress on the stability and robustness of power grids with high penetration of power electronics.

To Prevent or Promote Grid Expansion? Analyzing the Future Role of Power Transmission in the European Energy System

Future energy supply systems must become more flexible than they are today to accommodate the significant contributions expected from intermittent renewable power sources. Although numerous studies on planning flexibility options have emerged over the last few years, the uncertainties related to model-based studies have left the literature lacking a proper understanding of the investment strategy needed to ensure robust power grid expansion. To address this issue, we focus herein on two important aspects of these uncertainties: the first is the relevance of various social preferences for the use of certain technologies, and the second is how the available approaches affect the flexibility options for power transmission in energy system models. To address these uncertainties, we analyze a host of scenarios. We use an energy system optimization model to plan the transition of Europe’s energy system. In addition to interacting with the heating and transport sectors, the model integrates power flows in three different ways: as a transport model, as a direct current power flow model, and as a linearized alternating current power flow model based on profiles of power transfer distribution factors. The results show that deploying transmission systems contribute significantly to system adequacy. If investments in new power transmission infrastructure are restricted—for example, because of social opposition—additional power generation and storage technologies are an alternative option to reach the necessary level of adequacy at 2% greater system costs. The share of power transmission in total system costs remains widely stable around 1.5%, even if cost assumptions or the approaches for modeling power flows are varied. Thus, the results indicate the importance of promoting investments in infrastructure projects that support pan-European power transmission. However, a wide range of possibilities exists to put this strategy into practice.

Enhancing the power grid robustness against cascading failures under node-based attacks

In recent decades, many countries have suffered power outages and these accidents were often caused by a small disturbance, but because of the connection structure between the circuits, even a small mistake will cause the power grid cascade to fail and paralyze the network in a large area. In order to find a way to enhance the network robustness, this paper proposes three defense methods based on different ratio: the k-shell value ratio, degree ratio, and residual load ratio. We would compare the quality of the three defense methods by the relative size of the largest connected component after cascading failure. Besides, we also compare the time, the total number of crashed nodes, and the speed of cascading failure progress under three defense methods. From the experimental results, it is found that the defense methods based on the k-shell value ratio and the degree ratio have their own advantages in different situations.

Power outages, climate events and renewable energy: Reviewing energy storage policy and regulatory options for Australia

The incidence of harsh climate change and weather events is having profound impacts on energy networks. In recent times, Australia has suffered several network failures and power outages that have placed human lives and community infrastructure at extreme risk. Subsequently, a diverse array of energy storage systems have been commended as a potential solution for electricity network performance and resilience issues. At this critical juncture, this review suggests that an assemblage of currently lacking targeted energy storage systems and supply development policies will be required to grow and advance future domestic energy storage assets and infrastructure; while also providing a mechanism to address changing climatic conditions, variations in electricity demand and associated demand response, and a reduced dependency on coal-fired power generation. Importantly, the recognition of energy storage systems services value across the energy supply chain will be a critical component of future systems growth. In addition, while some specific regulatory changes to create a new energy capacity market will be required, the majority of electricity market rules and procedures will need to be maintained to allow entry of energy storage systems in scheduled and semi-scheduled modes. The review also identified the requirement for strong governance that supports continued renewable energy and energy storage investment, intergovernmental cooperation, and technical network upgrades. Ideally, the implementation of these policy options and regulations should result in a robust and resilient power grid, reduced emissions and economically efficient energy storage systems rollouts.

Robust power grid network design considering EM aging effects for multi-segment wires

This paper presents a number of power grid network design and optimization techniques that consider the electromigration (EM) effects for multi-segment interconnect wires. First, we consider a new EM immortality constraint due to EM void saturation volume for multi-segment interconnects. It helps reduce conservativeness in the EM-aware on-chip power grid design. Along with the EM nucleation phase immortality constraint, we show that both EM immortality constraints can be naturally integrated into the existing programming based power grid optimization framework. Second, to mitigate the overly conservativeness of the immortality constrained optimization methods, we further explore three strategies: we first size up failed wires to meet one of the immortality conditions subject to the design rules; second, we consider the EM-induced aging effects on power supply networks for a target lifetime, which allows some short-lived wires to fail and optimizes the remaining wires; third, we propose a large change sensitivity-based optimization scheme to perform localized fixing based on recently proposed coupled EM-IR drop analysis method. Numerical results on a number of IBM-format power grid networks demonstrate that the new method can reduce more power grid area compared to the existing EM immortality constrained optimizations. Moreover, the new method is able to optimize power grids with nucleated wires, which would not be possible with the existing methods. Results also show the sensitivity-based localized power girds fixing can fix EM-induced IR drop violations in a few minutes for synthesized power grid networks from ARM core designs.

Leveraging Generators With Complementary Capabilities for Robust Multistage Power Grid Operations

In this article, we develop online multistage decisions for procuring and dispatching generators with diverse capabilities to provably ensure reliability of power grid operations under high renewable uncertainty. We jointly consider both the day-ahead reliability assessment commitment (RAC) and the real-time dispatch problems. We first focus on the real-time dispatch problem and define “maximally robust algorithms,” in the sense that they are not dominated by any other algorithm in terms of reliability. We characterize a class of maximally robust algorithms using the concept of “safe dispatch set,” which also provides conditions for verifying grid reliability for RAC. However, in general, such safe dispatch sets may be difficult to compute. We then develop efficient computational algorithms for characterizing the safe dispatch sets. Specifically, for a simpler single-bus two-generator case, we show that the safe dispatch sets can be exactly characterized by a polynomial number of convex constraints. Then, based on this two-generator characterization, we develop a new solution for the multibus multigenerator case using the idea of virtual demand splitting (VDS), which can effectively compute a suitable subset of the safe dispatch set. A distinct advantage of our proposed approach is that it intelligently exploits the complementary capabilities of generators with different ramping capabilities, and thus leads to a larger safe dispatch set than existing approaches in the literature based purely on affine policies. Our numerical results on an IEEE 30-bus system demonstrate that a VDS-based solution outperforms the standard approaches in the literature in terms of reliability, without sacrificing economy.

Cost-Effective Placement of Phasor Measurement Units to Defend against False Data Injection Attacks on Power Grid

This study presents the phasor measurement unit (PMU) placement strategy in the presence of false data injection attacks which is one of the most serious security threats against power grid. It is focused on applications related to supervisory control and data acquisition (SCADA) systems where measurement data can be easily corrupted by adversaries without getting caught by the system. To safeguard power grids against malicious attacks, procedures have been proposed to facilitate the placement of secure PMUs to defend against false data injection attacks in a highly cost-effective way. It has formulated a method of identifying measurements that are vulnerable to false data injection attacks. It was discovered that a weak power grid can be transformed into a robust power grid by adding a few PMUs at vulnerable locations. Simulations on the IEEE standard test systems demonstrate the benefits of the proposed procedure.

From Megawatts to Kilowatts: A Review of Small Wind Turbine Applications, Lessons From The US to Brazil

Increased use of fossil fuels has contributed to global warming due to greenhouse gas emissions, which has led countries to implement policies that favor the gradual replacement of their use with renewable energy sources. Wind expansion in Brazil is a success story, but its adherence to distributed generation is still a big challenge. In this context, the authors of this paper argue that the development of robust and viable distributed power grids will also depend in the future on improving small wind generation as an important alternative to the diversity of decentralized power grids. In this study, the authors present an overview of the small-sized Aeolic (or wind) energy market in Brazil, with the objective to support the debate regarding its expansion. Promoting the small wind market in Brazil is still a big challenge, but lessons can be learned from the United States. In this context, the article uses the United States learning curve, analyzing barriers that were found, as well as public policies implemented to overcome them. The lessons learned in the American market may guide public policies aimed at fostering this technology in Brazil. If technological improvements, certification and introduction of financial incentives were implemented in Brazil, the small wind industry chain could grow substantially, building a trajectory to promote the low carbon economy.

Improvement strategy for robustness of power grid complex network by distributed photovoltaic power system

The robustness of power grids plays a very important role in reality. The blackout of power system can make a serious damage for the normal running of whole city or whole area and the loss caused by it is inestimable. This paper aims to propose a simulation method to evaluate the robustness of a power grid and some effective strategies to improve the robustness. Complex network keeps the most important characters of a power grid, and makes the simulation process concise. This paper studies how characters influence the robustness and proposes some conclusions and suggestions for enhancing effectively the robustness by changing these characters. In this paper, for the robustness of a network, it can be found that some parameters like tolerance coefficient are more dominant than others. Some parameters interact obviously, like capacity addition ratio and addition station ratio. Improper capacity addition strategy may have a negative effect on the network. And load node with big degree and small flow is likely an excellent choice to be added capacity by distributed photovoltaic power station. The simulation method and improvement strategy can be used in the engineering of power grid and establishment of distributed photovoltaic power system.

Bio-inspired design for robust power grid networks

Technological advances have created a world where humans are highly dependent on an uninterrupted electric power supply, yet extreme weather events and deliberate attacks continue to disrupt power systems. Inherently robust ecological networks present a rich source of robust design guidelines for modern power grids. Analyses of ecosystem networks in literature suggest that this robustness is a consequence of a unique preference for redundant pathways over efficient ones. The structural similarity between these two system-types is exploited here through the application of ecological properties and analysis techniques to long-term power grid design. The level of biological similarity between these two system-types is quantitatively investigated and compared by computing ecological network metrics for a set of synthetic power systems and food webs. The comparison substantiates the use of the ecological robustness metric for optimizing the design of power grid networks. A bio-inspired optimization model is implemented, which restructures the synthetic power systems to mimic ecosystem robustness. The bio-inspired optimal networks are evaluated using N-1, N-2, and N-3 contingency analyses to assess system performance under the loss of 1, 2, and 3 components respectively. The bio-inspired grids all experienced significantly fewer violations in each loss scenario compared to traditional configurations, further supporting the application of the ecological robustness metric for power system robustness. The results provide insights into how ecological robustness can guide the design of power systems for improved infrastructural resilience to better survive disturbances.

Optimal Robustness in Power Grids From a Network Science Perspective

In recent years, the frequent occurrences of large-scale blackouts highlight the necessity of enhancing the robustness of power grids. In this brief, based on the cascading failure model which considers both the network topology and electrical characteristics running in power grids, we study how the topological metrics affect the robustness of power grids. The topological metrics considered include both the connectivity of the network and the distribution of generators. We compare the results in typical complex network models and an IEEE 118-bus network. Moreover, by using the simulated annealing method, we find the optimal network topology to achieve the best network robustness, and compare the topological metrics before and after the optimization to verify our findings. The results show that, in order to enhance the robustness of power grids, it is better to make the network sparsely connected, and place the generators as hubs and decentralize these generators.