Modern power grids include a variety of renewable Distributed Energy Resources (DERs) as a strategy to comply with new environmental and renewable portfolio standards (RPSs) imposed by state and federal agencies. Typically, DERs include the use of power electronic (PE) interfaces to interactwith the power grid. Recently this interaction has not only been focused on supplying maximum available energy, but also on supporting the power grid under abnormal conditions such as low voltage/frequency conditions or non-unity power factor. Over the last few years, grid-following inverters (GFLIs) have proven their value while providing these ancillary grid-support services either at residential or utility scale. However, the use of grid-forming inverters (GFMIs) is gaining momentum as the penetration-level of DERs increases and system inertia decreases. Under abnormal operating conditions, GFMIs tend to better preserve grid stability due to their intrinsic ability to balance loadswithout the aid of coordination controls. In order to gain and propose fundamental insights into the interfacing of GFMIs to real time simulation, this paper analyzes the dynamics of two different GFMI simulation models in terms of stability and load changes using a Power Hardware-in-the-Loop (PHIL) simulation testbed.
Historically, photovoltaic inverters have been grid-following controlled, but with increasing penetrations of inverter-based generation on the grid, grid-forming inverters (GFMI) are gaining interest. GFMIs can also be used in microgrids that require the ability to interact and operate with the grid (grid-tied), or to operate autonomously (islanded) while supplying their corresponding loads. This approach can substantially improve the response of the grid to severe contingencies such as hurricanes, or to high load demands. During islanded conditions, GFMIs play an important role on dictating the system's voltage and frequency the same way as synchronous generators do in large interconnected systems. For this reason, it is important to understand the behavior of such grid-forming inverters under fault scenarios. This paper focuses on testing different commercially available grid-forming inverters under fault conditions.
The integration of communication-enabled grid-support functions in distributed energy resources (DER) and other smart grid features will increase the U.S. power grid's exposure to cyber-physical attacks. Unwanted changes in DER system data and control signals can damage electrical infrastructure and lead to outages. To protect against these threats, intrusion detection systems (IDSs) can be deployed, but their implementation presents a unique set of challenges in industrial control systems (ICSs), New approaches need to be developed that not only sense cyber anomalies, but also detect undesired physical system behaviors. For DER systems, a combination of cyber security data and power system and control information should be collected by the IDS to provide insight into the nature of an anomalous event. This allows joint forensic analysis to be conducted to reveal any relationships between the observed cyber and physical events. In this paper, we propose a hybrid IDS approach that monitors and evaluates both physical and cyber network data in DER systems, and present a series of scenarios to demonstrate how our approach enables the cyber-physical IDS to achieve more robust identification and mitigation of malicious events on the DER system.
This report documents the use of wind turbine inertial energy for the supply of two specific electric power grid services; system balancing and real power modulation to improve grid stability. Each service is developed to require zero net energy consumption. Grid stability was accomplished by modulating the real power output of the wind turbine at a frequency and phase associated with wide-area modes. System balancing was conducted using a grid frequency signal that was high-pass filtered to ensure zero net energy. Both services used Phasor Measurement Units (PMUs) as their primary source of system data in a feedforward control (for system balancing) and feedback control (for system stability).