电动汽车充电站规划研究
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摘要
随着经济的快速发展，资源匮乏和环境污染的问题越来越严重，人们对自身的健康和居住环境也越来越关注。目前庞大的汽车市场正在加剧资源的耗费，同时也为城市环境增添了更多的压力。而电动汽车因为其具有节能环保的特点正在成为汽车工业的主要发展方向。随着国家的政策支持和各大汽车厂商的积极参与，电动汽车的技术水平正日益提高。目前，已有一部分电动汽车的产品已经成型并投入示范运行，产业化和商业化的模式也在逐步完善。随着普及程度的增大，电动汽车的充电将会成为电网承担的又一重要负荷，对电网的影响也不可小视。作为电动汽车的基础配套，充电站必须先行进行规划和建设。
本文针对电动汽车的大规模应用，研究了其充电需求的预测和充电站的选址、定容、服务范围的划分等问题，主要工作如下：
（1）针对电动汽车充电需求的随机性、不确定性以及与居民生活出行的关系，构建了电动汽车常规充电需求和快速充电需求的蒙特卡洛预测模型；分析了电动汽车规模化应用后，其无序充电对电网负荷曲线带来的影响。研究了利用峰谷电价政策来引导电动汽车用户进行有序充电的措施，构建了峰谷电价时段优化模型，通过峰谷电价时段的优化，最大化地缩小了电网的峰值负荷与谷值负荷的差距，实现了削峰填谷，提高了设备的利用率。在优化峰谷时段的同时，还考虑了用户对于峰谷电价政策的敏感性，根据用户对于不同时段、不同峰谷电价差的响应程度，构建了考虑用户响应的峰谷电价时段综合优化模型，使得引导有序充电措施的制定更具合理性。
（2）针对城市区域电动汽车充电站的规划问题，在兼顾充电站运营者和电动汽车用户双方利益的基础上，提出了一种基于全社会成本最小化的规划方案优选模型。分析了路网结构、车流信息和用户路程损耗等因素对充电站选址和定容的影响。并且利用权重反映了路网中的车流密度对充电站服务范围的影响，通过加权Voronoi图实现了充电站服务范围的自动划分。
（3）研究了在配电网络容量约束条件下的城市电动汽车充电站的规划问题。利用排队论在服务系统优化设计方面的优势，构建了电动汽车充电站充电机的最优台数设计模型，设计时充分考虑了用户在充电站的等待成本和充电站的运营成本，从而使得充电站的容量配置既满足了用户的充电需求，又实现了资源的优化配置，避免了不必要的浪费。
（4）在考虑了电动汽车电量分布及行驶里程的情况下，提出了一种针对高速路段的充电站布局规划模型。以充电站的总体收益即路段中到充电站充电的电动汽车的数量期望值最大化为目标来确定高速公路沿线充电站的选址和间距。充电站内的充电机台数选择则兼顾了充电站运营者与顾客双方的利益，以充电站的服务成本和顾客的等待费用之和最小为目标来进行优化配置，同时利用排队论原理分析了充电站的服务水平和运行效率。
With the rapid development of economy, lack of resources and environmentalpollution problems are getting worse. People are increasingly concerned about theirhealth and living conditions. Currently, the huge vehicle market is not onlyintensifying resource-intensive, but also adding more pressure to the urbanenvironment. Because of its energy-saving and environment-friendly features, theelectric vehicle is becoming the main direction of the automotive industry. With thenational policy support and the active participation of the major car manufacturers, thetechnical level of the electric vehicle is increasing. Up to now, part of the electricvehicle has been formed and put into the demonstration run. Industrialization andcommercialization mode of the electric vehicle is gradually improving. As thepenetration increases, the charging of electric vehicles will become another importantload. There will be a very big impact on the grid. As the infrastructure and supporting,the charging station must first be planning and construction.
In this paper, some problems have been studied for large-scale application ofelectric vehicles, such as charging demand forecasting, charging stations locating andsizing, service scoping, etc. The main work in this dissertation is summarized asfollows:
(1) Electric vehicle charging is closely related to the habits of the residents. So ithas the characteristics of randomness and uncertainty. Based on this, a Monte Carloprediction model was constructed on conventional charging demand and fast chargingdemand. The impact of a large number of disorderly charging on the grid load curvehas been analyzed. In order to decrease this negative effect, Pricing Policy wasstudied to guide the electric vehicle charging orderly. And a peak-valley pricetime-period optimization model was put forward. Through the optimization ofpeak-valley price time-period, the gap between peak load and valley load wasnarrowed and electrical equipment utilization was improved. While optimizing thepeak-valley price time-period, the sensitivity of the user on the Pricing Policy hasbeen considered. Based on the user's response to different peak-valley price atdifferent time, an integrated optimization model was constructed to coordinate thesefactors. So that it can make orderly charging guide measures to develop more reasonable.
(2) Electric vehicle charging station planning in urban areas was studied.Considering the interests of charging station operators and electric vehicle drivers, anoptimization model for charging station planning based on the minimization of fullsocial cost was proposed. In the model, the impact on the charging station locatingand sizing from the road network, the traffic flow and the users’ loss on the way to thestation was analyzed. The role of traffic density in the service areas divided ofcharging stations was reflected by the weights. And the automatic partitioning ofservice areas was implemented by the weighted Voronoi diagram.
(3) Taking the distribution network structure and capacity constraints intoaccount, an optimization model for urban charging station planning was proposed.Based on the advantages of queuing theory in the service system design, theoptimization model of the charging station capacity configuration was constructedwith the consideration of charging station operating cost and users’ waiting cost. Inthis way, the capacity of the charging station configuration can not only meet the users’charging requirement, but also achieve the optimal allocation of resources and avoidunnecessary waste.
(4) Regarding the influences of the power distribution and the mileage of electricvehicles, a planning model of charging stations on the highway was established. Themaximum expectation of electric vehicles coming to stations for recharge from thehighway was chosen as the objective function for locating the charging station. Andthe number of chargers in the station was optimized in order to minimize the total costof station’s service and customers’ waiting time. In addition, this model also analyzedthe service level and operational efficiency of the charging station using the queuingtheory.
本文针对电动汽车的大规模应用，研究了其充电需求的预测和充电站的选址、定容、服务范围的划分等问题，主要工作如下：
（1）针对电动汽车充电需求的随机性、不确定性以及与居民生活出行的关系，构建了电动汽车常规充电需求和快速充电需求的蒙特卡洛预测模型；分析了电动汽车规模化应用后，其无序充电对电网负荷曲线带来的影响。研究了利用峰谷电价政策来引导电动汽车用户进行有序充电的措施，构建了峰谷电价时段优化模型，通过峰谷电价时段的优化，最大化地缩小了电网的峰值负荷与谷值负荷的差距，实现了削峰填谷，提高了设备的利用率。在优化峰谷时段的同时，还考虑了用户对于峰谷电价政策的敏感性，根据用户对于不同时段、不同峰谷电价差的响应程度，构建了考虑用户响应的峰谷电价时段综合优化模型，使得引导有序充电措施的制定更具合理性。
（2）针对城市区域电动汽车充电站的规划问题，在兼顾充电站运营者和电动汽车用户双方利益的基础上，提出了一种基于全社会成本最小化的规划方案优选模型。分析了路网结构、车流信息和用户路程损耗等因素对充电站选址和定容的影响。并且利用权重反映了路网中的车流密度对充电站服务范围的影响，通过加权Voronoi图实现了充电站服务范围的自动划分。
（3）研究了在配电网络容量约束条件下的城市电动汽车充电站的规划问题。利用排队论在服务系统优化设计方面的优势，构建了电动汽车充电站充电机的最优台数设计模型，设计时充分考虑了用户在充电站的等待成本和充电站的运营成本，从而使得充电站的容量配置既满足了用户的充电需求，又实现了资源的优化配置，避免了不必要的浪费。
（4）在考虑了电动汽车电量分布及行驶里程的情况下，提出了一种针对高速路段的充电站布局规划模型。以充电站的总体收益即路段中到充电站充电的电动汽车的数量期望值最大化为目标来确定高速公路沿线充电站的选址和间距。充电站内的充电机台数选择则兼顾了充电站运营者与顾客双方的利益，以充电站的服务成本和顾客的等待费用之和最小为目标来进行优化配置，同时利用排队论原理分析了充电站的服务水平和运行效率。
With the rapid development of economy, lack of resources and environmentalpollution problems are getting worse. People are increasingly concerned about theirhealth and living conditions. Currently, the huge vehicle market is not onlyintensifying resource-intensive, but also adding more pressure to the urbanenvironment. Because of its energy-saving and environment-friendly features, theelectric vehicle is becoming the main direction of the automotive industry. With thenational policy support and the active participation of the major car manufacturers, thetechnical level of the electric vehicle is increasing. Up to now, part of the electricvehicle has been formed and put into the demonstration run. Industrialization andcommercialization mode of the electric vehicle is gradually improving. As thepenetration increases, the charging of electric vehicles will become another importantload. There will be a very big impact on the grid. As the infrastructure and supporting,the charging station must first be planning and construction.
In this paper, some problems have been studied for large-scale application ofelectric vehicles, such as charging demand forecasting, charging stations locating andsizing, service scoping, etc. The main work in this dissertation is summarized asfollows:
(1) Electric vehicle charging is closely related to the habits of the residents. So ithas the characteristics of randomness and uncertainty. Based on this, a Monte Carloprediction model was constructed on conventional charging demand and fast chargingdemand. The impact of a large number of disorderly charging on the grid load curvehas been analyzed. In order to decrease this negative effect, Pricing Policy wasstudied to guide the electric vehicle charging orderly. And a peak-valley pricetime-period optimization model was put forward. Through the optimization ofpeak-valley price time-period, the gap between peak load and valley load wasnarrowed and electrical equipment utilization was improved. While optimizing thepeak-valley price time-period, the sensitivity of the user on the Pricing Policy hasbeen considered. Based on the user's response to different peak-valley price atdifferent time, an integrated optimization model was constructed to coordinate thesefactors. So that it can make orderly charging guide measures to develop more reasonable.
(2) Electric vehicle charging station planning in urban areas was studied.Considering the interests of charging station operators and electric vehicle drivers, anoptimization model for charging station planning based on the minimization of fullsocial cost was proposed. In the model, the impact on the charging station locatingand sizing from the road network, the traffic flow and the users’ loss on the way to thestation was analyzed. The role of traffic density in the service areas divided ofcharging stations was reflected by the weights. And the automatic partitioning ofservice areas was implemented by the weighted Voronoi diagram.
(3) Taking the distribution network structure and capacity constraints intoaccount, an optimization model for urban charging station planning was proposed.Based on the advantages of queuing theory in the service system design, theoptimization model of the charging station capacity configuration was constructedwith the consideration of charging station operating cost and users’ waiting cost. Inthis way, the capacity of the charging station configuration can not only meet the users’charging requirement, but also achieve the optimal allocation of resources and avoidunnecessary waste.
(4) Regarding the influences of the power distribution and the mileage of electricvehicles, a planning model of charging stations on the highway was established. Themaximum expectation of electric vehicles coming to stations for recharge from thehighway was chosen as the objective function for locating the charging station. Andthe number of chargers in the station was optimized in order to minimize the total costof station’s service and customers’ waiting time. In addition, this model also analyzedthe service level and operational efficiency of the charging station using the queuingtheory.
引文
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