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

Hierarchical Pricing Game for Balancing the Charging of Ride-Hailing Electric Fleets

Due to the ever-increasing popularity of ride-hailing services and the indisputable shift towards alternative fuel vehicles, the intersection of the ride-hailing market and smart electric mobility provides an opportunity to trade different services to achieve societal optimum. In this work, we present a hierarchical, game-based, control mechanism for balancing the simultaneous charging of multiple ride-hailing fleets. The mechanism takes into account sometimes conflicting interests of the ride-hailing drivers, the ride-hailing company management, and the external agents such as power-providing companies or city governments that will play a significant role in charging management in the future. The upper-level control considers charging price incentives and models the interactions between the external agents and ride-hailing companies as a Reverse Stackelberg game with a single leader and multiple followers. The lower-level control motivates the revenue-maximizing drivers to follow the company operator's requests through surge pricing and models the interactions as a single leader, multiple followers Stackelberg game. We provide a pricing mechanism that ensures the existence of a unique Nash equilibrium of the upper-level game that minimizes the external agent's objective at the same time. We provide theoretical and experimental robustness analysis of the upper-level control with respect to parameters whose values depend on sensitive information that might not be entirely accessible to the external agent. For the lower-level algorithm, we combine the Nash equilibrium of the upper-level game with a quadratic mixed integer optimization problem to find the optimal surge prices. Finally, we illustrate the performance of the control mechanism in a case study based on real taxi data from the city of Shenzhen in China

Smart Grid Technologies in Europe: An Overview

The old electricity network infrastructure has proven to be inadequate, with respect to modern challenges such as alternative energy sources, electricity demand and energy saving policies. Moreover, Information and Communication Technologies (ICT) seem to have reached an adequate level of reliability and flexibility in order to support a new concept of electricity network—the smart grid. In this work, we will analyse the state-of-the-art of smart grids, in their technical, management, security, and optimization aspects. We will also provide a brief overview of the regulatory aspects involved in the development of a smart grid, mainly from the viewpoint of the European Unio

An Efficient and Secure Energy Trading Approach with Machine Learning Technique and Consortium Blockchain

In this paper, a secure energy trading mechanism based on blockchain technology is proposed. The proposed model deals with energy trading problems such as insecure energy trading and inefficient charging mechanisms for electric vehicles (EVs) in a vehicular energy network (VEN). EVs face two major problems: finding an optimal charging station and calculating the exact amount of energy required to reach the selected charging station. Moreover, in traditional trading approaches, centralized parties are involved in energy trading, which leads to various issues such as increased computational cost, increased computational delay, data tempering and a single point of failure. Furthermore, EVs face various energy challenges, such as imbalanced load supply and fluctuations in voltage level. Therefore, a demand-response (DR) pricing strategy enables EV users to flatten load curves and efficiently adjust electricity usage. In this work, communication between EVs and aggregators is efficiently performed through blockchain. Moreover, a branching concept is involved in the proposed system, which divides EV data into two different branches: a Fraud Chain (F-chain) and an Integrity Chain (I-chain). The proposed branching mechanism helps solve the storage problem and reduces computational time. Moreover, an attacker model is designed to check the robustness of the proposed system against double-spending and replay attacks. Security analysis of the proposed smart contract is also given in this paper. Simulation results show that the proposed work efficiently reduces the charging cost and time in a VEN.publishedVersio

Generalized Wardrop Equilibrium for Charging Station Selection and Route Choice of Electric Vehicles in Joint Power Distribution and Transportation Networks

This paper presents the equilibrium analysis of a game composed of heterogeneous electric vehicles (EVs) and a power distribution system operator (DSO) as the players, and charging station operators (CSOs) and a transportation network operator (TNO) as coordinators. Each EV tries to pick a charging station as its destination and a route to get there at the same time. However, the traffic and electrical load congestion on the roads and charging stations lead to the interdependencies between the optimal decisions of EVs. CSOs and the TNO need to apply some tolling to control such congestion. On the other hand, the pricing at charging stations depends on real-time distributional locational marginal pricing, which is determined by the DSO after solving the optimal power flow over the power distribution network. This paper also takes into account the local and the coupling/infrastructure constraints of EVs, transportation and distribution networks. This problem is modeled as a generalized aggregative game, and then a decentralized learning method is proposed to obtain an equilibrium point of the game, which is known as variational generalized Wardrop equilibrium. The existence of such an equilibrium point and the convergence of the proposed algorithm to it are proven. We undertake numerical studies on the Savannah city model and the IEEE 33-bus distribution network and investigate the impact of various characteristics on demand and prices

A survey on enhancing grid flexibility through bidirectional interactive electric vehicle operations

Smart grids (SG) constitute a revolutionary concept within the energy sector, enabling the establishment of a bidirectional communication infrastructure. This infrastructure significantly improves control, efficiency, and overall service quality in power systems. The study provides an in-depth survey on the classification of EVs, including both plug-in and non-plug-in EVs, and the integration process of V2G, including bidirectional power flow analysis. Moreover, various control strategies for EV integration are explored, ranging from centralized and decentralized to hierarchical control structures. Further, the research thoroughly examines the potential benefits of EV integration and addresses associated challenges, such as battery degradation, infrastructure requirements, cybersecurity and communication issues, grid congestion, and consumer behavior. The study goes beyond theoretical exploration and offers a comprehensive simulation analysis. This analysis leverages the storage capabilities of EVs to provide grid support services. A real-time dynamic dispatch strategy is formulated to integrate EVs into the automatic generation control of multi-energy systems. The findings demonstrate that EVs can effectively mitigate forecasting errors in a power network heavily reliant on wind energy sources. Consequently, the storage capabilities of EVs contribute to enhancing grid flexibility in managing the intermittency of renewable energy resources

Development of the Plug-in Electric Vehicle Charging Infrastructure via Smart-Charging Algorithms

Electricity generation and the transportation sector make up a large portion of greenhouse gas emissions in the United States. Meeting ambitious reductions in greenhouse gasses requires large scale adoption of plug-in electric vehicles (PEVs) and has led to several policies and laws aimed at incentivizing PEV sales. An inadequate charging infrastructure, however, could be a major obstacle for a large-scale adoption of PEVs. Large electrical demands from PEVs could negatively affect circuitry, increase electricity costs, and exacerbate stress to local electrical components during times of high electricity usage. These issues, however, can be addressed by deploying smart-charging strategies.This work is focused on the development of smart-charging protocols for workplace battery electric vehicle (BEV) charging. Three comprehensive smart-charging protocols with different applications are proposed. Each protocol is developed with varying degrees of focus on communication requirements and privacy concerns. The BEV-based Optimization Protocol is a decentralized, non-iterative strategy that allows BEVs to individually schedule their charging schedules. The Octopus Charger-based MILP Protocol allows octopus chargers (i.e., charging stations with multiple cables) to independently schedule charging for their assigned BEVs. The Real-Time Octopus Charger-based MILP Protocol allows octopus chargers to schedule BEV charging in real time, without prior information from BEVs. By using the appropriate cost signal and assignment algorithms, the proposed protocols can manage a parking structure demand load while reducing the number of installed charging stations. Driving patterns from the National Household Travel Survey were used to perform simulations, to verify and quantify the effectiveness of each protocol. The proposed protocols resulted in improved peak load reductions for all simulated smart-charging scenarios, when compared with uncontrolled charging. By using octopus chargers, all protocols were able to reduce the number of charging stations needed at parking structures, while meeting the charging requests of all BEVs. Time-Of-Use rate plans from Southern California Edison were used to estimate monthly electricity costs for the simulated parking structures. The smart-charging protocols resulted in reduced electricity costs for most cases studied, when compared to uncontrolled charging