ABSTRACT. Transactive energy frameworks optimally schedule the energy pattern of the participants to improve the social welfare of the market. Peer-to-peer (P2P) trading platforms model the interactions between the different market players to develop a competitive deregulated structure. Demand Response (DR) is another conventional optimization problem which aims to optimally decide the consumption pattern of the customers to reduce the cost of the electricity and peak demand on the system. In this work, we present a two-stage optimization framework to combine the P2P and DR problems in the form of an iterative fashion using the ADMM and Mixed Integer Linear Programming (MILP). The first stage optimizes the DR framework based on the utility and P2P price while using the MILP to find the optimal schedule of the participants. The second stage optimizes the P2P market based on the demand schedule of the first stage using the ADMM optimization.

ABSTRACT. The integration of increasingly distributed energy resources (DERs) makes the grid more dynamic. The power system network control, supervision, and protection require a system dynamics model. The current modeling approach uses a synchronous generation-based model which cannot capture the dynamics of a converter-dominated power grid. Therefore, this research aims to apply computationally efficient and accurate neural Ordinary Differential Equations (NODEs) to model and infer the critical states information of the power system frequency dynamics. The NODEs-based framework is implemented in Python and trained using a system designed in MATLAB by generating C-code and then interfacing with python. The trained model with a log square chirp signal can predict the states under square as well as a step excitation signal, which validates the applicability of the NODES-based framework to model the frequency dynamics of the future power grids.

ABSTRACT. Implementation of networked grids requires multiple simultaneous fast topological changes to transform the electrical grid into disconnected power balanced islands. Hence, a framework that can rapidly detect and identify topology errors in multiple switching events simultaneously is required. This paper proposes a combined conventional and data-driven approach where first the normalized measurement residuals are used for error detection and then a neural network is trained using both residuals and measurements to distinguish error types and identify the erroneous components. The topology check is done on two different levels. In the lower level a state estimation problem is solved for each of the smaller grids individually. Decoupled systems require less computation time and power, which is essential for the time sensitive task at hand. The identification step, however, is attempted using a NN model at higher-level due to the possibility of the topology error being in the links between the smaller grids. A case study utilizing the proposed method is performed on different topological variations of the IEEE_14Bus system. The results show promising potential of the proposed algorithm.

ABSTRACT. The accuracy of distribution system state estimation may be significantly impacted by the existence of bad measurements and unexpected topology errors. This paper proposes a data-driven Graph Convolutional Network (GCN) for anomaly detection, including bad measurements and topology change events. Compared to many existing machine learning approaches, the proposed approach embeds both spatial-temporal measurement correlations, which allows us to detect and distinguish different anomalies. Numerical results carried out on the IEEE 37-node system demonstrate that the proposed-based method can obtain high accuracy in detecting bad data and topology changes as compared to other approaches, even in the presence of high PV penetrations.

ABSTRACT. The power variability of utility-scale solar PV plants causes reduction in the damping of the electromechanical oscillations (EMOs). A ∆-automatic generation control (∆-AGC) is introduced in the secondary frequency control loop to compensate for the reduction in the damping of EMOs during sudden drops in PV generation. ∆-AGC temporarily changes the operating points of the synchronous generators. The operation points of synchronous generators are changed by modifying the participation factors (PFs) in the AGC using a look-up table and a fuzzy logic approach developed based on empirical studies. Typical results of conventional AGC and with ∆-AGC are compared. The ∆-AGC approach provides a practical solution to improve power system stability with large utility-scale solar PV plants.

ABSTRACT. Modern power grids consist of interconnected components, which means that if one component fails, it can trigger a chain reaction, leading to cascading failures throughout the network. Hence, modeling the cascading failures and the ability to predict impending failures is essential for reliable grid operations. A challenge in developing an accurate cascade model is the complexity of the various dependencies that exist in a typical power grid. Capturing all these intrinsic dependencies is a complex problem that is often computationally not feasible due to many grid uncertainties. This research introduces graph-based resilient clustering techniques as a solution to ease the complexity of modeling the failure dependencies. Future research directions will focus on using detected communities to predict possible impending cascading failures in power grids. Additionally, clusters from the power grid can be used to establish power grid monitoring measures that can prevent subsequent failures.

ABSTRACT. Increased awareness of climate change is leading to large volumes of distributed energy resources (DERs), such as solar photovoltaic (PV) plants being integrated into the power distribution system. DERs are variable and uncertain due to their power generation being based on changing weather conditions. Digital twins (DTs) will aid system operators in counteracting the complexity introduced by these DERs. DTs can be built based on historical data by using artificial intelligence-based approaches. This allows the DT to learn and better model the behavior of the physical system. These data-driven digital twins (DD-DTs) use neural networks to create a variety of PV power prediction models for the 1 MW PV plant located at Clemson University in South Carolina, USA. Real-time measurements are taken every minute by Internet of Things (IoT) sensors and are archived. Both feedforward and feedback neural networks are explored. Typical results of DT predictions are presented.

ABSTRACT. Renewable energy generation sources (RESs) are gaining increased popularity in distributed power generation technology. Planning and managing increasing levels of RESs, specifically solar photovoltaic (PV) generation sources is becoming increasingly challenging, especially considering highly variable weather conditions. Estimations provide situational awareness of solar PV power generations under varying weather conditions. A digital twin (DT) can replicate PV plant behaviors and characteristics in a virtual platform, providing realistic solar PV estimations. Furthermore, neural networks (NNs), a popular paradigm of artificial intelligence (AI) may be used to adequately learn the relationship between input data and output data for data-driven DTs (DD-DTs). In this study, NN-based DD-DTs for Clemson University’s 1 MW solar PV plant located in South Carolina, USA, are developed to perform realistic solar PV power generation estimations. Typical results for two DD-DT architectures are presented.

ABSTRACT. A Wave Energy Converter (WEC) system can turn the wave energy into the high-pressure flow that the desalination system needs to pump saltwater into the reverse osmosis membrane and create the pressure needed to produce fresh water. The objective of this research is to track the maximum permeate flow in a WEC and to keep the salinity of the fresh water within a desired level. We experimented different number of parallel membranes and piston areas for different sea-states to have the optimum results in terms of permeate flow and water salinity. After the experiment, the results showed that there is a strong correlation between maximum permeate flow and optimum piston area. Increasing the number of membranes helped increase the permeate flow linearly to a certain level, after which a saturation phenomenon was observed. The study also highlights production of the maximum freshwater production with desired salinity and offers insights for future research.

ABSTRACT. This research presents a comprehensive simulation model for a 3 MW doubly fed induction generator (DFIG) wind turbine. The model captures the complex subsystems of modern wind turbines, including the rotor blades, drive train, and generator system, reflecting the turbine's behavior across all operational ranges. The model's accuracy is verified using startup and wind gust scenarios, fault conditions, and real-time hardware-in-the-loop simulation. Modifications are made to enable operation at low speeds without power generation, and a torque-to-speed module is implemented for external input in real-time simulation. This research offers a significant advancement in modeling DFIG wind turbines, aiding improved design and control of wind energy systems.

ABSTRACT. Rural communities in Alaska (AK) have distinct energy landscapes caused by frigid winters, unique terrain, low populations, geographic isolation, and limited infrastructure. In order to accurately reflect these landscapes, realistic models of power systems in remote AK communities are important. Generating synthetic models that do not expose private information, yet are realistic test cases, is a valuable solution. In this paper, we will explore a key component to developing synthetic, rural AK microgrid models: validation metrics.

ABSTRACT. Implementing new technologies into the Railbelt, such as solar and wind energy, as well as pumped storage hydro power can significantly reduce the CO2 equivalent emissions across the grid. However, the implementation of variable renewable energy technologies introduces intermittency in energy production. When peak production from the variable renewables and peak loads are at different times, this presents an issue of what to do with the excess energy produced. The challenge this poster delves into is long duration energy storage for the Railbelt electrical grid. Given the vast difference in peak and valley loads between winter and summer in Alaska there is a strong need for long duration energy storage. This poster works to explore the use of existing infrastructure such as abandoned mining lands and depleted natural gas reservoirs to sustainably implement long duration storage in the Railbelt.

ABSTRACT. This work proposes a TSO-DSO framework including DER set-point dispatch using sensitivity analysis. We address all three steps of TSO-DSO integration stating by estimating the capability curve of an unbalanced distribution system and then modeling of capability curve on the transmission side grid and estimating the reactive power needed from DSO during emergencies, and ending by dispatching the requested reactive power from TSO. The proposed interaction framework is tested on an IEEE 37-node distribution network connected to IEEE 9 bus transmission system. The simulation shows that using sensitivity-aware dispatch would reduce the number of communication signals between DERs and DSO and that can decrease the risk of communication contingency.

ABSTRACT. This work on Nuclear Small Modular Reactors (SMRs) in Alaska's Railbelt electric grid explores the implementation of SMRs along the Railbelt as a decarbonization technology to meet a carbon-emission-free goal by 2050. This project aims to select SMR(s) large enough to offset current natural gas and coal generation when installed. In addition to nuclear generation, new and existing solar and wind generation will be included in the power system simulator models.

ABSTRACT. To combat climate change, the United States joined 193 Parties in committing to the Paris Agreement, which aims to limit global warming to 2 degrees Celsius or less, and decarbonizing transportation will be a key requirement for achieving this goal. School buses (SBs) are a common form of student transport in the U.S. with nearly all SBs today powered by fossil fuels (primarily diesel). As a result, SBs have historically been a concerning source of both greenhouse gas emissions and local air pollutants with negative health impacts for students and others living nearby. Electric SBs (ESBs) are a promising emerging technology for decarbonizing student transport, however, ESB adoption in the U.S. remains at an early stage (<1%) with many outstanding uncertainties [1]. This study aims to elucidate several of these by taking inventory of the total SB stock within states and studying real-world SB operating profiles to infer potential battery range requirements, daily charging opportunities, and charging infrastructure requirements for ESBs. In addition, we observe the geographic trends of early-stage ESB adoption, which can be used to better understand early adopter patterns and train vehicle technology adoption models.

ABSTRACT. The modern power system transmission network operation is highly dynamic due to the high penetration of inverter-based resources and active systems. The state-of-the-art (SOTA) transmission network topology processing (TNTP) based on the supervisory control and data acquisition (SCADA) system is inefficient for modern power system operational applications. A hierarchical approach for TNTP (H-TNTP) based on substation configuration identification using the branch current and node voltage measurements is proposed to overcome the shortcomings of the SOTA approach. A modified two-area four-machine power system model with two grid-connected solar Photovoltaic (PV) plants is utilized as the test bed and simulated in the Real-Time Digital Simulator (RTDS). Furthermore, the proposed HTNTP is utilized to demonstrate the improved performance of energy management system (EMS) operational applications by implementing rapid automatic generation control (AGC) reconfiguration under contingencies.

ABSTRACT. Load curtailment is one of the strategies for preserving the stability of the power system when the demand for electricity exceeds the supply level. The major goal of this study is to determine an ideal load ratio share for load shedding by taking into account several performance indices. The analysis is performed through RTS - 96 bus test system.

ABSTRACT. Location of large scale Distributed Energy Resources(DERs) imposes many challenges on transmission planning as the traditional transmission planning doesn’t consider large scale DERs. Newer additions of large scale DERs on an implemented transmission planning might impact the network in several ways such as the need for new lines to accommodate DER integration or existing lines become obsolete. Integration of large scale DERs will result in building new lines to accommodate intended power flow will cost transmission utilities heavily if the integration happens at the low voltage lines than in high voltage lines. This extra burden on the transmission utilities can be compensated by providing incentives accordingly. This work focuses on understanding and quantifying the impacts of DER locations on transmission lines of different voltage levels by analyzing the impacts of DERs in terms of line loadings.

ABSTRACT. Fine granularity electricity consumption data is critical for building energy management and designing electric retrofits. Methods have been developed for producing hourly building electric load profiles without smart meters. Many of these techniques are built upon monthly utility bills, some leveraging time-of-use data. This work proposed an adaptive building electric load profiling technique, which improves upon the limitations of existing work by introducing a transition period that is not always included in the utility bills, while also considering the impacts of seasonal weather changes. The proposed method is tested on a gas-heated and a fully electric building. Results show the gas-heated building exhibits better profiling performance compared to the fully electric building, whose electric load is more sensitive to environmental temperature changes, resulting in error outside of the acceptable error threshold during shoulder seasons. However, this may be acceptable as shoulder seasons do not meaningfully impact electric retrofits.

ABSTRACT. As the United States pursues a cleaner energy future, it faces several new challenges because of different technical issues. Such examples include but are not limited to the integration of distributed energy resources (DERs) into grid or lowering of the national carbon footprint level. Moreover, the critical infrastructure of the nation exhibits several vulnerabilities that render it susceptible to cyber-attackers and other threats. For instance, the grid comprises numerous outdated components, where even a simple event like wildlife tripping over a power line can lead to a portion of the grid shutting down [1]. Furthermore, several utility companies openly disclose specific metrics about their generation plants, substations, and high-voltage lines, making them easily identifiable targets for a planned attack [2], [3]. Thus, a well-coordinated attack on a single key component of the power grid could trigger a blackout in a particular region. While the continuous development of the power grid has been recognized for a considerable time, the evolving threat landscape, including cyber-physical attacks, demands an ongoing evaluation of proactive measures to comprehend and reduce potential consequences. While previous efforts have aimed to comprehend the impacts of critical system components, there is still a great need to comply with today’s standards for reliable and secure operations of the grid. Given the constantly changing nature of the electric power grid, the necessity for an up-to-date analysis has become crucial.

Highly motivated by the above mentioned challenges, this work aims to conduct comprehensive simulations and analyses on three large-scale synthetic test systems provided by Texas A&M grid repository [4]: ACTIVSg10K, ACTIVSg2000, and ACTIVSg70K. Additionally, through the use of PowerWorld Simulator and Python programming, our analyses will aim to observe critical grid elements such as substations, power generating units, and high-voltage transmission lines amongst various test cases. By simulating and inducing outages on these vital components, this study seeks to identify the relevant elements that, when disrupted, may lead to damaging system integrity. Furthermore, the proposed work will record instances where multiple critical components are disturbed simultaneously, simulating a planned attack, to observe the consequences of such threat.

The significance of the proposed study is to provide consequential enumeration analysis that will support the identification of vulnerabilities in the power grid. By thoroughly examining the critical grid elements, this study will contribute to identify volatile components in the power system that will aid in reliable operations of the grid as well as towards achieving a sustainable and resilient energy future. The proposed strategic approach will also result into the establishment of a more secured electric power grid.

ABSTRACT. The bidirectional capability of Electric Vehicle (EV) charging stations enhances vehicle-to-grid (V2G) operations, offering benefits to both EV owners and electric utilities. Estimating number of users based on idle time and excess kilowatt-hour (kWh) are crucial factors for V2G scenarios. This work focuses on utilizing the dataset parameters such as connect time, disconnect time, charge completion time, requested kWh, and delivered kWh. These parameters are considered from a real-time charging station dataset recorded during the historical period of 2018-2021. Multiple machine learning (ML) classification methods are deployed. The preliminary results indicates that Wednesdays contain maximum number of users (1,823) based on the idle time. While, Fridays have 1,556 users for 30% excess kWh to contribute for V2G services.

ABSTRACT. This poster discusses an electric (E) and magnetic (H) field data gathering process across a series of High Voltage (HV) transmission lines. Data was collected from one DC and four AC Transmission (Tx) lines. Multiple DJI UAV (M2EA, M30, and M300) were used to collect E/H field measurements with an onboard set-up. These measurements included parameters such as E field in V/m, H field in mG, Battery voltage in V, Battery current A, Battery percentage in %, Battery Temperature in F, and latitude and longitude. The preliminary findings indicate that distance and sensor orientation affect E/H field distribution.