Power systems have evolved significantly during the last two decades with the advent of Distributed Energy Resources (DERs) like solar PV. Traditionally, large power plants were considered as the sole source of energy in the power systems. However, DERs connected to the transmission and the distribution systems are creating a paradigm shift from a centralized generation to a distributed one. Though the variable power output from these DERs poses challenges to the reliable operation of the grid, it also presents opportunities to design control and coordination approaches to improve system efficiency and operational reliability. Moreover, building new transmission lines to meet ever-increasing load demand is not always viable. Thus, the industry is leaning towards developing non-wires alternatives. Considering the existing limitations of the transmission system, line congestions, and logistic/economic constraints associated with its capacity expansion, leveraging DERs to supply distribution system loads is attractive and thus capturing the attention of researchers and the electric power industry. The primary objective of this dissertation is to develop a framework that enables DERs to supply local area load by co-simulating the power system and transactive system representations of the network. To realize this objective, a novel distributed optimization and game theory-based network representation is developed that optimally computes the power output of the Home Microgrids/DER aggregators. Moreover, the optimum operational schedules of the DERs within these Home Microgrids/DER aggregators are also computed. The novel electrical-transactive co-simulation ensures that the solution is optimum in the context of power systems i.e. power flow constraints are not violated while the payoffs are maximized for the Home Microgrids/DER aggregators. The transactive mechanism involves two-way iterative signaling. The signaling is modeled as an infinite strategy, multiplayer, non-cooperative game, and a novel theory is developed for the game model. The dissertation also introduces a novel concept of ranking the Home Microgrids/DER aggregators according to their historic performance, thus leading to fairness, higher participation, and transparency. Significant advantages offered by the framework include consumption of local generation, transmission upgrade deferral, mitigation of line congestions in peak periods, and reduced transmission systems losses. / Doctor of Philosophy / In past, electricity was primarily produced by the large fossil fuel-based and nuclear power plants, usually located farther away from the populated areas where the bulk of the electricity consumption occurs. The electricity from the power plants is carried by the transmission lines to the populated areas where it is distributed to end-users via a distribution network. However, during the last two decades, issues like global warming and depleting fossil fuels have led to the development and increased adoption of renewable energy resources like solar photovoltaics (PV), wind turbines, etc. These resources are commonly known as Distributed Energy Resources (DERs), and they are connected to both the transmission and the distribution systems. Initially, they were mainly used to supply the load within the facility in which they are installed. However, the electric load (demand) continues to grow while adding new fossil fuel-based plants and transmission lines are becoming logistically/economically challenging. Thus, researchers are working on developing techniques that can enable DERs to supply the loads in the distribution system to which they are connected. This dissertation develops a method to use DERs for load support in the distribution systems. Specifically, the buildings that house the DERs can use the energy generated by the DERs to supply the local load (building load), and once the total generation exceeds the load demand, the building can inject the power into the distribution system to support the local area load. The proposed framework considers the electric network constraints like limits of lines supplying the power and limits of the transformers. The proposed work also develops a new method to maximize the benefit (in terms of profit) for the DER owners. A ranking system is introduced for the DER owners that enhances the transparency and fairness of the process. The key benefits offered by the proposed work include reduced losses in the transmission system, more energy consumed closer to the point of generation, and avoidance of transmission line and large central generation additions.

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The emergence of blockchain technology and increasing penetration of distributed energy resources (DERs) have created a new opportunity for peer-to-peer (P2P) energy trading. However, challenges arise in such transactive markets to ensure individual rationality, incentive compatibility, budget balance, and economic efficiency during the trading process. This thesis creates an hour-ahead P2P energy trading network based on the Hyperledger Fabric blockchain and explores a comparative analysis of different auction mechanisms that form the basis of smart contracts. Considered auction mechanisms are discriminatory and uniform k-Double Auction with different k values. This thesis also investigates effects of four consumer and prosumer bidding strategies: random, preference factor, price-only game-theoretic approach, and supply-demand game-theoretic approach. A custom simulation framework that models the behavior of the transactive market is developed. Case studies of a 100-home microgrid at various photovoltaic (PV) penetration levels are presented using typical residential load and PV generation profiles in the metropolitan Washington, D.C. area. Results indicate that regardless of PV penetration levels and employed bidding strategies, discriminatory k-DA can outperform uniform k-DA. Despite so, discriminatory k-DA is more sensitive to market conditions than uniform k-DA. Additionally, results show that the price-only game-theoretic bidding strategy leads to near-ideal economic efficiencies regardless of auction mechanisms and PV penetration levels. / MS

The future electricity grid is likely to be increasingly complex and uncertain due to the introduction of new technologies in the grid, the increased use of control and communication infrastructure, and the uncertain political climate. In recent years, the transactive energy market framework has emerged as the key framework for future electricity market design in the electricity grid. However, most of the work done in this area has focused on developing retail level transactive energy markets. There seems to be an underlying assumption that wholesale electricity markets are ready to support any retail market design. In this dissertation, we focus on designing wholesale electricity markets that can better support transactive retail market. On the highest level, this dissertation contributes towards developing tools and models for future electricity market designs. A particular focus is placed on the relationship between wholesale markets and investment planning. Part I of this dissertation uses relatively simple models and case studies to evaluate key impediments to flexible transmission operation. In doing so, we identify several potential areas of concern in wholesale market designs: 1. There is a lack of consideration of demand flexibility both in the long-run and in the short-run 2. There is a disconnect between operational practices and investment planning 3. There is a need to rethink forward markets to better manage resource adequacy under long-term uncertainties 4. There is a need for more robust modeling tools for wholesale market design In Part II and Part III of this dissertation, we make use of mathematical decomposition and agent-based simulations to tackle these concerns. Part II of this dissertation uses Benders Decomposition and Lagrangian Decomposition to spatially and temporally decompose a power system and operation problem with active participation of flexible loads. In doing so, we are able to not only improve the computational efficiency of the problem, but also gain various insights on market structure and pricing. In particular, the decomposition suggests the need for a coordinated investment market and forward energy market to bridge the disconnect between operational practices and investment planning. Part III of this dissertation combines agent-based modeling with state-machine based modeling to test various spot, forward, and investment market designs, including the coordinated investment market and forward energy market proposed in Part II of this dissertation. In addition, we test a forward energy market design where 75% of load is required to be purchased in a 2-year-ahead forward market and various transmission cost recovery strategies. We demonstrate how the different market designs result in different investment decisions, winners, and losers. The market insights lead to further policy recommendations and open questions. Overall, this dissertation takes initial steps towards demonstrating how mathematical decomposition and agent-based simulations can be used as part of a larger market design toolbox to gain insights into different market designs and rules for the future electricity grid. In addition, this dissertation identifies market design ideas for further studies, particularly in the design of forward markets and investment cost recovery mechanisms.

abstract: This work introduces self-organizing techniques to reduce the complexity and burden of coordinating distributed energy resources (DERs) and microgrids that are rapidly increasing in scale globally. Technical and financial evaluations completed for power customers and for utilities identify how disruptions are occurring in conventional energy business models. Analyses completed for Chicago, Seattle, and Phoenix demonstrate site-specific and generalizable findings. Results indicate that net metering had a significant effect on the optimal amount of solar photovoltaics (PV) for households to install and how utilities could recover lost revenue through increasing energy rates or monthly fees. System-wide ramp rate requirements also increased as solar PV penetration increased. These issues are resolved using a generalizable, scalable transactive energy framework for microgrids to enable coordination and automation of DERs and microgrids to ensure cost effective use of energy for all stakeholders. This technique is demonstrated on a 3-node and 9-node network of microgrid nodes with various amounts of load, solar, and storage. Results found that enabling trading could achieve cost savings for all individual nodes and for the network up to 5.4%. Trading behaviors are expressed using an exponential valuation curve that quantifies the reputation of trading partners using historical interactions between nodes for compatibility, familiarity, and acceptance of trades. The same 9-node network configuration is used with varying levels of connectivity, resulting in up to 71% cost savings for individual nodes and up to 13% cost savings for the network as a whole. The effect of a trading fee is also explored to understand how electricity utilities may gain revenue from electricity traded directly between customers. If a utility imposed a trading fee to recoup lost revenue then trading is financially infeasible for agents, but could be feasible if only trying to recoup cost of distribution charges. These scientific findings conclude with a brief discussion of physical deployment opportunities. / Dissertation/Thesis / Doctoral Dissertation Systems Engineering 2019

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.

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.

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.