GridSpice - A Virtual Test Bed for Smart Grid

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1 GridSpice - A Virtual Test Bed for Smart Grid Amit Narayan Director, Smart Grid Modeling & Simulation Research Stanford University Optimized Distribution Systems for Renewables Workshop April 2012

2 GridSpice: Project Overview o Team: o Kyle Anderson, lead developer, PhD student, CS o Jimmy Du, undergrad EE o Yizheng Liao, MS student, EE o Vikas Yendluri undergrad, EE o Past Students o Alex Cousland, Rishi Guarpuay, Vijay Bhatt, Jeffrey Wong o Prof Abbas El Gamal, faculty PI o Amit Narayan, Project Director o Funding: o TomKat Center o Cisco Systems o GE

3 Power Networks Motivation Demand is becoming elastic Supply is becoming distributed and variable Storage is becoming viable Power Generation Transmission Distribution GridSpice Wholesale Markets Retail Markets Power Markets Power Consumption Massive amounts of data - from smart meters, sensors and buildings Can we leverage data and computational power to understand & optimize the grid operation?

4 Design Objectives Modeling the interactions between all participants of a smart grid Power Markets o Comprehensive Modeling Capability o System-level view instead of Transmission & Distribution Network Smart Meters & Smart Devices Demand Response Aggregators Generation Cos, IPPs & LSEs Wholesale Markets Retail Markets o o o o component -level Steady-state operation Agent-based modeling High-Performance algorithms Real-world calibration Residential, C&I Consumers o o Open-Source availability Cloud-Deployment

5 GRIDSPICE GridSpice Status Available in open source under a BSD License Code available for download at Cloud hosted version available to Stanford collaborators

6 GridSpice - Applications Renewable Integration Trading & Risk Demand Response Distributed Automation Analytics Electric Vehicle Utility Scale Storage Distributed Energy Resources Micro Grids

7 GridSpice Major Components I N P U T A N D G U I Transmission Network and Wholesale Markets Weather, Initial Conditions, Time-step (T_t) Load Information Weather, Initial Conditions, Time-step (T_d) Real-time Prices Sim-Controller Locational Marginal Prices Based on Optimal DC flow Distribution Network Customer Owned Assets Retail Contracts Aggregated Load Information O U T P U T R E P O R T S

8 P & Q V & < GRIDSPICE Transmission / Market Server Model Database Results GridSpice Simulation Controller User-Interface Server (GWT) Distribution Servers User

9 P & Q V & < GRIDSPICE Transmission / Market Server (MATPOWER & Gridlab-D) Model Database Results GridSpice Simulation Controller User-Interface Serer (GW Distribution Servers (Gridlab-D) er Us

10 GRIDSPICE User Interface inside the Browser

11 GRIDSPICE Explorer View

12 GRIDSPICE Element Editor

13 GRIDSPICE Model Database User gets an account on the server Account tied to Google ID User can upload models to the account User defines permissions for model (shared or private) Private models encrypted using AES and user-provided key Client authenticated using gmail (all models) & encryption key (private models)

14 GRIDSPICE Model Upload

15 P & Q V & < GRIDSPICE Transmission / Market Server Model Database Results GridSpice Simulation Controller User-Interface Server (GWT) Distribution Servers User

16 GRIDSPICE GridSpice Simulation Handler Initialization Validates models and returns results to user Partitions simulation agents into logical subgroups (Transmission & Distribution) Provisions available machines for simulation. Monitoring Polls simulation servers for errors Reports simulation progress & intermediate results to client UI Completion Aggregates result output files from all recorded agents Sends results to client UI Turns off machines if necessary

17 GRIDSPICE Distribution Network Simulation Each feeder is simulated on a separate machine The powerflow solver is run at the distribution network. Results passed to the parent bus on the transmission network.

18 GRIDSPICE Prototypical Feeder Models (PNNL) Feeder models based on prototypical feeders provided by PNNL Models for homes and commercial buildings constructed using load forecasting Uses smart meter data from SPP pilot program kv kva Description Moderate suburban and rural Moderate suburban and light rural Small urban center Heavy suburban Light rural Light urban Moderate suburban Light suburban Moderate urban Light rural Heavy urban Moderate urban Heavy suburban Heavy urban with rural spur Light suburban and moderate urban Light rural Heavy suburban and moderate urban Moderate suburban and heavy urban Moderate rural Moderate suburban and urban Moderate suburban and light urban Heavy suburban and moderate urban Moderate suburban and light urban Single large commercial or industrial

19 GRIDSPICE Generators Current Generator Outputs (Tight Min/Max settings) Next-Day Generator Schedules P & Q MATPOWER (Real-Time) Voltage & Angle Prices Distribution Powerflow Run as Needed(15 min) Prices MATPOWER (Day Ahead Market) Distribution Powerflow Run every 24 Hours Load Forecast (including Distribution Losses)

20 Example: CVR and DR Hot-spots can DR be used? IVVCDR Anderson, Narayan, IEEE SmartGridComm

21 Network & Retail Market Node Voltage for Hot Summer Day DR Penetration V set TWA 0% 122.1V 120V 20% 122.6V 120V 40% 123.1V 120V 60% 119.2V 118V 80% 118.8V 118V 100% 118.8V 118V IVVC 122.1V 120V Minimum V des

22 Conclusion GridSpice: Open Source Cloud Based Platform for Modeling and Simulation of Smart Grid. No Hardware to Purchase No Software to Install Available in Open Source