ACN-Sim: An Open-Source Simulator for Data-Driven Electric Vehicle Charging Research

ACN-Sim is a data-driven, open-source simulation environment designed to accelerate research in the field of smart electric vehicle (EV) charging. It fills the need in this community for a widely available, realistic simulation environment in which researchers can evaluate algorithms and test assumptions. ACN-Sim provides a modular, extensible architecture, which models the complexity of real charging systems, including battery charging behavior and unbalanced three-phase infrastructure. It also integrates with a broader ecosystem of research tools. These include ACN-Data, an open dataset of EV charging sessions, which provides realistic simulation scenarios and ACN-Live, a framework for field-testing charging algorithms. It also integrates with grid simulators like MATPOWER, PandaPower and OpenDSS, and OpenAI Gym for training reinforcement learning agents.Comment: 9 pages, 8 figures. [v2] Update timezone issue with Fig. 8 where x-axis and background load was shifted by 3 hour

On the Control of Active End-nodes in the Smart Grid

The electrical grid has substantially changed in recent years due to the integration of several disruptive load and generation technologies into low-voltage distribution networks, which are meant to smarten it and improve its efficiency. These technologies have subjected the grid to unprecedented amounts of variability and uncertainty that threaten its reliability and could reduce its efficiency. Even a low penetration of these disruptive technologies may cause equipment overloads, voltage deviations beyond permissible operating thresholds, and bidirectional power flows in distribution networks. The smart grid will comprise a vast number of active end-nodes, including electric vehicle chargers, solar inverters, storage systems, and other elastic loads, that can be quickly controlled to adjust their real and reactive power contributions. Given the availability of inexpensive measurement devices and a broadband communication network that connects measurement devices to controllers, it is possible to incorporate potentially disruptive technologies into distribution networks while maintaining service reliability, using some novel control mechanisms, which are the focus of this thesis. In this thesis, we propose a new paradigm for the control of active end-nodes at scale. This control paradigm relies on real-time measurements of the states of the distribution network and the end-nodes rather than long-term predictions. We use an optimal control framework to design mechanisms that balance a set of system-level and user-level objectives. We study control of active end-nodes in two different contexts: a radial distribution system and a grid-connected public electric vehicle charging station powered by on-site solar generation. We develop both a feedback controller and an open-loop controller, and propose centralized and distributed algorithms for solving optimal control problems. We implement and validate these control mechanisms using extensive numerical simulations and power flow analysis on a standard test system

The Adaptive Charging Network Research Portal: Systems, Tools, and Algorithms

Millions of electric vehicles (EVs) will enter service in the next decade, generating gigawatt-hours of additional energy demand. Charging these EVs cleanly, affordably, and without excessive stress on the grid will require advances in charging system design, hardware, monitoring, and control. Collectively, we refer to these advances as smart charging. While researchers have explored smart charging for over a decade, very few smart charging systems have been deployed in practice, leaving a sizeable gap between the research literature and the real world. In particular, we find that research is often based on simplified theoretical models. These simple models make analysis tractable but do not account for the complexities of physical systems. Moreover, researchers often lack the data needed to evaluate the performance of their algorithms on real workloads or apply techniques like machine learning. Even when promising algorithms are developed, they are rarely deployed since field tests can be costly and time-consuming. The goal of this thesis is to develop systems, tools, and algorithms to bridge these gaps between theory and practice. First, we describe the architecture of a first-of-its-kind smart charging system we call the Adaptive Charging Network (ACN). Next, we use data and models from the ACN to develop a suite of tools to help researchers. These tools include ACN-Data, a public dataset of over 80,000 charging sessions; ACN-Sim, an open-source simulator based on realistic models; and ACN-Live, a platform for field testing algorithms on the ACN. Finally, we describe the algorithms we have developed using these tools. For example, we propose a practical and robust algorithm based on model predictive control, which can reduce infrastructure requirements by over 75%, increase operator profits by up to 3.4 times, and significantly reduce strain on the electric power grid. Other examples include a pricing scheme that fairly allocates costs to users considering time-of-use tariffs and demand charges and a data-driven approach to optimally size on-site solar generation with smart EV charging systems.</p

Not according to plan: Exploring gaps in city climate planning and the need for regional action

As the country's primary economic and population centers, cities drive most greenhouse gas (GHG) emissions and will absorb most climate-related costs. And the growing frequency of floods, fires, droughts, and heat waves puts cities of all sizes in greater danger.To reduce these costs and amplify benefits, cities need to reduce emissions (or "decarbonize") their built environment. Eliminating fossil fuel consumption from their transportation, building, and electricity sectors is essential; collectively, these sectors produce nearly two-thirds of national GHG emissions. However, achieving those reductions will require more than simply relying on new federal rules and funding, including those in the Inflation Reduction Act. Local planners, policymakers, and practitioners need to coordinate on new infrastructure investments.One of the first steps cities have taken is the drafting of "climate action plans"—many of which pledge specific carbon reductions. Yet even as these plans proliferate, cities leaders are struggling to hit their targets. One gap in city climate planning and action is internal, with cities often failing to specify detailed strategies that will advance their goals. The other gap is regional: Individual cities do not have the fiscal, technical, or programmatic capacity to single-handedly drive decarbonization across their metropolitan regions, and often, they do not coordinate with other jurisdictions.This report attempts to better understand why cities are failing to meet their targets and what can be learned from the planning practices that are working well. By evaluating the most comprehensive decarbonization plans across 50 of the country's largest cities, the report judges how well the strategies and actions in these plans prepare cities for meaningful, accountable decarbonization

Federal Public Lands Policy and the Climate Crisis and Proposed Policy: Sequential Mitigation and Net Conservation Benefit

Professor Fischman\u27s contributions to this colleciton include the sections, Federal Public Lands Policy and the Climate Crisis and Proposed Policy: Sequential Mitigation and Net Conservation Benefit.https://www.repository.law.indiana.edu/facbooks/1231/thumbnail.jp

Interactive Demand Shifting in the context of Domestic Micro-Generation

The combination of ubiquitous computing and emerging energy technologies is radically changing the home energy landscape. Domestic micro-generation, dominated by solar photovoltaic, is increasing at a rapid pace. This represents an opportunity for creating and altering energy behaviours. However, these transformations generate new challenges that we call the domestic energy gap: domestic electricity consumption and microgeneration are out of sync. Micro-generation is mainly uncontrollable production relying on weather while domestic energy consumption tends to happen mostly during the evening. This thesis focuses on understanding and supporting new domestic practices in the context of domestic solar electricity generation, looking at ‘Demand-Shifting’. Specifically, we look at how can digital tools leverage Demand-Shifting practices in the context of domestic micro-generation? Relying on a mixed-method approach, we provide a qualitative and quantitative answer with the collaboration of 38 participating households in several field studies including two spanning more than eight months. Through a deep investigation of laundry and electric mobility routines in the context of domestic micro-generation, we emphasised a natural engagement into Demand-Shifting which appeared as a complex and time-consuming task for participants which was not visible when we analysed their quantitative data. We revealed this complexity through Participatory Data Analyses, a method we designed to analyse the data in collaboration with the participating householders. This provided us with a comprehensive view of the relationship between domestic micro-generation and daily routines. Finally, we highlight the need for timely and contextual support through the deployment of interventions in-the-wild. Building on discussions of our findings in perspective of the literature, we propose a conceptual framework to support domestic interactive Demand-Shifting

Plug-in hybrid and battery electric vehicles in South Africa: market forecasts

This paper uses diffusion modeling to forecast the sales of Plug-in Hybrid and Battery Electric Vehicles (PH/BEVs) in South Africa. First the potential benefits of PH/BEVs in South Africa are scrutinized. The global PH/BEV market is analyzed along with the goals and enticement policies of the countries that are best positioned for a widespread uptake of PH/BEVs. The supply and demand challenges facing the market for PH/BEVs in South Africa are evaluated with a review of current and proposed public policies. The total sales of PH/BEVs in South Africa are then forecast for the medium and long term using an adapted multiple technology generation Bass model with variable parameters for vehicle purchase price and running cost. Two scenarios are examined involving varying oil prices, electricity prices, government support and PH/BEV technological development

Multi-objective Smart Charge Control of Electric Vehicles

With the increasing integration of electric vehicles and renewable energy sources in electricity networks, key opportunities in terms of a cleaner environment and a sustainable energy portfolio are unlocked. However, the widespread deployment of these two technologies, can entail significant challenges for the electricity grid and in a larger context for the society, when they are not optimally integrated. In this context, smart charging of electric vehicles and vehicle-to-grid technologies are being proposed as crucial solutions to achieve economic, technical and environmental benefits in future smart grids. The implementation of these technologies involves a number of key stakeholders, namely, the end-electricity user, the electric vehicle owner, the system operators and policy makers. For a wider and efficient implementation of the smart grid vision, these stakeholders must be engaged and their aims must be fulfilled. However, the financial, technical and environmental objectives of these stakeholders are often conflicting, which leads to an intricate paradigm requiring efficient and fair policies. With this focus in mind, the present research work develops multi-objective optimisation algorithms to control the charging and discharging process of electric vehicles. Decentralised, hybrid and real-time optimisation algorithms are proposed, modelled, simulated and validated. End user energy cost, battery degradation, grid interaction and CO2 emissions are optimised in this work and their trade-offs are highlighted. Multi-criteria-decision-making approaches and game theoretical frameworks are developed to conciliate the interests of the involved stakeholders. The results, in the form of optimal electric vehicle charging/discharging schedules, show improvements along all the objectives while complying with the user requirements. The outcome of the present research work serves as a benchmark for informing system operators and policy makers on the necessary measures to ensure an efficient and sustainable implementation of electro-mobility as a fundamental part of current and future smart grids

Secure Large Scale Penetration of Electric Vehicles in the Power Grid

As part of the approaches used to meet climate goals set by international environmental agreements, policies are being applied worldwide for promoting the uptake of Electric Vehicles (EV)s. The resulting increase in EV sales and the accompanying expansion in the EV charging infrastructure carry along many challenges, mostly infrastructure-related. A pressing need arises to strengthen the power grid to handle and better manage the electricity demand by this mobile and geo-distributed load. Because the levels of penetration of EVs in the power grid have recently started increasing with the increase in EV sales, the real-time management of en-route EVs, before they connect to the grid, is quite recent and not many research works can be found in the literature covering this topic comprehensively. In this dissertation, advances and novel ideas are developed and presented, seizing the opportunities lying in this mobile load and addressing various challenges that arise in the application of public charging for EVs. A Bilateral Decision Support System (BDSS) is developed here for the management of en-route EVs. The BDSS is a middleware-based MAS that achieves a win-win situation for the EVs and the power grid. In this framework, the two are complementary in a way that the desired benefit of one cannot be achieved without attaining that of the other. A Fuzzy Logic based on-board module is developed for supporting the decision of the EV as to which charging station to charge at. GPU computing is used in the higher-end agents to handle the big amount of data resulting in such a large scale system with mobile and geo-distributed nodes. Cyber security risks that threaten the BDSS are assessed and measures are applied to revoke possible attacks. Furthermore, the Collective Distribution of Mobile Loads (CDML), a service with ancillary potential to the power system, is developed. It comprises a system-level optimization. In this service, the EVs requesting a public charging session are collectively redistributed onto charging stations with the objective of achieving the optimal and secure operation of the power system by reducing active power losses in normal conditions and mitigating line congestions in contingency conditions. The CDML uses the BDSS as an industrially viable tool to achieve the outcomes of the optimization in real time. By participating in this service, the EV is considered as an interacting node in the system-wide communication platform, providing both enhanced self-convenience in terms of access to public chargers, and contribution to the collective effort of providing benefit to the power system under the large scale uptake of EVs. On the EV charger level, several advantages have been reported favoring wireless charging of EVs over wired charging. Given that, new techniques are presented that facilitate the optimization of the magnetic link of wireless EV chargers while considering international EMC standards. The original techniques and developments presented in this dissertation were experimentally verified at the Energy Systems Research Laboratory at FIU

Linehaul Trucking Systems Decarbonization Analysis

Greenhouse gases (e.g., carbon dioxide, methane, and nitrous oxide) emitted by human activities are inarguably contributing to a changing climate. The transportation sector – which relies heavily on combusting fossil fuels such as gasoline and diesel and has long been a dominant contributor to greenhouse gas emissions – must be part of the solution to reduce emissions. This report explores the ways in which linehaul (heavy truck freight traveling long distances) can decarbonize. Both short-haul (commercial trips less than 250 miles from start to finish) and long-haul (trips over 250 miles) trucking are evaluated. The report focuses on three diesel truck alternatives: renewable natural gas (upgraded biogas) trucks, battery electric trucks, and hydrogen gas-powered fuel cell electric trucks. This report analyzes the opportunities and challenges that multiple alternative powertrains present and addresses how each powertrain could be used to advance decarbonization and zero-emissions initiatives, depending on the priorities of linehaul owners. It seeks to guide further research and investments so that the linehaul transportation industry can move past technical limitations into a position where trucking decarbonization can be a reality. Research insight was based on an extensive literature review, the Argonne National Laboratory’s transportation emissions and economic modeling tools, academic and fuel-vendor interviews, and a summer internship on Amazon’s Transportation Sustainability team. The Argonne models used were the 2019 versions of Greenhouse gases, Regulated Emissions and Energy in Transportation (GREET) and Alternative Fuel Life-Cycle Environmental and Economic Transportation (AFLEET). Five criteria were determined to influence the fit of alternative powertrains for linehaul trucking: greenhouse gas reduction potential, vehicle availability, vehicle functionality, cost, and scalability. Ability to meet zero-emissions vehicle targets is a consideration within the greenhouse gas reduction criterion. Alternative transportation systems become competitive when their total cost of operations are near diesel parity, their carbon footprints from well-to-wheel (across the fuel supply chain, including fuel use) are lower than the diesel vehicle status-quo – especially if they approach zero emissions, and if they are scalable. All three powertrains can, under the right conditions, improve upon the emissions scenario of business-as-usual diesel dependence. All alternative powertrains require a facilitative market and policy environment.Master of ScienceSchool for Environment and SustainabilityUniversity of Michiganhttp://deepblue.lib.umich.edu/bitstream/2027.42/163663/1/Dodinval_Claire_Practicum.pd