Battelle introduces Grid Command Distribution services and software for rapid modeling of smart grid distribution circuits
|Screen shot of a Grid Command Distribution “heatmap” analysis for a neighborhood. Source: Battelle and AEP (data). Click to enlarge.|
Battelle recently unveiled its new Grid Command Distribution services and software for utilities. The software is a front-end for the open-source GridLAB-D, a distribution system simulation and analysis tool developed at Pacific Northwest National Laboratory (PNNL), a Department of Energy (DOE) lab managed by Battelle. Battelle staff developed the new Grid Command Distribution software internally as part of its work over the past two years as part of an ongoing smart grid demonstration project in Ohio: AEP Ohio’s gridSMART program, sponsored by the DOE.
The new offering greatly shortens the time—from 4-5 days to less than a minute in some cases, according to Battelle—required to build extremely detailed planning models for the analysis of distribution circuits on a smart grid that encompass a plethora of devices, technologies and operating policies such as energy storage systems, line configurations, transformers, demand response tariffs, Volt-VAR optimization (VVO), plug-in vehicle charging, water heater loads, and so on. (VVO seeks to optimize voltage at all points along the distribution feeder under all loading conditions, thereby increasing grid efficiency.)
Grid Command Distribution allows utilities quickly to build circuit models with multiple configurations; assess complex resource deployment scenarios; and provide insight into grid sensitivity and capacity under changing conditions. The offering thus helps utilities make informed decisions before they invest in new technologies—for example, modeling the impact of alternative energy sources and advanced technologies on the grid to improve investment and operational efficiency.
If GridLAB is the calculation engine, this does your configuration and establishes what scenarios to examine. This is [also] used for the analysis afterward.—Jason Black, a Research Leader at Battelle
The software has three main components:
A pre-processing step in which it imports data from existing utility formats and turn it into a GridLAB-D object;
A post-processing step, greatly simplifying the ability to parse and understand GridLAB files; and
A scripting interface that allows users to build macros—scripts to build repetitive tasks—that integrate seamlessly with the rest of the tool.
The AEP project wanted us to model 32 distribution circuits, to evaluate smart grid technology on a particular circuit. These model are incredibly detailed, and include everything from the substation to the house and everything in it—plug load, heating cooling. There is a tremendous amount of detail.
They were spending 4-5 days to build a circuit model. To build a circuit model [with the new software] takes us less than a minute. We can automate the initial model building process. We allow uses to put together the base model, and use a software wizard to select the technology, then it goes off and runs it in GridLAB.
The last piece of what we’ve done is to build a powerful post-processing capability in the tool. Double-click on the file, open it up, plot, do all kind of statistical-based analysis on the file or sets of files. We have tried to abstract away a lot of the difficult things about setting up the model to get the user to the analysis phase quicker. We want to answer these questions quickly and efficiently.—Ivan Tornes, Principal Research Scientist, Battelle, development lead on Grid Command Distribution
As a sample use case, Tornes said, a utility might want to examine the application of volt/VAR across different circuits to see which ones would benefit the most. With the tool, staff can quickly set of models of several circuits, apply volt/VAR, and get the answer back in a short period of time to help determine investments, to make a rate case before the local public utilities commission (PUC), and so on. The software can serve as a planning tool or a tool for scenario exploration.
Another example could be determining the impact of concentrations of plug-in vehicles (PEVs): exploring the distribution density over a circuit and specifying how many chargers per house (e.g., two in a two-PEV household) and then seeing what then happens to the transformers in the neighborhood, seeing what off-peak charging might do to usage patterns, and so on. “There are lots of things to consider and look at,” Tornes said.
Grid Command Distribution allows the utilities to examine PEVs in concert with other technologies to see their combined effect—e.g., the addition of solar panels, batteries, volt/VAR.
While GridLAB-D is open source, Battelle’s Grid Command Distribution is part of a full-service portfolio it offers that includes Grid Command Active Demand Management and Grid Command Transmission (powered by HELM technology from Gridquant). (Earlier post.) Active Demand Management gives consumers and producers control over everyday energy decisions. HELM, a proprietary suite of software and services originally developed by Gridquant, offers utilities visibility into transmission grid conditions even up to the point of collapse.
Currently, said Black, Grid Command Distribution is completely independent from the operational pieces and is only for planning. In the future, he said it’s likely that Battelle would look to at least create an interface between the software systems or even fuse them.
AEP gridSMART. With a total budget of $150 million, half of which comes from the government, AEP Ohio and its partners are building a secure, interoperable, and integrated Smart Grid infrastructure in Ohio that demonstrates the ability to maximize distribution system efficiency and reliability, and consumer use of demand response programs to reduce energy consumption, peak demand costs, and fossil fuel emissions.
AEP Ohio is implementing Smart Grid technology over 58 13kV circuits from 10 distribution stations and 12 34.5kV circuits from six distribution stations.
Included in this project is a redistribution management system, integrated volt-VAR control, distribution automation, advanced meter infrastructure, home area networks, community energy storage, sodium sulfur battery storage, and renewable generation sources. These technologies will be combined with two-way consumer communication and information sharing, demand response, dynamic pricing, and consumer products, such as plug-in hybrid vehicles.
GridLAB-D. GridLAB-D is a flexible simulation environment that can be integrated with a variety of third-party data management and analysis tools. At its core, GridLAB-D has an advanced algorithm simultaneously to determine the state of millions of independent devices, each described by models and equations relevant to the particular domain. GridLAB-D relies on advanced physical models to describe the interdependencies of each of the devices. This helps to avert the danger of erroneous or misapplied assumptions. The advantages of this algorithm over traditional finite difference-based simulators are:
it handles unusual situations much more accurately;
it handles widely disparate time scales, ranging from sub-seconds to many years; and
it is very easy to integrate with new modules and third-party systems.