Doctor of Philosophy, The Ohio State University, 2017, Electrical and Computer Engineering

The advent of microgrids has shifted the focus from centralized power generation to a more distributed manner, involving a mix of different distributed energy resources (DERs). Reciprocating engine driven synchronous generators (referred as gensets) are a common DER used for distributed generation. One of the key concerns with such power networks is the aspect of frequency regulation under large disturbances, especially in an islanded mode of operation, without the support of the utility grid. This works looks at possible solution methods for mitigating large frequency disturbances in an islanded microgrid. Due to steep load changes, the gensets undergo large frequency swings and can be even vulnerable to stalling. The benefits of smart loads are analyzed in this work to prevent such occurrence by temporarily reducing the transient overload on gensets. Another solution to mitigate large frequency deviation is the integration of energy storage system (ESS), but the effectiveness depends on its operation as a grid-forming or a grid-following unit. Important metrics such as frequency nadir during load changes in the islanded microgrid are computed to show the usefulness of ESS in islanded microgrids. For this purpose, analytical methods using reduced-order models are developed and found to provide accurate estimates of frequency deviations under power system disturbances. Generally, ESS units are interfaced with an inverter and when operated in grid-forming mode can offer desired dynamic frequency behavior in an islanded microgrid. Similarly, other inverter-based DERs can also provide good frequency regulation as they share the larger portion of the transient overload compared to gensets. However, under certain scenarios the inverter-based DERs are found to collapse due to this large transient loading and can bring down the whole microgrid system as a result. A better coordination between the different DERs in a mixed source microgrid is facilitated in this work to gua (open full item for complete abstract)

Committee: Mahesh Illindala (Advisor); Jin Wang (Committee Member); Jiankang Wang (Committee Member); Alexander Lindsey (Committee Member) Subjects: Electrical Engineering

Master of Science, University of Toledo, 2010, Electrical Engineering

Demand-Side Management (DSM) can be defined as the implementation of policies and measures to control, regulate, and reduce energy consumption. This document introduces home energy management through dynamic distributed resource management and optimized operation of household appliances in a DSM based simulation platform. The principal purpose of the simulation platform is to illustrate customer-driven DSM operation, and evaluate an estimate for home electricity consumption while minimizing the customer's cost. A heuristic optimization algorithm i.e. Binary Particle Swarm Optimization (BPSO) is used for the optimization of DSM operation in the platform. The platform also simulates the operation of household appliances as a Hybrid Renewable Energy System (HRES). The resource management technique is implemented using an optimization algorithm, i.e. Particle Swarm Optimization (PSO), which determines the distribution of energy obtained from various sources depending on the load. The validity of the platform is illustrated through an example case study for various household scenarios.

Committee: Dr. Lingfeng Wang PhD (Advisor); Dr. Vijay Devabhaktuni PhD (Advisor); Dr. Gursel Serpen PhD (Committee Member) Subjects: Computer Science; Electrical Engineering; Energy; Technology

Doctor of Philosophy, University of Toledo, 2019, Electrical Engineering

The increasing complexity in the power grid, which is driven by distributed energy resources (DER) such as distributed generation, storage systems, and controllable loads, demands advanced control strategies for effective energy management. This dissertation demonstrates the development and implementation of two control strategies for managing DER. The first control objective is mitigating variability generated by PV output power using a battery energy storage system (BESS). The proposed method uses a novel adaptive moving average and adaptive state of charge (SoC) correction control method to achieve a better tradeoff between battery utilization and degree of PV power smoothness. The second control objective is achieving demand response, which refers to the mechanism that can shift the consumption of a load, such as the energy system in a building, to balance demand and supply of electricity, without harming the thermal comfort of the building's occupants and the free-will of the consumer. This is accomplished with PNNL's Intelligent Load Control (ILC), which manages the power consumption of loads based on priority criteria. ILC's unidirectional and bidirectional capability and their respective applications are demonstrated with the testbed. Additionally, this dissertation also explores the real-life implementation of these control strategies at the Scott Park campus (SPC) of the University of Toledo (UT). The SPC consists of eight buildings, a 1 MW solar array, and a 130-kWh BESS. For the dissertation work, a communication and control infrastructure has been created on the SPC electrical network by integrating an Internet of Things (IoT) system using Eclipse VOLTTRON. This infrastructure establishes communication and control between various devices on the SPC, which is used to validate and implement the proposed control strategies.

Committee: Raghav Khanna (Advisor); Mansoor Alam (Committee Member); Daniel Georgiev (Committee Member); Richard Molyet (Committee Member); Michael Heben (Committee Member) Subjects: Electrical Engineering; Energy

Master of Science, The Ohio State University, 2018, Electrical and Computer Engineering

This thesis addresses the coordinated operation of networked microgrids (MGs), distributed energy resources (DERs), and Volt-VAR control devices in the implementation of Volt-VAR optimization (VVO). Although our formulation is focused on implementing conservation voltage reduction (CVR), it can be extended for other VVO objectives e.g. losses minimization, peak demand shaving, or energy consumption reduction. We assume that the distribution network operator (DNO) has to make decisions anticipating the decisions of the MG operators. The hierarchy shown in this problem is related to a Stackelberg game. Hence, we formulate this problem as a bi-level optimization problem where the upper-level problem corresponds to the DNO and the lower-level problems correspond to each MG. The DNO as well as each MG are assumed to be independent entities with their individual objective functions. The objective of the DNO depends on the objective of the VVO objective. In particular, in the case of CVR, the objective is to minimize the load demand and losses of the distribution system. Conversely, we assume that the objective of the MG operators is to minimize the operation costs of dispatching DGs within the MG and buying/selling electricity from/to DNO i.e. an economic dispatch. The integration of DERs at the distribution level changes the response of the grid to a VVO strategy. We consider that DERs are located at the distribution network and within each microgrid. We study the four voltage-power control modes of DERs stated in the IEEE Std. 1547-2018, namely constant power factor, voltage-reactive power, active--reactive power, and constant reactive power. Finally, we validate our formulation in a modified IEEE 33-node test system. The effectiveness of CVR with the different voltage-power control modes of DERs is analyzed. The findings of this work are significant for the implementation of VVO in active distribution systems with networked microgrids.

Committee: Mahesh Illindala Ph.D. (Advisor); Jiankang Wang Ph.D. (Committee Member) Subjects: Electrical Engineering

Doctor of Philosophy, The Ohio State University, 2018, Electrical and Computer Engineering

The aggregation of distributed energy resources (DERs) near the loads to form microgrids was proposed for solving numerous problems faced by the traditional electric power distribution system. To address the growth in microgrid deployment, it is essential to guarantee that these systems are highly robust under all operating conditions, or in other words, they remain stable despite uncertainties. This ability to withstand disturbances and adapt to changing operation scenarios has been identified as a key aspect of the future electric grids. In this dissertation, different scenarios that cause collapse or may lead to instability in a mixed source microgrid are analyzed. Robust control theory is applied to determine the range of stable operation for uncertain parameters. Then, robustness metrics inspired in ecological systems are explored for proposing guidelines to the most robust configurations for a microgrid. Finally, shifting to a non-linear analysis, a controller to aid with coordination of controls is proposed to prevent a cascading collapse when a mixed source microgrid in exposed to a large load change.

Committee: Mahesh Illindala Dr. (Advisor); Rama Yedavalli Dr. (Other) Subjects: Electrical Engineering

Doctor of Philosophy, Case Western Reserve University, 2018, EECS - System and Control Engineering

The future distribution system is envisioned to be a network of distributed energy resources (DER), being able to operate in both the grid connected and islanded modes. In essence, the future electrical distribution systems will operate as medium-voltage (MV) microgrids. This dissertation presents a study of the optimal power flow (OPF) based on generalized benders decomposition (GBD) for optimally scheduling DERs and managing voltage regulation device operations to enable the economic and secure operations of the future MV distribution systems. Key model considerations include multi-phase unbalanced distribution system network, conservation voltage reduction (CVR), and multi-interval energy scheduling. Further, the optimal operating decisions are studied when the MV microgrid is in different operational modes, such as the 1) grid-connected mode, 2) islanded mode, and 3) grid-connected to islanded mode transition. Excellent algorithm performance has been achieved on the IEEE test feeder models. The use of an external engine for solving the unbalanced power flow and obtaining the sensitivities for the decomposed sub-problems allows the OPF to handle scaled-up models with the increased number of decision variables, constraints, and network buses. To support solution of large-scale problems, parallel computational strategies are recommended in order to achieve solution performance required by operations. Among the output of the GBD-based OPF, the primal solution provides optimal operation set point decisions while the dual solution provides system marginal-cost based energy prices such as the locational marginal prices (LMP) for single-phase nodes. These important OPF outcomes can facilitate the economic electricity market design in the distribution system involving both DERs and end-use demands. In this dissertation, a new method based on the GBD-based OPF has also been proposed using the unbalanced power system model linearized around the near-optimal operational (open full item for complete abstract)

Committee: Mingguo Hong PhD (Advisor); Kenneth Loparo PhD (Committee Member); Vira Chankong PhD (Committee Member); Evren Gurkan-Cavusoglu PhD (Committee Member) Subjects: Electrical Engineering; Energy

Doctor of Philosophy, The Ohio State University, 2016, Electrical and Computer Engineering

Large-scale power outages are rare but extreme accidents. They are usually caused by severe weather events and overloading caused cascading failures. Nowadays, with climate change and ever-increasing load demand, power blackouts are happening more frequently. In order to ensure reliable power delivery to customers, resilient distribution systems are envisaged, because of their characteristics of high reliability, power quality, advanced protection, and optimal restoration. During extreme events, they can provide uninterruptible power supply to critical loads, quickly detect and accurately isolate fault areas, and reestablish with an optimal restoration plan. This dissertation first proposes to develop community microgrids within distribution systems by integrating local distributed energy resources (DERs) and neighboring load centers, especially critical loads. Community microgrids can be useful means of providing resilient electricity service by enabling sustainable operations and supporting critical loads in the event of power disruptions. When an extreme event happens, the distribution system can be seamlessly partitioned into several energy-independent community microgrids. Then, the important customers are supplied with uninterrupted power by local DERs. After fault isolation, distribution systems are restored by reconnecting these community microgrids. The DER selection for community microgrids is mainly determined by the levelized cost of energy (LCOE) based quantitative assessment in conjunction with the quality functional deployment (QFD) tool. Subsequently, the capacity planning of dispatchable generation units, like natural gas gensets and battery energy storage system (BESS), is elaborated. The goal of this sizing scheme is to keep adequate reserve margin to ride through unforeseen events, like uncertainties from loads and renewables, loss of generation, etc. This is because when community microgrids work in the islanded mode, the critical loads co (open full item for complete abstract)

Committee: Mahesh Illindala Dr. (Advisor); Jin Wang Dr. (Committee Member); Jiankang Wang Dr. (Committee Member) Subjects: Electrical Engineering