Electric vehicles (EVs) offer a cleaner mode of personal transportation and a new way to store energy, but also present challenges to the grid due to additional distributed storage and load. With rising numbers of EVs on the streets, problems can include voltage limits violation or line congestion, mainly at the distribution level. All those operating devices in the grid, from network operators/managers to EVs owners, need fast communication with low latency, high security and reliability. The book addresses EVs as a driving source for realizing smart grid operation. It provides chapters from multidisciplinary academic and industry communities related to EVs charging schemes and technologies, and its associated communication, networking and information architectures. This book for researchers and practitioners provides a basis for full integration of EVs into the grid through extensive use of ICT tools in: (i) transport as energy storage system (TES) modelling, simulation and optimisation processes; (ii) vehicle on-line optimal control, estimation and prediction; (iii) energy system strategic planning with renewables; and (iv) supporting services such as those related to smart grid via smart charging.
Inspec keywords: wind power plants; energy management systems; analytic hierarchy process; smart power grids; battery powered vehicles; battery storage plants; demand side management; vehicular ad hoc networks; electric vehicle charging; frequency control; solar power stations; telecommunication computing; vehicle-to-grid; energy storage; power engineering computing; renewable energy sources; machine-to-machine communication; power markets
Other keywords: grid integration and management of EVs through machine-to-machine communication; smart grid; power-demand management in a smart grid using electric vehicles; electric buses; distributed energy storage; secondary frequency control; advanced mobility communication; multicriteria optimization of electric vehicle fleet charging and discharging schedule; vehicle-to-grid battery storage; small-size electric energy system; renewable energy integration; wind-powered charging station; V2G services; renewable generators; peer-to-peer energy market; energy management
Subjects: Energy resources and fuels; Physics literature and publications; General and management topics; Wind power plants; Frequency control; Solar power stations and photovoltaic power systems; Control of power systems and devices; Power systems; Direct energy conversion and energy storage; Power engineering computing; Energy storage; Computer networks and techniques; Energy utilisation; Communications computing; Computer communications; General electrical engineering topics; Other power stations and plants; Power transmission, distribution and supply; Transportation; Radio links and equipment; Energy resources
The urban and interurban mobility environment has been evolving in recent decades. Traditional factors that have been pushing these changes have been changes in motorisation, residential and urban sprawl, and the spatial distribution of the population, the workplaces and other societal relevant locations. Currently, more than a half of the world population is living in cities, reaching almost 72% in the European Union (EU), and it is estimated that over 80% of the European population will live in urban areas by 2020. The growth of the cities will also generate new city mobility challenges to be solved.
The development of vehicle-to-grid (V2G) technology for electric vehicles (EVs) enables prosumers to incorporate other energy storages and participate in largescale bidirectional energy trading. However, the mobility of EVs makes it significantly different from any other conventional storage. For a mass-penetration of EVs, uncoordinated planning and random distribution of V2G may result in power grid instability and power quality degradation. Therefore, a real-time coordinated and automated V2G system in a distributed way is essential for effective energy management. Machine-to-machine (M2M) communication enabling bidirectional information flow between EVs and other power system components is a key element to mitigate challenges associated with their grid integration and manage all parties autonomously. M2M technology assists in improving energy efficiency and reducing any potential risk of instability in power systems. In this chapter, an indepth study of an M2M communication-based coordinated management of EVs is presented. It includes a step-by-step description and practical implementation process of the data logging system, data transmission, and its automatic processing mechanism at the server. Additionally, the scalability issue for EV M2M communication under a 4G Long Term Evolution transceiver base station is extensively examined. Various numerical simulations with and without radio network scheduling are also presented to provide a detailed understanding of this scalability issue, taken into consideration communication delays and blocking rate.
The electric vehicles (EVs) have been dramatically increased and popularized in recent years. The ability to export the power to the grid via the vehicle -to -grid (V2G) technology makes the EVs become the promising solutions for reducing the peak demand in the power grid but could also severely increase the fluctuated penetration if no scheduling mechanisms are deployed. Therefore, it is an essential task to provide the optimal EV charging and discharging scheduling. However, to practically reinforce the scheduling relies not only on the ability to offer it in the efficient, reliable and scalable approaches, but also on the willingness of the participation from EVs owners. The cloud -based energy management service (EMS) satisfies the needs to practically deploy the scheduling mechanisms for the heterogeneous EVs and provide the incentives for customers' participation. The cloud computing is introduced with its characteristics and is utilized for the design of an extensive cloud -based framework, which provides the energy management as a service (EMaaS) to suggest the optimal electricity usage and trading options for every participated customer. The framework and the procedure of the cloud -based EMS are illustrated, and the EVs charging and discharging scheduling for the cloud -based EMS is formulated and implemented. The scheduling results for both EVs with and without the V2G ability are discussed with various examples.
Commercial fleet of electric vehicles (EV) is serving clients for different purposes, but each of those services is requiring some time to be fulfilled. When the vehicle is charging or discharging using the vehicle-to-grid (V2G) concept, the waiting time to service increases, and the vehicle owner is suffering losses due to the reduced service quality. In this chapter, we are exploring the scheduling optimization problem of an EV fleet controlled by the aggregator. The optimization of vehicle daily scheduling is needed in order to increase the revenues from offering ancillary services and reduce costs of service quality loss. New, practical multi-criteria decision-making methodologies for the daily scheduling of EV fleet are proposed. Criteria that have to be fulfilled simultaneously are the minimization of the service waiting time (SWT), maximization of the revenues coming from the frequency regulation services and the minimization of the costs incurred by the vehicle charging, including the costs of battery degradation. The stochastic nature of vehicles driving patterns (time the car owner has to calculate for the service provision) is considered using the queuing theory. The proposed methodology has been successfully implemented on two cases of EV commercial fleet daily scheduling.
The proliferation of intermittent renewable-energy-based power systems and the emergence of new types of loads are likely to introduce new powe-quality and power-demand management challenges in a smart grid. An additional level of complication gets added when the system deals with a mass penetration of uncontrolled mobile energy sources and loads, that is, electric vehicles (EVs), to the grid. However, the use of an advanced EV management technique can overcome the challenges through an intelligent bidirectional energy transfer process. This chapter highlights various control and optimization techniques to manage the power demand of both single and multiple customers in a smart grid using EVs. The techniques cover both the energy resource and load-management approaches. Energy-resource management technique for single customer coordinates between EV, photovoltaics (PV) and battery storages based on the peak and off-peak load conditions, to minimize the peak load and electricity cost with an increased efficiency. Likewise, the energy-resource management for multiple customers, controls the aggregated PVs, battery storage and aggregated EVs in a parking lot to flatten the energy demand curve, and reduce the peak load energy costs. In this process, a controller reads the real-time household power consumption data through smart meter, PV power generation under real environment, the state of-charge (SOC) for both EVs and battery storage, and the EV availability, to intelligently control the power flow from/to the energy sources to reduce the grid load demand. Additionally, an advanced charge management technique for both aggregated EVs and single EV is developed. On the contrary, the load management technique models a load-scheduling technique for a demand response (DR)-based home energy management systems (HEMS) that minimizes the electricity cost for the consumer and incorporates operational constraints for individual loads and energy sources. A Mixed-Integer Linear Programming (MILP)-based optimization model is formulated to determine the optimal scheduling of operation for residential loads and DERs according to a day-ahead time-of-use (TOU) electricity tariff. Peak load constraint is also incorporated into the optimization model to address grid reliability issues such as demand peaks, rebound peaks and congestion in the grid. The fmdings in this chapter suggest that an intelligent management technique can substantially reduce the power demand of the grid using EVs and reduce the impact of intermittent sources and thus improve the load factor.
This chapter describes the energy-management problem of a small-size electric energy system (SSEES), which comprises a set of electric vehicles (EVs); a set of flexible demands that can shift their energy consumption according to the system needs; and renewable generating units, such as solaror wind-powered units. We consider an energy-management system (EMS) in charge of all the components in the SSEES. This EMS determines the power scheduling of each component and guarantees that all the technical and economic constraints of these components are satisfied. Moreover, the EMS guarantees that the technical constraints of the SSEES are met. In order to compensate the energy deviations, the SSEES is connected to the main grid from which the EMS can buy/sell the deficit/surplus energy to the electricity market. Particularly, we model the day-ahead (DA) market. The considered problem is characterized by a number of uncertainties including the energy market prices and the production of renewable generating units. These uncertainties are modeled using a set of scenarios. Therefore, the resulting problem is formulated as a two-stage stochastic programming problem. In the first stage, the EMS decides the power bought from/sold to the DA market. In the second stage, and once the actual realization of the different uncertainty sources is known, the EMS decides the actual power scheduling of each component in the SSEES.
Electric vehicles (EVs) are a major component of future electric grids, both for the increase in electricity demand and the flexibility they can add to the grid. Vehicle-to-grid and vehicle-to-building pilots have been tested and some have been approved by grid operators, but the EVs' possibilities shall be further enhanced. In previous works, the authors proposed a peer-to-peer energy market between EVs that largely reduced the expenses of their costly day-charging. This chapter further expands the model by taking into account the long-term effects of the market, which reduce the impact of the electric grid prices forecast on the market. The ratio between EVs that offer energy and those that demand energy is shown to be a good indicator for the market price forecast. Almost all energy demand occurs in pairs zone-time in which the number of offering EVs is more than five times the number of demanding EVs, for which the market price is very close to the electricity price at night.
The number of electric vehicles (EVs) is expected to increase significantly in the future to combat air pollution and reduce reliance on fossil fuels. This will impact the power system. However, with appropriate charging and discharging through vehicle-to-grid (V2G) operation, EV batteries could replace some stationary energy storage to provide support for the power system and benefit EV owners. This raises the questions of when and how EV battery storage should be dispatched, taking into account both vehicle users' and power system requirements and priorities, as well as the constraints of the battery system. This chapter proposes a decentralized dispatch strategy based on the analytic hierarchy process (AHP) taking into account the relative importance of the different criteria such as cost, battery state of charge (SOC), power system contingency and load levelling. The proposed ARP -based dispatch strategy is tested on an IEEE Reliability Test System (RTS) with different EV numbers and capacities to investigate the efficacy of such an approach. The simulation results demonstrate the feasibility and benefits of the strategy.
Even though the Li-ion battery price has dropped significantly in recent years, this storage technology is still quite expensive and, in order to avoid overspending, this study proposes using EV's batteries as Battery Energy Storage System. One of the main disadvantages of this scheme is that the total available battery's capacity is variable, but it is dependent on the EV's mobility. In this chapter, an optimization model is developed to minimize the electricity cost purchased from the utility grid by the building manager. This optimization model takes into account several parameters: the availability of the EVs parked at the parking lot (that affects the available storage capacity, which varies at each time period, as EVs come and go along the day), the total energy consumption demanded by the building, the self -PV generation and, finally, the retail energy prices. With this information, the optimization algorithm determines how the EV batteries' charge/ discharge of all parked vehicles should be managed to minimize the cost of electricity purchased from the grid.
Current electric power systems are experiencing a steady growth in renewable electricity generation and in energy consumption from electric vehicles (EV), involving a number of impacts on the system that increase with the penetration level and the system weakness. As conventional synchronous generation is being displaced by renewable generators, they must take their share in the ancillary services provision and supplementary equipment has to be installed, with its associated cost. This chapter describes how EVs, already connected to the grid with appropriate electronic converters and controls, can be used for contributing to the provision of frequency and voltage control, thus avoiding additional investment in supplementary equipment. The chapter also presents innovative approaches for both ancillary services, based on previous developments in electricity generation from wind and wave energies.
The introduction of electric vehicles (EVs) is another alternative to reduce GHG emissions in these isolated systems, because these vehicles are more suitable for the short average daily distances on the islands. EVs can also contribute to integrating renewable energies in these isolated systems, becoming flexible loads and helping to reduce the peak-valley ratio and flattering the energy demand curve. Additionally, the mobility characteristics in islands make them appropriate to promote the electrification of the transportation sector. Introducing V2G as an energy storage system presents advantages such as making use of large-scale distributed storage systems; the batteries of EVs present very low cost for grid operators, easy implementation, and compatibility with the environmental policy. In this work the option of charging an EV fleet as a distributed energy storage system to increase the participation of renewable energy in an isolated power system has been presented.
This chapter focuses on a solar- and wind-powered charging station for electric buses, which is equipped with a set of backup batteries that serve as energy storage. Additional batteries are used to compensate for the variable and stochastic energy generation of the climate-driven solar and wind sources. The research presented here focuses on analysing the charging station's reliability in terms of covering observed load, its economic performance, impact on the power system and the potential to reduce greenhouse gas emissions (in CO2 equivalent). The results show that such a station can not only minimise electric buses' impact on the environment and ensure satisfactory reliability levels but also be a profitable investment.
In this work, we evaluate the effect of charging and discharging plan on integration of electric vehicles (EVs) with wind penetrated distribution grid. A probabilistic model is established by considering the stochastic environment of wind and the accessibility of network constraints. Next, a methodology based on fuzzy approach is applied to model the load profile of EVs with the stochastic availability of EVs, that is, arrival and departure times. Based on this stochastic load profile of EVs, particle swarm optimization with evolution strategy (ESPSO) procedure is applied to incorporate EVs into the distribution grid. Peak load shaving is discussed with vehicle to grid (V2G), having considered the EV charging cost, degradation cost of EV battery, and frequency regulation incomes along with spinning reserve earnings in single objective function. The study is further extended with a varied range of EV penetration in the distribution network
Large number of electric vehicles influences the aggregate power request significantly. Demand estimation is typically intended for the occasionally changing load designs. Though, with the fleet of electric vehicles, daily charging request makes conventional strategies less precise. In this work, prime site for centralized charging of electric vehicles and its charging scheme in a modified IEEE-34 distribution network is planned. To determine the optimum site, an hourly load demand is changed at specified junction nodes and the equivalent voltage sensitivity indexes are calculated. These indexes are derived from the Newton-Raphson load flow analysis in proposed distribution network and aid in choosing appropriate site for charging. Next, in the study, optimum charging of electric vehicles at selected node is conferred based on real-time pricing and wind power output data. The charging based on real-time price would lead to shift in the situations of peak load occurrence in the network. Similarly, charging based on wind power availability would aid in optimizing the customer's benefit in terms of cost. The peak period is shifted to valley period wherein real-time price is low or wind output is high.
The increasing penetration of electric vehicles (EVs) and renewable generators (RGs) in the power grid is an inevitable trend to combat air pollution and reduce the usage of fossil fuels. This will challenge distribution networks, which have constrained capacity. However, appropriate dispatch of electric vehicles via vehicle-to-grid (V2G) operation in coordination with the distributed renewable generators can provide support for the grid, reduce the reliance on traditional fossil-fuel generators and benefit EV users. This paper develops a novel agent-based coordinated dispatch strategy for EVs and distributed renewable generators, taking into account both grid's and EV users' concerns and priorities. This optimal dispatch problem is formulated as a distributed multi-objective constraint optimisation problem utilizing the Analytic Hierarchy Process and is solved using a dynamic-programming-based algorithm. The proposed strategy is tested on a modified UK Generic Distribution System (UKGDS). The electricity network model is simplified using a virtual sub-node concept to alleviate the computation burden of a node's agent. Simulation results demonstrate the feasibility and stability of this dispatch strategy.