Unsplittable Load Balancing in a Network of Charging Stations Under QoS Guarantees

The operation of the power grid is becoming more stressed, due to the addition of new large loads represented by Electric Vehicles (EVs) and a more intermittent supply due to the incorporation of renewable sources. As a consequence, the coordination and control of projected EV demand in a network of fast charging stations becomes a critical and challenging problem. In this paper, we introduce a game theoretic based decentralized control mechanism to alleviate negative impacts from the EV demand. The proposed mechanism takes into consideration the non-uniform spatial distribution of EVs that induces uneven power demand at each charging facility, and aims to: (i) avoid straining grid resources by offering price incentives so that customers accept being routed to less busy stations, (ii) maximize total revenue by serving more customers with the same amount of grid resources, and (iii) provide charging service to customers with a certain level of Quality-of-Service (QoS), the latter defined as the long term customer blocking probability. We examine three scenarios of increased complexity that gradually approximate real world settings. The obtained results show that the proposed framework leads to substantial performance improvements in terms of the aforementioned goals, when compared to current state of affairs.Comment: Accepted for Publication in IEEE Transactions on Smart Gri

Near-optimal Online Algorithms for Joint Pricing and Scheduling in EV Charging Networks

With the rapid acceleration of transportation electrification, public charging stations are becoming vital infrastructure in a smart sustainable city to provide on-demand electric vehicle (EV) charging services. As more consumers seek to utilize public charging services, the pricing and scheduling of such services will become vital, complementary tools to mediate competition for charging resources. However, determining the right prices to charge is difficult due to the online nature of EV arrivals. This paper studies a joint pricing and scheduling problem for the operator of EV charging networks with limited charging capacity and time-varying energy cost. Upon receiving a charging request, the operator offers a price, and the EV decides whether to admit the offer based on its own value and the posted price. The operator then schedules the real-time charging process to satisfy the charging request if the EV admits the offer. We propose an online pricing algorithm that can determine the posted price and EV charging schedule to maximize social welfare, i.e., the total value of EVs minus the energy cost of charging stations. Theoretically, we prove the devised algorithm can achieve the order-optimal competitive ratio under the competitive analysis framework. Practically, we show the empirical performance of our algorithm outperforms other benchmark algorithms in experiments using real EV charging data

Smart green charging scheme of centralized electric vehicle stations

This paper presses a smart charging decision-making criterion that significantly contributes in enhancing the scheduling of the electric vehicles (EVs) during the charging process. The proposed criterion aims to optimize the charging time, select the charging methodology either DC constant current constant voltage (DC-CCCV) or DC multi-stage constant currents (DC-MSCC), maximize the charging capacity as well as minimize the queuing delay per EV, especially during peak hours. The decision-making algorithms have been developed by utilizing metaheuristic algorithms including the Genetic Algorithm (GA) and Water Cycle Optimization Algorithm (WCOA). The utility of the proposed models has been investigated while considering the Mixed Integer Linear Programming (MILP) as a benchmark. Furthermore, the proposed models are seeded using the Monte Carlo simulation technique by estimating the EVs arriving density to the EVS across the day. WCOA has shown an overall reduction of 13% and 8.5% in the total charging time while referring to MILP and GA respectively

Market based intelligent charging system for electric vehicles

The existing electrical infrastructure is very unlikely to expand overnight. Therefore, a smart solution is certainly needed to integrate the additional load which electric vehicles (EV) bring to the network. The aim of the thesis is to study the electricity market, different intelligences related to electric vehicle charging and establish an algorithm that produces an optimized charging schedule for electric vehicles. The algorithm ensures a cost profit for user and takes part in demand response by shifting the timing of charging loads based on energy prices. The core intelligences integrated to the EV charging system in the thesis are cost optimization, peak shaving and load shifting. The algorithm follows the hourly unit cost related to the energy consumption and distribution fee in order to find the cheapest time slot for charging operation. It allocates as high charging power as possible to the cheapest time slots and then start selecting the expensive time slots until the battery is charged to desired state of charge. Along this process, the algorithm continuously calculates the maximum charging power available after other house-hold usage. The Elspot area price of Finland for 2018 added with 0.3 cents/kWh margin and 24% VAT are used as energy prices. Distribution unit prices include time-of-use pricing for day and nighttime energy use in addition to the fixed fuse-based fee. By following these unit prices, the algorithm shifts the load from high demand to low demand hours in order to minimize the total costs. The algorithm is implemented in MATLAB and tested through a case study on different type of Finnish detached houses. Detached houses with different load profile data are used as input for charging a 75 kWh EV with a 10 kW and 7.5 kW charger in different cases, where the other inputs remain same for all the test cases. The Elspot area price of Finland for 2018 added with 0.3 cents/kWh margin and 24% VAT are used as energy prices. Different day and night-time distribution prices are applied depending on the consumption. The simulation results are compared to regular EV charging, where the charging operation starts right after the EV is plugged in and finishes charging within shortest time. The results from the simulation are investigated from user’s and grid’s point of view. From user’s perspective, all the charging events with intelligent charging have costs savings over regular charging. The monetary profit is higher for higher charger rating (10 kW). In cases where the household usage is low, the proportional profit is high. From grid point of view, over 99% of the load gets shifted to night-time for 10 kW charger cases. For the 7.5kW charger, the amount of shifted load is over 97%, which is a little lower than 10 kW charger cases because of longer charging time. The findings of the case study validate the use of smart charging algorithm in order to ensure cost savings for the user

A chronological literature review of electric vehicle interactions with power distribution systems

In the last decade, the deployment of electric vehicles (EVs) has been largely promoted. This development has increased challenges in the power systems in the context of planning and operation due to the massive amount of recharge needed for EVs. Furthermore, EVs may also offer new opportunities and can be used to support the grid to provide auxiliary services. In this regard, and considering the research around EVs and power grids, this paper presents a chronological background review of EVs and their interactions with power systems, particularly electric distribution networks, considering publications from the IEEE Xplore database. The review is extended from 1973 to 2019 and is developed via systematic classification using key categories that describe the types of interactions between EVs and power grids. These interactions are in the framework of the power quality, study of scenarios, electricity markets, demand response, demand management, power system stability, Vehicle-to-Grid (V2G) concept, and optimal location of battery swap and charging stations.Introduction General Overview Chronological Review: Part I Chronological Review: Part II Brief Observations Conclusions and Future Works Final Reflections Author Contributions Funding Acknowledgments Conflicts of Interest Reference

Optimal electric vehicle scheduling : A co-optimized system and customer perspective

Electric vehicles provide a two pronged solution to the problems faced by the electricity and transportation sectors. They provide a green, highly efficient alternative to the internal combustion engine vehicles, thus reducing our dependence on fossil fuels. Secondly, they bear the potential of supporting the grid as energy storage devices while incentivizing the customers through their participation in energy markets. Despite these advantages, widespread adoption of electric vehicles faces socio-technical and economic bottleneck. This dissertation seeks to provide solutions that balance system and customer objectives under present technological capabilities. The research uses electric vehicles as controllable loads and resources. The idea is to provide the customers with required tools to make an informed decision while considering the system conditions. First, a genetic algorithm based optimal charging strategy to reduce the impact of aggregated electric vehicle load has been presented. A Monte Carlo based solution strategy studies change in the solution under different objective functions. This day-ahead scheduling is then extended to real-time coordination using a moving-horizon approach. Further, battery degradation costs have been explored with vehicle-to-grid implementations, thus accounting for customer net-revenue and vehicle utility for grid support. A Pareto front, thus obtained, provides the nexus between customer and system desired operating points. Finally, we propose a transactive business model for a smart airport parking facility. This model identifies various revenue streams and satisfaction indices that benefit the parking lot owner and the customer, thus adding value to the electric vehicle --Abstract, page iv