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This poster presents an Autonomous Microgrid Enabled by Grid-forming Power Electronic Converters. Experimental results from a microgrid with 8 grid-forming inverters are presented.
This presentation discusses the performance evaluation of a net load management (NLM) engine that balances load and generation in an isolated community to power a critical facility after a grid interruption event (e.g., the loss of a large generation unit). This NLM engine is particularly important for microgrid systems because it provides a high-speed, cost-optimal control solution to coordinate grid-forming inverters and to dispatch grid-following inverters and deferrable loads in microgrid systems to enhance grid resilience and reliability. The NLM algorithm cost-optimally dispatches the grid-following inverters and deferrable loads based on the demanded power and load priorities, and the grid-forming inverters use droop control to form system voltages and share active and reactive power. A controller-hardware-in-the-loop platform is developed to evaluate the control performance of the NLM algorithm with two sequential contingency events of lost generation units. The experimental results indicate that the NLM engine can maintain system stability, achieve the targeted system voltage and frequency, and balance load and generation to serve the critical facility with improved system resilience and reliability.
The proliferation of distributed inverter-based resources (IBRs) raises the questions if these IBRs can be used to blackstart microgrids and distribution feeders after major outages. In this paper, we propose and evaluate an autonomous microgrid restoration concept using grid-forming (GFM) IBRs and smart circuit creakers (SCBs). The concept is first explored in simulation platform and then a hardware testbed containing actual GFM inverters is developed to demonstrate these functionalities. A combination of dispatchable virtual oscillator control (dVOC) and droop-based control schemes have been designed for GFM inverter controls in the software simulations and hardware testbed. Subsequently, operation of SCBs using two distinct principles have been demonstrated that can restore or connect portions of the network.
As power systems are transforming with increasing penetrations of inverter-based resources (IBRs), system restoration using IBRs has drawn attention. Using distributed grid-forming (GFM) assets located near critical loads, either three-phase or single-phase, to establish microgrid voltages in the absence of a bulk grid, a distribution system could obtain high system survivability. For swift and secure recovery of a critical load in a single-phase lateral, local single-phase GFM inverters can form a microgrid, and then it can be combined with a neighbouring grid with the inverters remaining in GFM mode for voltage and frequency regulation until the bulk grid comes online. It leads to dynamic interoperation of single-phase GFM inverters with three-phase ones in the black start process. This paper studies the novel approach with electromagnetic transient (EMT) simulations. To evaluate the potential and the technical challenges of the heterogeneous IBR-driven black start, three-phase and single-phase GFM inverter models are developed, including negative-sequence control for voltage balance and a phase-by-phase current limiter (three-phase) and current magnitude limiter (single-phase). To examine dynamic aspects of the black-start process, the EMT simulation also models transformer and motor dynamics emulating their inrush and startup behavior as well as network dynamics. Involvement of grid-following assets to facilitate the black-start process is also modeled. By allowing multiple GFM inverters to collectively black start without leader-follower coordination, regardless of phases, a system can achieve extreme resilience. An inverter-driven black start of a heavily unbalanced 2-MVA distribution feeder using 1 three-phase and 3 single-phase GFM inverters is demonstrated. The simulation shows the heterogeneous system can maintain stability with the single-phase GFM dynamics coupled with the three-phase one.
On a global scale, over 1.3 billion lack access to electricity (85% in rural areas), and approximately 2.8 billion people rely on traditional biomass for cooking. The World Health Organization estimates that household air pollution from inefficient stoves causes more premature deaths than malaria, tuberculosis, and HIV/AIDS. Increasing demand for energy has led to dramatic increases in emissions. The need for reliable electricity and limiting emissions drives research on Resilient Hybrid Energy Systems (RHESs), which provide cleaner energy by combining wind, solar, and biomass energy with traditional fossil energy, increasing production efficiency and reliability and reducing generating costs and emissions. Microgrids have been shown as an efficient means of implementing RHESs, with some focused mainly on reducing the environmental impact of electric power generation. The technical challenges of designing, implementing, and applying microgrids involve conducting a cradle-to-grave Life Cycle Analysis (LCA) to evaluate these systems’ environmental and economic performance under diverse operating conditions to evaluate resiliency. A sample RHES was developed and used to demonstrate the implementation in rural applications, where the system can provide reliable electricity for heating, cooling, lighting, and pumping clean water. The model and findings can be utilized by other regions around the globe facing similar challenges.
Practical, vendor-agnostic interoperability guidelines for the secondary control architecture of microgrids (MGs) with multiple grid-forming (GFM) inverter-based resources (IBRs) have not yet been developed. Therefore, this paper proposes a generic and vendor-agnostic secondary control architecture that operates with all GFM IBRs and synchronous generators. This secondary control does not require the use of additional measurement devices in the MG and utilizes the inherent communication systems of the GFM units, such as Modbus TCP/IP. The practical challenges of Modbus registers, such as packet loss and quantization error, and their detrimental impacts on secondary control actions are investigated. The proposed three-stage modification for any secondary control architecture to mitigate these impacts includes: 1) averaging the data read, 2) situational event-triggering of the controller, and 3) finite iteration of the controlling action. The proposed method is validated using a three-phase electric power, 480 V, 60 Hz, 500 kVA laboratory hardware microgrid with commercial two GFM IBRs and one diesel generator. The experimental results corroborates the fact the proposed modification in the secondary control architecture is advantageous for practical usage under erroneous measurements.
The prevalence of distributed renewable generators motivates microgrid operators to exploit demand-side flexible resources (DFRs). Due to their dispersed nature, distributed DFR coordination has been a vibrant research area, while there are several issues awaiting to be addressed. On one hand, DFR power is internally coupled through power flow, while DFR usually cannot access grid information. On the other hand, in time-restricted scenarios, solution feasibility cannot be guaranteed by conventional dual-based algorithms. To fill these gaps, we propose a distributed DFR coordination framework with all-time feasibility. The proposed framework accounts for the distinct access of microgrid entities to grid information. A distributed and all-time feasible algorithm is proposed for optimal DFR coordination, which allows DFRs to make local decisions without violating constraints throughout iterations. The effectiveness of the proposed algorithm is demonstrated through case studies. The impact of peer-to-peer communication links on algorithm convergence is also investigated, which emphasizes the balance between communication investment and algorithm performance.
The purpose of this framework is to provide a resource for PUCs and State Energy Offices as they develop policies, regulations, and programs to support microgrids. Through shared understanding of the roles of these different entities, the framework aims to foster productive collaboration to support microgrid development.
Quantum key distribution (QKD) provides a potent solution to securely distribute keys for two parties. However, QKD itself is vulnerable to denial of service (DoS) attacks. A flexible and resilient QKD-enabled networked microgrids (NMs) architecture is needed but does not yet exist. In this article, we present a programmable quantum NMs (PQNMs) architecture. It is a novel framework that integrates both QKD and software-defined networking (SDN) techniques capable of enabling scalable, programmable, quantum-engineered, and ultra-resilient NMs. Equipped with a software-defined adaptive post-processing approach, a two-level key pool sharing strategy and an SDN-enabled event-triggered communication scheme, these PQNMs mitigate the impact of DoS attacks through programmable post-processing and secure key sharing among QKD links, a capability unattainable using existing technologies. Through comprehensive evaluations, we validate the benefits of PQNMs and demonstrate the efficacy of the presented strategies under various circumstances. Extensive results provide insightful resources for building QKD-enabled NMs in practice.
Multi-microgrid formation (MMGF) is a promising solution for enhancing power system resilience. This paper proposes a new deep reinforcement learning (RL) based model-free on-line dynamic MMGF scheme. Additionally, the dynamic MMGF problem is formulated as a Markov decision process, and a complete deep RL framework is specially designed for the topologytransformable micro-grids. In order to reduce the large action space caused by flexible switch operations, a topology transformation method is proposed and an action-decoupling Q-value is applied. Then, a convolutional neural network (CNN) based multi-buffer double deep Q-network (CM-DDQN) is developed to further improve the learning ability of the original DQN method. The proposed deep RL method provides real-time computing to support the on-line dynamic MMGF scheme, and the scheme handles a long-term resilience enhancement problem using an adaptive on-line MMGF to defend changeable conditions. The effectiveness of the proposed method is validated using a 7-bus system and the IEEE 123-bus system. The results show strong learning ability, timely response for varying system conditions and convincing resilience enhancement.
The microgrid (MG) plays a crucial role in the energy transition, but its nonlinearity presents a significant challenge for large-signal power systems studies in the electromagnetic transient (EMT) time scale. In this paper, we develop a large-signal linear MG model that considers the detailed dynamics of the primary and zero-control levels based on the Koopman operator (KO) theory. Firstly, a set of observable functions is carefully designed to capture the nonlinear dynamics of the MG. The corresponding linear KO is then analytically derived based on these observables, resulting in the linear representation of the original nonlinear MG with observables as the new coordinate. The influence of external input on the system dynamics is also considered during the derivation, enabling control of the MG. We solve the voltage control problem using the traditional linear quadratic integrator (LQI) method to demonstrate that textbook linear control techniques can accurately control the original nonlinear MG via the developed KO-linearized MG model. Our proposed KO linearization method is generic and can be easily extended for different control objectives and MG structures using our analytical derivation procedure. Here, we validate the effectiveness of our methodology through various case studies.
This document provides a proposed format for reporting the results of microgrid optimization analysis. While the proposed format assumes that the modeling is conducted as part of a renewable energy retrofit of an existing diesel micro-grid, the format can certainly be adopted for other situations.
The concept of net-zero carbon microgrids (NZMs) has received significant interest in recent years considering its promises to provide both energy resilience and carbon reduction. This paper investigates the practical constraints of NZM deployment in a government facility, exploring various cases revolving around the objectives of cost, emission, and energy resilience. The technoeconomic results for one of the Idaho National Laboratory (INL) facilities are discussed to identify a practical and economic approach to achieve the net-zero target considering both present and future scenarios. The study shows the carbon reduction, resilience, and economic success of net-zero initiatives are tied directly to the energy portfolio of its electricity provider. The NZMs typically required a hybrid mix of renewable generation and storage assets to maximize onsite clean generation and to provide reliable power during grid outages. Besides supporting the ongoing net-zero effort at INL, this work also provides a framework that can be directly used in other facilities aspiring to become a net zero in the future.
In the face of unprecedented challenges in environmental sustainability and grid resilience, there is an increasingly held consensus regarding the adoption of distributed and renewable energy resources such as microgrids (MGs), and the utilization of flexible electric loads by demand response (DR) to potentially drive a necessary paradigm shift in energy production and consumption patterns. However, the potential value of distributed generation and demand flexibility has not yet been fully realized in the operation of MGs. This study investigates the pricing and operation strategy with DR for a MG retailer in an integrated energy system (IES). Based on co-optimizing retail rates and MG dispatch formulated as a mixed integer quadratic programming (MIQP) problem, our model devises a dynamic pricing scheme that reflects the cost of generation and promotes DR, in tandem with an optimal dispatch plan that exploits spark spread and facilitates the integration of renewables, resulting in improved retailer profits and system stability. Main issues like integrated energy coupling and customer bill reduction are addressed during pricing to ensure rates competitiveness and customer protection. By evaluating on real datasets, the system is demonstrated to optimally coordinate storage, renewables, and combined heat and power (CHP), reduce carbon dioxide emission while maintaining profits, and effectively alleviate the PV curtailment problem. Finally, the model can be used by retailers and MG operators to optimize their operations, as well as regulators to design new utility rates in support of the ongoing transformation of energy systems.
Today's selection of DC microgrid buildings features a diverse set of electrical topologies and turnkey solutions, each with specific design trade-offs and optimizations. Designers desperately need standardized metrics and procedures for measurement and verification (M&V) to analyze and compare the advantages of each DC solution to traditional AC building networks. This work develops M&V procedures for quantifying and comparing the energy efficiency and power quality in buildings. To calculate full-building efficiency, this work introduces the measurement-informed modeling method, a procedure that develops and refines a building's energy model with metered data. To quantify power quality, this work defines a new voltage quality index that applies to both AC and DC buildings. This article describes the equipment, instrumentation, and operation necessary to calculate the efficiency and power quality. It then demonstrates these methods with a set of field tests. We report these M&V procedures can ultimately be used to compare and improve the efficiency and power quality of various DC topologies.
Building and microgrid designs with highly-distributed electrical storage have potential advantages over today's conventional topologies with centralized storage. Herein this paper studies the capital cost benefits of several residential behind-the-meter distributed-storage topologies, including AC and DC versions of systems with load-packaged batteries and resilient sub-networks. The study begins by defining the block configuration of each topology. This work then develops a model for the cost of the power electronics necessary to interface with the storage elements. Finally, the analysis develops a model for the total cost of each storage topology, incorporating the installation and soft costs. The results suggest that while the cost of power electronics is lower in centralized topologies, the total cost is lower for distributed storage due to the avoided costs of installation and permitting. This paper also explores the benefits of load-packaged batteries for savings in electrical infrastructure.
In this paper, a big data-based approach is proposed for the security improvement of an unplanned microgrid islanding (UMI). The proposed approach contains two major steps: the first step is big data analysis of wide-area monitoring to detect a UMI and locate it; the second step is particle swarm optimization (PSO)-based stability enhancement for the UMI. First, an optimal synchrophasor measurement device selection (OSMDS) and matching pursuit decomposition (MPD)-based spatial-temporal analysis approach is proposed to significantly reduce the volume of data while keeping appropriate information from the synchrophasor measurements. Second, a random forest-based ensemble learning approach is trained to detect the UMI. When combined with grid topology, the UMI can be located. Then the stability problem of the UMI is formulated as an optimization problem and the PSO is used to find the optimal operational parameters of the UMI. An eigenvalue-based multiobjective function is proposed, which aims to improve the damping and dynamic characteristics of the UMI. Finally, the simulation results demonstrate the effectiveness and robustness of the proposed approach.
Rising penetration of consumer-owned Distribution Grid Resources (DGRs), increasingly managed by third party aggregators and enrolled in grid services and wholesale market programs, can create localized congestion in distribution networks. Managing these constraints is challenging due to a persistent coordination and information gap: utilities are accountable for reliability and have network topology and state visibility, while aggregators control the DGR capability needed to relieve congestion. This survey synthesizes congestion management solutions for distribution systems with high DGR penetration, covering both market-based mechanisms (distribution level markets, locational pricing, flexibility auctions) and non-market-based solutions (network reconfiguration, direct DGR control, demand response, curtailment, etc.). The literature is organized across three decision horizons: long term planning, operational planning, and real-time operation. Special attention is devoted to emerging distribution system operator architectures and coordination frameworks spanning transmission system operators, aggregators, and microgrids. Drawing on recent case studies and implementations, we distill best practices, identify key technical and economic barriers, and outline research directions. The evidence points to a shift toward integrated congestion management that combines market signals with technical controls, enabled by improved monitoring, forecasting, and closed loop control capabilities.