Parametic Model for Optimization of Battery Capacity and Power Transmitters of On-line Electric Vehicles in Closed/Open Environments

An on-line electric vehicle(OLEV)uses a wireless charging phenomenon,in which power transmitters are installed beneath the road and the OLEV’s battery is charged remotely.This paper deals with the optimization of two key economic and design parameters,i.e.,the size of the battery and the power transmitters allocation.A complete model configuration of the OLEV system,including the vehicle design and power transmitter,is implemented using MATLAB/Simulink.The battery’s state of charge(SOC)rises and drops according to the vehicle’s velocity and power collection and consumption.The mixed integer programming(MIP)model is used for cost calculation.Therefore,with the help of the SOC graph and MIP model,the battery size and the number of power transmitters,along with their placements,are optimized.The proposed model is applicable to both closed and open environments as it accepts both regulated and deregulated velocities.Two test cases are performed for this purpose.The first test case deals with regulated velocity for which we have applied the KAIST campus OLEV’s velocity along with its 13 kWh battery size and 4 power transmitters,and then applied the suggested solution with the same velocity and route i.e.,8 power transmitters with shorter lengths and reduced battery size(3.25 kWh;one-fourth of the first case).SOC is found within limits at the end of the route,saving$1600 and validating the proposed model in this paper.For the second test case,we use deregulated velocity and optimize both parameters,using the same approach.

电网优化调度的模型-数据-知识融合方法研究评述及展望

随着新型电力系统时变不确定因素日益增多和非线性更为增强,电网调度决策的复杂度剧增,充分利用物理模型、数据驱动和调度员知识经验的互补特性,有望实现电网优化调度决策效率和场景适应性的提升。首先,从不确定性调度、数据驱动和知识驱动3方面分别梳理了电网优化调度相关新发展;其次,分析了模型-数据-知识融合的内涵,将3者融合架构分为主从驱动模式和对等驱动模式,分别评述了国内外学者的相关研究工作;最后,针对模型-数据-知识融合在电网优化调度中的应用现状分析了存在问题,并从模型-数据-知识融合效果的量化评价、调度可信知识的主动筛选和演绎、多模式融合的电网调度可靠智能决策和自主趋优演化4个方面对未来研究方向进行了展望。

Energy Management of a Battery Storage and D-STATCOM Integrated Power System Using the Fractional Order Sliding Mode Control

At present,the power system is more inclined towards disturbances,such as voltage variations and unbalanced load conditions,due to the grid's complexity and load growth.These challenges emphasize the integration of the compensating devices,such as battery storage(BS)and D-STATCOMs.In this regard,this current paper exhibits a novel energy management system(EMS)of a combined BS and D-STATCOM to compensate the power system during disturbances.The EMS is based on a fractional order sliding mode control(FOSMC)to drive the voltage source converters(VSCs)such that the active power is independently absorbed/injected by the BS,whereas the reactive power is independently absorbed/injected by the D-STATCOM depending upon the disturbance situation.FOSMC is a robust non-linear controller in which the Riemann-Liouville(RL)function is employed to design the sliding surface and the exponential reaching law is used to minimize the chattering phenomenon.The stability of the FOSMC in the proposed EMS is proved using the Lyapunov candidate function.In order to validate the performance of the proposed EMS,a model of a 400 V,180 kVA radial distributor along with a BS and D-STATCOM is simulated in MATLAB/Simulink environment in two test cases.The results prove that the proposed EMS with FOMSC effectively compensates the power system under voltage variations and unbalanced load conditions with rapid tracking,fast convergence and upright damping.Furthermore,the results have been compared with the classical proportional integral(PI)control and fixed frequency SMC(FFSMC),and they demonstrate the superiority of the proposed EMS with FOSMC in power system applications.