Abstract-The prevalence of the Smart Grid and its capabilities, has enabled sophisticated energy management that can be realized as a multi-constraint optimization problem and tailored to the specific scenario needs. In conjunction with the increasing introduction of Electric Vehicles (EVs), energy management tools can now consider expanded conditions including grid balance, cost optimization, EV characteristics, asset utilization, operational goals etc. In this work we analyze such a scenario and demonstrate how an EV fleet charging can be optimized in a timely manner while taking into consideration local conditions e.g., individual EV needs as well as global ones e.g., grid limits and energy price. We formalize a model that reflects the EV restrictions, and use it to assess an algorithmic approach that solves this non-linear optimization problem.

Abstract-In the emerging smart grid, using flexible infrastructures to realize better energy management via demand response scenarios is at its core. The potential of electric vehicles used to realize such flexibility is widely experimented with. In this work, we take a closer look at a specific case of enterpriseowned electric vehicles, parked at enterprise premises, and how their charging can be optimized in order to both adhere to the enterprise operational constraints, as well as consider dynamic changes stemming from other grid stakeholders. A simplified optimization using an evolutionary algorithm is realized, and the approach is evaluated under two scenarios of interest. Of specific interest to the many smart grid stakeholders are the DR scenarios [4], as these can yield additional (usually monetary) benefits to the involved stakeholders, while in parallel tackling key problems in the grid due to highly dynamic energy production stemming from Renewable Energy Sources (RES). The role of the Electric Vehicles (EVs) in smart grid is increasingly investigated [6], including their utilization as dynamic storage [7], since, if a critical mass of them is reached, they can have a significant energy impact on existing infrastructure, future planning and naturally in any energy optimization scenario. Index Terms-EnergyEVs can be an active participant in DR, since they provide flexibility during longer standing times. This is especially of interest when larger numbers of them are available, which is the case e.g., for EV fleets. Several uncertainties are coupled with individual EVs including, their presence, the authorization to centrally control charging, the acceptance by the consumer (EV owner), the impact on the EV battery, etc. However, many of these considerations, can be set aside in specific cases, such as those involving enterprise-owned cars.In this work we focus on this area and introduce two DR scenarios (a price-based and an incentive-based one) that are attractive for enterprise fleets of electric vehicles. The latter can react to DR events, with the flexibility given by long and predictable parking times without interfering with their operational plan. We introduce an optimization approach that allows operators of such EV fleets to react to two different types of DR events. Finally we evaluate these two scenarios and the optimization realized, with real world data both for available RES and enterprise EV fleet. II. DEMAND RESPONSE FOR DSO AND SUPPLIERSIn the context of enterprise-owned EVs, different motivations exist for shifting electrical loads over time. From a local point of view, such load shifts can help reducing consumption (due to EV charging) at times where electricity is expensive in order to reduce overall enterprise costs. In addition, EVs can also be used to prevent the overall power draw from exceeding technical or contractual limitations which could lead to physical damages or penalty fees. DR allows extending this concept from local boundary conditions to more global aspects, as due t...

Abstract-The rapid advances in the area of Smart Grid has led to the increased penetration of distributed energy resources such as photovoltaic panels and wind parks, as well as empowered the rise of the Electric Vehicles (EV). Futuristic scenarios are currently being investigated, one of which focuses on the use of EVs as a collective flexible storage, that could be utilized in a smart city neighbourhood to address unpredictability and intermittent behaviour of renewable resources, as well as a mean to increase the self-consumption factor. Our research focuses on the assessment of EVs as a promising alternative to static storage solutions. We use real-world data in simulations with the aim to investigate the relationship between different setups in smart cities and their impact. The results point out towards a promising future for EVs, as they can play a key part as part of a collective flexible storage.Index Terms-Electric Vehicles, Energy Management, Smart Grid, Microgrid, Renewable Integration, Energy Storage, Variable Storage I. MOTIVATIONThe Smart Grid vision [1] foresees increased distributed grid generation (DER) and increased interaction among existing and future energy related stakeholders. The aim is a transition towards a more efficient system, with lower energy losses, but also lower CO 2 emissions and increased distribution where the energy demand is synchronized with the supply and is produced in clearer ways where and when needed. To that sense we have seen the rise especially of renewables such as Photovoltaic (PV) and wind farms in smart cities as well as sophisticated efforts for their interaction with the consumers such as demand side management (DSM) and demand-response (DR) [2]. In Europe significant efforts [3] have been devoted in the last years towards the goal of realising the Smart Grid vision.The increase of DER and especially the renewable integration, leads to a highly dynamic behaviour that is increasingly difficult to be forecasted, and hence the deployment of storage solutions to deal with it play an increasing key role [4]. The traditional way to deal with energy deviations between demand and supply, is with the deployment of static storage solutions in key points of the infrastructure that can absorb in case of energy excess or feed-in energy deficit. However, in the Smart Grid era, this approach is challenged as the grid grows dynamically and static solutions may need to be more flexible and mobile to better address localized needs. Still, their cost is relative high [4] and "free" storage capacity is available due to the increased penetration of Electric Vehicles (EV), which is already proposed to be clustered as a variable storage [5].Especially when considering microgrids, the coupling of DER and (variable) storage solutions can complement other efforts also targeting demand and supply balance. In the microgrid, maximizing "self-consumption" may enable the creation of self-sustained communities that are more resilient and cost-effective, while they take optimal advantage of...

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