Plug In Electric Vehicles in Smart Grids

The advent of renewable energy is about to change power systems around the world. In this sense, operating a power system may become an even more complex task, with implications on the system security, reliability and market. The role played by the utilities may not be diminished, since they must be able to provide energy when intermittent sources are not available. In this near future, the plug-in electric vehicles will also have an important role for power distribution systems. But, at the same time, they have a big potential to help on integration of the renewable power generation in existing power systems. This chapter presents some of the existing plug-in electric vehicles technologies and discusses a few implication of this new scenario in the system operation.

The impact of Plug in Electric Vehicles (PEV) will be most significantly felt by the electric power distribution networks, and specifically by distribution transformers that exist on each neighborhood block and cul-de-sac as customers charge their PEVs. That impact is unlikely to be positive. Since PEV adoption is initially expected to cluster in neighborhoods where demand for PEVs is strongest, the new load may overload transformers, sap much-needed distribution capacity and also increase distribution network losses. Hence, the national goal of putting one million PEVs on the road by 2015 could easily impose a severe burden on the distribution network. Whether PEVs will help or hinder electricity provision will depend on how frequently and at what times the customers charge their vehicles. This behavior will be driven in part by the rate structures that are offered by utilities, as well as the price responsiveness of PEV owners to those rate structures. In this chapter, we propose a method to optimally charge the PEVs in order to minimize the system distribution network losses and to maximize energy transferred to PEVs. A novel short term prediction unit consisting of a receding time horizon method is proposed to forecast the PEV load and a multi objective bacterial foraging algorithm is used as an optimization tool. Also it is interesting to study the manner in which distribution network losses vary with PEV charging behavior. Hence the purpose of this chapter is to demonstrate a power management strategy using smart coordination approach to (a) design a charging and discharging infrastructure for the PEVs that maximizes energy delivered to PEV batteries and (b) reduce the distribution network losses to avoid overloading of the grid.

The advent of smart grids brings a set of new concepts not usually employed in current power systems. Plug-in electric vehicles fit this concept, since it is a low carbon emission device. However, an important characteristic of plug-in vehicles lies on the fact that it may become a source of energy during emergency conditions. Another aspect that may not be overlooked is the fact that the advantage of low carbon emission may be faded by the fact that charging these vehicles may deteriorate the network operating conditions. In this sense, a recharging policy must be addressed, so the system losses and voltage profile are adequately managed. This chapter deals with these topics, so the advent of plug-in electric vehicles may be understood as an important component of future smart grids.

In this chapter, we propose an optimal and distributed control strategy for plug-in electric vehicles’ (PEVs) charging as part of demand response in the smart grid. We consider an electricity market where users have the flexibility to sell back the energy stored in their PEVs or the energy generated from their distributed generators. The smart grid model in this chapter integrates a two-way communication system between the utility company and consumers. A price scheme considering fluctuation cost is developed to encourage consumers to lower the fluctuation in the demand response by charging and discharging their PEVs reasonably. A distributed optimization algorithm based on the alternating direction method of multipliers is applied to solve the optimization problem, in which consumers need to report their aggregated loads only to the utility company, thus ensuring their privacy. Consumers update the scheduling of their loads simultaneously and locally to speed up the optimization computing. We also extend the distributed algorithm to the asynchronous case, where communication loss exists in the smart grid. Using numerical examples, we show that the demand curve is flattened after the optimal PEV charging and load scheduling. We also show the robustness of the proposed method by considering estimation uncertainty on the overall next day load, and also the renewable energy. The distributed algorithms are shown to reduce the users’ daily bills with respect to different scenarios, thus motivating consumers to participate in the proposed framework.

The energy hub is defined as the multi-input multi-output energy converter. It usually consists of various converters like thermal generators, combined heat and power (CHP), renewable energies and energy storage devices. The plug-in electric vehicles as energy storage devices can bring various flexibilities to energy hub management problem. These flexibilities include emission reduction, cost reduction, controlling financial risks, mitigating volatility of power output in renewable energy resources, active demand side management and ancillary service provision. In this chapter a comprehensive risk hedging model for energy hub management is proposed. The focus is placed on minimizing both the energy procurement cost and financial risks in energy hub. For controlling the undesired effects of the uncertainties, the Information gap decision theory (IGDT) technique is used as the risk management tool. The proposed model is formulated as a mixed integer linear programming (MILP) problem and solved using General Algebraic Modeling System (GAMS). An illustrative example is analyzed to demonstrate the applicability of the proposed method.

In literature, several mechanisms are proposed to prevent Plug-in Electric Vehicles (PEVs) from overloading the distribution grid [1]. However, it is unclear how such technical mechanisms influence the market level control strategies of a PEV aggregator. Moreover, the presence of multiple aggregators in the same distribution grid further complicates the problem. Often, grid congestion management mechanisms are proposed to solve the potential interference between the technical and market objectives. Such methods come at the expense of additional complexity and costs, which is not beneficial for the large scale application of demand response. In our work, we investigate this problem by combining a simple low level voltage droop controller with an event driven control strategy for the coordination of charging PEVs. The approach is evaluated in different distribution grid settings, using two different market objectives for the aggregator.

Due to the expected increase in penetration levels of Plug-in Electric Vehicles (PEVs), the demand on the distribution power grid is expected to rise significantly during PEV charging. However, as PEV charging in many cases may not be time critical, they are suitable for load management tasks where the power consumption of PEVs is controlled to support the grid. Additionally, PEVs may also be enabled to inject power into the grid to lower peak demand or counteract the influence of intermittent renewable energy generation, such as that produced by solar photovoltaic panels. Further, PEV active rectifiers can be used to balance reactive power in a local area if required, to reduce the necessity for long distance transport of reactive power. To achieve these objectives, we adapt a known distributed algorithm, Additive Increase Multiplicative Decrease, to control both the active and reactive power consumption and injection. Here, we present this algorithm in a unified framework and illustrate the flexibility of the algorithm to accommodate different user objectives. We illustrate this with three scenarios, including a domestic scenario and a workplace scenario. In these scenarios the various objectives allow us to define a type of “fairness” for how the PEVs should adapt their power consumption, i.e. equal charging rates, or charging rates based on energy requirements. We then validate the algorithms by simulations of a simple radial test network. The simulations presented use the power simulation tool OpenDSS interlinked with MATLAB.

Employing plug-in electric vehicles (PEV) as energy buffers in a smart grid could contribute to improved power grid stability and facilitate the integration of renewable energies. While the technical feasibility of this concept termed vehicle-to-grid (V2G) has been extensively demonstrated, economic concerns remain a crucial barrier for its implementation into practice. A common drawback of previous economic viability assessments, however, is their static approach based on average values which neglects intrinsic system dynamics. Realistically assessing the economics of V2G requires modeling an intelligent agent as a homo economicus who exploits all available information with regard to maximizing its utility. Therefore, a smart control strategy built on real-time information, prediction and more sophisticated battery models is proposed in order to optimize an agent’s market participation strategy. By exploiting this information and by dynamically adapting the agent behavior at each time step, an optimal control strategy for energy dispatches of each single PEV is derived. The introduced cost-revenue model, the battery model, and the optimization model are applied in a case study building on data for Singapore. It is the aim of this work to provide a comprehensive view on the economic aspects of V2G which are essential for making it a viable business case.

With a large-scale introduction of plug-in electric vehicles (PEVs), a new entity, the PEV fleet aggregator, is expected to be responsible for managing the charging of, and for purchasing electricity for, the vehicles. This book chapter deals with the problem of an aggregator bidding into the day-ahead electricity market with the objective of minimizing charging costs while satisfying the PEVs’ flexible demand. The aggregator is assumed to potentially influence market prices, in contrast to what is commonly found in the literature. Specifically, the bidding strategy of the aggregator is formulated as a bi-level problem, which is implemented as a mixed-integer linear program. The upper-level problem represents the charging cost minimization of the aggregator, whereas the lower-level problem represents the market clearing. An aggregated representation of the PEV end-use requirements as a virtual battery, with time varying power and energy constraints, is proposed. This aggregated representation is derived from individual driving patterns. Since the bids of other market participants are not known to the aggregator ex ante, a stochastic approach is proposed, using scenarios based on historical data to describe such uncertain bids. The output of the proposed approach is a set of bidding curves, one for each hour of the day. Results show that by using PEV demand flexibility, the aggregator significantly reduces the charging cost. Additionally, the aggregator’s bidding strategy has an important impact on market prices.

This chapter focuses on the optimal operation of plug-in vehicle fleets in a microgrid characterized by the presence of other distributed resources, such as distributed generation units. The possible services that vehicle aggregators can provide in the microgrid are discussed and the control actions to be performed in order to obtain such services while integrating the vehicle aggregator actions with those of the other distributed resources of the grid are outlined. The problems with optimal operation are formulated as single-objective and multi-objective optimization problems, specifying, in both cases, objective functions, equality and inequality constraints. The differences and criticisms of both approaches are extensively analyzed in the chapter, where the two approaches are also implemented and solved with reference to the practical cases.

Smart Grid is transforming the way energy is being generated and distributed today, leading to the development of environment-friendly, economic and efficient technologies such as plug-in electric vehicles (PEVs), distributed energy resources and smart appliances at homes. Among these technologies, PEVs pose both a risk by increasing the peak load as well as an opportunity for the existing energy management systems by discharging electricity with the help of Vehicle-to-grid (V2G) technology. These complications, together with the PEV battery degradation, compound the challenge in the management of existing energy systems. In this context, microgrids are proposed as an aggregation unit to smartly manage the energy exchange of these different state-of-the-art technologies. In this chapter, we consider a microgrid with a high level of PEV penetration into the transportation system, widespread utilization of smart appliances at homes, distributed energy generation and community-level electricity storage units. We propose a mixed integer linear programming energy management optimization model to schedule the charging and discharging times of PEVs, electricity storage units, and running times of smart appliances. Our findings show that simultaneous charging and discharging of PEV batteries and electricity storage units do not occur in model solutions due to system energy losses.