The goal of this project is to provide a neutral tool for all parties involved in planning the future of the grid edge to analyze proposals in a consistent and transparent manner. Futurecasting will project the impact of changes in rate structures, consumer incentives, novel service models and technology availability on adoption of distributed energy resources and on the power consumption.
This new tool builds on the insights about consumer behavior derived from VISDOM by linking these insights to simulations of economic incentives created by scenarios of various potential changes. The tool will allow users—utilities and other energy firms, investors, regulators, and researchers—to understand how customers may react to incentives to produce change at the grid edge.
Futurecasting will also evaluate uncertainty in estimating the impacts of expected scenarios of future change.
Experiments with large, heterogenous datasets have been severely restricted in many areas—not just energy—both due to the difficulty of obtaining large, high quality data sets and due to the high infrastructure requirements for data storage and processing. Energy researchers are also hindered in accessing data due to privacy concerns and proprietary value.
To overcome these barriers, Bits & Watts will create a Grid Data Commons: a user-friendly, secure, cloud-based platform which we will use to aggregate and curate a large, heterogenous set of data about the diverse range of topics that affect the energy ecosystem, ranging from smart meter and weather data to satellite imagery. This resource will be vital for building the sophisticated models needed to better understand increasingly complex power systems. Grid Data Commons will develop and use a number of data-driven methodologies that shaped the World Wide Web to create the electricity data capital of the world.
This unique repository will make developing data science applications for power systems easier, faster and cheaper. Researchers will create not only shared data sets, but shared analytical tools and applications for technology, market and policy advances. Grid Data Commons will host publicly available data as well as secured project-specific data with strictly secure access. In time, researchers will be able to run experiments in an hour in the cloud that would take a month to run on a local computer.
Building on the Stanford research project GridSpice, Bits & Watts will develop the Virtual Megagrid. This open-source, cloud-based platform will model and simulate the entire smart grid: from central power plants to networked home appliances.
Good simulators exist individually for different parts of the electric system, such as generation, transmission and markets. In reality, electric power grids operate as a single, interconnected machine. Designing and planning the 21st century grid requires a simulator for the entire system. Based on real-world data, Bits & Watts researchers and partners will be able to test scenarios for variables as varied as intermittent generation penetration, asset allocation, proposed market policies and changing consumer behaviors. Testing new proposals on such a simulator will be faster, safer and cheaper than performing demonstration projects in the real world without prior simulation. The Virtual Megagrid platform could also be used to verify other research on a validated, objective system.
Improved understanding of consumers would enable utilities to target customers more effectively for demand-response and energy efficiency programs. It would also help distribution system operators manage consumption over small geographical areas. New sensors, advanced telecommunication and smart meters supply the data to better understand the consumer, but a clear picture of actual energy use habits has been missing.
Visualization & Insight for Demand Operations & Management, (VISDOM), analyzes raw electricity use data and additional diverse data sources—like geographic, demographic and weather information—for unprecedented understanding of customer behavior. VISDOM’s open-source software is being developed with several major utilities and demand-response companies. It incorporates statistical methods, new algorithms and machine learning in a graphical interface that is easy to use.
In addition, VISDOM enables response modeling for each customer segment. This helps evaluate planned changes by revealing how customer segments will respond to signals and to what degree.