Publications

Zia, Kaynat Z.; Papasani, Anusha P.; Lee, Wei J.; Rosewater, David M.

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Rosewater, David M.

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Rosewater, David M.

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West, Amanda W.; Rosewater, David M.

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Rosewater, David M.; Preger, Yuliya P.; Mueller, Jacob M.; Atcitty, Stanley A.; Willard, Steve W.; Smith, Morgan S.; Thompson, Joe T.; Long, Dirk L.

Energy storage technologies are positioned to play a substantial role in power delivery systems. They have the potential to serve as an effective new resource to maintain reliability and allow for increased penetration of renewable energy. However, because of their relative infancy, there is a lack of knowledge about how these resources truly operate over time. A data analysis can help ascertain the operational and performance characteristics of these emerging technologies. Rigorous testing and a data analysis are important for all stakeholders to ensure a safe, reliable system that performs predictably on a macro level. Standardizing testing and analysis approaches to verify the performance of energy storage devices, equipment, and systems when integrating them into the grid will improve the understanding and benefit of energy storage over time from technical and economic vantage points. Demonstrating the life-cycle value and capabilities of energy storage systems begins with the data that the provider supplies for the analysis. After a review of energy storage data received from several providers, some of these data have clearly shown to be inconsistent and incomplete, raising the question of their efficacy for a robust analysis. This report reviews and proposes general guidelines, such as sampling rates and data points, that providers must supply for a robust data analysis to take place. Consistent guidelines are the basis of a proper protocol and ensuing standards to (1) reduce the time that it takes for data to reach those who are providing the analysis; (2) allow them to better understand the energy storage installations; and (3) enable them to provide a high-quality analysis of the installations. The report is intended to serve as a starting point for what data points should be provided when monitoring. Readers are encouraged to use the guidance in the report to develop specifications for new systems, as well as enhance current efforts to ensure optimal storage performance. As battery technologies continue to advance and the industry expands, the report will be updated to remain current.

Rosewater, David M.

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Rosewater, David M.

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Rosewater, David M.

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Olis, Walker P.; Rosewater, David M.; Nguyen, Tu A.

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Rosewater, David M.; Lamb, Joshua H.; Hewson, John C.; Viswanathan, Vilayanur V.; Paiss, Matthew P.; Choi, Daiwon C.; Jaiswal, Abhishek J.

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Rosewater, David M.

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IEEE Power and Energy Society General Meeting

Headley, Alexander H.; Schenkman, Benjamin L.; Rosewater, David M.

Forward operating base (FOB) microgrids typically use diesel generators with discrete logic control to supply power. However, emerging energy storage systems can be added as spinning reserves and to increase the PV hosting capacity of microgrids to significantly reduce diesel consumption if resources are controlled appropriately. Discrete logic controllers use if/else statements to determine resource dispatch based on inputs such as net load and generator run times but do not account for the capabilities of energy storage systems explicitly. Optimal dispatch controllers could improve upon this architecture by optimizing dispatch based on forecasts of load and generation. However, optimal dispatch controllers are far less intuitive, require more processing power, and the level of potential improvement is unclear.This work seeks to address three points with regards to FOB microgrid operations. Firstly, the impact of energy storage systems on the adoption of solar generation in microgrids is discussed. Secondly, logic is added to the typical discrete controller decision tree to account for energy storage resources. Lastly, fuel savings with energy storage and solar generation using the new discrete control logic and optimal dispatch are compared based on load data measured from a real FOB. The results of these analyses show the potential impact of energy storage on fuel consumption in FOBs and gives guidance as to the appropriate control architecture for management of integrated resource microgrids.

Conference Record - Industrial and Commercial Power Systems Technical Conference

Zia, Kaynat; Papasani, Anusha; Rosewater, David M.; Lee, Wei J.

Arc flash hazard prediction methods have become more sophisticated because the knowledge about arc flash phenomenon has advanced since the publication of IEEE Std. 1584-2002 [17]. The IEEE Std. 1584-2018 [13] has added parameters for more accurate arc flash incident energy, arcing current and protection boundary estimation. The parameters in the updated estimation models include electrode configuration, open circuit voltage, bolted fault current, arc duration, gap width, working distance, and enclosure dimension. The sensitivity and effect changes of other parameters have been discussed the previous literatures [8] [9] [11] [2] [12] [15], this paper explains the fundamental theory on the selection of electrode configurations and performs sensitivity analysis of the enclosure dimension, that have been introduced in the IEEE Std. 1584-2018. According to the newly published model for incident energy (IE) estimation, the IE between VCB (Vertical Electrodes inside a metal Box) and HCB (Horizontal Electrodes inside a metal Box) can differ by a factor of two with other parameters constant. Using HCB as the worst-case scenario to determine the personal protection requirements [7] [10] may not be the best practice in all circumstances. This paper provides guidance for electrode configuration selection and a sensitivity analysis for determining a reasonable engineering margin when actual dimension is not available.

2020 International Symposium on Power Electronics, Electrical Drives, Automation and Motion, SPEEDAM 2020

Rosewater, David M.; Schenkman, Benjamin L.; Santoso, Surya

Battery energy storage systems are often controlled through an energy management system (EMS), which may not have access to detailed models developed by battery manu-facturers. The EMS contains a model of the battery system's performance capabilities that enables it to optimize charge and discharge decisions. In this paper, we develop a process for the EMS to calculate and improve the accuracy of its control model using the operational data produced by the battery system. This process checks for data salience and quality, identifies candidate parameters, and then calculates their accuracy. The process then updates its model of the battery based on the candidate parameters and their accuracy. We use a charge reservoir model with a first order equivalent circuit to represent the battery and a flexible open-circuit-voltage function. The process is applied to one year of operational data from two lithium-ion batteries in a battery system located in Sterling, MA USA. Results show that the process quickly learns the optimal model parameters and significantly reduces modeling uncertainty. Applying this process to an EMS can improve control performance and enable risk-averse control by accounting for variations in capacity and efficiency.

IEEE Transactions on Smart Grid

Rosewater, David M.; Baldick, Ross; Santoso, Surya

When batteries supply behind-the-meter services such as arbitrage or peak load management, an optimal controller can be designed to minimize the total electric bill. The limitations of the batteries, such as on voltage or state-of-charge, are represented in the model used to forecast the system's state dynamics. Control model inaccuracy can lead to an optimistic shortfall, where the achievable schedule will be costlier than the schedule derived using the model. To improve control performance and avoid optimistic shortfall, we develop a novel methodology for high performance, risk-averse battery energy storage controller design. Our method is based on two contributions. First, the application of a more accurate, but non-convex, battery system model is enabled by calculating upper and lower bounds on the globally optimal control solution. Second, the battery model is then modified to consistently underestimate capacity by a statistically selected margin, thereby hedging its control decisions against normal variations in battery system performance. The proposed model predictive controller, developed using this methodology, performs better and is more robust than the state-of-the-art approach, achieving lower bills for energy customers and being less susceptible to optimistic shortfall.

Rosewater, David M.

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Rosewater, David M.

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Rosewater, David M.

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Rosewater, David M.

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Rosewater, David M.; Baldick, Ross B.; Santoso, Surya S.

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IEEE Power and Energy Society General Meeting

Rosewater, David M.; Headley, Alexander H.; Mier, Frank A.; Santoso, Surya

Battery energy storage is being installed behind-the-meter to reduce electrical bills while improving power system efficiency and resiliency. This paper demonstrates the development and application of an advanced optimal control method for battery energy storage systems to maximize these benefits. We combine methods for accurately modeling the state-of-charge, temperature, and state-of-health of lithium-ion battery cells into a model predictive controller to optimally schedule charge/discharge, air-conditioning, and forced air convection power to shift a electric customer's consumption and hence reduce their electric bill. While linear state-of-health models produce linear relationships between battery usage and degradation, a non-linear, stress-factor model accounts for the compounding improvements in lifetime that can be achieved by reducing several stress factors at once. Applying this controller to a simulated system shows significant benefits from cooling-in-the-loop control and that relatively small sacrifices in bill reduction performance can yield large increases in battery life. This trade-off function is highly dependent on the battery's degradation mechanisms and what model is used to represent them.

Rosewater, David M.; Headley, Alexander H.; Mier, Frank A.; Santoso, Surya S.

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Rosewater, David M.; Headley, Alexander H.; Mier, Frank A.; Santoso, Surya S.

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Rosewater, David M.; Lao, K.W.; Siratarnsophon, P.S.; Santoso, S.S.

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Rosewater, David M.

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