Modeling The Effects Of Electric Power Disruption And Expansion On The Operations Of Ev Charging Stations

The projected and current adoption rates of electric vehicles are increasing. Since electric vehicles require that they be recharged continually over time, the energy needs to support them is immense and growing. Given existing infrastructure is insufficient to supply the projected energy needs, models are necessary to help decision makers plan for how to best expand the power grid to meet this need. A successful power grid expansion is one that enables charging stations to service the electric vehicle community. Thus, plans for power expansion need to be coordinated between the power grid and charging station investors. The infrastructure for the charging stations has to also be resilient and reliable to absorb this increase in load. Charging stations therefore should be included in the plans for post power disruption planning. In this work, two two-stage stochastic programming models are developed that can be used to determine a power grid expansion plan that sup- ports the energy needs, or load, from an uncertain set of electric vehicles geographically dispersed over a region. Another three-stage stochastic programming model is presented, where the decisions are made first to select which charging stations to install and expand uninterruptible power supply units and renewable energy sources. Then, when the disrup- tion occurs in the second-stage, repairs in power system and charging stations take place ahead of the arrival of panicked population to prepare for the expected surge in power de- mand. Finally, as demand is unveiled, managerial and operational decisions at the charging stations are made in the third-stage. To solve the mathematical models, we utilize hybrid approaches which mainly make use of Sample Average Approximation and Progressive Hedging algorithm. To validate the proposed model and gain key insights, we perform computational experiments using realistic data representing the Washington, DC area. Our computational results indicate the robustn

The projected and current adoption rates of electric vehicles are increasing. Since electric vehicles require that they be recharged continually over time, the energy needs to support them is immense and growing. Given existing infrastructure is insufficient to supply the projected energy needs, models are necessary to help decision makers plan for how to best expand the power grid to meet this need. A successful power grid expansion is one that enables charging stations to service the electric vehicle community. Thus, plans for power expansion need to be coordinated between the power grid and charging station investors. The infrastructure for the charging stations has to also be resilient and reliable to absorb this increase in load. Charging stations therefore should be included in the plans for post power disruption planning. In this work, two two-stage stochastic programming models are developed that can be used to determine a power grid expansion plan that supports the energy needs, or load, from an uncertain set of electric vehicles geographically dispersed over a region. Another three-stage stochastic programming model is presented, where the decisions are made first to select which charging stations to install and expand uninterruptible power supply units and renewable energy sources. Then, when the disruption occurs in the second-stage, repairs in power system and charging stations take place ahead of the arrival of panicked population to prepare for the expected surge in power demand. Finally, as demand is unveiled, managerial and operational decisions at the charging stations are made in the third-stage. To solve the mathematical models, we utilize hybrid approaches which mainly make use of Sample Average Approximation and Progressive Hedging algorithm. To validate the proposed model and gain key insights, we perform computational experiments using realistic data representing the Washington, DC area. Our computational results indicate the robustness of the proposed algorithm while providing a number of managerial insights to the decision makers.

This dissertation studies a framework in support electric vehicle (EV) charging station expansion and management decisions. In the first part of the dissertation, we present mathematical model for designing and managing electric vehicle charging stations, considering both long-term planning decisions and short-term hourly operational decisions (e.g., number of batteries charged, discharged through Battery-to-Grid (B2G), stored, Vehicle-to-Grid (V2G), renewable, grid power usage) over a pre-specified planning horizon and under stochastic power demand. The model captures the non-linear load congestion effect that increases exponentially as the electricity consumed by plugged-in EVs approaches the capacity of the charging station and linearizes it. The study proposes a hybrid decomposition algorithm that utilizes a Sample Average Approximation and an enhanced Progressive Hedging algorithm (PHA) inside a Constraint Generation algorithmic framework to efficiently solve the proposed optimization model. A case study based on a road network of Washington, D.C. is presented to visualize and validate the modeling results. Computational experiments demonstrate the effectiveness of the proposed algorithm in solving the problem in a practical amount of time. Finding of the study include that incorporating the load congestion factor encourages the opening of large-sized charging stations, increases the number of stored batteries, and that higher congestion costs call for a decrease in the opening of new charging stations. The second part of the dissertation is dedicated to investigate the performance of a collaborative decision model to optimize electricity flow among commercial buildings, electric vehicle charging stations, and power grid under power demand uncertainty. A two-stage stochastic programming model is proposed to incorporate energy sharing and collaborative decisions among network entities with the aim of overall energy network cost minimization. We use San Francisco, California as a testing ground to visualize and validate the modeling results. Computational experiments draw managerial insights into how different key input parameters (e.g., grid power unavailability, power collaboration restriction) affect the overall energy network design and cost. Finally, a novel disruption prevention model is proposed for designing and managing EV charging stations with respect to both long-term planning and short-term operational decisions, over a pre-determined planning horizon and under a stochastic power demand. Long-term planning decisions determine the type, location, and time of established charging stations, while short-term operational decisions manage power resource utilization. A non-linear term is introduced into the model to prevent the evolution of excessive temperature on a power line under stochastic exogenous factors such as outside temperature and air velocity. Since the research problem is NP-hard, a Sample Average Approximation method enhanced with a Scenario Decomposition algorithm on the basis of Lagrangian Decomposition scheme is proposed to obtain a good-quality solution within a reasonable computational time. As a testing ground, the road network of Washington, D.C. is considered to visualize and validate the modeling results. The results of the analysis provide a number of managerial insights to help decision makers achieving a more reliable and cost-effective electricity supply network.

Fast-Charging Infrastructure for Electric and Hybrid Electric Vehicles Comprehensive resource describing fast-charging infrastructure in electric vehicles, including various subsystems involved in the power system architecture needed for fast-charging Fast-Charging Infrastructure for Electric and Hybrid Electric Vehicles presents various aspects of fast-charging infrastructure, including the location of fast-charging stations, revenue models and tariff structures, power electronic converters, power quality problems such as harmonics & supraharmonics, energy storage systems, and wireless-charging, electrical distribution infrastructures and planning. This book serves as a guide to learn recent advanced technologies with examples and case studies. It also considers problems that arise, and the mitigation methods involved, in fast-charging stations in global aspects and provides tools for analysis. Sample topics covered in Fast-Charging Infrastructure for Electric and Hybrid Electric Vehicles include: Selection of fast-charging stations, advanced power electronic converter topologies for EV fast-charging, wireless charging for plug-in HEV/EVs, and batteries for fast-charging infrastructure Standards for fast-charging infrastructure and power quality issues (analysis of harmonic injection and system resonance conditions due to large-scale penetration of EVs and supraharmonic injection) For professionals in electric vehicle technology, along with graduate and senior undergraduates, professors, and researchers in related fields, Fast-Charging Infrastructure for Electric and Hybrid Electric Vehicles is a useful, comprehensive, and accessible guide to gain an overview of the current state of the art.

This book covers the recent research advancements in the area of charging strategies that can be employed to accommodate the anticipated high deployment of Plug-in Electric Vehicles (PEVs) in smart grids. Recent literature has focused on various potential issues of uncoordinated charging of PEVs and methods of overcoming such challenges. After an introduction to charging coordination paradigms of PEVs, this book will present various ways the coordinated control can be accomplished. These innovative approaches include hierarchical coordinated control, model predictive control, optimal control strategies to minimize load variance, smart PEV load management based on load forecasting, integrating renewable energy sources such as photovoltaic arrays to supplement grid power, using wireless communication networks to coordinate the charging load of a smart grid and using market price of electricity and customers payment to coordinate the charging load. Hence, this book proposes many new strategies proposed recently by the researchers around the world to address the issues related to coordination of charging load of PEVs in a future smart grid.

This authoritative new resource provides a comprehensive introduction to plug-in electric vehicles (PEVs), including critical discussions on energy storage and converter technology. The architecture and models for sustainable charging infrastructures and capacity planning of small scale fast charging stations are presented. This book considers PEVs as mobile storage units and explains how PEVS can provide services to the grid. Enabling technologies are explored, including energy storage, converter, and charger technologies for home and park charging. The adoption of EV is discussed and examples are given from the individual battery level to the city level. This book provides guidance on how to build and design sustainable transportation systems. Optimal arrival rates, optimal service rates, facility location problems, load balancing, and demand forecasts are covered in this book. Time-saving MATLAB code and background tables are included in this resource to help engineers with their projects in the field.

This book opens up new ways to develop mathematical models and optimization methods for interdependent energy infrastructures, ranging from the electricity network, natural gas network, district heating network, and electrified transportation network. The authors provide methods to help analyze, design, and operate the integrated energy system more efficiently and reliably, and constitute a foundational basis for decision support tools for the next-generation energy network. Chapters present new operation models of the coupled energy infrastructure and the application of new methodologies including convex optimization, robust optimization, and equilibrium constrained optimization. Four appendices provide students and researchers with helpful tutorials on advanced optimization methods: Basics of Linear and Conic Programs; Formulation Tricks in Integer Programming; Basics of Robust Optimization; Equilibrium Problems. This book provides theoretical foundation and technical applications for energy system integration, and the the interdisciplinary research presented will be useful to readers in many fields including electrical engineering, civil engineering, and industrial engineering.

Americans' safety, productivity, comfort, and convenience depend on the reliable supply of electric power. The electric power system is a complex "cyber-physical" system composed of a network of millions of components spread out across the continent. These components are owned, operated, and regulated by thousands of different entities. Power system operators work hard to assure safe and reliable service, but large outages occasionally happen. Given the nature of the system, there is simply no way that outages can be completely avoided, no matter how much time and money is devoted to such an effort. The system's reliability and resilience can be improved but never made perfect. Thus, system owners, operators, and regulators must prioritize their investments based on potential benefits. Enhancing the Resilience of the Nation's Electricity System focuses on identifying, developing, and implementing strategies to increase the power system's resilience in the face of events that can cause large-area, long-duration outages: blackouts that extend over multiple service areas and last several days or longer. Resilience is not just about lessening the likelihood that these outages will occur. It is also about limiting the scope and impact of outages when they do occur, restoring power rapidly afterwards, and learning from these experiences to better deal with events in the future.

This book comprises five peer-reviewed articles covering original research articles on the modeling and simulation of electricity systems for transport and energy storage. The topics include: 1 - Optimal siting and sizing methodology to design an energy storage system (ESS) for railway lines; 2 - Technical–economic comparison between a 3 kV DC railway and the use of trains with on-board storage systems; 3 - How to improve electrical feeding substations, by changing transformer technology and by installing dedicated high-power-oriented storage systems; 4 - Algorithm applied to a vehicle-to-grid (V2G) technology. 5 - Thermal investigation and optimization of an air-cooled lithium-ion battery pack.

This thesis explores the implications of the increased adoption of plug-in electric vehicles in California through its effect on the operation of the state's electric grid. The well-to-wheels emissions associated with driving an electric vehicle depend on the resource mix of the electricity grid used to charge the battery. We present a new least-cost dispatch model, EDGE-NET, for the California electricity grid consisting of interconnected sub-regions that encompass the six largest state utilities that can be used to evaluate the impact of growing electric vehicle demand on existing power grid infrastructure system and energy resources. This model considers spatiality and temporal dynamics of energy demand and supply when determining the regional impacts of additional charging profiles on the current electricity network. Model simulation runs for one year show generation and transmission congestion to be reasonable similar to historical data. Model simulation results show that average emissions and system costs associated with electricity generation vary significantly by time of day, season, and location. Marginal cost and emissions also exhibit seasonal and diurnal differences, but show less spatial variation. Sensitivity of demand analysis shows that the relative changes to average emissions and system costs respond asymmetrically to increases and decreases in electricity demand. These results depend on grid mix at the time and the marginal power plant type.In minimizing total system cost, the model will choose to dispatch the lowest-cost resource to meet additional vehicle demand, regardless of location, as long as transmission capacity is available. Location of electric vehicle charging has a small effect on the marginal greenhouse gas emissions associated with additional generation, due to electricity losses in the transmission grid. We use a geographically explicit, charging assessment model for California to develop and compare the effects of two charging profiles. Comparison of these two basic scenarios points to savings in greenhouse gas emissions savings and operational costs from delayed charging of electric vehicles. Vehicle charging simulations confirm that plug-in electric vehicles alone are unlikely to require additional generation or transmission infrastructure. EDGE-NET was successfully benchmarked against historical data for the present grid but additional work is required to expand the model for future scenario evaluation. We discuss how the model might be adapted for high penetrations of variable renewable energy resources, and the use of grid storage. Renewable resources such as wind and solar vary in California vary significantly by time-of-day, season, and location. However, combination of multiple resources from different geographic regions through transmission grid interconnection is expected to help mitigate the impacts of variability. EDGE-NET can evaluate interaction of supply and demand through the existing transmission infrastructure and can identify any critical network bottlenecks or areas for expansion. For this reason, EDGE-NET will be an important tool to evaluate energy policy scenarios.