text: An electric vehicle (EV) may be used as energy storage which allows the bi-directional electricity flow between the vehicle's battery and the electric power grid. In order to flatten the load profile of the electricity system, EV scheduling has become a hot research topic in recent years. In this paper, we propose a new formulation of the joint scheduling of EV and Unit Commitment (UC), called EVUC. Our formulation considers the characteristics of EVs while optimizing the system total running cost. We employ Chemical Reaction Optimization (CRO), a general-purpose optimization algorithm to solve this problem and the simulation results on a widely used set of instances indicate that CRO can effectively optimize this problem.
  @cite_18: For vehicle-to-grid (V2G) frequency regulation services, we propose an aggregator that makes efficient use of the distributed power of electric vehicles to produce the desired grid-scale power. The cost arising from the battery charging and the revenue obtained by providing the regulation are investigated and represented mathematically. Some design considerations of the aggregator are also discussed together with practical constraints such as the energy restriction of the batteries. The cost function with constraints enables us to construct an optimization problem. Based on the developed optimization problem, we apply the dynamic programming algorithm to compute the optimal charging control for each vehicle. Finally, simulations are provided to illustrate the optimality of the proposed charging control strategy with variations of parameters.
  @cite_4: The electricity and transportation industries are the main sources of greenhouse gas emissions on Earth. Renewable energy, mainly wind and solar, can reduce emission from the electricity industry (mainly from power plants). Likewise, next-generation plug-in vehicles, which include plug-in hybrid electric vehicles (EVs) and EVs with vehicle-to-grid capability, referred to as “gridable vehicles” (GVs) by the authors, can reduce emission from the transportation industry. GVs can be used as loads, energy sources (small portable power plants), and energy storages in a smart grid integrated with renewable energy sources (RESs). Smart grid operation to reduce both cost and emission simultaneously is a very complex task considering smart charging and discharging of GVs in a distributed energy source and load environment. If a large number of GVs is connected to the electric grid randomly, peak load will be very high. The use of traditional thermal power plants will be economically and environmentally expensive to support the electrified transportation. The intelligent scheduling and control of GVs as loads and or sources have great potential for evolving a sustainable integrated electricity and transportation infrastructure. Cost and emission reductions in a smart grid by maximum utilization of GVs and RESs are presented in this paper. Possible models for GV applications, including the smart grid model, are given, and results are presented. The smart grid model offers the best potential for maximum utilization of RESs to reduce cost and emission from the electricity industry.
  @cite_3: Continuing environmental concerns are moving electric vehicles into high gear of development. Plug-in type electric and hybrid vehicles are expected in the market anytime. The EV battery-charging load can potentially increase the demand on the utility distribution systems considerably. It is desirable that the EV battery charging load be contained during system off-peak hours without affecting the peak demand, thus utilizing the low cost electricity for charging EV. However, a sizable EV load can introduce a new peak in the early off-peak period. Optimization of the EV charging during the low cost off peak period is formulated to minimize the cost of EV charging in the context of Singapore system. The case studies presented show that significant saving is possible even at reasonable low levels of EV penetrations.
  @cite_23: The electrification of the vehicle fleet will result in an additional load on the power grid. Adequately dealing with such pluggable (hybrid) electrical vehicles (PHEV) forms part of the challenges and opportunities in the evolution towards Smart Grids. In this paper, we investigate the potential benefits of using control mechanisms, that could be offered by a Home Energy control box, in optimizing energy consumption stemming from PHEV charging in a residential use case. We present smart energy control strategies based on quadratic programming for charging PHEVs, aiming to minimize the peak load and flatten the overall load profile. We compare two strategies, and benchmark them against a business-as-usual scenario assuming full charging starting upon plugging in the PHEV. The first, local strategy only uses information at the home where the PHEV is charged: as a result the charging is optimized for local loads. The local strategy is compared to a global iterative strategy which controls the charging of multiple vehicles based on global load information over a residential area. Both strategies control the duration and rate of charging and result in charging schedules for each vehicle. We present quantitative simulation results over a set of 150 homes, and discuss the strategies in terms of complexity and performance (esp. resulting energy consumption), as well as their requirements concerning infrastructure and communication.
  @cite_25: The power system and transportation sector are our planet's main sources of greenhouse gas emissions. Renewable energy sources (RESs), mainly wind and solar, can reduce emissions from the electric energy sector; however, they are very intermittent. Likewise, next generation plug-in vehicles, which include plug-in hybrid electric vehicles and electric vehicles with vehicle-to-grid capability, referred to as gridable vehicles (GVs) by the authors, can reduce emissions from the transportation sector. GVs can be used as loads, energy sources (small portable power plants) and energy storage units in a smart grid integrated with renewable energy sources. However, uncertainty surrounds the controllability of GVs. Forecasted load is used in unit commitment (UC); however, the actual load usually differs from the forecasted one. Thus, UC with plug-in vehicles under uncertainty in a smart grid is very complex considering smart charging and discharging to and from various energy sources and loads to reduce both cost and emissions. A set of valid scenarios is considered for the uncertainties of wind and solar energy sources, load and GVs. In this paper, an optimization algorithm is used to minimize the expected cost and emissions of the UC schedule for the set of scenarios. Results are presented indicating that the smart grid has the potential to maximally utilize RESs and GVs to reduce cost and emissions from the power system and transportation sector.
summary: Existing work of V2G operation scheduling of EVs can be divided into two classes: charging-only scheduling and bi-directional scheduling. In charging-only scheduling, the algorithms try to optimize the electricity flow from the power grid to the batteries of EVs. For example, Shrestha optimized the EV charging cycles to off-peak periods to flatten the demand curve, in order to reduce the charging cost @cite_3 . Mets presented a smart energy control strategy to charge residential plug-in hybrid EVs (PHEVs) to smooth the system load profile @cite_23 . However, with the development of V2G technology, bi-directional charging, i.e., V2G and G2V, is possible, and bi-directional scheduling algorithm has attracted much research recently. The role of EVs in the power system may change during the day from loads to sources, and vice versa. Binary particle swarm optimization was employed to tackle the V2G scheduling problem to minimize the total running cost and reduce green house gas emission in @cite_4 and @cite_25 . Han proposed an aggregator for V2G frequency regulation in @cite_18 , aiming to maximize the revenue.