With the progressive exhaustion of fossil energy and growing concerns about climate change,it has been ob served that distributed energy resources such as photovoltaic(PV)systems and electric vehicles(EVs)are being in...With the progressive exhaustion of fossil energy and growing concerns about climate change,it has been ob served that distributed energy resources such as photovoltaic(PV)systems and electric vehicles(EVs)are being increasingly integrated into distribution systems.This underscores the in creasing imperative for a thorough analysis to evaluate reliabili ty from the perspectives of distribution systems and EV charg ing services,taking into account the stochastic nature of PV and EV load demands.This paper presents an approach for the reliability assessment of distribution systems that incorporate PV and EVs considering reliability models for both PV systems and EV battery systems.It also defines new indices to investi gate the adequacy and customer-side reliability for EV charging services.The developed methodology utilizes a Monte Carlo simulation-based approach and is showcased using the modified Roy Billinton Test System(RBTS)Bus 4 distribution system.The results illustrate that reliability indices for EV charging ser vices,such as percentage of charging energy not supplied(PCENS),average EV interruption frequency index(AEVIFI)and average EV interruption duration index(AEVIDI),are im proved under the proposed approach.展开更多
A de-centralised load management technique exploiting the flexibility in the charging of Electric Vehicles (EVs) is presented. Two charging regimes are assumed. The Controlled Charging Regime (CCR) between 16:30 hours...A de-centralised load management technique exploiting the flexibility in the charging of Electric Vehicles (EVs) is presented. Two charging regimes are assumed. The Controlled Charging Regime (CCR) between 16:30 hours and 06:00 hours of the next day and the Uncontrolled Charging Regime (UCR) between 06:00 hours and 16:30 hours of the same day. During the CCR, the charging of EVs is coordinated and controlled by means of a wireless two-way communication link between EV Smart Charge Controllers (EVSCCs) at EV owners’ premises and the EV Load Controller (EVLC) at the local LV distribution substation. The EVLC sorts the EVs batteries in ascending order of their states of charge (SoC) and sends command signals for charging to as many EVs as the transformer could allow at that interval based on the condition of the transformer as analysed by the Distribution Transformer Monitor (DTM). A real and typical urban LV area distribution network in Great Britain (GB) is used as the case study. The technique is applied on</span></span><span><span><span style="font-family:""> </span></span></span><span><span><span style="font-family:"">the LV area when its transformer is carrying the future load demand of the area on a typical winter weekday in the year 2050. To achieve the load management, load demand of the LV area network is decomposed into Non-EV <span>load and EV load. The load on the transformer is managed by varying the EV load in an optimisation objective function which maximises the capacity uti</span>lisation of the transformer subject to operational constraints and non-disruption of daily trips of EV owners. Results show that with the proposed load management technique, LV distribution networks could accommodate high uptake of EVs without compromising the useful normal life expectancy of distribution transformers before the need for capacity reinforcement.展开更多
文摘With the progressive exhaustion of fossil energy and growing concerns about climate change,it has been ob served that distributed energy resources such as photovoltaic(PV)systems and electric vehicles(EVs)are being increasingly integrated into distribution systems.This underscores the in creasing imperative for a thorough analysis to evaluate reliabili ty from the perspectives of distribution systems and EV charg ing services,taking into account the stochastic nature of PV and EV load demands.This paper presents an approach for the reliability assessment of distribution systems that incorporate PV and EVs considering reliability models for both PV systems and EV battery systems.It also defines new indices to investi gate the adequacy and customer-side reliability for EV charging services.The developed methodology utilizes a Monte Carlo simulation-based approach and is showcased using the modified Roy Billinton Test System(RBTS)Bus 4 distribution system.The results illustrate that reliability indices for EV charging ser vices,such as percentage of charging energy not supplied(PCENS),average EV interruption frequency index(AEVIFI)and average EV interruption duration index(AEVIDI),are im proved under the proposed approach.
文摘A de-centralised load management technique exploiting the flexibility in the charging of Electric Vehicles (EVs) is presented. Two charging regimes are assumed. The Controlled Charging Regime (CCR) between 16:30 hours and 06:00 hours of the next day and the Uncontrolled Charging Regime (UCR) between 06:00 hours and 16:30 hours of the same day. During the CCR, the charging of EVs is coordinated and controlled by means of a wireless two-way communication link between EV Smart Charge Controllers (EVSCCs) at EV owners’ premises and the EV Load Controller (EVLC) at the local LV distribution substation. The EVLC sorts the EVs batteries in ascending order of their states of charge (SoC) and sends command signals for charging to as many EVs as the transformer could allow at that interval based on the condition of the transformer as analysed by the Distribution Transformer Monitor (DTM). A real and typical urban LV area distribution network in Great Britain (GB) is used as the case study. The technique is applied on</span></span><span><span><span style="font-family:""> </span></span></span><span><span><span style="font-family:"">the LV area when its transformer is carrying the future load demand of the area on a typical winter weekday in the year 2050. To achieve the load management, load demand of the LV area network is decomposed into Non-EV <span>load and EV load. The load on the transformer is managed by varying the EV load in an optimisation objective function which maximises the capacity uti</span>lisation of the transformer subject to operational constraints and non-disruption of daily trips of EV owners. Results show that with the proposed load management technique, LV distribution networks could accommodate high uptake of EVs without compromising the useful normal life expectancy of distribution transformers before the need for capacity reinforcement.
文摘针对目前城市电动汽车(electric vehicle,EV)充电站存在盲目建设、规划不合理导致的部分充电站利用率低、用户充电满意度低等问题,同时为适应“双碳”目标下发展大规模EV的充电站规划需求,提出一种基于蒙特卡洛模拟和回声状态网络(echo state network,ESN)拟合的城市EV时空充电负荷预测方法,进一步开展EV充电站规划研究。首先考虑城市交通路网结构和区域主要功能,将待规划区域进行网格划分并作为待建充电站备选位置;利用蒙特卡洛方法对各类EV进行多种模式的出行链模拟,获取各网格区域内的EV充电负荷数据集;为拟合各网格内EV充电负荷的多样化分布特征,建立基于回声状态网络ESN学习算法的EV时空充电负荷预测模型,实现一定EV保有量下待规划区内EV时空充电负荷的预测。进一步考虑待规划网格区域内的最大充电预测负荷等约束条件﹑以充电站的建设和运维成本、EV用户充电出行成本以及配网损耗的综合成本最小为目标,建立EV充电站的规划模型,利用粒子群算法进行模型求解得到待规划区的充电站建设位置、数量及容量;最后以某城区EV充电负荷预测及充电站规划为例进行计算,验证了所提方法及模型的有效性。
