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Fault diagnostics are extremely important to decide proper actions toward fault isolation and system restoration. The growing integration of inverter-based distributed energy resources imposes strong influences on fault detection using traditional overcurrent relays. This paper utilizes emerging graph learning techniques to build a new temporal recurrent graph neural network models for fault diagnostics. The temporal recurrent graph neural network structures can extract the spatial-temporal features from data of voltage measurement units installed at the critical buses. From these features, fault event detection, fault type/phase classification, and fault location are performed. Compared with previous works, the proposed temporal recurrent graph neural networks provide a better generalization for fault diagnostics. Moreover, the proposed scheme retrieves the voltage signals instead of current signals so that there is no need to install relays at all lines of the distribution system. Therefore, the proposed scheme is generalizable and not limited by the number of relays installed. The effectiveness of the proposed method is comprehensively evaluated on the Potsdam microgrid and IEEE 123-node system in comparison with other neural network structures.

Hydrogen electrolyzers, with their operational flexibility, can be configured as smart dynamic loads which can provide grid services and facilitate the integration of more renewable energy sources into the electrical grid. However, to enable this ability, the electrolyzer system should be able to control both active and reactive power in coordination with the low-level controller of the electrolyzer via the power electronics system interface between the utility grid and electrolyzer. This paper discusses power converter topologies and the control scheme of this power electronics interface for electrolyzer applications to enable electricity grid services. For the sake of unity, in this paper, we consider the power converter system interfacing the utility grid at the line-to-line root mean square (RMS) value of 480 VAC-60 Hz and supplying to the 3500 A-750 kW PEM electrolyzer stack.

This paper presents a real-time simulation and hardware-based approach for systematic integration of Distributed Energy Resources (DERs) in advanced distribution grids with a special focus on resilience. Advanced distribution grids are considered to be functionally more sophisticated than traditional ones. The desirable advanced functionalities include – reconfiguration, real-time sensing, DERs, self-healing, etc. Some of these functionalities are currently being realized by microgrids as well. However, it is not feasible to convert each section of a distribution grid into a microgrid, but can be instituted with functionalities by design and controls at relatively lower costs. Interconnection of DERs, including energy storage to improve reliability and resilience is presented in details. Resilience of distribution grids is gaining greater importance and research towards enhancing it utilizing DERs is a key area. A real-time resilience framework with Analytical Hierarchical Processes (AHP) is developed that adapts to changing configurations, DERs, switching operations, grid conditions, etc. to provide an accurate and adoptable composite resilience metric. This framework and the composite resilience metric can play a unique role in operational and design decisions for operating future distribution grids. Advanced functionalities such as scenario-based reconfiguration in distribution grids are considered, with resilience metrics as performance criteria for choosing a preferred combination. Simulations are performed using Digital Real-Time Simulator (DRTS) and characterized response of flow battery validated against actual flow battery hardware is imposed to provide realistic results. Reconfiguration program is interfaced with DRTS for bi-directional real-time communication. Key contributions include enhancement of resilience of distribution grids using energy storage system under dynamic operating conditions.

An integrated testbed using digital real-time simulator (DRTS) and a high-performance computing (HPC) cluster is presented here to compare speed and performance of computational schemes to mitigate time-critical issues in electric power systems. The first approach in this testbed validation is taken by running a set of closed-loop load shedding algorithms to compare and contrast two paradigms of arresting cascading failure propagation. Two algorithms involve solving DC and AC power flow model-based optimization problems to compute load shedding at different buses, while a model-based stochastic search using parallel computing provides a viable alternative. The algorithms are implemented in the DRTS-HPC testbed for the IEEE 14-bus benchmark transmission system. As a proof of the concept, simulation results are presented for implementation of closed-loop load-shedding algorithms for cascading failures in the DRTS-HPC testbed.

Due to increased penetration of low-inertia resources into the electric grid, challenges are increasing for maintaining wide-area system stability. Grid stability assessment requires a faithful representation of the multiple-physics interaction at the system level, and timescales of interaction varying in orders of magnitude, from microseconds to seconds to several minutes. Along with the simulation-based techniques, hardware-in-the-loop (HIL), controller HIL, and power HIL techniques have been developed to better understand the emergent behavior of the system with emerging technologies. US National Laboratories have played a vital role in research and development to understand the behavior of individual technologies and devices integrated to the electric grid. Each national laboratory forwards a technological and strategic initiative tied core and enabling capabilities. Due to strategic, efficiency, and economic reasons, not all the labs have assets to conduct research on all technologies concomitantly, so it becomes crucial to integrate the labs across geographies to understand the interplay of different technologies together at the system level. This approach avoids duplication of the assets at different lab facilities and helps understand the integrated system behavior of various technologies representative of actual grid conditions by connecting multiple national labs. This paper talks about techniques of connecting three national laboratories to enable co-emulation of electrical–mechanical–thermal characteristics of devices and systems. Such an approach can be used to understand the dynamic and transient interaction of multi-physics in a system level, at-scale emulation using real-time simulation tools and techniques.

This paper presents a decision support system (DSS) for multi-objective dispatch of an electric microgrid considering cost of operation, peak load reduction, and emissions. Discrete compromise programming (DCP) is used as the multi-criteria decision analysis (MCDA) technique for providing the decision support to the distribution system operator (DSO) by ranking various alternatives based on preference of the objectives. The focus of this paper is the application of DCP-based MCDA to select feasible dispatch solutions closest to the preference of the DSO when multiple objectives are considered in the microgrid dispatch. This technique obtains non-dominated solutions in case of conflicting objectives without generating a Pareto front, and hence avoiding prohibitive computational cost. DCP can be used for MCDA in both exact and metaheuristic dispatch algorithms. Uncertainty in renewable energy forecasting and load demand is included through a scenario-based approach by sampling empirical distributions. The dispatch algorithm considers a two hour look-ahead time horizon. Simulations are performed on a notional electric microgrid with diesel generator, solar photovoltaic, and energy storage. The microgrid is based on the IEEE 13-node test feeder and the results are presented.

Widespread adoption of Electric Vehicles (EVs) for light, medium, and heavy-duty applications has gained significant interest. Based on ownership and user preferences, light duty and local delivery medium-duty EVs are typically connected to grid distribution networks whereas larger medium duty and heavy-duty EVs are typically connected to distribution or even sub-transmission networks. Challenges with at-scale EVs and charging infrastructure supporting them include interoperability, distribution network upgrades, surge in demand charges, power quality issues, voltage stability, etc. Based on real-world data, steady-state and dynamic assessments of light-duty EV charging with a focus on DC Fast Charging (DCFC) for weak distribution grids is presented. Three main areas of contributions in this paper include - steady state and dynamic power quality measurements and assessments using real world data, transient assessments of light duty EV integration with weak distribution networks (IEEE 13 node feeder system) under grid-connected and microgrid modes, and formulation of an intelligent charging decision algorithm to enable an impact-minimal charging.

International audience; We present a new performance metric for co-optimization of dispatch and reserves in microgrids. A metric based on NERC criteria is used for each asset to quantity its reliability-based value. A system level metric is obtained through the individual performance metrics, dispatched capacity, and net dispatchable capacity available as reserve. Simulations are performed on a notional microgrid with a dispatchable and a non-dispatchable distributed energy resource to demonstrate the calculation of the metric.

We use a deterministic cascade propagation model based on thermal overloading limits on the transmission lines to achieve a load shedding protection mechanism to mitigate cascading failures. Departing from the linearized DC-power flow based models, we adopt a more faithful AC-power flow model to compute the line flows. For AC power flow, we adopt the branch flow model, taking inspiration from the recent optimal power flow formulations, and use tools from convex optimization to solve the optimal load shedding problem. The results are demonstrated using a IEEE 14-bus test system in a Digital Real-Time Simulator (DRTS) environment.

Real-time (RT) simulator is a powerful tool for analyzing operational and control algorithms in electric power systems engineering. For understanding the dynamic and transient behavior of a power systems, significant RT computation capabilities are essential. A single unit of RT simulator has limited simulation capabilities. The most common way of augmenting simulation capability is using a bank of locally connected RT simulators. However, creating a large-sized bank of RT simulators involves significant financial investments and hence may not be feasible at all research facilities. Power and energy systems research facilities that use RT simulators are at diverse physical locations. In addition to RT simulators, research facilities around the world house an array of facilities with unique power, energy, and control systems for innovative research. To leverage these unique research facilities, geographically distributed RT simulation based on Wide Area Network (WAN) is required. Typical RT simulators perform simulations with time-steps in the order of milliseconds to microseconds, whereas data latency for communication on WAN may be as high as a few hundred milliseconds. Such communication latency between RT simulators may lead to inaccuracies and instabilities in geographically distributed RT simulations. In this paper, the effect of communication latency on geographically distributed RT simulation is discussed and analyzed. In order to reduce the effect of the communication latency, a Real-Time Predictor (RTP), based on linear curve fitting is developed and integrated into the distributed RT simulation environment. Two geographically distributed digital RT simulators are used to perform dynamic simulations of an electric power system with a fixed communication latency and the predictor. Empirical results demonstrate the effects of communication latency on the simulation and the performance of the RTP to improve the accuracy of simulations.

A novel approach using decoupled processes to harness offshore renewable energy, from marine hydrokinetics, ocean waves, wind, and solar to produce liquid air, is presented in this paper. Offshore renewables interconnection using submarine medium- and high-voltage direct current technologies are used to produce liquid air that can be transported to end-use locations using repurposed liquefied natural gas tankers. Two important possibilities arise from using the proposed technology. The first possibility allows the integration with conventional thermal cycles to improve efficiency. This approach can be used to leverage efficiencies of thermal systems that have already reached a plateau in the maximum achievable efficiency via design and operation optimization. The second possibility is related to the incorporation of low temperature cycles to recover waste heat from other thermal processes. This is important considering that waste heat accounts for more than 60% of the consumed energy in the United States. A detailed technical description of the complete cycle from cryogen generation to end-use of energy is provided. Estimation of efficiency enhancement for thermal plants using liquid air including waste heat recovery is presented.

Interconnection of digital real-time simulators over wide-area communication networks is an innovative approach to extend local real-time simulation capabilities to enable large-scale simulations. Furthermore, it allows the integration of geographically distributed assets as Power Hardware-in-the-Loop and Controller Hardware-in-the-Loop, thus providing a flexible framework for performing unique research experiments. In most cases, it is not possible to perform large scale real-time simulations and comprehensive experiments locally due to lack of simulation capacity and unavailability of unique assets. Main challenge associated with geographically distributed real-time simulation is to ensure simulation fidelity of the same degree as in the case when the entire simulation is performed at the same location. Simulation fidelity in geographically distributed real-time simulation is investigated and an empirical characterization is provided in this paper. Fidelity degradation caused by different values of time delay and sending rate of data exchange between two digital real-time simulators is presented. Two methods for representation of interface quantities in co-simulation interface algorithms are considered for performing simulations. The first method is based on representation of interface quantities as root mean square of magnitude, frequency, and phase angle of the current and voltage waveforms. The second method utilizes representation of interface quantities in form of time-varying Fourier coefficients, known as dynamic phasors. The empirical study is performed for transmission-distribution co-simulation using two racks of Real-Time Digital Simulator (RTDS®).

Real-Time Simulations (RTS) are increasingly being used to understand the complex device and system level interactions in power grids. RTS provides the capability to create detailed, highly accurate, and diverse set of power and control system components at low time steps (order of microseconds) that are based on “real-world clock-time.” RT simulator is a unique architecture with specialized processors and communication boards that allow time synchronization of simulations and the clocktime. Lean operating systems, specialized processors, faster communications, etc. are the typical attributes of RT simulators. RT simulators provide a unique capability of interfacing with power and control components via analog and digital interfaces. However, RT simulators have limited computational capability that constrains the size of power and control systems that can be simulated. Multiple simulators connected locally is typically used to increase the computation capability, however this is not always economical. Additionally, RT simulators are used at facilities with unique test infrastructure in the form of grid emulators, inverters, photovoltaic, wind turbine, microgrids, etc. Performing distributed RTS via Internet can augment simulation capacity and leverage unique infrastructure that is dispersed in academia and research laboratories. Research related to distributed RTS and its application in electric power engineering is discussed in this chapter.

Supercapacitors are finding wider applications in modern power systems due to a controllable fast dynamic response. Use of power electronically interfaced supercapacitors for frequency support is a proven technique. In practical applications the heat generated from the Equivalent Series Resistance (ESR) can significantly reduce the life of supercapacitor. Hence thermal issues must be addressed for optimal operation. It is infeasible to use traditional optimization control methods to mitigate the impacts of frequent cycling due to the lack of active thermal management. This paper proposes a Front End Controller (FEC) using Generalized Predictive Control based on real time receding optimization. The constraints in the optimization are based on thermal management to enhance the efficiency of utilization life of the supercapacitors. A rigorous mathematical derivation is provided and supporting simulation results are obtained using Real Time Digital Simulator to demonstrate the effectiveness of the proposed technique.

The divergent needs of the various microgrid participants make the dispatch in microgrids more complicated than typical methods. A review of the experience in Fort Collins suggests that regulations should be developed to address the potential financial conflict issues where generation companies may see microgrids as competition, and also that the cost of integrating assets into a microgrid need to be lowered.

This paper presents modeling procedures and validated results for the IEEE-13 node test distribution feeder using the real-time digital simulation platform RTDS-RSCAD. Models for distribution feeders in this real-time platform are rare, and this paper provides a detailed approach for developing this type of model. The platform-specific models, challenges, assumptions, solutions, and steady-state validation are presented. The feeder model developed here is aimed to be extended to an electric microgrid with integrated distributed energy resources (DER), which can be used to perform microgrid dispatch simulations. As demand curve information is important for microgrid dispatch simulations and is not available for the test feeder, a method for demand curve synthesis is presented. In addition, a method for DER sizing and placement is presented for design of a potential microgrid. Keywords—asset sizing, asset placement, distribution feeder model, HOMER, IEEE-13 node, real time, simulation, validation.

This paper explores and presents the capabilities of real time hardware-in-the-loop (RT-HIL) digital simulation for electric microgrids. RT-HIL is used by academia and industry for the simulation and testing of power systems at both the component and system levels. A literature survey is presented to identify existing real time simulation systems and some relevant applications. Simulations using RT-HIL involve modeling the interface between the components and systems in greater detail for increased understanding of interface issues. This is aimed at de-risking and hastening the penetration of new technologies such as microgrids.

Electric microgrids generate a significant amount of measurement data by virtue of the constituent distributed smaller capacity assets. Data reduction for performance quantification and visualization techniques for microgrids is presented here. The case study is performed on an operating microgrid in Fort Collins, Colorado, USA. Relevant NERC metrics are calculated and two more system level metrics are developed. Alternative approaches to peak load reduction calculations are also presented. A visualization GUI is developed and discussed as a deliverable to the power system operator for enhanced situational awareness.