Scalable software tools and methods for smart grid modeling and optimization
- The electric grid in its current incarnation-- comprising utility companies, power system operators, market players and other agents-- is undergoing rapid change. Increasing adoption of distributed energy resources, electric vehicles, and demand response programs are adding further stress to the aging grid infrastructure. In order to realize the benefits of these new technologies, engineers need new tools to model and optimize the complex interactions. We address this need with GridSpice, a software framework with the computational scale and the modeling capability to represent diverse scenarios in large interconnected grid systems. GridSpice leverages existing best-in-class point tools and fuses them together in a modular architecture. The GridSpice framework is designed to run on the cloud, giving researchers and engineers the ability to elastically utilize many machines to perform complex simulations. After describing the GridSpice framework, we introduce a method to optimize centralized storage in wholesale markets. Specifically, this method maximizes the expected present value (PV) of an independently operated electric energy storage (EES) unit cooptimized to perform both energy arbitrage (EA) and regulation service (RS). The method is particularly suitable for fast-ramping EES units with a high power-to-capacity ratio, such as a grid-scale battery. The method is applied to two sample battery technologies using market data from the Independent Service Operator of New England (ISONE), illustrating how the cost and performance of storage technologies translates to financial returns. Next, we present a method for optimizing energy storage on a low voltage distribution grid with high penetrations of renewable distributed generation (RDGs). The method shows how intelligently storage can alleviate local volt/var violations, increase the maximum safe RDG penetration level, and simultaneously reduce the average cost of energy by shifting aggregate load from peak to off-peak hours (i.e., energy arbitrage). Finally, we show how to characterize the return on investment of storage and solar installations as a function of the technology parameters, penetration level, and limitations on the communication network.
|Type of resource
|electronic; electronic resource; remote
|1 online resource.
|Stanford University, Department of Electrical Engineering.
|El Gamal, Abbas A
|El Gamal, Abbas A
|Statement of responsibility
|Submitted to the Department of Electrical Engineering.
|Thesis (Ph.D.)--Stanford University, 2015.
- © 2015 by Kyle Ture Anderson
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