The Critical Role of Public Charging Infrastructure

Editors: Peter Fox-Penner, PhD, Z. Justin Ren, PhD, David O. JermainA decade after the launch of the contemporary global electric vehicle (EV) market, most cities face a major challenge preparing for rising EV demand. Some cities, and the leaders who shape them, are meeting and even leading demand for EV infrastructure. This book aggregates deep, groundbreaking research in the areas of urban EV deployment for city managers, private developers, urban planners, and utilities who want to understand and lead change

Dynamic Modeling and Real-time Management of a System of EV Fast-charging Stations

Demand for electric vehicles (EVs), and thus EV charging, has steadily increased over the last decade. However, there is limited fast-charging infrastructure in most parts of the world to support EV travel, especially long-distance trips. The goal of this study is to develop a stochastic dynamic simulation modeling framework of a regional system of EV fast-charging stations for real-time management and strategic planning (i.e., capacity allocation) purposes. To model EV user behavior, specifically fast-charging station choices, the framework incorporates a multinomial logit station choice model that considers charging prices, expected wait times, and detour distances. To capture the dynamics of supply and demand at each fast-charging station, the framework incorporates a multi-server queueing model in the simulation. The study assumes that multiple fast-charging stations are managed by a single entity and that the demand for these stations are interrelated. To manage the system of stations, the study proposes and tests dynamic demand-responsive price adjustment (DDRPA) schemes based on station queue lengths. The study applies the modeling framework to a system of EV fast-charging stations in Southern California. The results indicate that DDRPA strategies are an effective mechanism to balance charging demand across fast-charging stations. Specifically, compared to the no DDRPA scheme case, the quadratic DDRPA scheme reduces average wait time by 26%, increases charging station revenue (and user costs) by 5.8%, while, most importantly, increasing social welfare by 2.7% in the base scenario. Moreover, the study also illustrates that the modeling framework can evaluate the allocation of EV fast-charging station capacity, to identify stations that require additional chargers and areas that would benefit from additional fast-charging stations

ABSCEV: An agent-based simulation framework about smart transportation for reducing waiting times in charging electric vehicles

[EN] Fuel has been the main source of energy for cars for many years, but the non-renewable resources are limited in the planet. In this context, electric vehicles (EVs) are increasingly replacing the previous kind of cars. However, as the number of EVs increases, some challenges arise such as the reduction of waiting times in the queues of fast charging stations. The current work addresses this challenge by means of social coordination mechanisms. In particular, this work presents an agent-based simulation framework for simulating the effects of different coordination policies in the route planning of EV drivers for charging their vehicles on their trips. In this manner, researchers and professionals can test different coordination mechanisms for this purpose. This framework has been experienced by simulating an adaptive strategy based on the implicit communication through booking systems in the charging stations. This strategy was compared with another common strategy, which was used as the control mechanism. This comparison was done by simulating several scenarios in two Spanish cities (i.e. Madrid and Zaragoza). The experimental results show that the current approach was useful to propose a route planning strategy that had statistically significant improvements in the reduction of waiting times in charging stations and also in the global trip times. In addition, the evolutions of pathfinding execution times and the numbers of interchanged messages did not show any overloading pattern over the time. (C) 2018 Elsevier B.V. All rights reservedWe acknowledge the research project "Construccion de un framework para agilizar el desarrollo de aplicaciones mviles en el ambito de la salud" funded by University of Zaragoza and Foundation Ibercaja with grant reference JIUZ-2017-TEC-03. This work has been supported by the program "Estancias de movilidad en el extranjero Jose Castillejo para jovenes doctores" funded by the Spanish Ministry of Education, Culture and Sport with reference CAS17/00005. We also acknowledge support from "Universidad de Zaragoza", "Fundacion Bancaria Ibercaja" and "Fundacion CAI" in the "Programa Ibercaja-CAI de Estancias de Investigacion" with reference IT1/18. This work acknowledges the research project "Desarrollo Colaborativo de Soluciones AAL" with reference TIN2014-57028-R funded by the Spanish Ministry of Economy and Competitiveness. It has also been supported by "Organismo Autonomo Programas Educativos Europeos" with reference 2013-1-CZ1-GRU06-14277. We also acknowledge support from project "Sensores vestibles y tecnologa movil como apoyo en la formacin y practica de mindfulness: prototipo previo aplicado a bienestar" funded by University of Zaragoza with grant number UZ2017-TEC-02.García-Magariño, I.; Palacios-Navarro, G.; Lacuesta Gilaberte, R.; Lloret, J. (2018). ABSCEV: An agent-based simulation framework about smart transportation for reducing waiting times in charging electric vehicles. Computer Networks. 138:119-135. https://doi.org/10.1016/j.comnet.2018.03.01411913513

Grid-Connected Distributed Wind-Photovoltaic Energy Management: A Review

Energy management comprises of the planning, operation and control of both energy production and its demand. The wind energy availability is site-specific, time-dependent and nondispatchable. As the use of electricity is growing and conventional sources are depleting, the major renewable sources, like wind and photovoltaic (PV), have increased their share in the generation mix. The best possible resource utilization, having a track of load and renewable resource forecast, assures significant reduction of the net cost of the operation. Modular hybrid energy systems with some storage as back up near load center change the scenario of unidirectional power flow to bidirectional with the distributed generation. The performance of such systems can be enhanced by the accomplishment of advanced control schemes in a centralized system controller or distributed control. In grid-connected mode, these can support the grid to tackle power quality issues, which optimize the use of the renewable resource. The chapter aims to bring recent trends with changing requirements due to distributed generation (DG), summarizing the research works done in the last 10 years with some vision of future trends

Fast-timescale Control Strategies for Demand Response in Power Systems.

Concerns over climate change have spurred an increase in the amount of wind and solar power generation on the grid. While these resources reduce carbon emissions, the physical phenomena that they rely on - wind and sunlight - are highly stochastic, making their generated power less controllable. Demand-side strategies, which modulate load in a controllable manner, have been proposed as a way to add flexibility to the grid. Resources with innate flexibility in their load profile are particularly suited to demand response (DR) applications. This work examines two such loads: heating, ventilation, and air conditioning (HVAC) systems, and plug-in electric vehicle (PEV) fleets. HVAC systems can vary the timing of power consumption due to the thermal inertia inherent in their associated building(s). The first part of this thesis explores the efficacy of using commercial HVAC for DR applications. Results are presented from an experimental testbed that quantify performance, in terms of accuracy in perturbing the load in a desired manner, as well as the efficiency of this process. PEVs offer very fast response times and may eventually represent a significant load on the power system. The second part of this thesis develops several control strategies to manage PEV power consumption in an environment where communication resources are limited, both to prevent detrimental system effects such as transformer overload, and to provide ancillary services such as frequency regulation to the grid.PhDElectrical Engineering: SystemsUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/116627/1/ianbeil_1.pd

The role of transmission networks in the evolution of a low carbon electricity system in the UK

The UK’s commitments to addressing climate change require a radical restructuring of the electricity sector. This thesis examines what role the electricity transmission networks could play in this transformation. In order to examine the possible role of policy making within a socio-technical system under conditions of long-term uncertainty, a novel scenario method is developed which accounts for political values, actor dynamics and technological networks. The approach is used to examine possible pathways for the electricity transmission network within alternative policy value-sets, which are defined by the level of locational signal provided to generators in respect of their network usage, and the degree of anticipatory or strategic planning involved in network policy. The scenarios emphasise the importance of a locational signal which acts at the operational timescale as well as the investment timescale. They also suggest a role for strategic coordination, particularly to join up planning across onshore, offshore and interconnector regimes. However, due to the range of possible generation and network configurations the scenarios span, they do not support the idea of a central design authority working to a single network blueprint. Specific policy recommendations aim to incorporate these suggestions within the grain of the existing policy trajectory and its prevailing value system. The two principle policy recommendations are therefore, the inclusion of a locational signal within the BSUoS charge in order to better reflect network usage at the operational timescale, and the establishment of an independent body with a remit to identify and contribute needs cases for cross-regime strategic coordination opportunities. The latter recommendation could be achieved with some adaption and clarification of the remit of the ENSG.Open Acces

Operations research models of technology transitions and the role of policy support

Technology exists to fulfill functions in society, and technological innovations are continuously proposed to fulfill a particular function more effectively than an incumbent technology. These innovations are disseminated through society in a process called technology diffusion, and may ultimately replace an incumbent system in what is known as a technology transition. Due to the complex and uncertain underlying processes of technology adoption and diffusion, technical systems are resistant to transition to possibly superior alternatives. To address market, systemic, and structural failures preventing a desired technology transition, a policymaker, or other motivated agent, may strategically intervene to stimulate or accelerate the diffusion process. The success or failure of such policy intervention carries crucial implications for climate change mitigation, healthcare advances, and any other aspect of society that technology touches. However, existing models of optimal technology policy design omit or otherwise offer crude representations of these underlying processes and are largely case-specific at the expense of gleaning generalizable insights. The goal of this dissertation is to advance the operations research modeling of technology transitions and the role of policy support. Through a variety of powerful operations research methodologies and relevant case studies, the individual projects in this dissertation offer novel models of technology transitions and insights into real-world technology policy, especially in the energy and climate domain. The three core chapters of this dissertation begin with the development of an applied energy system optimization model to assess a real-world climate policy, then move on to present two novel theoretical models that yield more general, analytical insights into technology policy decision making. Chapter 2 addresses the growing importance of cities in climate change mitigation with the development of an energy system optimization model for urban-scale decarbonization. Our optimization model determines the least-cost power and transportation technology pathways to achieve a policy goal of net-zero greenhouse gas emissions and is used to analyze the Community Climate Plan adopted by Austin, Texas. We find that the policy objective can be achieved at a modest 2.7% increase in net present power and transportation costs relative to business-as-usual. The optimal decarbonization pathway proceeds through two distinct stages, first reducing power sector emissions, then electrifying transportation. Solar PV expands in the long run with or without the climate plan based on favorable cost projections, but the policy causes wind to replace natural gas as a complement to solar PV. Our findings also highlight the substantial value of intelligently scheduled battery storage operations and electric vehicle charging. While the energy system optimization model of Chapter 2 captures numerous decisions for a complex urban energy system, it carries limiting assumptions about how technology diffusion occurs and the role of a policymaker in supporting a technology transition. Addressing these larger questions motivates the project in Chapter 3, which describes the development of two stylized models of technology policy decision making under uncertainty. The first model is a Markov reward process (MRP) that represents policy interventions with one-time, upfront costs, while the second is a Markov decision process (MDP) that represents interventions with recurring costs. For each model, we derive analytical expressions for the policymaker's willingness to pay (WTP) to raise the probabilities of advancing a technology development or diffusion process at various stages and compare and contrast the behaviors of the MRP and MDP models. Most notably, our analytical findings elucidate how the different cost-accounting schemes and the possibility of regressing from a more advanced development or diffusion stage back to an earlier one affect the WTP. Then, we conduct numerical sensitivity analysis to explore how the optimal technology policy portfolio varies with certain parameters, and present a case study on lithium-ion batteries for electric vehicles to demonstrate the practical application of our model to technology policy decision making. In Chapter 4, we narrow our focus on technology transitions to infrastructure-dependent technologies common in energy, transportation, and telecommunications systems. Policymakers seeking to promote the diffusion of infrastructure-dependent technologies are often confronted with the chicken-and-egg problem: consumers are reluctant to adopt the technology without adequate infrastructure available, and firms are reluctant to invest in infrastructure without a sufficient number of adopters. This chicken-and-egg problem can hinder the diffusion of new technologies and prolong the timeframe over which existing technological systems remain locked-in. In this paper, we formulate a stylized model of technology policy decision making from the perspective of a policymaker who seeks to stimulate the market penetration of an infrastructure-dependent technology. Our model is a bilevel optimization problem in which a policymaker (leader) maximizes net social benefits by setting the levels of two incentives: a subsidy for a profit-maximizing firm (follower) to invest in infrastructure that raises the benefit of adoption to consumers, and a direct subsidy for consumers to adopt the technology. We analytically derive the firm's optimal infrastructure investment response to the upper-level policy decisions, and show that the bilevel model is equivalent to a quadratic program. To bypass non-convexity, we develop a custom solution strategy based on decomposition, and find that it performs better than directly applying an off-the-shelf solver to the potentially non-convex problem. Finally, we present a case study on the diffusion of battery electric vehicles and obtain insights into how a policymaker should allocate resources to charging infrastructure and vehicle incentives. The three projects of this dissertation employ operations research methods to model technology transitions and the role of policy support. While each captures a variety of phenomena affecting technology transitions and optimal technology policy decision making, there remain thought-provoking questions that future research can address. We conclude this dissertation with proposed research directions and contemplate the high-level, real-world implications of this work.Operations Research and Industrial Engineerin