AGENT BASED, DISTRIBUTED CONTROL STRATEGIES AND OPTIMIZATION OF PLUG-IN ELECTRIC VEHICLES ON SMART/MICROGRID ARCHITECTURES

This thesis will present strategies for the use of plug-in electric vehicles on smart and microgrids. MATLAB is used as the design tool for all models and simulations. First, a scenario will be explored using the dispatchable loads of electric vehicles to stabilize a microgrid with a high penetration of renewable power generation. Grid components for a microgrid with 50% photovoltaic solar production will be sized through an optimization routine to maintain storage system, load, and vehicle states over a 24-hour period. The findings of this portion are that the dispatchable loads can be used to guard against unpredictable losses in renewable generation output. Second, the use of distributed control strategies for the charging of electric vehicles utilizing an agent-based approach on a smart grid will be studied. The vehicles are regarded as additional loads to a primary forecasted load and use information transfer with the grid to make their charging decisions. Three lightweight control strategies and their effects on the power grid will be presented. The findings are that the charging behavior and peak loads on the grid can be reduced through the use of distributed control strategies.

Using a Prediction and Option Generation Paradigm to Understand Decision Making

In many complex and dynamic domains, the ability to generate and then select the appropriate course of action is based on the decision maker's "reading" of the situation--in other words, their ability to assess the situation and predict how it will evolve over the next few seconds. Current theories regarding option generation during the situation assessment and response phases of decision making offer contrasting views on the cognitive mechanisms that support superior performance. The Recognition-Primed Decision-making model (RPD; Klein, 1989) and Take-The-First heuristic (TTF; Johnson & Raab, 2003) suggest that superior decisions are made by generating few options, and then selecting the first option as the final one. Long-Term Working Memory theory (LTWM; Ericsson & Kintsch, 1995), on the other hand, posits that skilled decision makers construct rich, detailed situation models, and that as a result, skilled performers should have the ability to generate more of the available task-relevant options. The main goal of this dissertation was to use these theories about option generation as a way to further the understanding of how police officers anticipate a perpetrator's actions, and make decisions about how to respond, during dynamic law enforcement situations. An additional goal was to gather information that can be used, in the future, to design training based on the anticipation skills, decision strategies, and processes of experienced officers. Two studies were conducted to achieve these goals. Study 1 identified video-based law enforcement scenarios that could be used to discriminate between experienced and less-experienced police officers, in terms of their ability to anticipate the outcome. The discriminating scenarios were used as the stimuli in Study 2; 23 experienced and 26 less-experienced police officers observed temporally-occluded versions of the scenarios, and then completed assessment and response option-generation tasks. The results provided mixed support for the nature of option generation in these situations. Consistent with RPD and TTF, participants typically selected the first-generated option as their final one, and did so during both the assessment and response phases of decision making. Consistent with LTWM theory, participants--regardless of experience level--generated more task-relevant assessment options than task-irrelevant options. However, an expected interaction between experience level and option-relevance was not observed. Collectively, the two studies provide a deeper understanding of how police officers make decisions in dynamic situations. The methods developed and employed in the studies can be used to investigate anticipation and decision making in other critical domains (e.g., nursing, military). The results are discussed in relation to how they can inform future studies of option-generation performance, and how they could be applied to develop training for law enforcement officers.

DEVELOPMENT AND VALIDATION OF THE ANTERIOR CRUCIATE LIGAMENT INJURY-RISK-ESTIMATION QUIZ (ACL-IQ)

Over 2 million Anterior Cruciate Ligament (ACL) injuries occur annually worldwide resulting in considerable economic and health burdens (e.g., suffering, surgery, loss of function, risk for re-injury, and osteoarthritis). Current screening methods are effective but they generally rely on expensive and time-consuming biomechanical movement analysis, and thus are impractical solutions. In this dissertation, I report on a series of studies that begins to investigate one potentially efficient alternative to biomechanical screening, namely skilled observational risk assessment (e.g., having experts estimate risk based on observations of athletes movements). Specifically, in Study 1 I discovered that ACL injury risk can be accurately and reliably estimated with nearly instantaneous visual inspection when observed by skilled and knowledgeable professionals. Modern psychometric optimization techniques were then used to develop a robust and efficient 5-item test of ACL injury risk prediction skill—i.e., the ACL Injury-Risk-Estimation Quiz or ACL-IQ. Study 2 cross-validated the results from Study 1 in a larger representative sample of both skilled (Exercise Science/Sports Medicine) and un-skilled (General Population) groups. In accord with research on human expertise, quantitative structural and process modeling of risk estimation indicated that superior performance was largely mediated by specific strategies and skills (e.g., ignoring irrelevant information), independent of domain general cognitive abilities (e.g., metal rotation, general decision skill). These cognitive models suggest that ACL-IQ is a trainable skill, providing a foundation for future research and applications in training, decision support, and ultimately clinical screening investigations. Overall, I present the first evidence that observational ACL injury risk prediction is possible including a robust technology for fast, accurate and reliable measurement—i.e., the ACL-IQ. Discussion focuses on applications and outreach including a web platform that was developed to house the test, provide a repository for further data collection, and increase public and professional awareness and outreach (www.ACL-IQ.org). Future directions and general applications of the skilled movement analysis approach are also discussed.