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.
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.
@cite_18 @cite_4 @cite_3 @cite_23 @cite_25
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The vehicle electrification will have a significant impact on the power grid due to the increase in electricity consumption. It is important to perform intelligent scheduling for charging and discharging of electric vehicles (EVs). However, there are two major challenges in the scheduling problem. First, it is challenging to find the globally optimal scheduling solution which can minimize the total cost. Second, it is difficult to find a distributed scheduling scheme which can handle a large population and the random arrivals of the EVs. In this paper, we propose a globally optimal scheduling scheme and a locally optimal scheduling scheme for EV charging and discharging. We first formulate a global scheduling optimization problem, in which the charging powers are optimized to minimize the total cost of all EVs which perform charging and discharging during the day. The globally optimal solution provides the globally minimal total cost. However, the globally optimal scheduling scheme is impractical since it requires the information on the future base loads and the arrival times and the charging periods of the EVs that will arrive in the future time of the day. To develop a practical scheduling scheme, we then formulate a local scheduling optimization problem, which aims to minimize the total cost of the EVs in the current ongoing EV set in the local group. The locally optimal scheduling scheme is not only scalable to a large EV population but also resilient to the dynamic EV arrivals. Through simulations, we demonstrate that the locally optimal scheduling scheme can achieve a close performance compared to the globally optimal scheduling scheme. This paper proposes an intelligent method for scheduling usage of available energy storage capacity from plug-in hybrid electric vehicles (PHEV) and electric vehicles (EV). The batteries on these vehicles can either provide power to the grid when parked, known as vehicle-to-grid (V2G) concept or take power from the grid to charge the batteries on the vehicles. A scalable parking lot model is developed with different parameters assigned to fleets of vehicles. The size of the parking lot is assumed to be large enough to accommodate the number of vehicles performing grid transactions. In order to figure out the appropriate charge and discharge times throughout the day, binary particle swarm optimization is applied. Price curves from the California ISO database are used in this study to have realistic price fluctuations. Finding optimal solutions that maximize profits to vehicle owners while satisfying system and vehicle owners constraints is the objective of this study. Different fleets of vehicles are used to approximate varying customer base and demonstrate the scalability of parking lots for V2G. The results are compared for consistency and scalability. Discussions on how this technique can be applied to other grid issues such as peaking power are included at the end. Vehicle-to-grid (V2G) has the potential of reducing the cost of owning and operating electric vehicles (EVs) while increasing utility system flexibility. Unidirectional V2G is a logical first step because it can be implemented on standard J1772 chargers and it does not degrade EV batteries from cycling. In this work an optimal combined bidding formulation for regulation and spinning reserves is developed to be used by aggregators. This formulation takes into account unplanned departures by EV owners during contract periods and compensates accordingly. Optional load level and price constraints are also developed. These algorithms maximize profits to the aggregator while increasing the benefits the customers and utility. Simulations over a three month period on the ERCOT system show that implementation of these algorithms can provide significant benefits to customers, utilities, and aggregators. Comparisons with bidirectional V2G show that while the benefits of unidirectional V2G are significantly lower, so are the risks. Research predicts that in 2030, around 30 of all vehicles in Belgium will be plug-in hybrid electric vehicles (PHEVs). Because most PHEVs are charged after working hours, the existing peak load in the evening will increase significantly. Large peak loads cause more expensive production and can even damage the electricity infrastructure. In a Smart Grid, the charging of PHEVs can be controlled to reduce peak load, denoted as demand side management (DSM). The goal of our research is to compare several solutions for DSM of PHEVs. This paper takes a first step by benchmarking a multi-agent solution against an optimal quadratic programming (QP) scheduler solution. Simulations show that a QP scheduler is able to optimally flatten peak loads while sufficiently charging the PHEV batteries. However, this solution is unfeasible in practice because it scales poorly and requires complete information on when and how much PHEVs need to charge beforehand, which is not available. The MAS solution proves to be scalable and adaptable to incomplete and unpredictable information while peaks are still reduced with an efficiency up to 95 compared to the QP scheduler.