Design and Implementation of a Blockchain-Based Energy Trading Platform for Electric Vehicles in Smart Campus Parking Lots

This paper proposes a blockchain-based energy trading platform for electric vehicles in smart campus parking lots. Smart parking lots are smart places capable of supporting both parking and charging services for electric vehicles. The electric vehicle owner may want to charge energy at a low price and sell it during peak hours at a higher price. The proposed system architecture consists of two layers: the physical infrastructure layer and the cyber infrastructure layer. The physical infrastructure layer represents all of the physical components located in the campus distribution power system, such as electric vehicles charging stations, transformers, and electric feeders, while the cyber infrastructure layer supports the operation of the physical infrastructure layer and enables selling/buying energy among participants. Blockchain technology is a promising candidate to facilitate auditability and traceability of energy transactions among participants. A real case of a parking lot with a realistic parking pattern in a university campus is considered. The system consists of a university control center and various parking lot local controllers (PLLCs). The PLLC broadcasts the electricity demand and the grid price, and each electric vehicle owner decides whether to charge/discharge based on their benefits. The proposed system is implemented on Hyperledger Fabric. Participants, assets, transactions, and smart contracts are defined and discussed. Two scenarios are considered. The first scenario represents energy trading between electric vehicles as sellers and the PLLC as a buyer, while the second scenario involves energy trading between electric vehicles as buyers and the PLLC as a seller. The proposed platform provides profits for participants, as well as enables balancing for the university load demand locally. Document type: Articl

A Fully Decentralized Hierarchical Transactive Energy Framework for Charging EVs with Local DERs in Power Distribution Systems

The penetration rates of both electric vehicles (EVs) and distributed energy resources (DERs) have been increasing rapidly as appealing options to address the global problems of carbon emissions and fuel supply issues. However, uncoordinated EV charging activities and DER generation result in operational challenges for power distribution systems. Therefore, this article has developed a hierarchical transactive energy (TE) framework to locally induce and coordinate EV charging demand and DER generation in electric distribution networks. Based on a modified version of the alternating direction method of multipliers (ADMMs), two fully decentralized (DEC) peer-to-peer (P2P) trading models are presented, that is, an hour-ahead market and a 5-min-ahead real-time market. Compared to existing P2P electricity markets, this research represents the first attempt to comprehensively incorporate alternating current (ac) power network constraints into P2P electricity trading. The proposed TE framework not only contributes to mitigating operational challenges of distribution systems, but also benefits both EV owners and DER investors through secured local energy transactions. The privacy of market participants is well preserved since the bid data of each participant are not exposed to others. Comprehensive simulations based on the IEEE 33-node distribution system are conducted to demonstrate the feasibility and effectiveness of the proposed method

Power System Steady-State Analysis with Large-Scale Electric Vehicle Integration

It is projected that the electric vehicle will become a dominant method of transportation within future road infrastructure. Moreover, the electric vehicle is expected to form an additional role in power systems in terms of electrical storage and load balancing. This paper considers the latter role of the electric vehicle and its impact on the steady-state stability of power systems, particularly in the context of large-scale electric vehicle integration. The paper establishes a model framework which examines four major issues: electric vehicle capacity forecasting; optimization of an object function; electric vehicle station siting and sizing; and steady-state stability. A numerical study has been included which uses projected United Kingdom 2020 power system data with results which indicate that the electric vehicle capacity forecasting model proposed in this paper is effective to describe electric vehicle charging and discharging profiles. The proposed model is used to establish criteria for electric vehicle station siting and sizing and to determine steady-state stability using a real model of a small-scale city power system

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

Optimal behavior of a PEV parking lot in renewable-based power systems

There have been a lot of developments in terms of Plug-in Electric Vehicles (PEVs) regarding many different subjects, and with some variations between authors. On this basis, it is intended to sum up a lot of contents being approached, and help understanding them. Followed by the development and analysis of a model in order to better understand the functionality of these new developments. First a state of the art is presented where the new development are presented, these will include management of the PEV’s, uncontrolled or controlled (i.e. aggregated) and their capability of using V2G and G2V technologies are analyzed. Afterwards, electricity markets are approached where real world applications are shown and different market types are categorized in order to a better understanding of the subject. The interaction of the PEVs with some renewable energy resources (e.g. solar, wind and biomass) is presented. To finalize, models of PEVs are categorized and multiple types of modules, the related variables, applied methods, and the considered parameters are presented. For a case analysis, a model that includes a parking lot of PEVs will be studied, which includes renewable energy resources, wind and solar. The objective is to analyze the impact of these on the market participation of the parking lot and also on the distribution grid. These analyses will be made on size variations, grid placement and also constraint variations of the model.Tem havido muitos desenvolvimentos em relação a veículos elétricos com tecnologia Plug-in (PEVs), sendo um tema abrangente com bastantes tópicos a serem estudados, sendo que existem também diferentes abordagens do tema por diferentes autores. Tendo isto em consideração, o objetivo inicial será a recolha de informação relativo a esta área e a sua sumarização de modo a possibilitar uma maior compreensão sobre a área. De seguida, o modelo desenvolvido será efetuada a sua análise, tendo em consideração alguns destes desenvolvimentos mencionados previamente. Primeiramente um estado da arte será apresentado onde os recentes desenvolvimentos na área serão apresentados. Estes desenvolvimentos incluem a possibilidade de gestão e manuseamento dos veículos, controlados ou descontrolados (i.e. agregador), e a possibilidade da utilização das tecnologias veiculo para a rede (V2G) e rede para o veículo (G2V) é analisada. De seguida, são analisado os mercados de energia onde serão apresentados casos reais e diferentes tipos de Mercado serão descriminados. A interação dos PEVs com algumas energias renováveis (e.g. Solar, Vento e biomassa) é apresentada. Finalizando modelos de PEVs serão categorizados fazendo distinção entre eles, entre tipo de modelos, variáveis, métodos aplicados, e os parâmetros considerados por estes mesmos. Como caso de estudo é apresentada a análise de um modelo que conta com um parqueamento de PEV, inclui energias renováveis. O objetivo é o de analisar os efeitos das energias renováveis na participação do mercado do parqueamento e o impacte na rede de distribuição. Esta análise será feita pela variação na potência instalada das renováveis, localização na rede do parqueamento e variação nas limitações do modelo

PV Charging and Storage for Electric Vehicles

Electric vehicles are only ‘green’ as long as the source of electricity is ‘green’ as well. At the same time, renewable power production suffers from diurnal and seasonal variations, creating the need for energy storage technology. Moreover, overloading and voltage problems are expected in the distributed network due to the high penetration of distributed generation and increased power demand from the charging of electric vehicles. The energy and mobility transition hence calls for novel technological innovations in the field of sustainable electric mobility powered from renewable energy. This Special Issue focuses on recent advances in technology for PV charging and storage for electric vehicles

Smart Sustainable Mobility: Analytics and Algorithms for Next-Generation Mobility Systems

To this date, mobility ecosystems around the world operate on an uncoordinated, inefficient and unsustainable basis. Yet, many technology-enabled solutions that have the potential to remedy these societal negatives are already at our disposal or just around the corner. Innovations in vehicle technology, IoT devices, mobile connectivity and AI-powered information systems are expected to bring about a mobility system that is connected, autonomous, shared and electric (CASE). In order to fully leverage the sustainability opportunities afforded by CASE, system-level coordination and management approaches are needed. This Thesis sets out an agenda for Information Systems research to shape the future of CASE mobility through data, analytics and algorithms (Chapter 1). Drawing on causal inference, (spatial) machine learning, mathematical programming and reinforcement learning, three concrete contributions toward this agenda are developed. Chapter 2 demonstrates the potential of pervasive and inexpensive sensor technology for policy analysis. Connected sensing devices have significantly reduced the cost and complexity of acquiring high-resolution, high-frequency data in the physical world. This affords researchers the opportunity to track temporal and spatial patterns of offline phenomena. Drawing on a case from the bikesharing sector, we demonstrate how geo-tagged IoT data streams can be used for tracing out highly localized causal effects of large-scale mobility policy interventions while offering actionable insights for policy makers and practitioners. Chapter 3 sets out a solution approach to a novel decision problem faced by operators of shared mobility fleets: allocating vehicle inventory optimally across a network when competition is present. The proposed three-stage model combines real-time data analytics, machine learning and mixed integer non-linear programming into an integrated framework. It provides operational decision support for fleet managers in contested shared mobility markets by generating optimal vehicle re-positioning schedules in real time. Chapter 4 proposes a method for leveraging data-driven digital twin (DT) frameworks for large multi-stage stochastic design problems. Such problem classes are notoriously difficult to solve with traditional stochastic optimization. Drawing on the case of Electric Vehicle Charging Hubs (EVCHs), we show how high-fidelity, data-driven DT simulation environments fused with reinforcement learning (DT-RL) can achieve (close-to) arbitrary scalability and high modeling flexibility. In benchmark experiments we demonstrate that DT-RL-derived designs result in superior cost and service-level performance under real-world operating conditions