Peppanen, Jouni P.; Reno, Matthew J.; Thakkar, Mohini T.; Grijalva, Santiago G.; Harley, Ronald G.
The many new distributed energy resources being installed at the distribution system level require increased visibility into system operations that will be enabled by distribution system state estimation (DSSE) and situational awareness applications. Reliable and accurate DSSE requires both robust methods for managing the big data provided by smart meters and quality distribution system models. This paper presents intelligent methods for detecting and dealing with missing or inaccurate smart meter data, as well as the ways to process the data for different applications. It also presents an efficient and flexible parameter estimation method based on the voltage drop equation and regression analysis to enhance distribution system model accuracy. Finally, it presents a 3-D graphical user interface for advanced visualization of the system state and events. Moreover, we demonstrate this paper for a university distribution network with the state-of-the-art real-time and historical smart meter data infrastructure.
Accurately representing the local solar variability at timescales relevant to distribution grid operations (30-s and shorter) is essential to modeling the impact of solar photovoltaics (PV) on distribution feeders. Due to a lack of available high-frequency solar data, some distribution grid studies have used synthetically-created PV variability or measured PV variability from a different location than their study location. In this work, we show the importance of using accurate solar PV variability inputs in distribution studies. Using high-frequency solar irradiance data from 10 locations in the United States, we compare the ramp rate distributions at the different locations, use a quantitative metric to describe the solar variability at each location, and run distribution simulations using representative 1-week samples from each location to demonstrate the impact of locational solar variability on the number of voltage regulator tap change operations. Results show more than a factor of 3 difference in the number of tap change operations between different PV power variability samples based on irradiance from the different locations. Errors in simulated number of tap changes of up to -70% were found when using low-frequency (e.g., 15-min) solar variability.
This manual provides the documentation of the MATLAB toolbox of functions for using OpenDSS to simulate the impact of solar energy on the distribution system. The majority of the functio ns are useful for interfacing OpenDSS and MATLAB, and they are of generic use for commanding OpenDSS from MATLAB and retrieving information from simulations. A set of functions is also included for modeling PV plant output and setting up the PV plant in th e OpenDSS simulation. The toolbox contains functions for modeling the OpenDSS distribution feeder on satellite images with GPS coordinates. Finally, example simulations functions are included to show potential uses of the toolbox functions. Each function i n the toolbox is documented with the function use syntax, full description, function input list, function output list, example use, and example output.
The use of residential PV grid-tie inverters to supply reactive power as a benefit to the distribution grid has been widely proposed, however, there is little insight into how much of a benefit can be achieved from this control under varying system operating points. This paper seeks to demonstrate the effectiveness of a linearized versus nonlinear reactive power dispatch solution on a highly unbalanced distribution feeder under differing load profiles, insolation levels, and penetration rates of PV in the feeder. The results are analyzed to determine the system operating points that are favorable to reactive power control and the overall effectiveness of each solution in realistic feeder states.
With rising adoption of solar energy, it is increasingly important for utilities to easily assess potential interconnections of photovoltaic (PV) systems. In this analysis, we show the maximum feeder voltage due to various PV interconnections and provide visualizations of the PV impact to the distribution system. We investigate the locational dependence of PV hosting capacity by examining the impact of PV system size on these voltages with regard to PV distance and resistance to the substation. We look at the effect of increasing system size on line loading and feeder violations. The magnitude of feeder load is also considered as an independent variable with repeated analyses to determine the effect on the PV impact analysis. A technique is presented to determine and visualize the maximum capacity for possible PV installations for distribution feeders.
This manual provides the documentation of the MATLAB toolbox of functions for using OpenDSS to simulate the impact of solar energy on the distribution system. The majority of the functions are useful for interfacing OpenDSS and MATLAB, and they are of generic use for commanding OpenDSS from MATLAB and retrieving information from simulations. A set of functions is also included for modeling PV plant output and setting up the PV plant in the OpenDSS simulation. The toolbox contains functions for modeling the OpenDSS distribution feeder on satellite images with GPS coordinates. Finally, example simulations functions are included to show potential uses of the toolbox functions. Each function in the toolbox is documented with the function use syntax, full description, function input list, function output list, example use, and example output.
Distributed photovoltaic (PV) projects must go through an interconnection study process before connecting to the distribution grid. These studies are intended to identify the likely impacts and mitigation alternatives. In the majority of the cases, system impacts can be ruled out or mitigation can be identified without an involved study, through a screening process or a simple supplemental review study. For some proposed projects, expensive and time-consuming interconnection studies are required. The challenges to performing the studies are twofold. First, every study scenario is potentially unique, as the studies are often highly specific to the amount of PV generation capacity that varies greatly from feeder to feeder and is often unevenly distributed along the same feeder. This can cause location-specific impacts and mitigations. The second challenge is the inherent variability in PV power output which can interact with feeder operation in complex ways, by affecting the operation of voltage regulation and protection devices. The typical simulation tools and methods in use today for distribution system planning are often not adequate to accurately assess these potential impacts. This report demonstrates how quasi-static time series (QSTS) simulation and high time-resolution data can be used to assess the potential impacts in a more comprehensive manner. The QSTS simulations are applied to a set of sample feeders with high PV deployment to illustrate the usefulness of the approach. The report describes methods that can help determine how PV affects distribution system operations. The simulation results are focused on enhancing the understanding of the underlying technical issues. The examples also highlight the steps needed to perform QSTS simulation and describe the data needed to drive the simulations. The goal of this report is to make the methodology of time series power flow analysis readily accessible to utilities and others responsible for evaluating potential PV impacts.