Smart EV Charging for Improved Sustainable Mobility

The landscape of energy generation and utilization is witnessing an unprecedented change. We are at the threshold of a major shift in electricity generation from utilization of conventional sources of energy like coal to sustainable and renewable sources of energy like solar and wind. On the other hand, electricity consumption, especially in the field of transportation, due to advancements in the field of battery research and exponential technologies like vehicle telematics, is seeing a shift from carbon based to Lithium based fuel. Encouraged by 1. Decrease in the cost of Li – ion based batteries 2. Breakthroughs in battery chemistry research - resulting in increased drive range 3. Government incentives and tariff concessions by utilities for EV owners in the form of tax credits, EV – only parking spaces, free charging equipment etc., the automobile market, especially the passenger vehicle market, is witnessing a steady growth in the sale of electric vehicles. This has resulted in Electric Vehicles contributing to the electricity load resulting in two challenges 1. At the supply end, it contributes as a potential micro energy storage system to fit the time gap between the demand for electricity and the supply of renewable and/or low cost electricity generation; and, 2. At the consumer-end, it creates a necessity to make energy consumption as sustainable and renewable as possible, while preserving battery life. In this thesis work we attempt to provide multiple practical solutions to address these needs by advancing existing technologies in the industry. Firstly, we have developed a “Joint EV-Grid Solution for Robust and Low-Complexity Smart Charing”, where we have designed and implemented a distributed smart charging algorithm, which runs in the EV with load and pricing information collected from Grid through the charging station. It is responsible for optimizing the charge plan of the user’s vehicle based on his/her preference and ensure a full charge before departure. The objective could be minimizing the electricity cost per charge session or maximizing the renewable energy usage. For instance, by setting the preference to optimize the algorithm according to “Price”, the additional demand is scheduled to off-peak hours (i.e., incurring the least cost). Alternatively, by setting the preference to “Renewables” the EV charges based on the maximum availability of renewable energy sources, thereby maximizing the utilization of renewable energy resources which may lead to reduced cost, if not minimize it. Furthermore, we have improved on our initial approach by introducing “Smart Charging Solution through Usage/Charging Pattern Learning” where we have used machine learning algorithms like Logistic regression and Fuzzy Logic to enable EVs to learn the usage and charging pattern of users and prepare a charging plan that is personalized at the users’ end and prevents potential smart-changing-caused demand peaks by distributing the net load throughout the day. Through our experiment studies we were successful in creating a distributed Charging algorithm and a Machine Learning system that could cater to the said requirements through innovative charging strategies. Consequently, helping us create a sustainable, win – win situation for both electricity consumer and producer

Distributed Optimal Vehicle Grid Integration Strategy with User Behavior Prediction

With the increasing of electric vehicle (EV) adoption in recent years, the impact of EV charging activities to the power grid becomes more and more significant. In this article, an optimal scheduling algorithm which combines smart EV charging and V2G gird service is developed to integrate EVs into power grid as distributed energy resources, with improved system cost performance. Specifically, an optimization problem is formulated and solved at each EV charging station according to control signal from aggregated control center and user charging behavior prediction by mean estimation and linear regression. The control center collects distributed optimization results and updates the control signal, periodically. The iteration continues until it converges to optimal scheduling. Experimental result shows this algorithm helps fill the valley and shave the peak in electric load profiles within a microgrid, while the energy demand of individual driver can be satisfied.Comment: IEEE PES General Meeting 201

Development of a multi criteria model for assisting EV user charging decisions

Electric Vehicles offer one of the most efficient solutions towards the direction of providing sustainable transportation systems. However, a broader market uptake of Electric Vehicle--based mobility is still missing. The lack of sufficient infrastructure (Electric Vehicle charging stations) in combination with the lack of information about their availability appears as a major limitation, leading to low user acceptance. Additional, technology based, assistance services provided to Electric Vehicle users is a key solution to unlock the full potential of their utilization. This paper presents a multi-factor dynamic optimization model using multi-criteria analysis to select the best alternatives for Electric Vehicle charging within a smart grid with the goal of supporting a larger uptake of Electric Vehicle -based mobility. The application provides assistance to the Electric Vehicle drivers through functionalities of energy price, cost and travel time of the electric vehicle to the charging station, the specifications of vehicles and stations, the status of the charging stations as well as the user\u27s preferences. The proposed model is developed by incorporating PROMETHEE II and Analytic Hierarchy Process methodologies to provide the best charging solutions after considering all possible options for each Electric Vehicle user. The multi-criteria analysis algorithm is not only limited to comparing alternative charging options at a specific time but also looks at several starting times of charging. A simulated case study is implemented to examine the functionality of the proposed model. From the results, it is evident that by applying the findings of this work entrepreneurial community and industry can develop new services that will improve user satisfaction, electromobility, urban mobility, and sustainability of cities. At the same time, academia, leveraging the methodology and factors that influence the choice of charging station, can conduct further research on digital innovations that will contribute to the consolidation of e-mobility ensuring the sustainability of cities, while accelerating digital transformation in the transport sector

Transforming Energy Networks via Peer to Peer Energy Trading: Potential of Game Theoretic Approaches

Peer-to-peer (P2P) energy trading has emerged as a next-generation energy management mechanism for the smart grid that enables each prosumer of the network to participate in energy trading with one another and the grid. This poses a significant challenge in terms of modeling the decision-making process of each participant with conflicting interest and motivating prosumers to participate in energy trading and to cooperate, if necessary, for achieving different energy management goals. Therefore, such decision-making process needs to be built on solid mathematical and signal processing tools that can ensure an efficient operation of the smart grid. This paper provides an overview of the use of game theoretic approaches for P2P energy trading as a feasible and effective means of energy management. As such, we discuss various games and auction theoretic approaches by following a systematic classification to provide information on the importance of game theory for smart energy research. Then, the paper focuses on the P2P energy trading describing its key features and giving an introduction to an existing P2P testbed. Further, the paper zooms into the detail of some specific game and auction theoretic models that have recently been used in P2P energy trading and discusses some important finding of these schemes.Comment: 38 pages, single column, double spac

Real-Time Bi-directional Electric Vehicle Charging Control with Distribution Grid Implementation

As electric vehicle (EV) adoption is growing year after year, there is no doubt that EVs will occupy a significant portion of transporting vehicle in the near future. Although EVs have benefits for environment, large amount of un-coordinated EV charging will affect the power grid and degrade power quality. To alleviate negative effects of EV charging load and turn them to opportunities, a decentralized real-time control algorithm is developed in this paper to provide optimal scheduling of EV bi-directional charging. To evaluate the performance of the proposed algorithm, numerical simulation is performed based on real-world EV user data, and power flow analysis is carried out to show how the proposed algorithm improve power grid steady state operation. . The results show that the implementation of proposed algorithm can effectively coordinate bi-directional charging by 30% peak load shaving, more than 2% of voltage drop reduction, and 40% transmission line current decrease

Battery-Conscious, Economic, and Prioritization-Based Electric Vehicle Residential Scheduling

Advances in communication technologies and protocols among vehicles, charging stations, and controllers have enabled the application of scheduling techniques to prioritize EV fleet charging. From the perspective of users, residential EV charging must particularly address cost-effective solutions to use energy more efficiently and preserve the lifetime of the battery—the most expensive element of an EV. Considering this matter, this research addresses a residential EV charging scheduling model including battery degradation aspects when discharging. Due to the non-linear characteristics of charging and battery degradation, we consider a mixed integer non-linearly constrained formulation with the aim of scheduling the charging and discharging of EVs to satisfy the following goals: prioritizing charging, reducing charging costs and battery degradation, and limiting the power demand requested to the distribution transformer. The results shows that, when EVs are discharged before charging up within a specific state-of-charge range, degradation can be reduced by 5.3%. All charging requests are completed before the next-day departure time, with 16.35% cost reduction achieved by scheduling charging during lower tariff prices, in addition to prevention of overloading of the distribution transformer