Grid integrated distributed PV (GridPV).

Thakkar

et al. 2015

IEEE Trans. Smart Grid

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. We demonstrate this paper for a university distribution network with the state-of-the-art real-time and historical smart meter data infrastructure.

Section: B Developing Advanced Visualizationmentioning

confidence: 99%

Leveraging AMI Data for Distribution System Model Calibration and Situational Awareness

Peppanen

Thakkar

et al. 2015

IEEE Trans. Smart Grid

“…The line thickness represents the current flow in the lines. These plots were created with the GridPV Matlab toolbox [21], [22]. The feeder has a peak load of 6.41MW and operates at a native power factor of 0.917 at peak load.…”

Section: Feeder Characteristicsmentioning

confidence: 99%

Multi-Objective Advanced Inverter Controls to Dispatch the Real and Reactive Power of Many Distributed PV Systems.

Lave

Broderick

et al. 2016

Self Cite

The research presented in this report compares several real-time control strategies for the power output of a large number of PV distributed throughout a large distribution feeder circuit. Both real and reactive power controls are considered with the goal of minimizing network overvoltage violations caused by large amounts of PV generation. Several control strategies are considered under various assumptions regarding the existence and latency of a communication network. 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.

“…The tool can either be run using MATLAB or as an executable version.  The MATLAB tool requires an actual version of MATLAB, OpenDSS [6], and MATLAB GridPV Toolbox [7,8]. The GUI has been tested on a 64-bit Windows [6] and that the free R2014a MATLAB Runtime is installed (http://www.mathworks.com/products/compiler/mcr/).…”

Section: Requirementsmentioning

confidence: 99%

Tools for Enhanced Grid Operation and Optimized PV Penetration Utilizing Highly Distributed Sensor Data.

Peppanen

Seuss

et al. 2015

Self Cite

Increasing numbers of PV on distribution systems are creating more grid impacts, but it also provides more opportunities for measurement, sensing, and control of the grid in a distributed fashion. This report demonstrates three software tools for characterizing and controlling distribution feeders by utilizing large numbers of highly distributed current, voltage, and irradiance sensors. Instructions and a user manual is presented for each tool. First, the tool for distribution system secondary circuit parameter estimation is presented. This tool allows studying distribution system parameter estimation accuracy with user-selected active power, reactive power, and voltage measurements and measurement error levels. Second, the tool for multi-objective inverter control is shown. Various PV inverter control strategies can be selected to objectively compare their impact on the feeder. Third, the tool for energy storage for PV ramp rate smoothing is presented. The tool allows the user to select different storage characteristics (power and energy ratings) and control types (local vs. centralized) to study the tradeoffs between state-of-charge (SOC) management and the amount of ramp rate smoothing.