Optimal energy management and control of an industrial microgrid with plug-in electric vehicles

An industrial microgrid (IMG) consists in a microgrid involving manufacturer plants which are usually equipped with distributed generation facilities, industrial electric vehicles, energy storage systems, etc. In this paper, the problem of IMG efficient operation in presence of plug-in electric vehicles is addressed. To this purpose, schedule of the different device operations of IMGs has to be optimally computed, minimizing the operation cost while guaranteeing electrical network stability and production constraints. Such a problem is formulated in a receding horizon framework involving dynamic optimal power flow equations. Uncertainty affecting plug-in electric vehicles is handled by means of a chance constraint approach. The obtained nonconvex problem is then approximately solved by exploiting suitable convex relaxation techniques. Numerical simulations have been performed showing computational feasibility and robustness of the proposed approach against increased penetration of electric vehicles

Migrating towards Using Electric Vehicles in Fleets – Proposed Methods for Demand Estimation and Fleet Design

Carsharing and electric vehicles have emerged as sustainable transportation alternatives to mitigate transportation, environmental, and social issues in cities. This dissertation combines three correlated topics: carsharing feasibility, electric vehicle carsharing fleet optimization, and efficient fleet management. First, the potential demand for electric vehicle carsharing in Beijing is estimated using data from a survey conducted the summer of 2013 in Beijing. This utilizes statistical analysis method, binary logit regression. Secondly, a model was developed to estimate carsharing mode split by the function of utilization and appropriate carsharing fleet size was simulated under three different fleet types: an EV fleet with level 2 chargers, an EV fleet with level 3 chargers, and a gasoline vehicle fleet. This study also performs an economic analysis to determine the payback period for recovering the initial EV charging infrastructure costs. Finally, this study develops a fleet size and composition optimization model with cost constraints for the University of Tennessee, Knoxville motor pool fleet. This will help the fleet manage efficiently with minimum total costs and greater demand satisfaction. This dissertation can help guide future sustainable transportation planning and policy

Towards a comprehensive framework for V2G optimal operation in presence of uncertainty

As the global fleet of Electric Vehicles keeps increasing in number, the Vehicle To Grid (V2G) paradigm is gaining more and more attention. From the grid point of view an aggregate of electric vehicles can act as a flexible load, thus able to provide balancing services. The problem of computing the optimal day-ahead charging schedule for all vehicles in the fleet is a challenging one, especially because it is affected by many sources of uncertainty. In this paper we consider the uncertainty deriving from arrival and departure times, arrival energy and services market outcomes. We propose a general optimization framework to deal with the day ahead planning that encompasses different kind of use-cases. We adopt a robust paradigm to enforce the constraints and an expectation paradigm for the cost function. For all constraints and cost terms we propose an exact formulation or a very tight approximation, even in the case of piece-wise linear battery dynamics. Numerical results corroborates the theoretical findings

Demand Side Management of Electric Vehicles in Smart Grids: A survey on strategies, challenges, modeling, and optimization

The shift of transportation technology from internal combustion engine (ICE) based vehicles to electricvehicles (EVs) in recent times due to their lower emissions, fuel costs, and greater efficiency hasbrought EV technology to the forefront of the electric power distribution systems due to theirability to interact with the grid through vehicle-to-grid (V2G) infrastructure. The greater adoptionof EVs presents an ideal use-case scenario of EVs acting as power dispatch, storage, and ancillaryservice-providing units. This EV aspect can be utilized more in the current smart grid (SG) scenarioby incorporating demand-side management (DSM) through EV integration. The integration of EVswith DSM techniques is hurdled with various issues and challenges addressed throughout thisliterature review. The various research conducted on EV-DSM programs has been surveyed. This reviewarticle focuses on the issues, solutions, and challenges, with suggestions on modeling the charginginfrastructure to suit DSM applications, and optimization aspects of EV-DSM are addressed separatelyto enhance the EV-DSM operation. Gaps in current research and possible research directions have beendiscussed extensively to present a comprehensive insight into the current status of DSM programsemployed with EV integration. This extensive review of EV-DSM will facilitate all the researchersto initiate research for superior and efficient energy management and EV scheduling strategies andmitigate the issues faced by system uncertainty modeling, variations, and constraints

Elbilpolitikk fra et samfunnsøkonomisk perspektiv

This thesis focuses on the economics and polices for the electrification of transport. Over the last few years we have observed a rapid rise in the number of battery electric vehicles (BEVs) in Norway. This growth is the combined result of rapid technological change and a targeted national climate policy. The rising share of BEVs relative to the share of conventional vehicles could lead to socio-economic benefits such as reduced greenhouse gas emissions and local pollution, but it could also pose new challenges such as pressure on the capacity of the electricity distribution network. In addition, BEVs have similar negative externalities as fossil-fueled vehicles with regards to congestion, road wear and accidents. BEVs can mitigate some market failures and exacerbate others, creating a messy optimization problem for the social planner. This illustrates the need for new knowledge on mechanisms and welfare enhancing policies in the transport and electricity markets as they become more integrated. This thesis seeks to contribute to the body of knowledge on the subject, in the following introductory chapter and four independent chapters. The latter chapters are written as scientific papers that are either published or in the process of getting published in peer-reviewed journals.Denne avhandlingen tar for seg elbilpolitikk i et samfunnsøkonomisk perspektiv. De siste årene har vi opplevd en rask økning i antall elbiler i Norge. Denne veksten er et resultat av både rask teknologisk utvikling og en målrettet nasjonal klimapolitikk. Den økende andelen av elbiler i forhold til andelen konvensjonelle biler kan føre til samfunnsøkonomiske fordeler som reduserte klimagassutslipp og lokal forurensning, men det kan også gi nye utfordringer som press på kapasiteten til strømdistribusjonsnettet. I tillegg har elbiler tilsvarende eksterne kostnader som konvensjonelle biler med tanke på kø, veislitasje og ulykker. Elbiler kan dempe noen markedssvikt og forverre andre, og skape et rotete optimaliseringsproblem for samfunnsplanleggeren. Dette understreker behovet for ny kunnskap om den gjensidige påvirkningen mellom transport- og elektrisitetsmarkedet, og hva som kan være samfunnsmessig effektiv politikk. Denne avhandlingen bidrar til kunnskapen om emnet, i det følgende kappen og fire uavhengige kapitler. De siste kapitlene er skrevet som vitenskapelige artikler som enten er publisert eller i ferd med å bli publisert i fagfellevurderte tidsskrifter

Efficient operation of recharging infrastructure for the accommodation of electric vehicles: a demand driven approach

Large deployment and adoption of electric vehicles in the forthcoming years can have significant environmental impact, like mitigation of climate change and reduction of traffic-induced air pollutants. At the same time, it can strain power network operations, demanding effective load management strategies to deal with induced charging demand. One of the biggest challenges is the complexity that electric vehicle (EV) recharging adds to the power system and the inability of the existing grid to cope with the extra burden. Charging coordination should provide individual EV drivers with their requested energy amount and at the same time, it should optimise the allocation of charging events in order to avoid disruptions at the electricity distribution level. This problem could be solved with the introduction of an intermediate agent, known as the aggregator or the charging service provider (CSP). Considering out-of-home charging infrastructure, an additional role for the CSP would be to maximise revenue for parking operators. This thesis contributes to the wider literature of electro-mobility and its effects on power networks with the introduction of a choice-based revenue management method. This approach explicitly treats charging demand since it allows the integration of a decentralised control method with a discrete choice model that captures the preferences of EV drivers. The sensitivities to the joint charging/parking attributes that characterise the demand side have been estimated with EV-PLACE, an online administered stated preference survey. The choice-modelling framework assesses simultaneously out-of-home charging behaviour with scheduling and parking decisions. Also, survey participants are presented with objective probabilities for fluctuations in future prices so that their response to dynamic pricing is investigated. Empirical estimates provide insights into the value that individuals place to the various attributes of the services that are offered by the CSP. The optimisation of operations for recharging infrastructure is evaluated with SOCSim, a micro-simulation framework that is based on activity patterns of London residents. Sensitivity analyses are performed to examine the structural properties of the model and its benefits compared to an uncontrolled scenario are highlighted. The application proposed in this research is practice-ready and recommendations are given to CSPs for its full-scale implementation.Open Acces

A Cost-Effective Electric Vehicle Intelligent Charge Scheduling Method for Commercial Smart Parking Lots Using a Simplified Convex Relaxation Technique

Deployment of efficient and cost-effective parking lots is a known bottleneck for the electric vehicles (EVs) sector. A comprehensive solution incorporating the requirements of all key stakeholders is required. Taking up the challenge, we propose a real-time EV smart parking lot model to attain the following objectives: (a) maximize the smart parking lot revenue by accommodating maximum number of EVs and (b) minimize the cost of power consumption by participating in a demand response (DR) program offered by the utility since it is a tool to answer and handle the electric power usage requirements for charging the EV in the smart parking lot. With a view to achieving these objectives, a linear programming-based binary/cyclic (0/1) optimization technique is developed for the EV charge scheduling process. It is difficult to solve the problems of binary optimization in real-time given that the complexity of the problem increases with the increase in number of EV. We deploy a simplified convex relaxation technique integrated with the linear programming solution to overcome this problem. The algorithm achieves: minimum power consumption cost of the EV smart parking lot; efficient utilization of available power; maximization of the number of the EV to be charged; and minimum impact on the EV battery lifecycle. DR participation provide benefits by offering time-based and incentive-based hourly intelligent charging schedules for the EV. A thorough comparison is drawn with existing variable charging rate-based techniques in order to demonstrate the comparative validity of our proposed technique. The simulation results show that even under no DR event, the proposed scheme results in 2.9% decrease in overall power consumption cost for a 500 EV scenario when compared to variable charging rate method. Moreover, in similar conditions, such as no DR event and for 500 EV arrived per day, there is a 2.8% increase in number of EV charged per day, 3.2% improvement in the average state-of-charge (SoC) of the EV, 12.47% reduction in the average time intervals required to achieve final SoC