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Distributed energy is an attractive alternative to typical centralized energy sources specifically for remote locations not accessible to the electricity grid. With the continued advancement into new renewable technologies like solar, wind, fuel cell etc., off-grid standalone systems are becoming more attractive and even compeating on a cost basis for rural locations. Along with the environmental and sustainable movement, these technologies are only going to get more and more popular as time goes on. Power electronic converters are also advancing which will help the shift in electricity options. Creating innovative power electronic systems will be important when moving toward smaller, more e cient and higher power density solutions. As such, this thesis will aim to design and create an integrated power electronic system for an o -grid standalone solar application designed for remote rural locations with no access to electricity, or in locations which could bene t from such a system. It is designed for a DC input source from 24V-40V, such as a solar panel, and can operate four di erent loads; one 12V-24V 100 W DC load, charge a 48V battery, run three 5V cell phone charger outputs and run one 230V, 50Hz, 1 kW AC load. A boost converter, buck converter, phase shifted full bridge isolated DC-DC converter and a single phase inverter are implimented in the integrated system to achieve these outputs. A comparison of similar products on the market are presented and compared with the proposed design by showing the product speci cations, advantages and disadvantages of each. A discussion of each converter in the system is presented and will include operation, design and component selection. An in-depth design process for the inductor within the boost converter is presented and will cover core, winding design and an optimization algorithm using the Genetic Algorithm (GA) is used to compare di erent ferrite based C-C shaped inductors. More speci cally, the core material selected is Ferroxcube 3C97 and the inductor comparions are between di erent Litz bundled windings from New England Wire Tecnologies and a customized rectangular winding. The GA optimizes around the lowest volume by comparing the di erent inductor designs using the di erent Litz winding constructions and the custom rectangular winding constrictuion. The rectangular winding achieves the lowest volume and will be compared with a three phase interleaved boost design implimenting a CoilCraft inductor. The buck converter is the simplest converter and is designed using the traditional methods in literature. An in-depth design process for the phase shifted full bridge converter is also done wherein the zero voltage switching (ZVS) is achieved. The DC-AC inverter is the last converter designed within the integrated system and covers input capacitor sizing, and output lter design. There are speci c distributed energy standards that must be followed when connecting loads to the system and so the purpose of the lter is to lter out the voltage harmonics. The control techniques for each converter is also discussed and shown to operate in both simulation and in experimentally. The losses within the system are discussed and the required equations are de ned / Thesis / Master of Applied Science (MASc)

With the ever-growing concerns of environmental and climate concerns for energy consumption in our society, it is crucial to develop novel solutions that improve the efficient utilization of distributed energy resources for energy efficiency and demand response (DR). As such, there is a need to develop targeted energy programs, which not only meet the requirement of energy goals for a community but also take the energy use patterns of individual households into account. To this end, a sound understanding of the energy behavior of customers at the neighborhood level is needed, which requires operational analytics on the wealth of energy data from customers and devices. In this dissertation, we focus on data-driven solutions for customer energy behavior characterization with applications to distributed energy management and flexibility provision. To do so, the following problems were studied: (1) how different customers can be segmented for DR events based on their energy-saving potential and balancing peak and off-peak demand, (2) what are the opportunities for extracting Time-of-Use of specific loads for automated DR applications from the whole-house energy data without in-situ training, and (3) how flexibility in customer demand adoption of renewable and distributed resources (e.g., solar panels, battery, and smart loads) can improve the demand-supply problem. In the first study, a segmentation methodology form historical energy data of households is proposed to estimate the energy-saving potential for DR programs at a community level. The proposed approach characterizes certain attributes in time-series data such as frequency, consistency, and peak time usage. The empirical evaluation of real energy data of 400 households shows the successful ranking of different subsets of consumers according to their peak energy reduction potential for the DR event. Specifically, it was shown that the proposed approach could successfully identify the 20-30% of customers who could achieve 50-70% total possible demand reduction for DR. Furthermore, the rebound effect problem (creating undesired peak demand after a DR event) was studied, and it was shown that the proposed approach has the potential of identifying a subset of consumers (~5%-40% with specific loads like AC and electric vehicle) who contribute to balance the peak and off-peak demand. A projection on Austin, TX showed 16MWh reduction during a 2-h event can be achieved by a justified selection of 20% of residential customers. In the second study, the feasibility of inferring time-of-use (ToU) operation of flexible loads for DR applications was investigated. Unlike several efforts that required considerable model parameter selection or training, we sought to infer ToU from machine learning models without in-situ training. As the first part of this study, the ToU inference from low-resolution 15-minute data (smart meter data) was investigated. A framework was introduced which leveraged the smart meter data from a set of neighbor buildings (equipped with plug meters) with similar energy use behavior for training. Through identifying similar buildings in energy use behavior, the machine learning classification models (including neural network, SVM, and random forest) were employed for inference of appliance ToU in buildings by accounting for resident behavior reflected in their energy load shapes from smart meter data. Investigation on electric vehicle (EV) and dryer for 10 buildings over 20 days showed an average F-score of 83% and 71%. As the second part of this study, the ToU inference from high-resolution data (60Hz) was investigated. A self-configuring framework, based on the concept of spectral clustering, was introduced that automatically extracts the appliance signature from historical data in the environment to avoid the problem of model parameter selection. Using the framework, appliance signatures are matched with new events in the electricity signal to identify the ToU of major loads. The results on ~1500 events showed an F-score of >80% for major loads like AC, washing machine, and dishwasher. In the third study, the problem of demand-supply balance, in the presence of varying levels of small-scale distributed resources (solar panel, battery, and smart load) was investigated. The concept of load complementarity between consumers and prosumers for load balancing among a community of ~250 households was investigated. The impact of different scenarios such as varying levels of solar penetration, battery integration level, in addition to users' flexibility for balancing the supply and demand were quantitatively measured. It was shown that (1) even with 100% adoption of solar panels, the renewable supply cannot cover the demand of the network during afternoon times (e.g., after 3 pm), (2) integrating battery for individual households could improve the self-sufficiency by more than 15% during solar generation time, and (3) without any battery, smart loads are also capable of improving the self-sufficiency as an alternative, by providing ~60% of what commercial battery systems would offer. The contribution of this dissertation is through introducing data-driven solutions/investigations for characterizing the energy behavior of households, which could increase the flexibility of the aggregate daily energy load profiles for a community. When combined, the findings of this research can serve to the field of utility-scale energy analytics for the integration of DR and improved reshaping of network energy profiles (i.e., mitigating the peaks and valleys in daily demand profiles). / Doctor of Philosophy / Buildings account for more than 70% of electricity consumption in the U.S., in which more than 40% is associated with the residential sector. During recent years, with the advancement in Information and Communication Technologies (ICT) and the proliferation of data from consumers and devices, data-driven methods have received increasing attention for improving the energy-efficiency initiatives. With the increased adoption of renewable and distributed resources in buildings (e.g., solar panels and storage systems), an important aspect to improve the efficiency by matching the demand and supply is to add flexibility to the energy consumption patterns (e.g., trying to match the times of high energy demand from buildings and renewable generation). In this dissertation, we introduced data-driven solutions using the historical energy data of consumers with application to the flexibility provision. Specific problems include: (1) introducing a ranking score for buildings in a community to detect the candidates that can provide higher energy saving in the future events, (2) estimating the operation time of major energy-intensive appliances by analyzing the whole-house energy data using machine learning models, and (3) investigating the potential of achieving demand-supply balance in communities of buildings under the impact of different levels of solar panels, battery systems, and occupants energy consumption behavior. In the first study, a ranking score was introduced that analyzes the historical energy data from major loads such as washing machines and dishwashers in individual buildings and group the buildings based on their potential for energy saving at different times of the day. The proposed approach was investigated for real data of 400 buildings. The results for EV, washing machine, dishwasher, dryer, and AC show that the approach could successfully rank buildings by their demand reduction potential at critical times of the day. In the second study, machine learning (ML) frameworks were introduced to identify the times of the day that major energy-intensive appliances are operated. To do so, the input of the model was considered as the main circuit electricity information of the whole building either in lower-resolution data (smart meter data) or higher-resolution data (60Hz). Unlike previous studies that required considerable efforts for training the model (e.g, defining specific parameters for mathematical formulation of the appliance model), the aim was to develop data-driven approaches to learn the model either from the same building itself or from the neighbors that have appliance-level metering devices. For the lower-resolution data, the objective was that, if a few samples of buildings have already access to plug meters (i.e., appliance level data), one could estimate the operation time of major appliances through ML models by matching the energy behavior of the buildings, reflected in their smart meter information, with the ones in the neighborhood that have similar behaviors. For the higher-resolution data, an algorithm was introduced that extract the appliance signature (i.e., change in the pattern of electricity signal when an appliance is operated) to create a processed library and match the new events (i.e., times that an appliance is operated) by investigating the similarity with the ones in the processed library. The investigation on major appliances like AC, EV, dryer, and washing machine shows the >80% accuracy on standard performance metrics. In the third study, the impact of adding small-scale distributed resources to individual buildings (solar panels, battery, and users' practice in changing their energy consumption behavior) for matching the demand-supply for the communities was investigated. A community of ~250 buildings was considered to account for realistic uncertain energy behavior across households. It was shown that even when all buildings have a solar panel, during the afternoon times (after 4 pm) in which still ~30% of solar generation is possible, the community could not supply their demand. Furthermore, it was observed that including users' practice in changing their energy consumption behavior and battery could improve the utilization of solar energy around >10%-15%. The results can serve as a guideline for utilities and decision-makers to understand the impact of such different scenarios on improving the utilization of solar adoption. These series of studies in this dissertation contribute to the body of literature by introducing data-driven solutions/investigations for characterizing the energy behavior of households, which could increase the flexibility in energy consumption patterns.

As inverter-based distributed energy resources (DERs) such as photovoltaic (PV) and battery energy storage system (BESS) penetrate within the distribution system. New challenges regarding how to utilize these devices to improve power quality arises. Before, PV systems were required to disconnect from the grid during a large disturbance, but now smart inverters are required to have dynamically controlled functions that allows them to remain connected to the grid. Monitoring power flow at the point of common coupling is one of the many functions the controller should perform. Smart inverters can inject active power to pick up critical load or inject reactive power to regulate voltage within the electric grid. In this context, this thesis focuses on a high level and local control design that incorporates DERs. Different controllers are implemented to stabilize the microgrid in an Islanding and resiliency mode. The microgrid can be used as a resiliency source when the distribution is unavailable. An average model in the D-Q frame is calculated to analyze the inherent dynamics of the current controller for the point of common coupling (PCC). The space vector approach is applied to design the voltage and frequency controller. Secondly, using inverters for Volt/VAR control (VVC) can provide a faster response for voltage regulation than traditional voltage regulation devices. Another objective of this research is to demonstrate how smart inverters and capacitor banks in the system can be used to eliminate the voltage deviation. A mixed-integer quadratic problem (MIQP) is formulated to determine the amount of reactive power that should be injected or absorbed at the appropriate nodes by inverter. The Big M method is used to address the nonconvex problem. This contribution can be used by distribution operators to minimize the voltage deviation in the system. / Master of Science / Reliable power supply from the electric grid is an essential part of modern life. This critical infrastructure can be vulnerable to cascading failures or natural disasters. A solution to improve power systems resilience can be through microgrids. A microgrid is a small network of interconnected loads and distributed energy resources (DERs) such as microturbines, wind power, solar power, or traditional internal combustion engines. A microgrid can operate being connected or disconnected from the grid. This research emphases on the potentially use of a Microgrid as a resiliency source during grid restoration to pick up critical load. In this research, controllers are designed to pick up critical loads (i.e hospitals, street lights and military bases) from the distribution system in case the electric grid is unavailable. This case study includes the design of a Microgrid and it is being tested for its feasibility in an actual integration with the electric grid. Once the grid is restored the synchronization between the microgrid and electric must be conducted. Synchronization is a crucial task. An abnormal synchronization can cause a disturbance in the system, damage equipment, and overall lead to additional system outages. This thesis develops various controllers to conduct proper synchronization. Interconnecting inverter-based distributed energy resources (DERs) such as photovoltaic and battery storage within the distribution system can use the electronic devices to improve power quality. This research focuses on using these devices to improve the voltage profile within the distribution system and the frequency within the Microgrid.

Residential solar Photovoltaic (PV) installations are increasing at a very high pace in the United States. In 2017 there are approximately one million residential solar PV installations in the US. A significant share of these installations are downstream of distribution transformers and thus connected to the secondary. To precisely analyze voltage variations induced by PV systems into distribution systems, accurate models of load and PV systems connected to the secondary side of distribution transformers are required. In the work here we consider two secondary circuit modeling approaches, simple secondary and detailed secondary models. In simple secondary models all loads and all PV generators below a distribution transformer are modeled as an aggregate load and an aggregate PV generator. In the detailed secondary models all loads and PV systems below the distribution transformers are modeled individually and secondary conductors and service drops are also modeled. Using a cloud motion simulator, it is observed that employing the simple secondary models can lead to inaccurate and conservative results. Moreover, the locations with the greatest voltage changes are different in the two modeling approaches. This paper highlights the importance of utilizing detailed secondary models over simple secondary models in analyzing PV generation. / Master of Science / Power system planners and operators rely on computer-based modeling and analysis of the electric grid to ensure that electricity is delivered to consumers in a reliable manner. The current modeling is done either to simulate the high voltage transmission networks, or the primary distribution networks. Till now these modeling approaches have worked well as the electricity flow in the electric grid is largely unidirectional, i.e. power flows from the transmission network to the distribution network. Neglecting the secondary distribution network topology in such a structure of the electric grid does not introduce significant calculation errors. However, the rapid growth of residential solar PV based distributed generation over the last few years, which is expected to continue into the foreseeable future, has motivated the need to rethink this modeling and analysis paradigm. As the penetration levels of distributed generation increase, there will be bi-directional flow of electricity between the transmission and distribution networks. Accurate analysis of such a decentralized electric grid cannot be performed if secondary distribution network topology is neglected in the models. So, to precisely analyze voltage variations induced by PV systems into distribution systems, accurate models of load and PV systems connected to the secondary side of distribution transformers are required. This thesis highlights the importance of using detailed models of secondary distribution.

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.

The penetration of renewable and distributed generation sources (DGs) in power distribution systems has been increasing at an ever-faster rate. While DGs provide clean and affordable energy, their addition introduces new problems in the system operation. One of the main challenges due to the high penetration of DGs is the overvoltage issues that demand appropriate voltage control. This control is essential to maintain the power quality, energy efficiency, and voltage stability in the system. Voltage Regulators (VRs) and capacitor banks (CBs) are traditional control devices that are installed in the system to keep the desired voltage profile. However, they are not designed to operate in a way that can address the high frequency and magnitude changes occurring in systems with high penetration of DGs. Therefore, they need to be supplemented with voltage control performed by controlling the reactive power generation of the DGs. The coordination among these different control devices is essential for proper system operation. This thesis explores the design of the coordinated control of VRs, CBs, and DGs, by considering different control methods such as coordinated cooperative, predictive cooperative, and unified control of all voltage control devices. The proposed methods are implemented in a system with high penetration of DGs and tested by exploring the worst-case scenario in terms of DG sizing and placement. This scenario is determined analytically using sensitivities and verified using stochastic Monte Carlo simulation. The future generation of active power distribution systems need to be optimally controlled in order to be efficient, reliable, and resilient, while capable of effectively managing high penetration levels of DGs, and other controllable loads and devices. The important outcome of this thesis is the introduction of a practical voltage control method to achieve these goals.

The future of the electric grid requires advanced control technologies to reliably integrate high level of renewable generation and residential and small commercial distributed energy resources (DERs). Flexible loads are known as a vital component of future power systems with the potential to boost the overall system efficiency. Recent work has expanded the role of flexible and controllable energy resources, such as energy storage and dispatchable demand, to regulate power imbalances and stabilize grid frequency. This leads to the DER aggregators to develop concepts such as the virtual energy storage system (VESS). VESSs aggregate the flexible loads and energy resources and dispatch them akin to a grid-scale battery to provide flexibility to the system operator. Since the level of flexibility from aggregated DERs is uncertain and time varying, the VESSs’ dispatch can be challenging. To optimally dispatch uncertain, energy-constrained reserves, model predictive control offers a viable tool to develop an appropriate trade-off between closed-loop performance and robustness of the dispatch. To improve the system operation, flexible VESSs can be formulated probabilistically and can be realized with chance-constrained model predictive control. The large-scale deployment of flexible loads needs to carefully consider the existing regulation schemes in power systems, i.e., generator droop control. In this work first, we investigate the complex nature of system-wide frequency stability from time-delays in actuation of dispatchable loads. Then, we studied the robustness and performance trade-offs in receding horizon control with uncertain energy resources. The uncertainty studied herein is associated with estimating the capacity of and the estimated state of charge from an aggregation of DERs. The concept of uncertain flexible resources in markets leads to maximizing capacity bids or control authority which leads to dynamic capacity saturation (DCS) of flexible resources. We show there exists a sensitive trade-off between robustness of the optimized dispatch and closed-loop system performance and sacrificing some robustness in the dispatch of the uncertain energy capacity can significantly improve system performance. We proposed and formulated a risk-based chance constrained MPC (RB-CC-MPC) to co-optimize the operational risk of prematurely saturating the virtual energy storage system against deviating generators from their scheduled set-point. On a fast minutely timescale, the RB-CC-MPC coordinates energy-constrained virtual resources to minimize unscheduled participation of ramp-rate limited generators for balancing variability from renewable generation, while taking into account grid conditions. We show under the proposed method it is possible to improve the performance of the controller over conventional distributionally robust methods by more than 20%. Moreover, a hardware-in-the-loop (HIL) simulation of a cyber-physical system consisting of packetized energy management (PEM) enabled DERs, flexible VESSs and transmission grid is developed in this work. A predictive, energy-constrained dispatch of aggregated PEM-enabled DERs is formulated, implemented, and validated on the HIL cyber-physical platform. The experimental results demonstrate that the existing control schemes, such as AGC, dispatch VESSs without regard to their energy state, which leads to unexpected capacity saturation. By accounting for the energy states of VESSs, model-predictive control (MPC) can optimally dispatch conventional generators and VESSs to overcome disturbances while avoiding undesired capacity saturation. The results show the improvement in dynamics by using MPC over conventional AGC and droop for a system with energy-constrained resources.

In the context of the envisioned electric power delivery system of the future, the smart grid, this dissertation focuses on control and management strategies for integration of distributed energy resources in the power system. This work conceptualizes a hierarchical framework for the control of microgrids---the building blocks of the smart grid---and develops the notion of potential functions for the secondary control for devising intermediate set points to ensure feasibility of operation of the system. A scalar potential function is defined for each controllable unit of the microgrid such that its minimization corresponds to achieving the control goal. The set points are dynamically updated using communication within the microgrid. This strategy is generalized to (i) include both local and system-wide constraints and (ii) allow a distributed implementation. This dissertation also proposes and evaluates a simple yet elaborate distributed strategy to mitigate the transients of controllable devices of the microgrid using local measurements. This strategy is based on response monitoring and is augmented to the existing controller of a power system device. This strategy can be implemented based on either set point automatic adjustment (SPAA) or set point automatic adjustment with correction enabled (SPAACE) methods. SPAA takes advantage of an approximate model of the system to calculate intermediate set points such that the response to each one is acceptable. SPAACE treats the device as a generic system and monitors its response and modulates its set point to achieve the desired trajectory. SPAACE bases its decisions on the trend of variations of the response and accounts for inaccuracies and unmodeled dynamics. Case studies using the PSCAD/EMTDC software environment and MATLAB programming environment are presented to demonstrate the application and effectiveness of the proposed strategies in different scenarios.

In the context of the envisioned electric power delivery system of the future, the smart grid, this dissertation focuses on control and management strategies for integration of distributed energy resources in the power system. This work conceptualizes a hierarchical framework for the control of microgrids---the building blocks of the smart grid---and develops the notion of potential functions for the secondary control for devising intermediate set points to ensure feasibility of operation of the system. A scalar potential function is defined for each controllable unit of the microgrid such that its minimization corresponds to achieving the control goal. The set points are dynamically updated using communication within the microgrid. This strategy is generalized to (i) include both local and system-wide constraints and (ii) allow a distributed implementation. This dissertation also proposes and evaluates a simple yet elaborate distributed strategy to mitigate the transients of controllable devices of the microgrid using local measurements. This strategy is based on response monitoring and is augmented to the existing controller of a power system device. This strategy can be implemented based on either set point automatic adjustment (SPAA) or set point automatic adjustment with correction enabled (SPAACE) methods. SPAA takes advantage of an approximate model of the system to calculate intermediate set points such that the response to each one is acceptable. SPAACE treats the device as a generic system and monitors its response and modulates its set point to achieve the desired trajectory. SPAACE bases its decisions on the trend of variations of the response and accounts for inaccuracies and unmodeled dynamics. Case studies using the PSCAD/EMTDC software environment and MATLAB programming environment are presented to demonstrate the application and effectiveness of the proposed strategies in different scenarios.