Publications

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Battery energy storage systems (BESS) are a critical technology for integrating high penetration renewable power on an intelligent electrical grid. As limited energy restricts the steady-state operational state-of-charge (SoC) of storage systems, SoC forecasting models are used to determine feasible charge and discharge schedules that supply grid services. Smart grid controllers use SoC forecasts to optimize BESS schedules to make grid operation more efficient and resilient. This paper presents three advances in BESS SoC forecasting. First, two forecasting models are reformulated to be conducive to parameter optimization. Second, a new method for selecting optimal parameter values based on operational data is presented. Last, a new framework for quantifying model accuracy is developed that enables a comparison between models, systems, and parameter selection methods. The accuracies achieved by both models, on two example battery systems, with each method of parameter selection are then compared in detail. The results of this analysis suggest variation in the suitability of these models for different battery types and applications. The proposed model formulations, optimization methods, and accuracy assessment framework can be used to improve the accuracy of SoC forecasts enabling better control over BESS charge/discharge schedules.

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Batteries are designed to store electrical energy. The increasing variation in time value of energy has driven the use of batteries as controllable distributed energy resources (DER). This is enabled though low-cost power electronic inverters that are able to precisely control charge and discharge. This paper describes the software implementation of an open-source battery inverter fleet models in python. The Sandia BatterylnverterFleet class model can be used by scientists, researchers, and engineers to perform simulations of one or more fleets of similar battery-inverter systems connected to the grid. The program tracks the state- of-charge of the simulated batteries and ensures that they stay within their limits while responding to separately generated service requests to charge or discharge. This can be used to analyze control and coordination, placement and sizing, and many other problems associated with the integration of batteries on the power grid. The development of these models along with their python implementation was funded by the Grid Modernization Laboratory Consortium (GMLC) project 1.4.2. Definitions, Standards and Test Procedures for Grid Services from Devices.

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Power systems can become unstable under transient periods such as short-circuit faults, leading to equipment damage and large scale blackouts. Power system stabilizers (PSS) can be designed to improve the stability of generators by quickly regulating the exciter field voltage to damp the swings of generator rotor angle and speed. The stability achieved through exciter field voltage control can be further improved with a relatively small, fast responding energy storage system (ESS) connected at the terminals of the generator that enables electrical power damping. PSS are designed and studied using a single-machine infinite-bus (SMIB) model. In this paper, we present a comprehensive optimal-control design for a flexible ac synchronous generator PSS using both exciter field voltage and ESS control including estimation of unmeasurable states. The controller is designed to minimize disturbances in rotor frequency and angle, and thereby improve stability. The design process is based on a linear quadratic regulator of the SMIB model with a test system linearized about different operating frequencies in the range 10 Hz to 60 Hz. The optimal performance of the PSS is demonstrated along with the resulting stability improvement.

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Methods for benchmarking and comparison can either limit or accelerate the adoption of emerging energy storage technologies on the grid. This paper assesses the efficacy of the methods in the U.S. DOE Protocol for Uniformly Measuring and Expressing the Performance of Energy Storage to remove barriers to the technology's acceptance. The protocol enables standardized data collection to compare different technologies for energy storage applications fairly. We apply the relevant portions of the protocol to a 1-megawatt lithium-ion battery system to provide a critical assessment of procedures and methods it stipulates. Field experience and data will be invaluable to standards development organizations as they begin to consider these methods for codification.

Power systems in rural Alaska villages face a unique combination of challenges that can increase the cost of energy and lowers energy supply reliability. In the case of the remote village of Shungnak, diesel and heating fuel is either shipped in by barge or flown in by aircraft. This report presents a technical analysis of several energy infrastructure upgrade and modification options to reduce the amount of fuel consumed by the community of Shungnak. Reducing fuel usage saves money and makes the village more resilient to disruptions in fuel supply. The analysis considers demand side options, such as energy efficiency, alongside the installation of wind and solar power generation options. Some novel approaches are also considered including battery energy storage and the use of electrical home heating stoves powered by renewable generation that would otherwise be spilled and wasted. This report concludes with specific recommendations for Shungnak based on economic factors, and fuel price sensitivity. General conclusions are also included to support future work analyzing similar energy challenges in remote arctic regions.

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Foster confidence in the safety and reliability of energy storage systems.

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