Grid Edge Research Articles

Empowering the Grid Edge to Think: Applications of Artificial Intelligence for Virtual Power Plants in China

Empowering the Grid Edge to Think: Applications of Artificial Intelligence for Virtual Power Plants in China

A new voltage sensitivity-based distributed feedback online optimization for voltage control in active distribution networks

Rising sustainability, environmental, and decarbonization considerations have prompted a recent increase in the integration of inverter-based distributed energy resources (DER), such as photovoltaics (PV), into the power grid. This trend raises concerns about potential voltage violations within distribution systems, emphasizing the growing relevance of effective voltage regulation. Utilizing smart inverters for active and reactive power control proves highly efficient in addressing voltage rise issues within active distribution networks (ADN). This work proposes a distributed feedback online optimization (FOO) strategy for voltage regulation in ADNs. The FOO strategy utilizes online recursive least squares (RLS) sensitivity-based feedback, relying on local measurements to iteratively compute optimal reactive power set points for DERs, ensuring bus voltage regulation to within acceptable limits. The distributed control algorithm employs a primal-dual update approach, enabling coordinated information exchange between neighboring controllers. This online feedback-based control offers a promising solution for real-time coordinated voltage control in distribution grids, particularly with the increasing integration of inverter-based DERs at the grid's edge. The approach demonstrates robustness under varying conditions, communication dynamics, and topology changes, validated using real distribution feeders with actual solar PV production and consumption data. Advancements in sensing, communication systems, and smart grid tools further enhance the feasibility and scalability of this strategy for evolving power systems.

Design analysis of intelligent controller to minimize harmonic distortion and power loss of wind energy conversion system (grid connected)

<p>The controlling of internal parametric variations in addition to the non-linearity of a large conversion system of wind energy (WECS) is prime challenges to make the most of the generated energy, with less power loss and secure the proficiency (η) integration conventional grid. An adjustable speed control structure of grid-connected conversion system of wind energy (WECS), with the help of a Permanent Magnet Type Synchronous Generator with intelligent controller minimizes the power loss. The control system incorporates a pair of controllers dedicated to the converters of both the generator and grid edge. The controller at the generator side has the main function is to optimize power that can be withdrawal from the wind by intelligently regulating the turbine’s rotational speed. Meanwhile, the grid edge converter effectively manages active and reactive power by manipulating the d & q-axis current components, respectively. This paper discusses about the improvement in performance of the system when using Neuro-Fuzzy system as compared to Neural Network and Management of energy deliver system via direct control method. The findings reveal that the training time for Artificial Neural Networks (ANNs) is substantial, leading to the Neural Network-Direct Power Contol (NN-DPC) approach being the slowest option among the alternatives. Additionally, the NF-DPC system is less time-consuming than the NN-DPC, with a recorded duration of 24 seconds compared to the NN-DPC’s observation of 8 min and 5 s. However, it is worth noting that the NF-DPC system is somewhat more time-intensive than Common-Direct Power Contol (C-DPC).</p>

The Private Virtual Network Operator Model for Field Area Network

Uninterrupted grid edge connectivity is essential for distribution utilities driving towards a future where efficient, secure, and reliable field area network (FAN) operation is not just preferable but required. Communications and network connectivity have been rapidly evolving over the last several years and will continue to do so—including in the areas of spectrum options, carrier solutions, device manufacturers, core manufacturers, and network management options. Utilities have a greater choice of options to deploy, while needing to remain prudent in their investments. One of the options gaining traction in the United States is the private virtual network operator (PVNO) model.

Experimental investigation of a grid plasma jet array in a turbulent boundary layer for skin-friction drag reduction

Toward turbulent skin-friction drag reduction, a novel layout of plasma actuator with a grid anode is devised (grid edge length: L), capable of producing an array of wall-normal plasma jets. The characteristics of this plasma jet array and its interaction with a turbulent boundary layer are investigated experimentally with a particle imaging velocimetry. Results show that the quiescent flow field of the plasma actuator is dominated by a standing vortex ring attached at the inner sides of the square grid, a wall-normal jet flow issued from the grid center, and a downwash flow between adjacent grids. When a tandem array of 11 plasma jets are issued into the cross flow, an equilibrium stage is reached after the third jet. In this stage, the main body of the wall-normal jet bends noticeably to the cross flow, and its leeward side hosts a reverse flow zone, extending downstream to form a slender low-speed wedge (LSW). Two vortical structures are prominent: the streamwise counter-rotating vortex pair residing in the two sides of the jet body, and the arch-shape negative spanwise vortex situated on the LSW root. In the grid-middle plane, the production of turbulence is enhanced across the entire boundary layer, and a second production peak is identified at y+=26. Reduction of the spanwise-averaged wall shear stress is achieved downstream of plasma actuation at x/L≥19.4, and part of the drag reduction fruit earned by ejection is offset by the spanwise transportation of high-velocity fluids toward the middle plane.

Grid-Forming Control of Three-Phase and Single-Phase Converters Across Unbalanced Transmission and Distribution Systems

In this work, we investigate grid-forming control for power systems containing three-phase and single-phase converters connected to unbalanced distribution and transmission networks, investigate self-balancing between single-phase converters, and propose a novel balancing feedback for grid-forming control that explicitly allows to trade-off unbalances in voltage and power. We develop a quasi-steady-state power network model that allows to analyze the interactions between three-phase and single-phase power converters across transmission, distribution, and standard transformer interconnections. We first investigate conditions under which this general network admits a well-posed kron-reduced quasi-steady-state network model. Our main contribution leverages this reduced-order model to develop analytical conditions for stability of the overall network with grid-forming three-phase and single-phase converters connected through standard transformer interconnections. Specifically, we provide conditions on the network topology under which (i) single-phase converters autonomously self-synchronize to a phase-balanced operating point and (ii) single-phase converters phase-balance through synchronization with three-phase converters. Moreover, we establish that the conditions can be relaxed if a phase-balancing feedback control is used. Finally, case studies combining detailed models of transmission systems (i.e., IEEE 9-bus) and distribution systems (i.e., IEEE 13-bus) are used to illustrate the results for (i) a power system containing a mix of transmission and distribution connected converters and, (ii) a power system solely using distribution-connected converters at the grid edge.

Speeding at the Edge: An Efficient and Secure Redactable Blockchain for IoT-Based Smart Grid Systems

As a promising approach to extending cloud resources and services, blockchain-enabled internet of things (IoT)-based smart grid edge computing has attracted much attention. However, the edge node’s resource-constraint nature makes it difficult to store the entire chain as the sensing IoT data volume increases. To address this issue, we propose an FS scheme, a fast and secure multi-threshold trapdoor chameleon hash scheme which serves as the basis for block substitution at the edge nodes to solve the storage limitation problem. FS scheme is used to achieve a consensus-based block substitution, which allows t-out-of-n edge nodes to compute a hash collision collaboratively to reliably substitute a historical block without leaking the randomness R. Also, inspired by the rationale of fast polynomial interpolation, we optimize the FS scheme to FS-I to reduce the time complexity from O(nt) to O(tlog2t). In addition, we further optimize FS-I to FS-II by using a fast Fourier transform (FFT) to dramatically improve the computational efficiency of Lagrange interpolation, which leads to a significant improvement in terms of block substitution performance. Finally, We provide security analysis and evaluate the performance through comprehensive experiments and the results show that FS can achieve up to several magnitudes better than DTTCH. The results also demonstrate that the FS scheme can provide high service quality for large-scale IoT-based smart grid systems.

Decentralized State Estimation-Based Optimal Integral Model Predictive Control of Voltage and Frequency in the Distribution System Microgrids

The power distribution system is undergoing a shift from centralized to decentralized operation due to the large-scale integration of distributed energy resources (DER). To increase the scalability and reduce the dependency on communication networks, there is a strong need for a decentralized control framework that can maintain the nominal voltage and frequency in a microgrid. In this article, a novel decentralized state estimation-based optimal voltage and frequency restoration strategy is proposed. The framework computes the optimal integral model predicted control reference signal to the primary controller, that is unaffected by the local measurement noise, hence, ensuring stable power-sharing among the DERs. The proposed framework is built on a first-order DER model and a local/global measurements-based local state estimation technique, facilitating deployability to the grid edge devices. The capabilities of the proposed framework are tested and validated on a real-time simulator with a full system dynamic model considering an islanded section of the IEEE 123 node distribution network for the various grid events. It is observed that the framework offers significant transient performance improvement in comparison to the Linear Quadratic Regulator method, with around 70% faster voltage restoration when the communication latency is considered.

A Super-Efficient GSM Triplexer for 5G-Enabled IoT in Sustainable Smart Grid Edge Computing and the Metaverse.

Global concerns regarding environmental preservation and energy sustainability have emerged due to the various impacts of constantly increasing energy demands and climate changes. With advancements in smart grid, edge computing, and Metaverse-based technologies, it has become apparent that conventional private power networks are insufficient to meet the demanding requirements of industrial applications. The unique capabilities of 5G, such as numerous connections, high reliability, low latency, and large bandwidth, make it an excellent choice for smart grid services. The 5G network industry will heavily rely on the Internet of Things (IoT) to progress, which will act as a catalyst for the development of the future smart grid. This comprehensive platform will not only include communication infrastructure for smart grid edge computing, but also Metaverse platforms. Therefore, optimizing the IoT is crucial to achieve a sustainable edge computing network. This paper presents the design, fabrication, and evaluation of a super-efficient GSM triplexer for 5G-enabled IoT in sustainable smart grid edge computing and the Metaverse. This component is intended to operate at 0.815/1.58/2.65 GHz for 5G applications. The physical layout of our triplexer is new, and it is presented for the first time in this work. The overall size of our triplexer is only 0.007 λg2, which is the smallest compared to the previous works. The proposed triplexer has very low insertion losses of 0.12 dB, 0.09 dB, and 0.42 dB at the first, second, and third channels, respectively. We achieved the minimum insertion losses compared to previous triplexers. Additionally, the common port return losses (RLs) were better than 26 dB at all channels.

A Study on the Deployment of Mesoscale Chemical Hazard Area Monitoring Points by Combining Weighting and Fireworks Algorithms

In order to address the problems of redundancy and waste of resources in the deployment of monitoring points in mesoscale chemical hazard areas, we propose a method for the deployment of monitoring points in mesoscale chemical hazard areas by combining weight and fireworks algorithms. Taking the mesoscale chemical hazard monitoring area as the research background, we take the probabilistic sensing model of telemetry sensor nodes as the research object, make a reasonable grid division of the mesoscale monitoring area, calculate the importance of each grid and perform clustering, utilize the diversity of the fireworks algorithm and the rapidity of the solution to solve the monitoring point deployment model and discuss the relevant factors affecting the deployment scheme. The simulation results show that the proposed algorithm can achieve the optimal coverage monitoring for monitoring areas with different importance and reduce the number of monitoring nodes and redundancy; meanwhile, the relevant factors such as the grid edge length, the number of clusters, and the average importance of monitoring areas have different degrees of influence on the complexity of the algorithm and the deployment scheme.

Special Section on Local and Distributed Electricity Markets

Driven by the goals of clean energy and zero carbon emissions, the power industry is undergoing significant transformations. The rapid growth of diverse distributed energy resources (DERs) at grid edge such as rooftop photovoltaics (PVs) and electric vehicles is transforming the traditional centralized power grid management to a decentralized, bottom-up, and localized control paradigm. Establishing local and distribution-level electricity markets provides an effective solution to managing large amounts of small-scale DERs. New regulations such as the recent FERC Order 2222 in the U.S. open the door to DERs in the wholesale markets. Through coordinating the local and distribution-level markets with the transmission-level wholesale market, the DERs and prosumers can trade energy and flexibility locally with each other and meanwhile provide energy, flexibility and ancillary services to the bulk power grid. During this transition, there are many new technical challenges to address, calling for innovative ideas and interdisciplinary research in this promising direction. Advanced information and communication technologies (ICT) are needed, as a key enabler, for the development and practical implementation of local and distribution electricity markets. Research into local and distribution markets is strongly interdisciplinary, involving the state of the art in power engineering, economics, and digital/information technology. A broad spectrum of contributors from universities, industry, research laboratories and policy makers is sought to develop and present solutions and technologies that will facilitate and advance practical applications and implementations of local and distribution-level electricity markets to uncover the values of DERs.

2023 IEEE PES Grid Edge Technologies Conference and Exhibition: A Welcome Message from Jessica J. Bian, President, IEEE Power & Energy Society [Letter

2023 IEEE PES Grid Edge Technologies Conference and Exhibition: A Welcome Message from Jessica J. Bian, President, IEEE Power & Energy Society [Letter

A reactive power market for the future grid

As pressures to decarbonize the electricity grid increase, the grid edge is witnessing a rapid adoption of distributed and renewable generation. As a result, traditional methods for reactive power management and compensation may become ineffective. Current state-of-art for reactive power compensation, which rely primarily on capacity payments, exclude distributed generation (DG). We propose an alternative: a reactive power market at the distribution level designed to meet the needs of decentralized and decarbonized grids. The proposed market uses variable payments to compensate DGs equipped with smart inverters, at an increased spatial and temporal granularity, through a distribution-level Locational Marginal Price (d-LMP). We validate our proposed market with a case study of the US New England grid on a modified IEEE-123 bus, while varying DG penetration from 5% to 160%. Results show that our market can accommodate such a large penetration, with stable reactive power revenue streams. The market can leverage the considerable flexibility afforded by inverter-based resources to meet over 40% of reactive power load when operating in a power factor range of 0.6 to 1.0. DGs participating in the market can earn up to 11% of their total revenue from reactive power payments. Finally, the corresponding daily d-LMPs determined from the proposed market were observed to exhibit limited volatility.

DER Forecast Using Privacy-Preserving Federated Learning

With the increasing penetration of distributed energy resources (DERs) in grid edge, including renewable generation, flexible loads, and storage, accurate prediction of distributed generation and consumption at the consumer level becomes important. However, DER prediction based on the transmission of customer-level data, either repeatedly or in large amounts, is not feasible due to privacy concerns. In this article, a distributed machine learning approach, federated learning (FL), is proposed to carry out DER forecasting using a network of Internet of Things (IoT) nodes, each of which transmits a model of the consumption and generation patterns without revealing consumer data. We consider a simulation study that includes 1000 DERs and show that our method leads to an accurate prediction of preserving consumer privacy, while still leading to an accurate forecast. We also evaluate grid-specific performance metrics, such as load swings and load curtailment, and show that our FL algorithm leads to satisfactory performance. Simulations are also performed on the Pecan street data set to demonstrate the validity of the proposed approach on real data.

Guest Editorial: Special Section on ActiveDistribution Networks: Markets, Operations,Planning, and Regulation

The power system is in change, and the most fundamental changes are happening in the distribution grid. Formerly passive consumers get active and smart, storage and distributed generation appears, and the “grid edge” is upgraded with Intenet of Things (IoT). Gone are the days where distribution infrastructure consisted of passive assets, dimensioned for the worst case of load. We are now entering a new phase where active distribution grids are operated with the help of local markets, machine learning techniques, and distribution automation. Integrating renewable energy or electric vehicles and electrifying the last remaining fossil applications require a flexible and powerful active distribution grid.

Grid Edge Visibility: Gaps and a road map

Behind-The-Meter (BTM) resources at the grid edge are rapidly becoming an important component of the electric grid, requiring a substantial reconfiguration of traditional grid practices, such as policy changes, market redesign, and infrastructure upgrades. This adjustment is challenged by the fact that, by definition, grid edge elements are not easily observable by grid control entities. Increasing the visibility of these resources is therefore an important goal, one that is experiencing much research and discussion by various power system stakeholders. For example, policy makers are analyzing the tradeoffs of using grid edge meters to impose charges on grid edge electricity generation. System operators, such as the Midcontinent Independent System Operator (MISO) in the United States, can identify visibility information on the electrical location and the size of the grid edge resources as a critical consideration across the transmission-and-distribution (T&D) spectrum. This article summarizes the challenges and needs of grid entities resulting from the introduction of grid edge resources as well the gaps in the extant grid edge visibility frameworks.

Unleash Values From Grid-Edge Flexibility: An overview, experience, and vision for leveraging grid-edge distributed energy resources to improve grid operations

The power system landscape is presently undergoing a dramatic change with the rapid integration of numerous distributed energy resources (DERs) at the grid edge. Broadly, we consider grid-edge DERs as any electricity source, storage, and demand response program connected to the medium-to-low-voltage distribution system.

Advanced Metering Infrastructure for Distribution Planning and Operation: Closing the loop on grid-edge visibility

In the recent history of electric utilities, the potential to have visibility of the grid edge is gaining significance for the reliable planning and operation of a clean energy smart grid. Before the recent large-scale customer adoption of distributed energy resources (DERs), distribution networks were planned with a simpler fit-and-forget philosophy. The importance of customer-sited DERs to achieve climate change goals marks a major shift for utility operations. Changes to traditional fit-and-forget planning paradigms require better availability of grid-edge data. Smart metering is an enterprise-wide tool that is enabling visibility when and where it has been most needed. As of 2020, the rollout of smart metering, or advanced metering infrastructure (AMI), had reached more than 100 million meters in the United States, and nearly half of all electricity customers are now equipped with a smart meter ( <xref ref-type="fig" rid="fig1" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Figure 1</xref> ). Smart meters are quickly becoming a ubiquitous data capture feature of smart grids. AMI enables utilities to record and measure electricity usage and power-flow metrics at a minimum of hourly intervals and at least once a day. At a minimum, AMI enables interval metering, automatic meter reading enabling accurate and time-interval billing, and the ability to provide feedback on customer energy consumption.

Welcome to the Special Issue on Grid-Edge Computing With Behind-the-Meter Resources [Guest Editorial

The integration of distributed energy resources (DERs), such as solar photovoltaic systems, as well as other synergistic assets, including electric vehicles, energy storage, and smart appliances, in electric power systems has been dramatically increasing in the past few years. These assets have the capability to provide much-needed flexibility to electric power systems for improved grid reliability, resilience, and economic efficiency; however, most of these resources are located behind the meter (BTM) on customer premises, and their flexibility is not fully used in current grid operations. Grid-edge computing plays an important role in unlocking the great benefits and potential that BTM resources could provide to electric power systems by enhancing visibility and controllability at the grid edge.

A structured grid generation framework based on parameterization for the design of free-form gridshells

Free-form gridshells have become a regional landmark because of their light and smooth architectural style. Structured grids with regular and straightforward topological connections are often the best choice for the holistic design of free-form gridshells. However, it is difficult to create a structured grid that can embody the architectural connotation and facilitate manufacturing and construction process over free-form surfaces, especially surfaces with complex shapes. To generate structured grids with fair lines, regular cells, and uniform edges over surfaces and to better interactively control the direction and size of grids, this paper proposes a parameterization-based grid generation framework. Firstly, a bubble-packing method is applied to obtain an initial grid over the given surface. Secondly, a grid parameterization algorithm that is as shape-preserving as possible is proposed and used to produce the parameter domain of the initial grid. Thirdly, a fair, regular, and uniform planar structured grid is generated in the parameter domain, and the direction and size of the grid can be adjusted by changing corresponding parameters. Finally, the planar structured grid is mapped to the given surface to obtain the final grid. As part of the new contribution of this paper, the calculation method of the fairness index is improved to quantitatively evaluate the fairness of the structured grid. The regularity and uniformity are also evaluated in terms of grid cell shape quality and grid edge lengths. Three case studies are presented to demonstrate the validity of the proposed framework. The proposed framework can be effective at reducing mapping distortion and can produce grids that meet the requirements of fairness, regularity, and uniformity.