Distributed teams continue to emerge in response to the complex organizational environments brought about by globalization, technological advancements, and the shift toward a knowledge-based economy. These teams are comprised of members who hold the disparate knowledge necessary to take on cognitively demanding tasks. However, knowledge coordination between team members who are not co-located is a significant challenge, often resulting in process loss and decrements to the effectiveness of team level knowledge structures. The current effort explores the configuration dimension of distributed teams, and specifically how subgroup formation based on geographic location, may impact the effectiveness of a team's transactive memory system and subsequent team process. In addition, the role of task cohesion as a buffer to negative intergroup interaction is explored.

The changing nature of the energy grid in recent years has prompted key stakeholders to think of ways to address incoming challenges. Transactive energy is an approach that promises to dynamically align active grid elements coming up in the previously inactive consumers' side to achieve a reliable and smarter grid. This work models the distribution grid structure as a combination of microgrids. A blockchain-in-the loop simulation framework is modelled and simulated for a residential microgrid using power system simulators and transactive agents. Blockchain smart contracts are used to coordinate peer-to-peer energy transactions in the microgrid. The model is used to test three market coordination schemes: a simple auction-less scheme, an auction-less scheme with a normalized sorting metric and an hour ahead single auction scheme with penalties for unfulfilled bids. Case studies are presented of a microgrid with 30 homes, at different levels of solar and energy storage penetration within the microgrid, all equipped with responsive and unresponsive appliances and transactive agents for the HVAC systems. The auction-less scheme with a normalized sorting metric is observed to provide a fairer advantage to smaller solar installations in comparison to the simple auction-less method. It is then concluded that the auction-less schemes are most beneficial to users, as they would not need sophisticated forecasting technology to reduce penalties from bid quantity inaccuracies, as long as the energy mix within the microgrid is diverse enough. / Master of Science / The legacy energy industry involved the bulk transfer of energy from huge generation plants through long transmission lines to the end consumers. However, with the onset of improved renewable energy and information technologies, energy is now being generated closer to the consumer side with appliances capable of actively participating in the energy system now widely available. Transactive energy with blockchain has been proposed in order to dynamically coordinate these systems to work towards a more reliable and smarter grid using economic value in a transparent and secure way. This work models a transactive power grid as a combination of microgrids using a blockchain network to coordinate hourly peer-to-peer energy transactions. The blockchain-in-the-loop simulation model is used to compare three different market mechanisms in a residential microgrid of 30 homes with varying levels of solar panels, batteries and transactive thermostats installed. Two auction-less schemes - one with a normalized sorting metric - and an hour ahead single auction mechanism are analyzed. While the auction-less scheme with the normalized metric is seen to be fairer than the simple auction-less scheme, it is concluded that the auction-less schemes are most beneficial to residents. This is because sophisticated forecasting technology would not be needed like in the hour ahead auction scheme, provided that the microgrid has participants with diverse energy consumption and production profiles throughout the day.

The rapid growth of internet-connected smart devices capable of exchanging energy price information and adaptively controlling the consumption of connected loads, has paved the way for transactive control to make inroads in the modern grid. Transactive control frameworks integrate the wholesale and retail energy markets, and enable active participation of end users, thereby playing a key role in managing the rising number of distributed assets.However, the use of internet for the communication of data among the building, distribution,and transmission levels makes the system susceptible to external intrusions. A skilled adversary can potentially manipulate the exchanged data with the intention to inflict damage to the system or increase financial gains. In this thesis, the effect of such data integrity attacks on information exchanged between the distribution systems operator and end-users is investigated. Impact on grid operations is evaluated using different categories like operational, financial, user comfort and reliability parameters. It is shown that attack impact depends on a number of factors like attack duration, time of attack, penetration rate etc besides the attack magnitude. The effect of an attack continues to persist for some time after its removal and hence effective detection and mitigation strategies will be required to ensure system resilience and robustness. / Master of Science / Transactive energy is a framework where price-responsive loads adjust their energy consumption at a certain time according to the real-time energy price sent by the utility. Field demonstrations in recent years have shown that transactive control can effectively manage grid objectives and also monetarily benefit both the electric utility and end-users. Therefore, transactive energy is expected to make inroads into conventional grid operations in the next few years. As successful operation of such a market depends on the information exchanged among different stakeholders, a malicious adversary may try to inject false data and affect system operations. This thesis investigates how manipulating data in the transactive energy platform affects system operations and financial gains of different stakeholders. Understanding system behavior under attack conditions will help in formulating effective detection and mitigation strategies and enhancing system resilience.

The increasing penetration of electric vehicles (EVs) and other distributed energy resources (DERs) offers enhanced flexibility and resilience. During extreme conditions, grid-connected EVs and DERs can provide electricity service and restore critical loads when the utility system is unavailable. On the other hand, during normal operation, these proactive devices can provide ancillary services to alleviate voltage fluctuations and support frequency regulation. In comparison with other DERs, EVs are more flexible in providing ancillary services due to their mobile nature. However, the proliferation of EVs and DERs also introduces operational challenges to the distribution grid. For instance, EVs primarily fulfill their transportation needs. Uncoordinated charging of a large number of EVs can increase the burden on the distribution system. Due to the limited charging rate and battery size, it is generally impractical for a single EV to directly participate in the ancillary service market. A conventional distribution system is designed for unidirectional flow of electric energy. With the growing installation of DERs on the distribution system, the flow of electric energy is bi-directional and, therefore, there is a higher risk of protection miscoordination due to the fault currents resulting from DERs. With limited communication capability, these undetected protective device (PD) actuations can cause uncertainties and delay the service restoration process. This dissertation makes contributions to the coordination of EVs and DERs. It introduces four innovative models for EV coordination: 1) A transactive energy (TE) trading mechanism is proposed to coordinate EVs and aggregators. 2) Optimal tools are provided to assist EVs and aggregators in optimal decision making while participating in TE. 3) A charging station model is developed to allow EVs to provide ancillary service aligned with their mobile nature. 4) A utility function model is presented to capture the EV owners' behaviors for providing ancillary services and charging vehicles. Charging stations can estimate the electric energy demand and optimize ancillary service provision to meet their goals. Simulation cases validated that the proposed optimization tools can align EV owners' preferences in providing ancillary service to enhance distribution system operation flexibility. To enhance the resilience of distribution systems, two novel optimization strategies are presented: 1) An advanced outage management (AOM) is proposed to utilize smart meters and fault indicators (FIs) to identify the most credible outage scenario and fault locations. 2) An advanced feeder restoration (AFR) is developed to provide an optimal restoration strategy to enhance system resilience. The proposed optimization models have been validated with realistic simulation cases. / Doctor of Philosophy / As Electric Vehicles (EVs) and other Distributed Energy Resources (DERs) become more common, they are changing how our distribution systems work. For example, during power outages, grid-connected DERs and EVs can be deployed to sustain essential electricity services such as hospitals and communications. On the other hand, during a normal operating condition, they can help maintain the stability of our electricity systems. It is a technical challenge to integrate these new EV and DER devices into the existing power grid. For example, EVs are mainly designed for transportation. Their clustered charging patterns can significantly increase the electrical demand if they are not managed properly. Also, the limited battery capacity and charging speed make it difficult for a single vehicle to provide meaningful support to the grid operation. For the EV management side, this research is concerned with how to better integrate EVs and similar technologies into the power grid. Four key contributions of this dissertation are: 1) Developing a trading mechanism for EVs and aggregators of EVs to exchange energy and ancillary services efficiently; 2) Creating computational technologies to help these entities optimize their decisions while meeting their requirements; 3) Structuring charging station operations that cater to the preferences of EV owners while supporting grid operation; and 4) Modeling EV owners' decision-making to set optimal pricing and service strategies at charging stations. These mechanisms and strategies will allow EV owners to support the power grid while meeting their transportation needs. Moreover, the study addresses the issue of enhancement of the distribution system's capability to restore services under extreme conditions. It provides an advanced outage management method that utilizes remote monitoring and control technologies, including smart meters and fault indicators, to identify the location of electrical faults and reduce the outage areas. The advanced feeder restoration method determines an optimal strategy to restore the electricity service efficiently while keeping the distribution grid stable.

Variable generator output levels from renewable energies is an important technical obstacle to the transition from fossil fuels to renewable resources. Super grids and smart grids are among the most effective solutions to mitigate generation variability. In a super grid, electric utilities within an interconnected system can share generation and reserve units so that they can produce electricity at a lower overall cost. Smart grids, in particular demand response programs, enable flexible loads such as plug-in electric vehicles and HVAC systems to consume electricity preferntially in a grid-friendly way that assists the grid operator to maintain the power balance. These solutions, in conjunction with energy storage systems, can facilitate renewable integration. This study aims to provide an understanding of the achievable benefits from integrating demand response into wholesale and retail electricity markets, in particular in the presence of significant amounts of variable generation. Among the options for control methods for demand response, market-based approaches provide a relatively efficient use of load flexibility, without restricting consumers' autonomy or invading their privacy. In this regard, a model of demand response integration into bulk electric grids is presented to study the interaction between variable renewables and demand response in the double auction environment, on an hourly basis. The cost benefit analysis shows that there exists an upper limit of renewable integration, and that additional solutions such as super grids and/or energy storage systems are required to go beyond this threshold. The idea of operating an interconnection in an unified (centralized) manner is also explored. The traditional approach to the unit commitment problem is to determine the dispatch schedule of generation units to minimize the operation cost. However, in the presence of price-sensitive loads (market-based demand response), the maximization of economic surplus is a preferred objective to the minimization of cost. Accordingly, a surplus-maximizing hour-ahead scheduling problem is formulated, and is then tested on a system that represents a 20-area reduced model of the North America Western Interconnection for the planning year 2024. The simulation results show that the proposed scheduling method reduces the total operational costs substantially, taking advantage of renewable generation diversity. The value of demand response is more pronounced when ancillary services (e.g. real-time power balancing and voltage/frequency regulation) are also included along with basic temporal load shifting. Relating to this, a smart charging strategy for plug-in electric vehicles is developed that enables them to participate in a 5-minute retail electricity market. The cost reduction associated with implementation of this charging strategy is compared to uncontrolled charging. In addition, an optimal operation method for thermostatically controlled loads is developed that reduces energy costs and prevents grid congestion, while maintaining the room temperature in the comfort range set by the consumer. The proposed model also includes loads in the energy imbalance market. The simulation results show that market-based demand response can contribute to a significant cost saving at the sub-hourly level (e.g. HVAC optimal operation), but not at the super-hourly level. Therefore, we conclude that demand response programs and super grids are complementary approaches to overcoming renewable generation variation across a range of temporal and spatial scales. / Graduate / 0791 / sahandbehboodi@gmail.com

An approach to utilize Distributed Generation (DG) to minimize the total load shedding by analyzing the power system in Transactive energy framework is proposed. An algorithm to optimize power system in forward and spot markets to maximize an electric utility’s profit by optimizing purchase of power from DG is developed. The proposed algorithm is a multi-objective optimization with the main objective to maximize a utility’s profit by minimizing overall cost of production, load shedding, and purchase of power from distributed generators. This work also proposes a method to price power in forward and spot markets using existing LMP techniques. Transactive accounting has been performed to quantify the consumer payments in both markets. The algorithm is tested in two test systems; a 6-bus system and modified IEEE 14-bus system. The results show that by investing in DG, utility benefits from profit increase, load shedding reduction, and transmission line loading improvement.

Shared mental models (SMM) and Transactive memory systems (TMS) have been advocated as the main team learning mechanisms. Despite multiple appeals for collaboration, research in both these fields has progressed in parallel and little effort has been made to integrate these theories. The purpose of this study was to test the relationship between SMM and TMS in a field setting and examine their influence on various team effectiveness outcomes such as team performance, team learning, team creativity, team members' satisfaction and team viability. Contextual factors relevant to an organizational setting were tested and these included team size, tenure, country of origin, team reward and organizational support. Based on responses from 41 teams from 7 industries across two countries (US and India), results indicate that team size, country of origin and team tenure impact team performance and team learning. In addition, team reward and organizational support predicted team viability and satisfaction. Results indicated that TMS components (specialization, coordination and credibility) were better predictors of team outcomes than the omnibus TMS construct. In particular, TMS credibility predicted team performance and creativity while TMS coordination predicted team viability and satisfaction. SMM was measured in two different ways: an average deviation index and a 6-item scale. Both methods resulted in a conceptually similar interpretation although average deviation indices provided slightly better results in predicting effectiveness outcomes. TMS components moderated the relationship between SMM and team outcomes. Team performance was lowest when both SMM and TMS were low. However, contrary to expectations, high levels of SMM did not always result in effective team outcomes (performance, learning and creativity) especially when teams exhibited high TMS specialization and credibility. An interaction pattern was observed under conditions of low levels of SMM such that high TMS resulted in higher levels of team outcomes. The theoretical and practical implications of these results are discussed.

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Motivated by numerous drivers, blockchain-based peer-to-peer energy trade whitepapers surged in the past two years. Assuming disruption through blockchain technology, they envisioned a transformation of energy systems through technosocio- economic solutions. Few impartial and sober assessments of blockchain-based energy projects exist, and many publications praise disruptive potential without further examination. A more distant and critical perspective, however, is imperative for a responsible use of a novel, in particular disruptive, technology. This review aims at surveying the energy system envisioned by the projects through discussing the projects by their characteristics, their perspective on the transactive energy lifecycle and the energy ecosystem envisioned in the white papers. This review is descriptive and comparative in nature, and attempts to synthesize topics raised in the white papers through methods of grounded theory, as well as assessing the disruptive potential of blockchain technology in energy systems. Through this and a critical and neutral perspective, it strives to (soberly) contribute to a discussion on the digitization of elements of the energy system, and how blockchain-based use cases can contribute constructively to the problems at hand.

Abstract The Afterimage of Violence: Frames and Responsiveness in the Three Films Also Known as Jihadi, Umdrehen and When Things Occur This thesis explores the intersection of aesthetics, ethics, and politics in relation to artistic strategies that confront political violence in artistic lens-based media. It aims to expand understandings of the possibilities and limitations of depicting violent real-world events and investigates how images of violence elicit responses from viewers. This study focuses on three art films—Also Known as Jihadi (2017), Umdrehen (2022), and When Things Occur (2016)—that grapple with different instances of violence, examining their affective, embodied, and non-representational perspectives. The films are analyzed through a triangulation of Jacques Rancière's concept of the "distribution of the sensible," Judith Butler's notion of the frame, and Jill Bennett's idea of the transactive potential harbored in artworks. Drawing on Butler's question regarding the norms that govern which lives are recognized as human and worthy of protection, the thesis argues that the three art films function as afterimages of violence, which orchestrate affective responses and ethical engagement with the suffering of others. The analysis delves into the artistic strategies employed by the films to generate a more nuanced understanding of political violence, highlighting the interplay between cognitive associations, affective forces, and embodied perception. It explores the relationship between representation and responsiveness, investigating how art can disrupt established notions and judgments surrounding violent events and the lives affected by them. By analyzing the three selected films, the thesis articulates how art can generate critical engagement with political violence and cultivate empathic concern. The research aims to contribute to a broader understanding of the aesthetic and ethical implications of representing violence, emphasizing the potential of art to transform perceptions, foster new modes of seeing, and engender ethical responses.