arXiv subjects

Samrat Acharya

Publications and source records attributed to Samrat Acharya.

13 recordsLinked to original sources

arXiv2024

Impact of the Inflation Reduction Act and Carbon Capture on Transportation Electrification for a Net-Zero Western U.S. Grid

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.

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arXiv2024

Decoupling Power Quality Issues in Grid-Microgrid Network Using Microgrid Building Blocks

Microgrids are evolving as promising options to enhance reliability of the connected transmission and distribution systems. Traditional design and deployment of microgrids require significant engineering analysis. Microgrid Building Blocks (MBB), consisting of modular blocks that integrate seamlessly to form effective microgrids, is an enabling concept for faster and broader adoption of microgrids. Back-to-Back converter placed at the point of common coupling of microgrid is an integral part of the MBB. This paper presents applications of MBB to decouple power quality issues in grid-microgrid network serving power quality sensitive loads such as data centers, new grid-edge technologies such as vehicle-to-grid generation, and serving electric vehicle charging loads during evacuation before disaster events. Simulation results show that MBB effectively decouples the power quality issues across networks and helps maintain good power quality in the power quality sensitive network based on the operational scenario.

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arXiv2024

Microgrid Building Blocks for Dynamic Decoupling and Black Start Applications

Microgrids offer increased self-reliance and resilience at the grid's edge. They promote a significant transition to decentralized and renewable energy production by optimizing the utilization of local renewable sources. However, to maintain stable operations under all conditions and harness microgrids' full economic and technological potential, it is essential to integrate with the bulk grid and neighboring microgrids seamlessly. In this paper, we explore the capabilities of Back-to-Back (BTB) converters as a pivotal technology for interfacing microgrids, hybrid AC/DC grids, and bulk grids, by leveraging a comprehensive phasor-domain model integrated into GridLAB-D. The phasor-domain model is computationally efficient for simulating BTB with bulk grids and networked microgrids. We showcase the versatility of BTB converters (an integrated Microgrid Building Block) by configuring a two-microgrid network from a modified IEEE 13-node distribution system. These microgrids are equipped with diesel generators, photovoltaic units, and Battery Energy Storage Systems (BESS). The simulation studies are focused on use cases demonstrating dynamic decoupling and controlled support that a microgrid can provide via a BTB converter.

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arXiv2024

Dynamic Model of Back-to-Back Converter for System-Level Phasor Simulation

The power system is expected to evolve rapidly with the increasing deployment of power electronic interface and conditioning systems, microgrids, and hybrid AC/DC grids. Among power electronic systems, back-to-back (BTB) converters can be a powerful interface to integrate microgrids and networked microgrids. To study the integration of such devices into large power systems, a balance between power electronics model fidelity and system-level computational efficiency is critical. In system-level simulations of bulk power systems dominated by synchronous generators, detailed electromagnetic models of back-to-back converters may be unnecessary and also computationally inefficient. This paper focuses on developing a simple phasor model for back-to-back converters that can be easily integrated into powerflow solvers to facilitate large-scale power system simulations. The model is implemented using C$^{++}$ language and integrated into GridLAB-D, an open source software for distribution systems studies, as a potential new capability. The GridLAB-D phasor domain model is validated against the electromagnetic transient (EMT) simulation of the detailed switching model. Simulation results show that the phasor model successfully captures the dominant dynamics of the converter with significantly shorter simulation elapsed time.

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arXiv2023

MaDEVIoT: Cyberattacks on EV Charging Can Disrupt Power Grid Operation

This paper examines the feasibility of demand-side cyberattacks on power grids launched via internet-connected high-power EV Charging Stations (EVCSs). By distorting power grid frequency and voltage, these attacks can trigger system-wide outages. Our case study focuses on Manhattan, New York, and reveals that such attacks will become feasible by 2030 with increased EV adoption. With a single EVCS company dominating Manhattan, compromising a single EVCS server raises serious power grid security concerns. These attacks can overload power lines and trip over-frequency (OF) protection relays, resulting in a power grid blackout. This study serves as a crucial resource for planning authorities and power grid operators involved in the EV charging infrastructure roll-out, highlighting potential cyberthreats to power grids stemming from high-power EVCSs.

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arXiv2023

Smart Data Mapping for Connecting Power System Model and Geospatial Data

Knowing the geospatial locations of power system model elements and linking load models with end users and their communities are the foundation for analyzing system resilience and vulnerability to natural hazards. However, power system models and geospatial data for power grid assets are often developed asynchronously without close coordination. Creating a direct mapping between the two is a challenging task, mainly due to heterogeneous data structures, target uses, historical legacies, and human errors. This work aims to build an automatic data mapping workflow to connect the two, and to support energy grid resilience studies for Puerto Rico. The primary steps in this workflow include constructing graphs using geospatial data, and aligning them to the transmission networks defined in the power system data. The results have been evaluated against existing manual mapping practices for part of the Puerto Rico Power Grid model to illustrate the performance of such auto-mapping solutions.

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arXiv2023

Weather Sensitive High Spatio-Temporal Resolution Transportation Electric Load Profiles For Multiple Decarbonization Pathways

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.

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arXiv2023

Causative Cyberattacks on Online Learning-based Automated Demand Response Systems

Power utilities are adopting Automated Demand Response (ADR) to replace the costly fuel-fired generators and to preempt congestion during peak electricity demand. Similarly, third-party Demand Response (DR) aggregators are leveraging controllable small-scale electrical loads to provide on-demand grid support services to the utilities. Some aggregators and utilities have started employing Artificial Intelligence (AI) to learn the energy usage patterns of electricity consumers and use this knowledge to design optimal DR incentives. Such AI frameworks use open communication channels between the utility/aggregator and the DR customers, which are vulnerable to \textit{causative} data integrity cyberattacks. This paper explores vulnerabilities of AI-based DR learning and designs a data-driven attack strategy informed by DR data collected from the New York University (NYU) campus buildings. The case study demonstrates the feasibility and effects of maliciously tampering with (i) real-time DR incentives, (ii) DR event data sent to DR customers, and (iii) responses of DR customers to the DR incentives.

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arXiv2022

Outing Power Outages: Real-time and Predictive Socio-demographic Analytics for New York City

Electrical outages continue to occur despite technological innovations and improvements to electric power distribution infrastructure. In this paper, we describe a tool that was designed to acquire and collect data on electric power outages in New York City since July 2020. The electrical outages are then displayed on a front-end application, which is publicly available. We use the collected outage data to analyze these outages and their socio-economic impacts on electricity vulnerable population groups. We determined that there was a slightly negative linear relationship between income and number of outages. Finally, a Markov Influence Graph was created to better understand the spatial and temporal relationships between outages.

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arXiv2021

Cyber Insurance Against Cyberattacks on Electric Vehicle Charging Stations

Cyberattacks in the energy sector are commonplace. Load-altering cyberattacks launched via the manipulations of high-wattage appliances and assets are particularly alarming, as they are not continuously monitored by electric power utilities. Public Electric Vehicle Charging Stations (EVCSs) are among such high-wattage assets. Even EVCSs monitored by the electric power utilities and protected by state-of-the-art defense mechanisms are vulnerable to cyberattacks. Such cyberattacks cause financial losses to the EVCSs. In this paper, we propose cyber insurance for EVCSs to hedge the economic loss due to such cyberattacks and develop a data-driven cyber insurance design model for public EVCSs. Under mild modeling assumptions, we derive an optimal cyber insurance premium. Then, we ensure the robustness of this optimal premium and investigate the risk of insuring the EVCSs using a suitable risk assessment metric (Conditional Value-at-Risk). A case study with data from EVCSs in Manhattan, New York illustrates our results. Our results demonstrate that risk assessment is crucial for designing insurance premiums. Furthermore, the premium increases in proportion to the loss coverage offered for the EVCSs. This work informs the stakeholders involved in the roll-out and operation of public EVCSs about the benefits of cyber insurance and suggests that insurance premiums can be reduced by deploying state-of-the-art defense mechanisms.

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arXiv2021

Impact of Electric Vehicle Routing with Stochastic Demand on Grid Operation

Given the rise of electric vehicle (EV) adoption, supported by government policies and dropping technology prices, new challenges arise in the modeling and operation of electric transportation. In this paper, we present a model for solving the EV routing problem while accounting for real-life stochastic demand behavior. We present a mathematical formulation that minimizes travel time and energy costs of an EV fleet. The EV is represented by a battery energy consumption model. To adapt our formulation to real-life scenarios, customer pick-ups and drop-offs were modeled as stochastic parameters. A chance-constrained optimization model is proposed for addressing pick-ups and drop-offs uncertainties. Computational validation of the model is provided based on representative transportation scenarios. Results obtained showed a quick convergence of our model with verifiable solutions. Finally, the impact of electric vehicles charging is validated in Downtown Manhattan, New York by assessing the effect on the distribution grid.

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arXiv2020

A Hierarchical Approach to Multi-Energy Demand Response: From Electricity to Multi-Energy Applications

Due to proliferation of energy efficiency measures and availability of the renewable energy resources, traditional energy infrastructure systems (electricity, heat, gas) can no longer be operated in a centralized manner under the assumption that consumer behavior is inflexible, i.e. cannot be adjusted in return for an adequate incentive. To allow for a less centralized operating paradigm, consumer-end perspective and abilities should be integrated in current dispatch practices and accounted for in switching between different energy sources not only at the system but also at the individual consumer level. Since consumers are confined within different built environments, this paper looks into an opportunity to control energy consumption of an aggregation of many residential, commercial and industrial consumers, into an ensemble. This ensemble control becomes a modern demand response contributor to the set of modeling tools for multi-energy infrastructure systems.

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arXiv2020

Public Plug-in Electric Vehicles + Grid Data: Is a New Cyberattack Vector Viable?

High-wattage demand-side appliances such as Plug-in Electric Vehicles (PEVs) are proliferating. As a result, information on the charging patterns of PEVs is becoming accessible via smartphone applications, which aggregate real-time availability and historical usage of public PEV charging stations. Moreover, information on the power grid infrastructure and operations has become increasingly available in technical documents and real-time dashboards of the utilities, affiliates, and the power grid operators. The research question that this study explores is: Can one combine high-wattage demand-side appliances with public information to launch cyberattacks on the power grid? To answer this question and report a proof of concept demonstration, the study scrapes data from public sources for Manhattan, NY using the electric vehicle charging station smartphone application and the power grid data circulated by the US Energy Information Administration, New York Independent System Operator, and the local utility in New York City. It then designs a novel data-driven cyberattack strategy using state-feedback based partial eigenvalue relocation, which targets frequency stability of the power grid. The study establishes that while such an attack is not possible at the current penetration level of PEVs, it will be practical once the number of PEVs increases.

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