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GridSpice: A Virtual Platform for Modeling, Analysis, and Optimization of the Smart Grid

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Stephen Boyd, Electrical Engineering; Abbas El Gamal, Electrical Engineering; Amit Narayan, Smart Grid Research and Modeling; Dan O'Neill, Electrical Engineering; Benjamin Van Roy, Management Science and Engineering and Electrical Engineering

GridSpice is an open-source, cloud-based platform for modeling simulations of the smart grid. Although still in early development, GridSpice has been tested and critiqued by industry mentors at Cisco systems, and numerous students have used it for their final projects in the “Modern Power Systems” course.  More than 20 groups from industry and other academic institutions have submitted project proposals to the researchers to be considered for use in the next version of GridSpice. The research team is adding several applications to the platform, such as energy storage and electric vehicles, and they are standardizing the software to the electric power industry’s Common Information Model.  The early-stage GridSpice platform has been incorporated into GridLAB-D, where they have added modules to support cosimilation with matpower, distributed computing, and scripting in Python.

This research project is also developing a messaging application for quickly optimizing a dynamic system based on a great deal of data. The electrical network of the future will have many devices, such as generators, fixed demand, flexible demand and storage devices, and each device will have its own objective and dynamic limitations.  Maximizing the efficiency of the total network over a given time horizon, subject to the device and line constraints, is a large optimization problem.

Stephen Boyd’s research group has developed a decentralized method for solving this problem. At each step, each device exchanges simple messages with its neighbors in the network and then solves its own optimization problem, minimizing its own objective function, augmented by a term determined by the messages it has received. The method is completely decentralized, and needs no global coordination other than synchronizing iteration. In one resulting publication, Boyd describes the method’s speed and scaling, including the solution of a serial implementation problem with over 10 million variables in just 17 minutes. With decentralized computing, the solve time would be less than one second.

The high variability of renewable energy resources such as wind and solar presents significant challenges to the operation of the electric power grid. Gas-fired generators can be used to mitigate this variability, but they are costly to operate and produce harmful carbon emissions. Energy storage provides a more environmentally friendly alternative, but is costly to deploy in large amounts. Abbas El Gamal’s research team has demonstrated that storage can reduce fast-ramping generation by a factor that approaches the storage round-trip inefficiency as its capacity becomes large. Also, using the NREL-simulated wind-power dataset, the researchers developed expressions for the stationary distribution of storage and conventional generation. Finally, the researchers propose a two-threshold policy that trades off conventional generation savings with loss of load probability.

Publications and media

Q&A: Stanford smart grid project launched a decade-long journey for a recently acquired climate-tech startup May 2022

"Shapley value estimation for compensation of participants in demand response programs" IEEE Transactions onSmart Grid6 (2015): 2837-2844.

"GridSpice: A distributed simulation platform for the smart grid" IEEE Transactions on Industrial Informatics 10 (2014): 2354-2363.

"Security constrained optimal power flow via proximal message passing" Power Systems Conference (2014): 1-8.

"Dynamic network energy management via proximal message passing" Foundations and Trends in Optimization 1 (2014): 70-126.

"Operation and configuration of a storage portfolio via convex optimization" Preprints of the 18th IFAC World Congress (2011): 10487-10492.

"Simulating integrated volt/var control and distributed demand response using GridSpice" 1st IEEE International Workshop on Smart Grid Modeling and Simulation (2011): 84-89.

"Resource allocation via message passing" INFORMS Journal on Computing 23 (2011): 205-219.

"Modeling and analysis of the role of fast-response energy storage in the smart grid" 49th Annual Allerton Conference on Communication, Control, and Computing(2011): 719-726.

Awarded in 2010 as part of the TomKat's Smart Grid: Sustainable Grid Efforts.