Electric Vehicles In Smart Grid Research Articles

A Decision Function Based Smart Charging and Discharging Strategy for Electric Vehicle in Smart Grid

As the number of Electric Vehicle (EV) increases, the uncontrolled EV charging behaviors may cause the grid load fluctuations and other passive effects. In order to balance the EV charging load in the smart grid, an Electric Vehicle Charging and Discharging Strategy (EVCDS) which is based on a Charging Decision Function (CDF) as well as a Discharging Decision Function (DDF) is proposed. In the CDF and DDF, there are three sub-functions related to the residual energy of battery, EV’s charging habits, and the charging efficiency of charging station respectively. The residual energy of battery is used to calculate the excepted probability that satisfies the user’s mileage requirement, which is an important factor. The charging habits are used to calculate the second sub-function value, which stands for the user’s comfort. The charging efficiency and distance are combined together to calculate the third sub-function. All the sub-functions are weighted and combined into the CDF and DDF to decide whether to charge, discharge or do nothing. In the numerical results, we set up a scenario for the commercial vehicles and private vehicles. After compared with other strategies, EVCDS performs well in terms of reducing the charging demand fluctuations and improving the charging demand balance among charging stations.

Optimal Charging Schedule Coordination of Electric Vehicles in Smart Grid

The increasing penetration of electric vehicle (EV) at distribution system is expected in the near future leading to rising demand for power consumption. Large scale uncoordinated charging demand of EVs will eventually threatens the safety operation of the distribution network. Therefore, a charging strategy is needed to reduce the impact of charging. This paper proposes an optimal centralized charging schedule coordination of EV to minimize active power losses while maintaining the voltage profile at the demand side. The performance of the schedule algorithm developed using particle swarm optimization (PSO) technique is evaluated at the IEEE-33 Bus radial distribution system in a set time frame of charging period. Coordinated and uncoordinated charging schedule is then compared in terms of active power losses and voltage profile at different level of EV penetration considering 24 hours of load demand profile. Results show that the proposed coordinated charging schedule is able to achieve minimum total active power losses compared to the uncoordinated charging.

Distributed Scalable Autonomous Market-Based Demand Response via Residential Plug-In Electric Vehicles in Smart Grids

Flexibility in power demand, diverse usage patterns and storage capability of plug-in electric vehicles (PEVs) grow the elasticity of residential electricity demand remarkably. This elasticity can be used to form the daily aggregated demand profile and/or alter instantaneous demand of a system wherein a large number of residential PEVs share one electricity retailer. In this paper, we propose a demand response technique to manage vehicle-to-grid enabled PEVs’ electricity assignments (charging and discharging) in order to reduce the overall electricity procurement costs for a retailer bidding to a two-settlement electricity market, i.e., a day-ahead and a spot or real-time (RT) market. We show that our approach is decentralized, scalable, fast converging and does not violate users’ privacy. Extensive simulations show significant overall cost savings can be achieved for a retailer bidding to an operational electricity market by using the proposed algorithm. This technique becomes more needful when the power grid accommodates a large number of intermittent energy resources. There, RT demand altering is crucial due to more likely contingencies and hence more RT price fluctuations and even occurring the so-called black swan events . Finally, such retailer could offer better deals to customers as well to stay competitive.

Online Charging Scheduling Algorithms of Electric Vehicles in Smart Grid: An Overview

As an environment-friendly substitute for conventional fuel-powered vehicles, electric vehicles (EVs) and their components have been widely developed and deployed worldwide. The large-scale integration of EVs into power grid brings both challenges and opportunities to the system performance. On one hand, the load demand from EV charging imposes large impact on the stability and efficiency of power grid. On the other hand, EVs could potentially act as mobile energy storage systems to improve the power network performance, such as load flattening, fast frequency control, and facilitating renewable energy integration. Evidently, uncontrolled EV charging could lead to inefficient power network operation or even security issues. This spurs enormous research interests in designing charging coordination mechanisms. A key design challenge here lies in the lack of complete knowledge of events that occur in the future. Indeed, the amount of knowledge of future events significantly impacts the design of efficient charging control algorithms. This article focuses on introducing online EV charging scheduling techniques that deal with different degrees of uncertainty and randomness of future knowledge. Besides, we highlight the promising future research directions for EV charging control.

Distributed Online Energy Management for Data Centers and Electric Vehicles in Smart Grid

To avoid an additional large demand charge introduced by the adoption of electric vehicles (EVs), we investigate a joint energy management problem for geographically distributed data centers (DCs) and EVs of the employees in this paper. Specifically, we intend to minimize the long-term total cost of DCs and EVs by jointly scheduling DC workloads and EV charging demands, without violating heterogeneous EV charging requirements and the given power limits in all geographical locations. We first formulate a stochastic programming problem with the considerations of uncertainties in DC workloads, electricity prices, and EV energy demands. Since these uncertain system parameters are time-varying and the size of the formulated problem is large, we then propose a distributed online algorithm to solve the formulated problem based on Lyapunov optimization technique and a variant of alternating direction method of multipliers. Finally, extensive simulation results show the effectiveness of the proposed algorithm.

Risk management and participation planning of electric vehicles in smart grids for demand response

Demand response (DR) can serve as an effective tool to better balance the electricity demand and supply in the smart grid. It is defined as "the changes in electricity usage by end-use customers from their normal consumption patterns" in response to pricing and incentive payments. This paper focuses on new opportunities for DR with electric vehicles (EVs). EVs are potential distributed energy resources that support both the grid-to-vehicle and vehicle-to-grid modes. Their participation in the time-based (e.g., time-of-use) and incentive-based (e.g., regulation services) DR programs helps improve the stability and reduce the potential risks to the grid. Smart scheduling of EV charging and discharging activities also supports high penetration of renewables with volatile energy generation. This paper proposes a novel stochastic model from the Independent System Operator's perspective for risk management and participation planning of EVs in the smart grid for DR. The risk factors considered in this paper involve those caused by uncertainties in renewables (wind and solar), load patterns, parking patterns, and transmission lines' reliability. The effectiveness of the model in response to various settings such as the area type (residential, commercial, and industrial), the EV penetration level, and the risk level has been investigated.

Hybrid optimal online-overnight charging coordination of plug-in electric vehicles in smart grid

Abstract Optimal coordinated charging of plugged-in electric vehicles (PEVs) in smart grid (SG) can be beneficial for both consumers and utilities. This paper proposes a hybrid optimal online followed by overnight charging coordination of high and low priority PEVs using discrete particle swarm optimization (DPSO) that considers the benefits of both consumers and electric utilities. Objective functions are online minimization of total cost (associated with grid losses and energy generation) and overnight valley filling through minimization of the total load levels. The constraints include substation transformer loading, node voltage regulations and the requested final battery state of charge levels (SOCreq). The main challenge is optimal selection of the overnight starting time (toptimal-overnight,start) to guarantee charging of all vehicle batteries to the SOCreq levels before the requested plug-out times (treq) which is done by simultaneously solving the online and overnight objective functions. The online-overnight PEV coordination approach is implemented on a 449-node SG; results are compared for uncoordinated and coordinated battery charging as well as a modified strategy using cost minimizations for both online and overnight coordination. The impact of toptimal-overnight,start on performance of the proposed PEV coordination is investigated.

Decentralized Optimal Scheduling for Charging and Discharging of Plug-In Electric Vehicles in Smart Grids

This paper focuses on the procurement of load shifting service by optimally scheduling the charging and discharging of PEVs in a decentralized fashion. We assume that the energy flow between PEVs and the grid is bidirectional, i.e., PEVs can also release energy back into the grid as distributed generation, which is known as vehicle-to-grid (V2G). The optimal scheduling problem is then formulated as a mixed discrete programming (MDP) problem, which is NP-hard and extremely difficult to solve directly. To get over this difficulty, we propose a solvable approximation of the MDP problem by exploiting the shape feature of the base demand curve during the night, and develop a decentralized algorithm based on iterative water-filling. Our algorithm is decentralized in the sense that the PEVs compute locally and communicate with an aggregator. The advantages of our algorithm include reduction in computational burden and privacy preserving. Simulation results are given to show the performance of our algorithm.

Charging Schemes for Plug-In Hybrid Electric Vehicles in Smart Grid: A Survey

Plug-in hybrid electric vehicles (PHEVs) have emerged as an important tool in reducing greenhouse gas emissions, due to their lower dependency on fossil fuel. Since, for cost efficiency, PHEVs have a limited battery capacity, they must be recharged often and especially after trips. Thus, efficient battery charging plays an important role on the success of PHEVs commercial adoption. This paper surveys the state-of-the-art of existing PHEV battery charging schemes. We classify these schemes into four classes, namely, uncontrolled , indirectly controlled , smart , and bidirectional charging, and review various existing techniques within each class. For uncontrolled charging, existing studies focus on evaluating the impact of adding variable charging load on the smart grid. Various indirectly controlled charging schemes have been proposed to control energy prices, in order to indirectly influence the charging operations. Smart charging schemes can directly control a rich set of charging parameters to achieve various performance objectives, such as minimizing power loss, maximizing operator’s profit, ensuring fairness, and so on. Finally, bidirectional charging allows a PHEV to discharge energy into smart grid, such that the vehicle can act as a mobile energy source to further stabilize the grid, which is partially supplied by intermittent renewable energy sources. This survey provides a comprehensive one-stop introductory reference to quickly learn about the key features and technical challenges, addressed by existing PHEV battery charging schemes in smart grid.

A Review of Charge Scheduling of Electric Vehicles in Smart Grid

Electric vehicles (EVs) are being introduced by different manufacturers as an environment-friendly alternative to vehicles with internal combustion engines, with several benefits. The number of EVs is expected to grow rapidly in the coming years. However, uncoordinated charging of these vehicles can put a severe stress on the power grid. The problem of charge scheduling of EVs is an important and challenging problem and has seen significant research activity in the last few years. This review covers the recent works done in the area of scheduling algorithms for charging EVs in smart grid. The works are first classified into two broad classes of unidirectional versus bidirectional charging, and then, each class is further classified based on whether the scheduling is centralized or distributed and whether any mobility aspects are considered or not. It then reviews the key results in this field following the classification proposed. Some interesting research challenges that can be addressed are also identified.

A self-adapting similarity-based coalition formation approach for plug-in electric vehicles in smart grids

The Vehicle-To-Grid (V2G) concept is a key feature towards the integration of electric vehicles into smart grids. Through V2G sessions, plug-in electric vehicles (PEVs) can sell their surplus energy to the grid. However, profiting from V2G sessions is not trivial for singletons. Thereby, the formation of coalitions among PEVs has been proposed to tackle this issue. In this paper, we rely on domain knowledge in order to propose a novel modelling for such problem. Specifically, we investigate how the similarity among the PEVs’ energy profiles can be used to improve the formation of coalitions. The energy profile of a PEV estimates how long such PEV will be available for the V2G session. Based on this, we aim at maximising the coalitions’ duration. We empirically show that our approach is both efficient (it outperforms state-of-the-art algorithms in terms of runtime) and effective (solutions were 96.5% of optimum, on average).

Mobility aware scheduling for imbalance reduction through charging coordination of electric vehicles in smart grid

Electric vehicles (EVs) are being introduced by different manufacturers as an environment-friendly alternative to vehicles with internal combustion engines. The number of EVs is expected to grow rapidly in the coming years which will act as distributed loads in the demand side of the smart grid. On the supply side, an aggregator has to predict a load schedule 12–36 h in advance to charge the EVs and purchase energy accordingly from the day-ahead market. The goal of the aggregator is to schedule the EVs at different charging stations along the route of the EVs for charging so that the energy imbalance between the energy purchased and the energy consumed by the EVs is minimized while maximizing the number of EVs charged. In this work, we refer to this problem as the Bounded Maximum Energy Usage Maximum EV Charging Problem where mobile EVs communicate their charging preferences to the aggregator and the aggregator schedules EVs to different charging stations in their route so as to maximize both the purchased energy utilization (and therefore, minimum energy imbalance) and the number of EVs charged. We first prove that the problem is NP-complete. A pseudo-polynomial algorithm is proposed for a restricted version of the problem that can act as an upper bound for the energy usage. We also give an upper bound for the number of EVs charged. An offline and an online heuristic are then proposed to solve the problem. Detailed simulation results in different city traffic scenarios are presented to show the performance of the algorithms.

Optimal Charging Scheduling of Electric Vehicles in Smart Grids by Heuristic Algorithms

Transportation electrification has become an important issue in recent decades and the large scale deployment of electric vehicles (EVs) has yet to be achieved. The smart coordination of EV demand addresses an improvement in the flexibility of power systems and reduces the costs of power system investment. The uncertainty in EV drivers’ behaviour is one of the main problems to solve to obtain an optimal integration of EVs into power systems. In this paper, an optimisation algorithm to coordinate the charging of EVs has been developed and implemented using a Genetic Algorithm (GA), where thermal line limits, the load on transformers, voltage limits and parking availability patterns are taken into account to establish an optimal load pattern for EV charging-based reliability. This methodology has been applied to an existing residential low-voltage system. The results indicate that a smart charging schedule for EVs leads to a flattening of the load profile, peak load shaving and the prevention of the aging of power system elements.

Models for optimal charging scheduling for electric vehicles in smart grid

Electric vehicles have experienced high demand in the market, which will introduce a substantial new load to the current electricity grid. Furthermore, uncontrolled electric vehicle charging from residential users will exacerbate the existing peak load during evening hours. In this paper, we propose optimisation models to alleviate the impact of extra load from electric vehicles on the power grid. Particularly, two centralised models allow the controller to minimise the total electricity costs for all users. Consequently, load levelling of the entire power grid is achieved. On the other hand, a decentralised model allows each user to minimise his/her own electricity cost and studies the resulting system-wide load levelling. Our numerical results indicate significant improvements in the total cost and peak-to-average ratio by both the centralised and decentralised optimal charging scheduling models, when compared to the uncontrolled charging scenarios.

Strategic charging method for plugged in hybrid electric vehicles in smart grids; a game theoretic approach

Implementation of various incentive-based and time-based load management strategies has great potential to decrease peak load growth and customer electricity bill cost. In recent years, developments in Plug in Hybrid Electric Vehicles (PHEVs) have provided various environmental and economic advantages. However, high penetration of electric vehicles in to the grid may cause high peak loads at different times of the days. Using advanced metering and automatic chargers makes it possible to optimize the charging cost, and release generation capacities to provide sustainable electricity supply. Using an appropriate encouraging program is a simple way for vehicle owners to manage their energy consumption and shift the time of charging to proper time of the day; and therefore, to reduce their electricity bill. With these objectives, this paper proposes a new practical PHEVs’ charging scheduling programs aiming at optimizing customers charging cost by considering the generation capacity limitation and dynamic electricity price in different time slots of a day. Using a stochastic model for start time of charging and the duration of it in the proposed optimization algorithm, make this method a practical tool for modeling the vehicle owners’ charging behavior with the purpose of peak load shaving.

The role of electric vehicles in smart grids

The electrification of transportation is seen as one of the solutions to challenges such as global warming, sustainability, and geopolitical concerns on the availability of oil. From the perspective of power systems, an introduction of plug‐in electric vehicles presents many challenges but also opportunities to the operation and planning of power systems. On the one hand, if vehicles are considered regular loads without flexibility, uncontrolled charging can lead to problems at different network levels endangering secure operation of installed assets. However, with direct or indirect control approaches the charging of vehicles can be managed in a desirable way, e.g., shifted to low‐load hours. Furthermore, vehicles can be used as distributed storage resources to contribute to ancillary services for the system, such as frequency regulation and peak‐shaving power or help integrate fluctuating renewable resources. All these modes of operation need appropriate regulatory frameworks and market design if the flexibility of the vehicles is to be capitalized. In most of the proposed approaches, a so‐called aggregator could be in charge of directly or indirectly controlling the charging of vehicles and serve as an interface with other entities such as the transmission system operator or energy service providers. However, fully decentralized schemes without an aggregator are also conceivable, for instance, to provide primary frequency control. Communication also plays a key role, as in most of the control schemes a significant amount of information needs to be transmitted between vehicles and control entities. The management of electric vehicles as distributed resources fits well in the paradigm of smart grids, where an advanced use of communication technologies and metering infrastructure, increased controllability and load flexibility, and a larger share of fluctuating and distributed resources are foreseen.This article is categorized under: Energy Infrastructure > Economics and Policy Energy Systems Economics > Systems and Infrastructure Energy Systems Analysis > Systems and Infrastructure