Stations In Distribution System Research Articles

Shunt capacitor allocation by considering electric vehicle charging stations and distributed generators based on optimization algorithm

The two-stage optimization bear smell search algorithm (BSSA) based on fuzzy theory is proposed for determining the optimal location and size of dispersed generation sources, shunt capacitors, and electrical charging stations in distribution systems. In BSSA, in the first stage, the fuzzy method is used for measuring optimal Distributed Generations (DGs) and Shunt Capacitors (SCs) to improve the power factor, re-generation, power losses, and voltage specifications of the distribution system. In the second stage, the distribution system is considered as integrated with DGs and SCs, and the fuzzy BSSA is used to identify the optimal locations for the Electric Vehicle (EV) charging stations and number of vehicles in these stations. Moreover, in the proposed model, a lithium-ion EV battery is used for charging and the characteristic curves are used for analyzing the generated load current. The bear smell search algorithm, which is inspired by bears’ hunting in nature, is a novel meta-heuristic algorithm for solving optimization problems. If the complexity of optimization problems increases, this algorithm suffers from low convergence speed, which increases computation time. To resolve this problem, we propose a new development for this algorithm. Moreover, we examine the effects of EV load increase and uncertainty in DGs and the load distribution system on the performance of the distribution system. The simulation results show that the advantages of BSSA are more than those of the other optimization methods. The simulation results in a 51-bus distribution network indicate that the proposed method has better performance based on numerical analyses.

Coordinated allocation of distributed generation resources and electric vehicle charging stations in distribution systems with vehicle-to-grid interaction

With the implementation of vehicle-to-grid technologies, electric vehicles in distribution systems are becoming controllable resources and enabled to provide a lot of ancillary services (e.g. peak power shaving, voltage regulation, spinning reserve and so on). This phenomenon brings positive effects to the operation of distribution systems but simultaneously challenges the deployments of related power devices, especially the distributed generation resources. Based on this background, this paper proposes an optimization model to jointly deploy electric vehicle charging stations and distributed generation resources, during which the vehicle-to-grid function of electric vehicles is comprehensively considered. To make the optimization model ideally convex, linearized Distflow equations, as well as an exact second order conic relaxation are adopted and utilized in this paper. Consequently, the proposed model can be efficiently solved by off-the-shelf commercial solvers and the allocation schemes with minimal annualized social costs are obtained in polynomial time. Finally, a practical urban area fed by a 31-bus distribution system in China is selected as the test system to verify the effectiveness of the proposed approach, and numerical results are analyzed.

Online Distributed MPC-Based Optimal Scheduling for EV Charging Stations in Distribution Systems

The increasing popularity of electric vehicles (EVs) has made electric transportation a popular research topic. The demand for EV charging resources has significantly reshaped the net demand profile of power distribution systems. This paper proposes an online optimal charging strategy for multiple EV charging stations in distribution systems with power flow and bus voltage constraints satisfied. First, we formulate the online optimal charging problem as an optimal power flow problem that minimizes the total system energy cost based on short-term predictive models and operates in a time-receding manner with the latest system information. Then, the problem is convexified by a modified convex relaxation technique based on the bus injection model, so that the globally optimal solution can be obtained with high efficiency. Moreover, a distributed model predictive control based scheme is designed to solve the optimization problem per concerns regarding data privacy, individual economic interests, and EV uncertainties. The obtained optimal schedules are dispatched to the EVs parked at each charging station according to a fuzzy rule, which guarantees full charging at the departure time for each vehicle. The effectiveness of the proposed method is demonstrated via simulations on a modified IEEE 15-bus distribution system with charging stations located in both residential and commercial areas.

Electric Vehicle Battery Charging/Swap Stations in Distribution Systems: Comparison Study and Optimal Planning

Electric vehicle (EV) is a promising technology for reducing environmental impacts of road transport. In this paper, a framework for optimal design of battery charging/swap stations in distribution systems based on life cycle cost (LCC) is presented. The battery charging/swap station models are developed to compare the impacts of rapid-charging stations and battery swap stations. Meanwhile, in order to meet the requirements of increased power provided during the charging period, the distribution network should be reinforced. In order to control this reinforcement cost, stations should be placed at appropriate places and be scaled correctly. For optimal cost-benefit analysis and safety operation, the LCC criterion is used to assess the project and a modified differential evolution algorithm is adopted to solve the problem. The proposed method has been verified on the modified IEEE 15-bus and 43-bus radial distribution systems. The results show that battery swap station is more suitable for public transportation in distribution systems.

Optimal Planning of Electric-Vehicle Charging Stations in Distribution Systems

With the progressive exhaustion of fossil energy and the enhanced awareness of environmental protection, more attention is being paid to electric vehicles (EVs). Inappropriate siting and sizing of EV charging stations could have negative effects on the development of EVs, the layout of the city traffic network, and the convenience of EVs' drivers, and lead to an increase in network losses and a degradation in voltage profiles at some nodes. Given this background, the optimal sites of EV charging stations are first identified by a two-step screening method with environmental factors and service radius of EV charging stations considered. Then, a mathematical model for the optimal sizing of EV charging stations is developed with the minimization of total cost associated with EV charging stations to be planned as the objective function and solved by a modified primal-dual interior point algorithm (MPDIPA). Finally, simulation results of the IEEE 123-node test feeder have demonstrated that the developed model and method cannot only attain the reasonable planning scheme of EV charging stations, but also reduce the network loss and improve the voltage profile.