Optimal Dynamic Scheduling of Electric Vehicles in a Parking Lot Using Particle Swarm Optimization and Shuffled Frog Leaping Algorithm

This paper addresses the optimal charging scheduling problem for Electric Vehicles (EVs) in an intelligent workplace parking lot powered by both the Photovoltaic Power (PV) System and the Power Grid. Due to the uncertain charging requirements of different EVs and time-varying available renewable energy, the charging load from the parking lot may bring a new challenge to the Power Grid. By minimizing total cost of the parking lot, we design a dynamic charging scheduling scheme to manage the charging processes of EVs based on the real- time information of EVs and renewable energy from the PV system. Numerical simulations are carried out to demonstrate the efficiency of the designed charging scheduling scheme.

In this paper classic static and dynamic scheduling strategy is analyzed first, and then communication network of schedule ability judgment basis is given. An improved dynamic EDF scheduling algorithm is proposed in order to improve the scheduling task of real-time. The scheduling strategy is to change task priority according to the transmission error over deadline task when applying dynamic EDF scheduling strategy. True Time tool is used to build CAN network control system simulation platform. Dynamic EDF scheduling algorithm and improved scheduling algorithm are simulated respectively. The effectiveness of improved scheduling algorithm is verified by the simulation Keywords-Network control system; Scheduling Algorithm; True Time toolbox result.

PurposeAttitude control of unmanned aerial vehicle (UAV) is the purposeful manipulation of controllable external forces to establish a desired attitude, which is inner‐loop of the autonomous flight control system. In the practical applications, classical control methods such as proportional‐integral‐derivative control are usually selected because of simple and high reliability. However, it is usually difficult to select or optimize the control parameters. The purpose of this paper is to investigate an intelligent algorithm based classical controller of UAV.Design/methodology/approachAmong the many intelligent algorithms, shuffled frog leaping algorithm (SFLA) combines the benefits of the genetic‐based memetic algorithm as well as social behavior based particle swarm optimization. SFLA is a population based meta‐heuristic intelligent optimization method inspired by natural memetics. In order to improve the performance of SFLA, a different dividing method of the memeplexes is presented to make their performance balance; moreover, an evolution mechanism of the best frog is introduced to make the algorithm jump out the local optimum. The modified SFLA is applied to the tuning of the proportional coefficients of pitching and rolling channels of UAV flight control system.FindingsSimulation of a UAV control system in which the nonlinear model is obtained by the wind tunnel experiment show the rapid dynamic response and high control precision by using the modified SFLA optimized attitude controller, which is better than that of the original SFLA and particle swarm optimization method.Originality/valueA modification scheme is presented to improve the global searching capability of SFLA. The modified SFLA based intelligent determination method of the UAV flight controller parameters is proposed, in order to improve the attitude control performance of UAV.

Electric vehicles (EVs) are becoming more and more popular as they provide significant environmental benefits compared to fossil-fuel vehicles. However, they represent substantial loads on the power grid, and the scheduling of EV charging can be a challenge, especially in large parking lots. This paper presents a metaheuristic-based approach parallelized on multicore processors (CPU) and graphics processing units (GPU) to optimize the scheduling of EV charging in a single smart parking lot. The proposed method uses a particle swarm optimization algorithm that takes as input the arrival time, the departure time, and the power demand of the vehicles and produces an optimized charging schedule for all vehicles in the parking lot, which minimizes the overall charging cost while respecting the chargers’ capacity and the parking lot feeder capacity. The algorithm exploits task-level parallelism for the multicore CPU implementation and data-level parallelism for the GPU implementation. The proposed algorithm is tested in simulation on parking lots containing 20 to 500 EVs. The parallel implementation on CPUs provides a speedup of 7.1x, while the implementation on a GPU provides a speedup of up to 247.6x. The parallel implementation on a GPU is able to optimize the charging schedule for a 20-EV parking lot in 0.87 s and a 500-EV lot in just under 30 s. These runtimes allow for real-time computation when a vehicle arrives at the parking lot or when the electricity cost profile changes.

Generation bidding strategy in a pool based electricity market using Shuffled Frog Leaping Algorithm

Scheduled charging offers the potential for electric vehicles (EVs) to use renewable energy more efficiently, lowering costs and improving the stability of the electricity grid. Many studies related to EV charge scheduling found in the literature assume perfect or highly accurate knowledge of energy demand for EVs expected to arrive after the scheduling is performed. However, in practice, there is always a degree of uncertainty related to future EV charging demands. In this work, a Model Predictive Control (MPC) based smart charging strategy is developed, which takes this uncertainty into account, both in terms of the timing of the EV arrival as well as the magnitude of energy demand. The objective of the strategy is to reduce the peak electricity demand at an EV parking lot with PVarrays. The developed strategy is compared with both conventional EV charging as well as smart charging with an assumption of perfect knowledge of uncertain future events. The comparison reveals that the inclusion of a 24 h forecast of EV demand has a considerable effect on the improvement of the performance of the system. Further, strategies that are able to robustly consider uncertainty across many possible forecasts can reduce the peak electricity demand by as much as 39% at an office parking space. The reduction of peak electricity demand can lead to increased flexibility for system design, planning for EV charging facilities, deferral or avoidance of the upgrade of grid capacity as well as its better utilization.

Nowadays, vehicle to grid (V2G) capability of the electric vehicle (EV) is used in the smart distribution network (SDN). The main reasons for using the EVs, are improving air quality by reducing greenhouse gas emissions, peak demand shaving and applying ancillary service, and etc. So, in this chapter, a non-linear bi-level model for optimal operation of the SDN is proposed where one or more solar based-electric vehicle parking lots (PLs) with private owners exist. The SDN operator (SDNO) and the PL owners are the decision-makers of the upper-level and lower-level of this model, respectively. The objective functions at two levels are the SDNO’s profit maximization and the PL owners’ cost minimization. For transforming this model into the single-level model that is named mathematical program with equilibrium constraints (MPEC), firstly, Karush–Kuhn–Tucker (KKT) conditions are used. Furthermore, due to the complementary constraints and non-linear term in the upper-level objective function, this model is linearized by the dual theory and Fortuny-Amat and McCarl linearization method. In the following, it is assumed that the SDNO is the owner of the solar-based EV PLs. In this case, the proposed model is a single-level model. The uncertainty of the EVs and the solar system, as well as two programs, are considered for the EVs, i.e., controlled charging (CC) and charging/discharging schedule (CDS). Because of the uncertainties, a risk-based model is defined by introducing a Conditional Value-at-Risk (CVaR) index. Finally, the bi-level model and the single-level model are tested on an IEEE 33-bus distribution system in three modes; i.e., without the EVs and the solar system, with the EVs by controlled charging and with/ without the solar system, and with the EVs by charging/discharging schedule and with/without the solar system. The main results are reported and discussed.

Electric vehicle (EV) dynamic wireless charging system has become an emerging application in the area of the intelligent transportation system (ITS). However, an integrated design of the EV dynamic wireless charging system requires the considerations of both the economical and technical perspectives of a smart city. Specifically, most of the existing researches unilaterally consider the application of either placement strategy for power tracks (PTs) or dynamic vehicle-to-grid (V2G) scheduling. In this article, we propose a multistage system framework to account for an integrated EV dynamic wireless charging system in a smart city. First of all, an optimal placement strategy for PTs is developed based on city traffic information and EV energy demand. Then, having the optimal locations of PTs through the previous stage approach, the proposed dynamic V2G scheduling scheme is formulated to coordinate the schedules of EVs with the provision of daytime V2G ancillary services. Our simulation results present that the proposed multistage system model achieves improvements on both the placement strategy and V2G scheduling scheme. In addition, relatively low economic system costs can be obtained through our proposed model.

The expectation of a huge number of Electric Vehicles (EVs) into the market creates multiple technical problems. The power system is at risk due to the unsecured and unreliable operation resulting from unplanned charging and discharging. The unplanned EV charging and discharging change the load profile of the electric grid. Hence, it is required to develop an effective scheduling scheme for charging and discharging EVs. This research focuses on the minimization of the charging cost of a large number of Electric Vehicles (EVs) in a residential area and parking station. When a huge number of EVs are aggregated in the residential and parking stations, charging and discharging power should be a significant and viable contributor to the power grid. V2G (Vehicle-to-Grid) technology offers additional potential benefits to improve power quality and also system reliability. EVs can also supply G2V (Grid-to-Vehicle) power through the discharge of the battery, which benefits both EV owners and electric utilities. It is very difficult to schedule the configurations of EV charging and discharging in an optimized way so as to decrease the charging cost of EVs. The usage of renewable energy sources like solar Photovoltaic (PV) power, reduces the burden on the grid. The solar PV systems are likely to have significant surplus power during the daytime. In this research, optimal charging and discharging scheduling schemes for EVs are developed and the proposed work is carried out under the MATLAB environment with and without Photovoltaic (PV) systems. The realistic load data is collected from Transmission Corporation of Andhra Pradesh (APTRANSCO), India and the solar data is taken from the Indian Solar Resource Data website. The effectiveness of the scheduling scheme is rated according to its capability to reduce the total cost. Extensive simulation studies have been performed on the distribution system under consideration and the results have been presented. The results demonstrate that the charging cost of EVs can be reduced significantly with the use of solar PV power.

The rapid expansion of electric vehicles (EVs) brings forth a range of possibilities and obstacles for the electrical grid. EVs are a hopeful way to cut down on greenhouse gas emissions and reliance on fossil fuels, but the way they charge can put a lot of stress on the grid, which can cause problems for both grid operators and EV users. The uncontrolled approach to EV charging could result in the development of new peak loads. This paper formulates a problem for the minimisation of EV charging cost while reducing the load variance using a dynamic charging strategy. This dynamic charging strategy forecasts the charging price of EV for each time stamp based on the available energy generation. The proposed formulation has also considered the seasonal variation in load profile. The model is solved using different optimisation algorithm and different scenarios have been formulated to validate the effectiveness of the optimisation problem which has been formulated to achieve the objectives of minimised charging cost and load variance. The proposed strategy has improved the charging cost by 67% from other strategies of EV charging while reduction in load variance comes out to be 69%. This proposed formulation will help in the higher adoption of EV's and will subsequently lead to sustainable growth.

Future Paths of Electric Vehicle Adoption in the United States: Predictable Determinants, Obstacles and Opportunities

Gear drives are extremely critical elements of transmission systems. Designing gears that satisfy the application's requirements like load carrying capacity and strength whilst also being compact, lightweight, and economical is quite a challenging task. In recent times, a lot of research has been dedicated to optimizing speed reducers by making use of metaheuristic nature-inspired algorithms. The present work optimizes the weight of a spur speed reducer by employing two modified nature-inspired algorithms: Particle Swarm Optimization (PSO) and Shuffled Frog Leaping Algorithm (SFLA). Parameter optimization was carried out to select the best combination of c1, c2 and 𝜔 for the selected case study. It was found that there was a 1.1619% and 13.41% decrease in the cost function evaluation, as compared to the Crude Monte-Carlo and Stray Process methods used in the original case study, and around an 11% decrease as compared to results in published literature.

The accuracy of PV array model directly affects the output characteristics of the PV system. In this paper, a simplified engineering model for PV array is established and the influence on the output characteristics of PV array is analyzed when Rs, Rsh and α, β, γ change. According to the actual data of a PV power station, the shuffled frog leaping algorithm (SFLA) is adopted to identify the parameters in the model. The results show that the identification curve and the measured curve are in a better consistency. Then, the particle swarm optimization (PSO) algorithm is adopted to identify the same parameters and the identification results are compared with the identification results of SFLA. It shows that the output of PV array model based on the identification results of SFLA has a higher fitting degree with the actual output of PV power station which further verifies the superiority of SFLA.

Body Area Networks (BANs), targeting everywhere anytime health monitoring, operate in license-exempt frequency bands. They require a cross layer design that jointly considers the requirements of (1) access delay to the medium and (2) packet delivery ratio (PDR). In this paper, we experimentally evaluate the PDR of a BAN under static and dynamic scheduling schemes. Investigations show that static multi-hop routing enhances PDR with the help of packet relaying, but at the cost of using more transmission slots in the shared medium, which may compromise access delay. To reduce this cost, we propose a dynamic scheduling approach that adapts to BAN topology changes resulting from body movements. Our extensive experimental results indicate that for BANs colloccated with other wireless systems, our dynamic multi-hop scheduling is mandatory for satisfaction of the PDR constraint.

In this paper, the Authors present the designing of power system stabilizer (PSS) and static var compensator (SVC) based on chaos, particle swarm optimization (PSO) and shuffled frog leaping (SFL) Algorithms has been presented to improve the power system stability. Single machine infinite bus (SMIB) system with SVC located at the terminal of generator has been considered to evaluate the proposed SVC and PSS controllers. The coefficients of PSS and SVC controller have been optimized by Chaos, PSO and SFL algorithms. Fi-nally the system with proposed controllers is simulated for the special disturbance in input power of genera-tor, and then the dynamic responses of generator have been presented. The simulation results show that the system composed with recommended controller has outstanding operation in fast damping of oscillations of power system and describes an application of Chaos, PSO and SFL algorithms to the problem of designing a Lead-Lag controller used in PSS and SVC in power system.