Photovoltaic (PV) distributed energy resources (DER) have reached approximately 27 GW in the U.S., and the solar penetration rate continues to increase. This growth is expected to continue, causing challenges for grid operators who must maintain grid stability, reliability, and resiliency. To minimize adverse effects on the performance of electrical power system (EPS) with increasing levels of variable renewable generation, photovoltaic inverters must implement grid-support capabilities, allowing the DER to actively participate in grid support operations and remain connected during short-term voltage and frequency anomalies. These functions include voltage and frequency regulation features that adjust DER active and reactive power at the point of common coupling. To evaluate the risk of these functions conflicting with traditional distribution system voltage regulation equipment, researchers used several methods to quantify EPS-support function response times for autonomous voltage regulation functions (volt-var function). Based on this study, no adverse interactions between PV inverters with volt-var functions and load tap changing transformers or capacitor banks were discovered.
Photovoltaic (PV) distributed energy resources (DER) have reached approximately 27 GW in the U.S., and the solar penetration rate continues to increase. This growth is expected to continue, causing challenges for grid operators who must maintain grid stability, reliability, and resiliency. To minimize adverse effects on the performance of electrical power system (EPS) with increasing levels of variable renewable generation, photovoltaic inverters must implement grid-support capabilities, allowing the DER to actively participate in grid support operations and remain connected during short-term voltage and frequency anomalies. These functions include voltage and frequency regulation features that adjust DER active and reactive power at the point of common coupling. To evaluate the risk of these functions conflicting with traditional distribution system voltage regulation equipment, researchers used several methods to quantify EPS-support function response times for autonomous voltage regulation functions (volt-var function). Based on this study, no adverse interactions between PV inverters with volt-var functions and load tap changing transformers or capacitor banks were discovered.
This paper describes methods that a distribution engineer could use to determine advanced inverter settings to improve distribution system performance. These settings are for fixed power factor, volt-var, and volt-watt functionality. Depending on the level of detail that is desired, different methods are proposed to determine single settings applicable for all advanced inverters on a feeder or unique settings for each individual inverter. Seven distinctly different utility distribution feeders are analyzed to simulate the potential benefit in terms of hosting capacity, system losses, and reactive power attained with each method to determine the advanced inverter settings.
A control algorithm is designed to smooth the variability of PV power output using distributed batteries. The tradeoff between smoothing and battery size is shown. It is also demonstrated that large numbers of highly distributed current, voltage, and irradiance sensors can be utilized to control the distributed storage in a more optimal manner. It is also demonstrated that centralized energy storage control for PV ramp rate smoothing requires very fast communication, typically less than a 15-second update rate. Finally, advanced inverter dynamic reactive current is shown to provide voltage variability smoothing, hence reducing the number of voltage regulator tap changes without energy storage.
A control algorithm is designed to smooth the variability of PV power output using distributed batteries. The tradeoff between smoothing and battery size is shown. It is also demonstrated that large numbers of highly distributed current, voltage, and irradiance sensors can be utilized to control the distributed storage in a more optimal manner. It is also demonstrated that centralized energy storage control for PV ramp rate smoothing requires very fast communication, typically less than a 15-second update rate. Finally, advanced inverter dynamic reactive current is shown to provide voltage variability smoothing, hence reducing the number of voltage regulator tap changes without energy storage.
The research presented in this paper compares five real-time control strategies for the power output of a large number of distributed PV systems in a large distribution feeder circuit. Both real and reactive power controls are considered with the goal of minimizing network over-voltage violations caused by high penetrations of PV generation. The control parameters are adjusted to maximize the effectiveness of each control. The controls are then compared based on their ability to achieve multiple objectives. These objectives include minimizing the total number of voltage violations, minimizing the total amount of PV energy curtailed or reactive power generated, and maximizing the fairness of any control action among all PV systems. The controls are simulated on the OpenDSS platform using time series load and spatially-distributed irradiance data.
A central control algorithm was developed to utilize photovoltaic system advanced inverter functions, specifically fixed power factor and constant reactive power, to provide distribution system voltage regulation and to mitigate voltage regulator tap operations by using voltage measurements at the regulator. As with any centralized control strategy, the capabilities of the control require a reliable and fast communication infrastructure. These communication requirements were evaluated by varying the interval at which the controller sends dispatch commands and evaluating the effectiveness to mitigate tap operations. The control strategy was demonstrated to perform well for communication intervals faster than the delay on the voltage regulator (30 seconds). The communication reliability, latency, and bandwidth requirements were also evaluated.
As PV penetration on the distribution system increases, there is growing concern about how much PV each feeder can handle. A total of 216 medium-voltage distributions feeders have been analyzed in detail for their individual PV hosting capacity and the locational PV hosting capacity at all the buses on the feeder. A statistical analysis is performed on the hosting capacity results in order to compare correlation with feeder load, percent of issues caused, and the variation for different feeder voltages. Due to the large number of distribution systems simulated, the analysis provides novel insights into each of these areas. Investigating the locational PV hosting capacity also expands the conventional analytical methods that study only the worst-case PV scenario.
Efficient management and coordination of distributed energy resources with advanced automation schemes requires accurate distribution system modeling and monitoring. Big data from smart meters and photovoltaic (PV) micro-inverters can be leveraged to calibrate existing utility models. This paper presents computationally efficient distribution system parameter estimation algorithms to improve the accuracy of existing utility feeder radial secondary circuit model parameters. The method is demonstrated using a real utility feeder model with advanced metering infrastructure (AMI) and PV micro-inverters, along with alternative parameter estimation approaches that can be used to improve secondary circuit models when limited measurement data is available. The parameter estimation accuracy is demonstrated for both a three-phase test circuit with typical secondary circuit topologies and single-phase secondary circuits in a real mixed-phase test system.
The fourth solicitation of the California Solar Initiative (CSI) Research, Development, Demonstration and Deployment (RD&D) Program established by the California Public Utilities Commission (CPUC) supported the Electric Power Research Institute (EPRI), National Renewable Energy Laboratory (NREL), and Sandia National Laboratories (SNL) with data provided from Pacific Gas and Electric (PG&E), Southern California Edison (SCE), and San Diego Gas and Electric (SDG&E) conducted research to determine optimal default settings for distributed energy resource advanced inverter controls. The inverter functions studied are aligned with those developed by the California Smart Inverter Working Group (SIWG) and those being considered by the IEEE 1547 Working Group. The advanced inverter controls examined to improve the distribution system response included power factor, volt-var, and volt-watt. The advanced inverter controls examined to improve the transmission system response included frequency and voltage ride-through as well as Dynamic Voltage Support. This CSI RD&D project accomplished the task of developing methods to derive distribution focused advanced inverter control settings, selecting a diverse set of feeders to evaluate the methods through detailed analysis, and evaluating the effectiveness of each method developed. Inverter settings focused on the transmission system performance were also evaluated and verified. Based on the findings of this work, the suggested advanced inverter settings and methods to determine settings can be used to improve the accommodation of distributed energy resources (PV specifically). The voltage impact from PV can be mitigated using power factor, volt-var, or volt-watt control, while the bulk system impact can be improved with frequency/voltage ride-through.
We present a simple algorithm for identifying periods of time with broadband global horizontal irradiance (GHI) similar to that occurring during clear sky conditions from a time series of GHI measurements. Other available methods to identify these periods do so by identifying periods with clear sky conditions using additional measurements, such as direct or diffuse irradiance. Our algorithm compares characteristics of the time series of measured GHI with the output of a clear sky model without requiring additional measurements. We validate our algorithm using data from several locations by comparing our results with those obtained from a clear sky detection algorithm, and with satellite and ground-based sky imagery.