Microgrid is an essential part of the nation’s smart grid deployment plan, recognized especially for improving efficiency, reliability, flexibility, and resiliency of the electricity system. Since microgrid consists of different distributed generation units, microgrid scheduling and real-time dispatch play a crucial role in maintaining economic, reliable, and resilient operation. The control and optimization performances of the existing online approaches degrade significantly in microgrid applications with missing forecast information, large state space, and multiple probabilistic events. This dissertation focuses on these challenges and proposes efficient online learning and optimization-based approaches. For addressing the missing forecast challenges on online microgrid operations, a new fitted rolling horizon control (fitted-RHC) approach is proposed in Chapter 2. The proposed fitted-RHC approach is designed with a regression algorithm that utilizes the empirical knowledge obtain from the day-ahead forecast to make microgrid real-time decisions whenever the intra-day forecast data is unavailable. Simulation results show that the proposed fitted-RHC approach can achieve the optimal policy for the deterministic case study and perform efficiently with the uncertain environment in the stochastic case study. / Includes bibliography. / Dissertation (PhD)--Florida Atlantic University, 2021. / FAU Electronic Theses and Dissertations Collection

Electricity service providers (ESP) worldwide have increased their interest in the use of electrical distribution, transmission, generation, storage, and responsive load resources as integrated systems. Referred to commonly as "smart grid," their interest is driven by widespread goals to improve the operations, management and control of large-scale power systems. In this thesis I provide research into a novel agent-based simulation (ABS) approach for exploring smart grid system (SGS) dispatch, schedule forecasting and resource coordination. I model an electrical grid and its assets as an adaptive ABS, assigning an agent construct to every SGS resource including demand response, energy storage, and distributed generation assets. Importantly, real time is represented as an environment variable within the simulation, such that each resource is characterized temporally by multiple agents that reside in different times. The simulation contains at least as many agents per resource as there are time intervals being investigated. These agents may communicate with each other during the simulation, but only agents assigned to represent the same unique resource may exchange information between time periods. Thus, confined within each time interval, each resource agent may also interact with other resource agents. As with any agent-based model, the agents may also interact with the environment, in this case, containing forecasted environment, load and price information specific to each time interval. The resulting model is a time-independent global approach capable of: (1) capturing time-variant local grid conditions and distribution grid load balancing constraints; (2) capturing time-variant resource availability and price constraints, and finally, (3) simulating efficient unit-commitment real-time dispatches and schedule forecasts considering time-variant forecasted transactive market prices. This thesis details the need for such a system, discusses the form of the ABS, and analyzes the predictive behavior of the model through a critical lens by applying the resulting proof-of-concept simulation to a set of comprehensive validation scenarios. The resulting analysis demonstrates ABS as an effective tool for real-time dispatch and SGS schedule forecasting as applied to research, short-term economic operations planning and transactive systems alike. The model is shown to converge on economic opportunities regardless of the price or load-forecast shape and to correctly perform least-cost dispatch and schedule forecasting functionality.

A dissertation submitted to the Facaulty of Engineering and the Built Environment, University of the Witwatersrand in ful lment of the requirements of the degree of Master of science in Engineering September 2016 / Standalone microgrid studies are being done because an expansion of the existing utility grids to supply power to remote communities is not feasible. Standalone microgrids can be considered as one of the solutions for remote communities because power can be generated close to these communities and it minimizes cost related to power transmission. Renewable energy sources with large uctuations are frequently the source of power for these standalone microgrids. The uctuating nature of these renewable sources can often lead to frequent blackouts. This research is aimed at minimizing power uctuations using controllable energy storage systems. This MSc focuses on the analysis of the ramp rate and delay time requirements for controllable energy storage system used in standalone PV microgrids. Measured insolation data and recorded load demand data for typical domestic appliances are used in this study to analyze ramp rates present. The ramp rates are then used to determine the range of energy storage ramp rate and delay time required to maintain the microgrid voltage within the standardized range of 1pu 5%. From the recorded data it has been observed that PV power can be sampled from at least 1-second intervals without losing important information. The 1 second averaged ramp rates obtained from the insolation data measurements have been found to have the highest value of 0.12pu/sec. However, this ramp rate increases to 0.3pu/sec when the allowable microgrid voltage band is narrow (1pu 5%). These insolation ramp rates are very low compared to the ramp rates of typical loads that can be connected to a microgrid. This means that, if the energy storage system is speci ed to meet the load ramp rate requirements, it will be able to respond to the uctuating PV power. The results obtained from the simulations con rm that energy storage system ramp rate plays an important role in the stability of a standalone microgrid. The minimum allowable energy storage ramp rate was found to be 8.15pu/sec for load transients with a ramp time of 20ms. This value is 28 times the energy storage ramp rate required to cancel out insolation uctuations. This further con rms that energy storage system ramp rates must be speci ed using the load demand data. The maximum allowable delay time was also found to be 0.53s to maintain the microgrid voltage within the standardized range of 1pu 5%. This delay time is applicable when canceling out only the insolation uctuations. To cancel out load transient power uctuations, there should be no delay time. / MT2017

A dissertation submitted to the Faculty of Engineering and the Built Environment, University of the Witwatersrand, in ful lment of the requirements for the degree of Master of Science in Engineering. Johannesburg, 2016 / This research investigates the power sharing between distributed energy resources with voltage and frequency droop control. A case study based on voltage sources in an islanded microgrid is set up in the laboratory, referred to as: The Example Microgrid. The Example Microgrid consists of two synchronous generators, active and reactive power loads. A simulation model is constructed based on the laboratory set-up, where componentwise and system-wise testing are completed. The simulation results are validated with the experimental set-up, and it is concluded that the model accurately represents the physical system under steady-state conditions. Further simulation studies on conventional droop controllers are conducted based on the Example Microgrid model. The results indicate that the use of conventional droop control is inappropriate for small, low-voltage islanded microgrids. As a possible application of this work, three variations of adapted droop controllers are simulated and their performance evaluated. It is found that with the adapted droop controllers, the power sharing error can be minimised / M T 2016

Thesis (Master of Engineering in Electrical Engineering)--Cape Peninsula University of Technology, 2018. / As the electric energy demand grows, there is a significant increase in the penetration of renewable generation (RG) in the existing electrical grid network. Interconnecting of renewable generation technologies to an existing distribution system has proven to provide various benefits such as meeting the growing load demand and its contribution to energy system decarbonisation, long-term energy security and expansion of energy access to new energy consumers in the developing urban and rural areas. However, the aim of this thesis is to conduct a study on the impacts of renewable generation on the power quality of electrical grid. Therefore, this work aims at assessing the potential effects of Distributed Generation (DG) on the operation of electric power system by modelling of harmonics and voltage distortion. With different types of renewable generation available at present, it is believed that some designs contribute significantly to electrical network’s Power Quality (PQ). After the analysis of harmonic currents (chapter 6 and 7 of this thesis) introduced by renewable generation technologies, their negative impact on the power quality of the grid is seen to be apparent at point of connection (POC) but only within controlled limits. Analytical method for modeling of harmonic interactions between the grid and aggregated distributed generation technologies are investigated using DIgSILENT Power Factory software and the results obtained are discussed.

Smart grid has been described as the Energy Internet: Where Energy Technology meets Information Technology. The incorporation of such technology into vast existing utility infrastructures offers many advantages, including possibilities for new smart appliances, energy management systems, better integration of renewable energy, value added services, and new business models, both for supply- and demand-side management. Smart grid also replaces aging utility technologies that are becoming increasingly unreliable, as the average ages for many critical components in utility systems now exceed their original design lives. However, while smart grid offers the promise of revolutionizing utility delivery systems, many questions remain about how such systems can be rolled out at the state, regional, and national levels. Many unique regulatory and market structure challenges exist, which makes it critical to pick the right technology for the right situation and to employ it in the right manner. Technology Roadmapping may be a valuable approach for helping to understand factors that could affect smart grid technology and product development, as well as key business, policy and regulatory drivers. As emerging smart grid technologies are developed and the fledgling industry matures, a critical issue will be understanding how the combination of industry drivers impact one another, what barriers exist to achieving the benefits of smart grid technologies, and how to prioritize R&D and acquisition efforts. Since the planning of power grids often relies on regional factors, it will also be important investigate linkages between smart grid deployment and regional planning goals. This can be used to develop strategies for overcoming barriers and achieving the benefits of this promising new technology. This research builds upon existing roadmapping processes by considering an integrated set of factors, including policy issues, which are specifically tuned to the needs of smart grids and have not generally been considered in other types of roadmapping efforts. It will also incorporate expert judgment quantification to prioritize factors, show the pathways for overcoming barriers and achieving benefits, and discussing the most promising strategies for achieving these goals.

The increasing demand for and prevalence of distributed energy resources (DER) such as solar power, electric vehicles, and energy storage, present a unique set of challenges for integration into a legacy power grid, and accurate models of the low-voltage distribution systems are critical for accurate simulations of DER. Accurate labeling of the phase connections for each customer in a utility model is one area of grid topology that is known to have errors and has implications for the safety, efficiency, and hosting capacity of a distribution system. This research presents a methodology for the phase identification of customers solely using the advanced metering infrastructure (AMI) voltage timeseries. This thesis proposes to use Spectral Clustering, combined with a sliding window ensemble method for utilizing a long-term, time-series dataset that includes missing data, to group customers within a lateral by phase. These clustering phase predictions validate over 90% of the existing phase labels in the model and identify customers where the current phase labels are incorrect in this model. Within this dataset, this methodology produces consistent, high-quality results, verified by validating the clustering phase predictions with the underlying topology of the system, as well as selected examples verified using satellite and street view images publicly available in Google Earth. Further analysis of the results of the Spectral Clustering predictions are also shown to not only validate and improve the phase labels in the utility model, but also show potential in the detection of other types of errors in the topology of the model such as errors in the labeling of connections between customers and transformers, unlabeled residential solar power, unlabeled transformers, and locating customers with incomplete information in the model. These results indicate excellent potential for further development of this methodology as a tool for validating and improving existing utility models of the low-voltage side of the distribution system.

Power transmission grid infrastructures deliver electricity across large distance and are vital to the functioning of modern society. Increasingly these setups embody highly-coupled cyber-physical systems where advanced telecommunications networks are used to send status and control information to operate power transmission grid components, i.e., "smart grids". However, due to the high inter-dependency between the communication and power grid network layers, failure events can lead to further loss of control of key grid components, i.e., even if they are undamaged. In turn, such dependencies can exacerbate cascading failures and lead to larger electricity blackouts, particularly under disaster conditions. As a result, a range of studies have looked at modelling failures in interdependent smart grids. However most of these designs have not considered the use of proactive network-level survivability schemes. Indeed, these strategies can help maintain vital control connectivity during failures and potentially lead to reduced outages. Hence this thesis addresses this critical area and applies connection protection methodologies to reduce communication/control disruption in transmission grids. The performance of these schemes is then analyzed using detailed simulation for a sample IEEE transmission grid. Overall findings show a good reduction in the number of overloaded transmission lines when applying network-level recovery schemes.

Favorable price trends and increasing demand for renewable energy sources portend accelerating integration of solar photovoltaic (PV) generation into traditional electric power system networks. Managing the variable output of massive PV resources makes system frequency regulation more complex and expensive. ISOs must procure additional regulation and load following capacity, while power plants must supply more regulation work. In contrast to costly physical storage solutions, this thesis proposes to address the issue by reconfiguring the electricity market pricing structure to translate all power imbalances into real-time market price signals. More accurately determining the instantaneous value of energy, electric power markets could reward participants who can quickly respond to frequency fluctuations. By utilizing short term forward markets to monetize the risk associated with intermittency, the true cost of reliability is determined and could reduce wasteful capacity payments. This market redesign is an ideal open platform for disparate smart grid technologies which could encourage all suppliers, loads and generator, to offer supply or reduce consumption when it is needed most and could vastly improve frequency performance metrics.

An optimally designed thermosiphon for power electronics cooling is developed. There exists a need for augmented grid assets to facilitate power routing and decrease line losses. Power converter augmented transformers (PCATs) are critically limited thermally. Conventional active cooling system pumps and fans will not meet the 30 year life and 99.9% reliability required for grid scale implementation. This approach seeks to develop a single-phase closed-loop thermosiphon to remove heat from power electronics at fluxes on the order of 10 - 15 W/cm2. The passive thermosiphon is inherently a coupled thermal-fluid system. A parametric model and multi-physics design optimization code will be constructed to simulate thermosiphon steady state performance. The model will utilize heat transfer and fluid dynamic correlations from literature. A particle swarm optimization technique will be implemented for its performance with discrete domain problems. Several thermosiphons will be constructed, instrumented, and tested to verify the model and reach an optimal design.