Abstract-This paper describes how decentralized control theory can be used to analyze the control of multiple cooperative robotic vehicles. Models of cooperation are discussed and related to the input/output reachability, structural observability, and controllability of the entire system. Whereas decentralized control research in the past has concentrated on using decentralized controllers to partition complex physically interconnected systems, this work uses decentralized methods to connect otherwise independent nontouching robotic vehicles so that they behave in a stable, coordinated fashion. A vector Liapunov method is used to prove stability of two examples: the controlled motion of multiple vehicles along a line and the controlled motion of multiple vehicles in formation. Also presented are three applications of this theory: controlling a formation, guarding a perimeter, and surrounding a facility.

The U.S. Department of Energy's Energy Storage Systems (ESS) Program, through the support of Pacific Northwest National Laboratory (PNNL) and Sandia National Laboratories (SNL), facilitated the development of the protocol provided in this report. The focus of the protocol is to provide a uniform way of measuring, quantifying, and reporting the performance of ESSs in various applications; something that does not exist today and, as such, is hampering the consideration and use of this technology in the market. The availability of an applicationspecific protocol for use in measuring and expressing performance-related metrics of ESSs will allow technology developers, power-grid operators and other end-users to evaluate the performance of energy storage technologies on a uniform and comparable basis. This will help differentiate technologies and products for specific application(s) and provide transparency in how performance is measured. It also will assist utilities and other consumers of ESSs to make more informed decisions as they consider the potential application and use of ESSs, as well as form the basis for documentation that might be required to justify utility investment in such technologies. 4 ACKNOWLEDGMENTS

This paper describes the design and implementation of a proof-of-concept Pacific dc Intertie (PDCI) wide area damping controller and includes system test results on the North American Western Interconnection (WI). To damp inter-area oscillations, the controller modulates the power transfer of the PDCI, a ±500 kV dc transmission line in the WI. The control system utilizes real-time phasor measurement unit (PMU) feedback to construct a commanded power signal which is added to the scheduled power flow for the PDCI. After years of design, simulations, and development, this controller has been implemented in hardware and successfully tested in both open and closed-loop operation. The most important design specifications were safe, reliable performance, no degradation of any system modes in any circumstances, and improve damping to the controllable modes in the WI. The main finding is that the controller adds significant damping to the modes of the WI and does not adversely affect the system response in any of the test cases. The primary contribution of this paper, to the state of the art research, is the design methods and test results of the first North American real-time control system that uses wide area PMU feedback.

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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.