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 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.

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.

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.

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.

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.

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

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.