The electrification of transportation is critical to mitigate Greenhouse Gas (GHG) emissions. The United States (U.S.) government's Inflation Reduction Act (IRA) of 2022 introduces policies to promote the electrification of transportation. In addition to electrifying transportation, clean energy technologies such as Carbon Capture and Storage (CCS) may play a major role in achieving a net-zero energy system. Utilizing scenarios simulated by the U.S. version of the Global Change Analysis Model (GCAM-USA), we analyze the individual and compound contributions of the IRA and CCS to reach a clean U.S. grid by 2035 and net-zero GHG emissions by 2050. We analyze the contributions based on three metrics: i) transportation electrification rate, ii) transportation fuel mix, and iii) spatio-temporal charging loads. Our findings indicate that the IRA significantly accelerates transportation electrification in the near-term (until 2035). In contrast, CCS technologies, by enabling the continued use of internal combustion vehicles while still advancing torward net-zero, potentially suppresses the rate of transportation electrification in the long-term. This study underscores how policy and technology innovation can interact and sensitivity studies with different combination are essential to characterize the potential contributions of each to the transportation electrification.

Electrification of transport compounded with climate change will transform hourly load profiles and their response to weather. Power system operators and EV charging stakeholders require such high-resolution load profiles for their planning studies. However, such profiles accounting whole transportation sector is lacking. Thus, we present a novel approach to generating hourly electric load profiles that considers charging strategies and evolving sensitivity to temperature. The approach consists of downscaling annual state-scale sectoral load projections from the multi-sectoral Global Change Analysis Model (GCAM) into hourly electric load profiles leveraging high resolution climate and population datasets. Profiles are developed and evaluated at the Balancing Authority scale, with a 5-year increment until 2050 over the Western U.S. Interconnect for multiple decarbonization pathways and climate scenarios. The datasets are readily available for production cost model analysis. Our open source approach is transferable to other regions.

High penetration of intermittent generation increases uncertainty and variability in balancing reserve needs.New tools are needed to help the balancing authority system operator plan for intraday and intra-hour balance between generation and load.The Grid Reserve and Flexibility Planning tool (GRAF-Plan) helps plan for adequate balancing reserves for future years or seasons for expected wind and solar generation.It also assesses the flexibility of the scheduled generation fleet to meet such requirements.The estimations are based on utilities' operational practices (e.g., forecasting and time frame of reserve deployment), and it incorporates detailed data from renewable generation and load.Application of the tool in estimating reserve requirements in Central America under different levels of renewable generation (high and low) and for the Western Electricity Coordinating Council (WECC) 2030 Anchor Data Set scenario is discussed.

When electrified transit systems make grid aware choices, improved social welfare is achieved by reducing grid stress, reducing system loss, and minimizing power quality issues. Electrifying transit fleet has numerous challenges like non availability of buses during charging, varying charging costs and so on, that are related the electric grid behavior. However, transit systems do not have access to the information about the co-evolution of the grid's power flow and therefore cannot account for the power grid's needs in its day-to-day operation. In this paper we propose a framework of transportation-grid co-simulation, analyzing the spatio-temporal interaction between the transit operations with electric buses and the power distribution grid. Real-world data for a day's traffic from Chattanooga city's transit system is simulated in SUMO and integrated with a realistic distribution grid simulation (using GridLAB-D) to understand the grid impact due to transit electrification. Charging information is obtained from the transportation simulation to feed into grid simulation to assess the impact of charging. We also discuss the impact to the grid with higher degree of transit electrification that further necessitates such an integrated transportation-grid co-simulation to operate the integrated system optimally. Our future work includes extending the platform for optimizing the charging and trip assignment operations.

High penetration of grid-edge, inverter-based photovoltaic (PV) can cause significant voltage fluctuations not only at the distribution but also at the sub-transmission levels due to PV output intermittency. This paper proposes a reactive power planning tool for sub-transmission systems to mitigate voltage violations and fluctuations caused by high PV penetration and intermittency with a minimum investment cost. The planning tool considers all existing var assets in both sub-transmission and distribution systems to reduce the need of new equipment. It also closely coordinates with an optimization-based volt-var operational tool for selecting a set of scenarios with voltage violations due to PV intermittency and testing the final investment decision. The planning tool obtains an investment need for each scenario based on a proposed optimal power-flow framework. Because of a high number of discrete variables in this optimization problem, an efficient technique to recover the feasibility for the solution of the relaxed optimization problem is included. After obtaining separated solutions for all scenarios, two options are provided for making final planning decision: i) a conservative direct combination of investment need solutions and ii) a machine learning-based selection of representative investment needs at most time steps. The final investment decision options are verified using a realistic large-scale sub-transmission system and 5-min PV and load data. The results show a significant voltage performance improvement with a lower investment cost for additional var equipment compared to conventional approaches.

When electrified transit systems make grid aware choices, improved social welfare is achieved by reducing grid stress, reducing system loss, and minimizing power quality issues. Electrifying transit fleet has numerous challenges like non availability of buses during charging, varying charging costs and so on, that are related the electric grid behavior. However, transit systems do not have access to the information about the co-evolution of the grid's power flow and therefore cannot account for the power grid's needs in its day-to-day operation. In this paper we propose a framework of transportation-grid co-simulation, analyzing the spatio-temporal interaction between the transit operations with electric buses and the power distribution grid. Real-world data for a day's traffic from Chattanooga city's transit system is simulated in SUMO and integrated with a realistic distribution grid simulation (using GridLAB-D) to understand the grid impact due to transit electrification. Charging information is obtained from the transportation simulation to feed into grid simulation to assess the impact of charging. We also discuss the impact to the grid with higher degree of transit electrification that further necessitates such an integrated transportation-grid co-simulation to operate the integrated system optimally. Our future work includes extending the platform for optimizing the charging and trip assignment operations.

With a growing interest and awareness to support clean and sustainable sources of energy, several countries have ambitious plans to significantly increase the penetration of renewable energy in the electric grid. However, the sources of renewable energy, such as solar and wind, are highly intermittent and therefore can pose challenges in maintaining reliable system operations; one such challenge being flexibility requirements. This paper addresses the concerns of increasing flexibility needs with high renewable penetration and studies the coordinated integration of small modular nuclear reactor and inverter-based renewable generation sources in a system to achieve high levels of carbon-free and sustainable energy. In this paper, balancing reserve and short-term flexibility requirements were considered for the analysis. Also, a methodology was developed to calculate metrics for short-term flexibility needs arising from slow variability observed in load and renewable generation. Small modular reactors are considered as potential sources of generation flexibility to complement renewables.

Shunt or series capacitors, or fast static exciters can improve the voltage dip which follows the fault clearing. More detailed technical and economic studies are needed to determine the preferred solution.

While there are many advantages to electric publictransit vehicles, they also pose new challenges for fleet operators. One key challenge is defining a charge scheduling policy that minimizes operating costs and power grid disruptions while maintaining schedule adherence. An uncoordinated policy could result in buses running out of charge before completing their trip, while a grid agnostic policy might incur higher energy costs or cause adverse impact on the grid's distribution system. We present a grid aware decision theoretic framework for electric bus charge scheduling that accounts for energy price and grid load The framework co-simulates models for traffic (Simulation of Urban Mobility) and the electric grid (GridLAB_D), which are used by a decision theoretic planner to evaluate charging decisions with regard to their long-term effect on grid reliability and cost. We evaluated the framework on a simulation of Richland, WA's bus and grid network, and found that it could save over $100k per year on operating costs for the city compared togreedy methods.

With the rapid penetration of intermittent solar photovoltaic (PV) and other distributed energy resources (DER) into the grid, and subsequent changes in power flow patterns in both distribution and sub-transmission, voltage regulation is becoming a major challenge. It is prudent to leverage PV and DER to provide ancillary services to the grid, such as voltage regulation. Recently, a quasi-static, coordinated real-time sub transmission volt-var control algorithm (CReST-VCT) was developed for voltage regulation under high PV penetration by dispatching the reactive power settings of the shunt devices and PV inverters. The algorithm was validated offline, on a quasistatic study. Such quasi-static dispatching algorithms cannot guarantee its performance for highly nonlinear dynamical power systems. In this paper the performance of the algorithm is validated with a real time nonlinear dynamic simulation of a modified IEEE 118 bus system in Opal-RT. The real time simulation is used to emulate the actual system operation, providing a more realistic testing environment for CReST-VCT. The dispatch and control signals are communicated between the power system (Opal solver) and the control center (GAMS solver) in real-time with a MODBUS bridge. The results demonstrate i) system is stable with the new dispatch points ii) significant improvement in system-wide voltage profiles compared to an uncontrolled scenario. Another significant contribution of the work is developing a framework for dispatchable, long duration dynamic simulations that can be leveraged for market/dispatch studies.

The report presents the results of the development of the open-source suite of applications for synchrophasor analysis. The suite includes several software tools for oscillation analysis, power plant model validation, and frequency response analysis using synchrophasor measurements. All tools are based on the common framework and data sources. The developed tools have been used by different electrical utilities for synchrophasor analysis. The report includes several use cases based on the actual system PMU data.

This paper proposes a reactive power planning tool for sub-transmission systems to mitigate voltage violations and fluctuations caused by high photovoltaic (PV) penetration and intermittency with a minimum investment cost. The tool considers all existing volt-ampere reactive (var) assets in both sub-transmission and distribution systems to reduce the need of new equipment. The planning tool coordinates with an operational volt-var optimization tool to determine all scenarios with voltage violations and verify the planning results. The planning result of each scenario is the solution of a proposed optimal power-flow framework with efficient techniques to handle a high number of discrete variables. The final planning decision is obtained from the planning results of all selected violated scenarios by using two different approaches - direct combination of all single-step solutions and final investment decision based only on the scenarios that are representative for the power-flow voltage violations at most time steps. The final planning decision is verified using a realistic large-scale sub-transmission system and 5-minute PV and load data. The results show a significant voltage violation reduction with a less investment cost for additional var equipment compared to conventional approaches.

With the increasing availability of sensors, power system dynamic state estimation (PSDSE) is going to play a critical role in the reliable and efficient operation of power systems. The real-time measurements in today's power grid are obtained through various types of sensors having different sampling rates, e.g., the traditional SCADA systems with low sampling rates (generally 0.5-2 samples per second), and different groups of phasor measurement units having high sampling rates (usually 30-60 samples per second). We propose a multi-rate multi-sensor data fusion-based PSDSE framework to utilize the measurements coming from sensors with two different sampling rates. The continuous time-domain nonlinear dynamical and measurement equations are discretized at appropriate sampling periods to obtain two discrete models. Two separate estimators are developed using these models. State information of the intermediate time steps of the estimation having coarser sampling period is evaluated using model-based prediction. These two estimations are optimally combined or fused using Bar-Shalom-Campo formula. The proposed algorithm tracks the dynamic states successfully during transient events such as faults. The method is demonstrated by using the standard IEEE-9, 39, 57, and 118 bus systems. The fusion-based state estimator is shown to perform better than the individual state estimators.

In a state estimator, the presence of malicious or simply corrupt sensor data or bad data is detected by the high value of normalized measurement residuals that exceeds the threshold value, determined by the $\chi^{2}$ distribution. However, high normalized residuals can also be caused by another type of anomaly, namely gross modeling or topology error. In this paper we propose a method to distinguish between these two sources of anomalies - 1) malicious sensor data and 2) modeling error. The anomaly detector will start with assuming a case of malicious data and suspect some of the individual measurements corresponding to the highest normalized residuals to be `malicious', unless proved otherwise. Then, choosing a change of basis, the state space is transformed and decomposed into `observable' and `unobservable' parts with respect to these `suspicious' measurements. We argue that, while the anomaly due to malicious data can only affect the `observable' part of the states, there exists no such restriction for anomalies due to modeling error. Numerical results illustrate how the proposed anomaly diagnosis based on Kalman decomposition can successfully distinguish between the two types of anomalies.

High-voltage direct current (HVDC) transmission lines are increasingly being installed in power systems around the world, and this trend is expected to continue with advancements in power electronics technology. These advancements are also bringing multiterminal direct current (MTDC) systems closer to practical application. In addition, the continued deployment of phasor measurement units makes dynamic information about a large power system readily available for highly controllable components, such as HVDC lines. All these trends have increased the appeal of modulating HVDC lines and MTDC systems to provide grid services in addition to bulk power transfers. This paper provides a literature survey of HVDC and MTDC damping controllers for interarea oscillations in large interconnected power systems. The literature shows a progression from theoretical research to practical applications. There are already practical implementations of HVDC modulation for lines in point-to-point configuration, although the modulation of MTDC systems is still in the research stage. As a conclusion, this paper identifies and summarizes open questions that remain to be tackled by researchers and engineers.

The ‘smart grid’ is one of the largest critical infrastructure systems of any nation. Preventing the grid from data integrity attacks is vital for reliable operation of the grid. Various Phasor Measurement Units (PMUs) and other intelligent electronic devices play crucial role in real-time operations of the grid. Control actions are taken based on information received from such devices. However, all these modern measurement systems communicate with the control center via wireless network making them vulnerable to several security threats, data integrity attack being one of them. This paper addresses the issues associated with the injection of malicious data into the measurements. A model based ‘trustiness’ technique has been proposed to mitigate the adverse effect of such attacks in the performance of the electrical power grid. The proposed method has been demonstrated using the IEEE 14-bus test system.

In recent times dynamic state estimation has been identified as a requirement for the modern power system. The availability of Phasor Measurement Units (PMUs) has made the tracking of power system dynamic states in real time quite feasible. However, the measurement system of today's grid consists of a combination of (1) traditional slow SCADA data with low sampling rates and (2) fast PMUs having high sampling rates. Such measurements with different sampling rates pose a serious challenge in integrating them into power system dynamic state estimator. This paper addresses this problem as a special case of multi-rate multi-sensor data fusion problem and appropriately adopts a method to integrate PMU and SCADA data in the power system dynamic state estimator. The proposed method has been demonstrated using a IEEE 14 bus test case.