Publications

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This report proposes a reformulation of U.S. ISO/RTO-managed wholesale electric power mar- kets for improved reliability and e ciency of system operations. Current markets do not specify or compensate primary frequency response. They also unnecessarily limit the participation of new technologies in reserve markets and o er insu cient economic inducements for new capacity invest- ment. In the proposed market reformulation, energy products are represented as physically-covered rm contracts and reserve products as physically-covered call option contracts. Trading of these products is supported by a backbone of linked ISO/RTO-managed forward markets with planning horizons ranging from multiple years to minutes ahead. A principal advantage of this reformulation is that reserve needs can be speci ed in detail, and resources can o er the services for which they are best suited, without being forced to conform to rigid reserve product de nitions. This should improve the business case for electric energy storage and other emerging technologies to provide reserve. In addition, the facilitation of price discovery should help to ensure e cient energy/reserve procurement and adequate levels of new capacity investment.

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As the amount of renewable generation increases, the inherent variability of wind and photovoltaic systems must be addressed in order to ensure the continued safe and reliable operation of the nation's electricity grid. Grid-scale energy storage systems are uniquely suited to address the variability of renewable generation and to provide other valuable grid services. The goal of this report is to quantify the technical performance required to provide di erent grid bene ts and to specify the proper techniques for estimating the value of grid-scale energy storage systems.

The valuation of an electricity storage device is based on the expected future cash flow generated by the device. Two potential sources of income for an electricity storage system are energy arbitrage and participation in the frequency regulation market. Energy arbitrage refers to purchasing (stor- ing) energy when electricity prices are low, and selling (discharging) energy when electricity prices are high. Frequency regulation is an ancillary service geared towards maintaining system frequency, and is typically procured by the independent system operator in some type of market. This paper outlines the calculations required to estimate the maximum potential revenue from participating in these two activities. First, a mathematical model is presented for the state of charge as a function of the storage device parameters and the quantities of electricity purchased/sold as well as the quantities o ered into the regulation market. Using this mathematical model, we present a linear programming optimization approach to calculating the maximum potential revenue from an elec- tricity storage device. The calculation of the maximum potential revenue is critical in developing an upper bound on the value of storage, as a benchmark for evaluating potential trading strate- gies, and a tool for capital nance risk assessment. Then, we use historical California Independent System Operator (CAISO) data from 2010-2011 to evaluate the maximum potential revenue from the Tehachapi wind energy storage project, an American Recovery and Reinvestment Act of 2009 (ARRA) energy storage demonstration project. We investigate the maximum potential revenue from two di erent scenarios: arbitrage only and arbitrage combined with the regulation market. Our analysis shows that participation in the regulation market produces four times the revenue compared to arbitrage in the CAISO market using 2010 and 2011 data. Then we evaluate several trading strategies to illustrate how they compare to the maximum potential revenue benchmark. We conclude with a sensitivity analysis with respect to key parameters.

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With the continuing development of more capable data gathering sensors, comes an increased demand on the bandwidth for transmitting larger quantities of data. To help counteract that trend, a study was undertaken to determine appropriate lossy data compression strategies for minimizing their impact on target detection and characterization. The survey of current compression techniques led us to the conclusion that wavelet compression was well suited for this purpose. Wavelet analysis essentially applies a low-pass and high-pass filter to the data, converting the data into the related coefficients that maintain spatial information as well as frequency information. Wavelet compression is achieved by zeroing the coefficients that pertain to the noise in the signal, i.e. the high frequency, low amplitude portion. This approach is well suited for our goal because it reduces the noise in the signal with only minimal impact on the larger, lower frequency target signatures. The resulting coefficients can then be encoded using lossless techniques with higher compression levels because of the lower entropy and significant number of zeros. No significant signal degradation or difficulties in target characterization or detection were observed or measured when wavelet compression was applied to simulated and real data, even when over 80% of the coefficients were zeroed. While the exact level of compression will be data set dependent, for the data sets we studied, compression factors over 10 were found to be satisfactory where conventional lossless techniques achieved levels of less than 3.

The development of tools for complex dynamic security systems is not a straight forward engineering task but, rather, a scientific task where discovery of new scientific principles and math is necessary. For years, scientists have observed complex behavior but have had difficulty understanding it. Prominent examples include: insect colony organization, the stock market, molecular interactions, fractals, and emergent behavior. Engineering such systems will be an even greater challenge. This report explores four tools for engineered complex dynamic security systems: Partially Observable Markov Decision Process, Percolation Theory, Graph Theory, and Exergy/Entropy Theory. Additionally, enabling hardware technology for next generation security systems are described: a 100 node wireless sensor network, unmanned ground vehicle and unmanned aerial vehicle.

This report contains the results of a research effort on advanced robot locomotion. The majority of this work focuses on walking robots. Walking robot applications include delivery of special payloads to unique locations that require human locomotion to exo-skeleton human assistance applications. A walking robot could step over obstacles and move through narrow openings that a wheeled or tracked vehicle could not overcome. It could pick up and manipulate objects in ways that a standard robot gripper could not. Most importantly, a walking robot would be able to rapidly perform these tasks through an intuitive user interface that mimics natural human motion. The largest obstacle arises in emulating stability and balance control naturally present in humans but needed for bipedal locomotion in a robot. A tracked robot is bulky and limited, but a wide wheel base assures passive stability. Human bipedal motion is so common that it is taken for granted, but bipedal motion requires active balance and stability control for which the analysis is non-trivial. This report contains an extensive literature study on the state-of-the-art of legged robotics, and it additionally provides the analysis, simulation, and hardware verification of two variants of a proto-type leg design.

This report describes an integrated approach for designing communication, sensing, and control systems for mobile distributed systems. Graph theoretic methods are used to analyze the input/output reachability and structural controllability and observability of a decentralized system. Embedded in each network node, this analysis will automatically reconfigure an ad hoc communication network for the sensing and control task at hand. The graph analysis can also be used to create the optimal communication flow control based upon the spatial distribution of the network nodes. Edge coloring algorithms tell us that the minimum number of time slots in a planar network is equal to either the maximum number of adjacent nodes (or degree) of the undirected graph plus some small number. Therefore, the more spread out that the nodes are, the fewer number of time slots are needed for communication, and the smaller the latency between nodes. In a coupled system, this results in a more responsive sensor network and control system. Network protocols are developed to propagate this information, and distributed algorithms are developed to automatically adjust the number of time slots available for communication. These protocols and algorithms must be extremely efficient and only updated as network nodes move. In addition, queuing theory is used to analyze the delay characteristics of Carrier Sense Multiple Access (CSMA) networks. This report documents the analysis, simulation, and implementation of these algorithms performed under this Laboratory Directed Research and Development (LDRD) effort.

As part of DARPA's Software for Distributed Robotics Program within the Information Processing Technologies Office (IPTO), Sandia National Laboratories was tasked with identifying military airborne and maritime missions that require cooperative behaviors as well as identifying generic collective behaviors and performance metrics for these missions. This report documents this study. A prioritized list of general military missions applicable to land, air, and sea has been identified. From the top eight missions, nine generic reusable cooperative behaviors have been defined. A common mathematical framework for cooperative controls has been developed and applied to several of the behaviors. The framework is based on optimization principles and has provably convergent properties. A three-step optimization process is used to develop the decentralized control law that minimizes the behavior's performance index. A connective stability analysis is then performed to determine constraints on the communication sample period and the local control gains. Finally, the communication sample period for four different network protocols is evaluated based on the network graph, which changes throughout the task. Using this mathematical framework, two metrics for evaluating these behaviors are defined. The first metric is the residual error in the global performance index that is used to create the behavior. The second metric is communication sample period between robots, which affects the overall time required for the behavior to reach its goal state.

The goal of this research was to develop and demonstrate cooperative 3-D plume tracing algorithms for miniature autonomous underwater vehicles. Applications for this technology include Lost Asset and Survivor Location Systems (L-SALS) and Ship-in-Port Patrol and Protection (SP3). This research was a joint effort that included Nekton Research, LLC, Sandia National Laboratories, and Texas A&M University. Nekton Research developed the miniature autonomous underwater vehicles while Sandia and Texas A&M developed the 3-D plume tracing algorithms. This report describes the plume tracing algorithm and presents test results from successful underwater testing with pseudo-plume sources.

This report describes the development of a miniature mobile microrobot device and several microsystems needed to create a miniature microsensor delivery platform. This work was funded under LDRD No.10785, entitled, ''Integrated Microsensors for Autonomous Microrobots''. The approach adopted in this project was to develop a mobile platform, to which would be attached wireless RF remote control and data acquisition in addition to various microsensors. A modular approach was used to produce a versatile microrobot platform and reduce power consumption and physical size.