Electric distribution systems around the world are seeing an increasing number of utility-owned and non-utility-owned (customer-owned) intelligent devices and systems being deployed. New deployments of utility-owned assets include self-healing systems, microgrids, and distribution automation. Non-utility-owned assets include solar photovoltaic generation, behind-the-meter energy storage systems, and electric vehicles. While these deployments provide potential data and control points, existing centralized control architectures do not have the flexibility or the scalability to integrate the increasing number or variety of devices. The communication bandwidth, latency, and the scalability of a centralized control architecture limit the ability of these new devices and systems from being engaged as active resources. This paper presents a standards-based architecture for distributed power system controls that increases operational flexibility by coordinating centralized and distributed control systems. The system actively engages utility and non-utility assets using a distributed architecture to increase reliability during normal operations and resiliency during extreme events. Results from laboratory testing and preliminary field implementations, as well as details of an ongoing full-scale implementation at Duke Energy, are presented.

A hardware-in-the-loop (HIL) microgrid testbed for the evaluation and assessment of microgrid operation and control system has been presented in this paper. The HIL testbed is composed of a real-time digital simulator (RTDS) for modeling of the microgrid, multiple NI CompactRIOs for device level control, a prototype microgrid energy management system (MicroEMS), and a relay protection system. The applied communication-assisted hybrid control system has been also discussed. Results of function testing of HIL controller, communication, and the relay protection system are presented to show the effectiveness of the proposed HIL microgrid testbed.

Microgrids have long been deployed to provide power to customers in remote areas as well as critical industrial and military loads. Today, they are also being proposed as grid-interactive solutions for energy-resilient communities. Such microgrids will spend most of the time operating while synchronized with the surrounding utility grid but will also be capable of separating during contingency periods due to storms or temporary disturbances such as local grid faults. Properly designed and grid-integrated microgrids can provide the flexibility, reliability, and resiliency needs of both the future grid and critical customers. These systems can be an integral part of future power system designs that optimize investments to achieve operational goals, improved reliability, and diversification of energy sources.

Power hardware-in-the-loop (PHIL) simulation, where actual hardware under text is coupled with a real-time digital model in closed loop, is a powerful tool for analyzing new methods of control for emerging distributed power systems. However, without careful design and compensation of the interface between the simulated and actual systems, PHIL simulations may exhibit instability and modeling inaccuracies. This paper addresses issues that arise in the PHIL simulation of a hardware battery inverter interfaced with a simulated distribution feeder. Both the stability and accuracy issues are modeled and characterized, and a methodology for design of PHIL interface compensation to ensure stability and accuracy is presented. The stability and accuracy of the resulting compensated PHIL simulation is then shown by experiment.

This article introduces a novel multirate cosimulation architecture that overcomes previous challenges integrating faster real-time, microseconds-scale, power-hardware-in-the-loop (PHIL) with larger scale but slower, real-time, seconds-scale, quasi-static time-series (QSTS) simulation. Specifically, an intermediate reduced-equivalent electromagnetic transient (EMT) model duplicates the key power system topology to capture high-speed dynamics and converge hardware-power system interactions between QSTS updates. Exchanging multidimensional parameter vectors (loads, control status, etc.) between the QSTS and EMT models enables capturing the interactions of the rich, high-node-count (thousands of electrical nodes) QSTS model with the hardware. This architecture offers full spatial resolution from QSTS, including individual load dynamics, actual distribution management system controls, and nodal voltages. Simultaneously, the intermediate EMT model provides more detailed high-speed transients of key distributed energy resource hardware interactions. In addition, we use careful PHIL interface design and the exchange of complex power data, rather than current, to further improve performance. This cosimulation architecture is demonstrated by testing a simulated distribution system with an interconnected 500-kVA advanced photovoltaic (PV) inverter in PHIL. The architecture successfully captured PV local volt–volt ampere reactive (volt-VAR) control interactions with the larger network under time-varying electrical and weather conditions.

Penetration levels of solar photovoltaic (PV) generation on the electric grid have increased in recent years. In the past, most PV installations have not included grid-support functionalities. But today, standards such as the upcoming revisions to IEEE 1547 recommend grid support and anti-islanding functions-including volt-var, frequency-watt, volt-watt, frequency/voltage ride-through, and other inverter functions. These functions allow for the standardized interconnection of distributed energy resources into the grid. This paper develops and tests low-level inverter current control and high-level grid support functions. The controller was developed to integrate advanced inverter functions in a systematic approach, thus avoiding conflict among the different control objectives. The algorithms were then programmed on an off-the-shelf, embedded controller with a dual-core computer processing unit and field-programmable gate array (FPGA). This programmed controller was tested using a controller-hardware-in-the-loop (CHIL) test bed setup using an FPGA-based real-time simulator. The CHIL was run at a time step of 500 ns to accommodate the 20-kHz switching frequency of the developed controller. The details of the advanced control function and CHIL test bed provided here will aide future researchers when designing, implementing, and testing advanced functions of PV inverters.