Jun 21 2019
New software created at Sandia could enable business enterprises and homeowners to find it easy to manage solar panels on rooftops.
Matthew Reno, a Sandia National Laboratories engineer, helped develop new software that can perform quasi-static time series analysis to show how rooftop solar panels interact with the electrical grid throughout the year. (Image credit: Randy Montoya)
The software can perform a thorough, second-by-second simulation, called quasi-static time series analysis, which demonstrates utility companies how rooftop solar panels at a particular business or house would communicate with a local electrical grid over the course of a year.
Utility companies are in need of the analysis since they must supply electricity at the standard voltage used to operate all devices, ranging from phone chargers to refrigerators. Huge amounts of solar generation in one part of a city can result in extreme voltage fluctuations, which can harm household electronics.
To date, grid analysis of this type has not been feasible outside research environment since previous models required a number of days to run a single case. When compared to conventional simulations used by utility companies, the new simulations are more detailed, and Sandia researchers believe that they will lead to more solar panel installations.
When installing new solar panels on the grid, utility companies will analyze how a new system interacts with the grid, typically by doing a snapshot, power-flow simulation to determine if the impacts will be OK or not. However, doing a snapshot of one instance in time is conservative because of the uncertainty about impacts that happen at various times on solar panels, such as the weather. This can lead to increased connection costs or homeowners living in parts of the city having unnecessarily low limits for adding solar panels, especially in sunny states.
Matthew Reno, Engineer, Sandia National Laboratories
In a three-year project that was financially supported by DOE’s Solar Energy Technologies Office as part of the Grid Modernization Lab Consortium, Sandia, the National Renewable Energy Laboratory, Georgia Tech University, the Electric Power Research Institute, and CYME International—the largest software vendor for utility companies—used a four-part strategy to accelerate time-series analyses to gain more accurate knowledge about the impact of the use of solar energy on the grid.
“The project tackled four main areas to make time series analysis faster—with the idea that each of the four areas was developed independently—and then they were combined,” stated Reno. “For example, if each method makes the analysis 10 times faster, when we combine two methods, it could be 100 times faster.”
According to Reno, an analysis is currently performed 1000 times faster compared to what was possible two years earlier. The software can perform a simulation that used to require 36 hours and complete it within five minutes on a standard desktop computer.
Simulating a Year on the Grid
Initially, the team studied ways to minimize how often the electric grid power flow had to be simulated over time. Rather than individually simulating each second of the year, the software runs through the simulated year at a different speed, considering whether it is day or night or likely to be sunny or cloudy at that time of year.
You can jump through time faster at some points and put the computational effort where it is most needed, using event-based simulation or variable time-step. During cloudy periods with varying amounts of sunlight, the simulation slows down and looks at the impact second by second. When there is low variability, like at night when everything is fairly stable, the simulation can jump forward with larger time steps, making the program more efficient.
Matthew Reno, Engineer, Sandia National Laboratories
The second part of the project involved speeding up the individual simulations by updating the formulas used to compute the power flow. The researchers collaborated with the developers of the commercial and open-source electric distribution system software that is used most commonly to enhance memory management, input and output, and algorithms for large datasets.
The third part focused on bringing down the complexity of the power-grid model used by the software while retaining its accuracy. The smaller, more efficient model assists the software in solving problems more rapidly by focusing its analysis on crucial parts of the grid.
The focus of the fourth part of the project was on altering the way a standard business computer performs the analysis to guarantee the use of all the processing cores.
“In quasi-static time series analysis, each second is dependent on the one that came before, which means everything had to be done sequentially, on a single processor, on a single core,” stated Reno. “Your desktop computer could have seven other cores sitting there doing nothing while a single core performed the simulation for the entire year by itself.”
The new software breaks up parts of the grid or parts of the year, assigns them to each available computing core, and runs them concurrently.
Evaluating Smart Grid Controls and New Tech
Although the project’s focus has been on supporting rooftop solar, according to Reno, the new software can also perform assessment of smart grid controls and new technology.
As we look to the future with new smart grid applications and controls, utility companies are going to have continued need to use time-series analysis to see how new electric car charging will impact neighborhoods, investigate the best energy storage controls and applications or determine how smart home controls, like thermostats and lights, can benefit their grids. In order to understand the new benefits and controllability of the smart grid, these companies will have to be able to simulate it first.
Matthew Reno, Engineer, Sandia National Laboratories
The team of researchers has published over 30 papers, and the advancements are being shared with universities, utilities, and other researchers in many ways. CYME International has been directly adding components of the code into its commercial software, and optimized time-series analysis tools have been added into the most recent versions of EPRI’s OpenDSS Distribution System Simulator and in Sandia’s GridPV MATLAB Toolbox.