The rise of Vehicle-to-Grid (V2G) technology provides an innovative solution for building a more flexible and efficient new power system. However, the large-scale application of V2G technology requires crossing a series of technical and standard barriers. At present, there is an increasingly urgent need for standardization among the various links of "vehicle-pile-network", including communication protocols, safety standards, charging interface specifications and data interaction formats, which put forward high requirements for the popularization and integration of V2G technology. In this paper, based on the typical scene of V2G, the development status of V2G standards at home and abroad is summarized and analyzed in depth; secondly, the construction method of V2G standard system based on six-dimensional structural model method is proposed, and the V2G standard system is proposed by using the work decomposition method; finally, combined with the development trend of electric vehicle technology, the design thinking and suggestions of V2G standard system in China are put forward.

This paper discusses the optimal scheduling technology of Electric Vehicles (EVs) in the consumption of new energy generation. In the context of energy transition, EVs, as distributed energy storage resources, have significant advantages for peak shaving and valley filling in the power system. The development of new energy generation, especially distributed Photovoltaic (PV), poses new challenges to grid regulation. This paper proposes a joint optimal dispatch model for EVs and new energy sources based on the summation search algorithm, aiming to improve the level of new energy consumption. The model achieves the optimal scheduling of EV charging stations through the objective function and constraints, and improves the system operation efficiency. Case analysis verifies the effectiveness of the proposed technique and shows that reasonable control of EV charging moments can significantly improve the level of wind power consumption, which is of great significance to achieve the "double carbon" goal.

With the rapid increase of electric vehicle ownership, the disorderly grid-connected charging of electric vehicles will bring great uncertainty to the smooth load of the distribution network, so it is important to optimize the regulation of electric vehicle clusters. To this end, an optimal regulation strategy for electric vehicle load clusters based on Demand Response is proposed. Through the analysis of the source load characteristics of electric vehicles, the elastic service rate and electricity incentive are innovatively proposed. Finally, the data of electric vehicles in the northern Hebei region is used for example simulation. The results show that the demand response-based EV cluster optimization and regulation strategy can reduce the charging cost of users and get a better dispatching effect while reducing the system load fluctuation.

以电动汽车为代表的需求侧资源通过聚合运营,将为电力系统提供大量的可调节资源,缓解系统平衡运行的矛盾.但其商业运营模式尚未成熟,关键原因之一是电动汽车聚合价值评估难,难以支撑相关市场交易机制的构建.通过引入价值网络建模方法,建立电动汽车聚合运营价值创造模式,构建电动汽车聚合运营价值网络模型.进而基于电动汽车聚合商视角,分析了聚合体系内多主体价值交换关系,提出了聚合模式下多主体价值获取路径.最后,结合国内某区域市场环境,对有无聚合商时的市场各主体效益进行分析.仿真结果表明电动汽车聚合运营使得各主体都能产生增量收益,在现有模式下聚合商获益最大.市场补偿系数、新能源消纳比例增量是影响各方受益的关键因素.

Referring to the problem of increasing load fluctuation caused by random grid-connected charging of large-scale electric vehicles, this paper proposes an evaluation method considering V2G response potential of electric vehicles. Based on the key factors of electric vehicles' participation in power grid dispatching, a clustering method of electric vehicles participating in V2G regarding the battery state of charge (SOC) and vehicle residence time is proposed in this paper, and a calculation model for evaluating the potential of electric vehicles participating in V2G is also established. The effectiveness of the proposed method is verified by multi-scenario comparative analysis, which provides a basis for optimizing and regulating the charging load of electric vehicles through load aggregators on the premise of meeting the charging demand of electric vehicles.

Aiming at the problem of “1 hour to charge and 4 hours just to queue” during the National Day holiday in 2021, in order to reduce the problems of poor charging experience and reduced confidence in high-speed travel caused by too long charging queuing time, this paper proposes a charging path planning. Based on the state model of electric vehicles (EV), charging stations, traffic network and distribution network, this paper fully considers the charging resources around the expressway network when planning the charging path for users on the expressway network, and proposes a new method considering the “EV-pile-road-grid” state electric vehicle charging path planning. By calling the Baidu map API interface and the data of the charging piles of the Internet of Vehicles Platform, the optimized selection of charging stations is completed, and a feasible navigation path with the shortest travel (time) is finally formed.

为了解决传统集中式电动汽车充电服务费统一定价难以满足新型电力系统精度与灵活性需求的问题,提出了一种基于智能合约的电动汽车充电服务费自适应调整机制.通过建立考虑购售电双方收益的充电服务费优化模型,从购售电双方收益及负荷引导需求两方面对充电服务费进行优化;同时运用智能合约技术,制定更加精确、更具针对性的单座充电站的充电服务费调整策略.通过算例进行仿真验证,结果表明相较于传统定价模式,所提自适应定价模式下的负荷引导能力和交易双方的收益水平都具备显著优势.

Against the background of carbon neutrality, the power dispatching operation mode has undergone great changes. It not only gradually realizes the coordinated control of source–grid–load–storage, but also strives to realize the multi-level coordination of the transmission network, distribution network and microgrid. Disorderly charging and discharging of large-scale electric vehicles (EVs) will have a great negative impact on the distribution network, but aggregating EVs and guiding them to charge and discharge in an orderly manner will play a positive role in delaying investment in the distribution network. Therefore, it is urgent to adopt an effective scheduling control strategy for electric vehicle charging and discharging. First, a variety of indexes were set to analyze the influence of EVs access on distribution network and the correlation between the indexes. Then, by defining the EVs penetration rate and the load simultaneous rate, the charging load planning of EVs was calculated. Based on the simultaneous load rate, the regional electricity load plan was calculated, and a configuration model of distribution capacity suitable for charging loads in different regions was constructed. Finally, an optimal dispatch model for electric vehicles considering the safety of distribution network was proposed and the distribution transformer capacity allocation model was used as the optimization target constraint. Compared with most optimized dispatch models used to maximize aggregator revenues and reduce peak-to-valley differences and load fluctuations in distribution networks, this model could effectively reduce unnecessary investment while meeting regional distribution transformer needs and maintaining distribution network security. Taking the improved IEEE 34-bus systems as an example, the simulation analysis was carried out and the investment demand of distribution network under the condition of disordered and orderly charge and discharge was compared. The results show that the proposed optimal scheduling method can effectively reduce the load fluctuation of distribution network, keep the voltage offset within the allowable voltage deviation range, and can effectively delay the investment of distribution network.

With the increasing number of electric vehicles, the scale of the market also increases. In the past, the electric vehicle market had problems such as opaque information, numerous levels and data leakage, which were criticized for the impact of the overall development and policies of the electric vehicle industry. In view of the problems existing in the transparency and security of big data management transactions of the Internet of vehicles, this paper combs the commercial operation framework of the Internet of Vehicles Platform, analyses the feasibility and necessity of establishing the token system of the Internet of Vehicles Platform, and constructs the token economic system architecture of the Internet of Vehicles Platform and its development path.

In the incentive based demand response project, the baseline load calculation of electric vehicle users can provide quantitative basis for evaluating the power demand response and the load adjustment degree of users. The charging load of electric vehicle is different from the traditional electric load, which has greater flexibility and is more affected by user’s behavior. This paper analyzes the characteristics and influencing factors of electric vehicle charging load. On the basis of summarizing the common calculation methods of user baseline load at home and abroad, this paper puts forward the calculation method of electric vehicle baseline load based on classification, and finally analyzes the accuracy and applicability of this method combined with specific examples.

In order to improve the enthusiasm of electric vehicle users to participate in the possession of power grid regulation, and resolve the pricing problem of electric vehicle charging service fee, this paper proposes an automatic adjustment mechanism of electric vehicle charging service fee based on intelligent contract. Firstly, This paper designs the deployment architecture and transaction process of intelligent contract. After that, this paper establishes the user ' s response contribution model and the charging station revenue model. On this basis, the service rate adjustment rules considering the user ' s response contribution and the charging station revenue are designed and applied to the charging contract settlement. The differential pricing mechanism of service rate automatically adjusted according to the response effect of user participation in power grid regulation is realized.

Abstract Under the goal of carbon neutralization and carbon peak, China’s electric vehicle industry will usher in a high-speed development stage. Electric vehicles can achieve carbon emission reduction from two aspects of electric energy substitution and clean energy substitution. In this paper, the carbon emission reduction benchmark of electric vehicles is determined, and the Bass model is used to predict the number of electric vehicles. On this basis, the carbon dioxide emissions reduced when electric vehicles replace fuel vehicles to meet the travel requirements and the carbon dioxide emissions of electric vehicles with different proportions of renewable energy generation are analyzed. Finally, the benefits of carbon emission reduction under decentralized and centralized modes are studied to determine the subsidy range of electric vehicle users.

Under the goal of carbon neutralization and carbon peaking, peak shaving auxiliary servicetrading market is an important part of China's power grid construction in the future. This paper studies the current background of China's peak shaving auxiliary service trading market from the aspects of price formation mechanism and market trading mechanism; and puts forward a combined prediction model of parallel deep learning. The innovation of the model is that it uses a prediction framework that has not been used in other models: the results of each algorithm in the first round of prediction are taken as the input variables in the second prediction. While fusing each algorithm to make up for the defects of a single algorithm, the original data are expanded.The parallel deep learning model overcomes the problem of insufficient price related parameter data caused by market competition and data confidentiality.Comparing the results predicted by using a single model with those predicted by using a parallel deep learning model, the parallel deep learning prediction model improves the goodness of fit (R2) from 0.55 to 0.92. The prediction method can also be applied to other short-term prediction which is difficult to summarize the law due to too few original data or large data volatility, and has certain application and popularization significance.

A large number of renewable energy and EVs (electric vehicles) are connected to the grid, which brings huge peak shaving pressure to the power system. If we can make use of the flexible characteristics of EVs and effectively aggregate the adjustable resources of EVs to participate in power auxiliary services, this situation can be alleviated to a certain extent. In this paper, a two-stage physical and economic adjustable capacity evaluation model of EVs for peak shaving and valley filling ancillary services is constructed. The main steps are as follows: with the help of the deep learning ability of the AC (Actor-Critic) algorithm, the optimal physical charging scheme of EV fleet is determined to minimize the grid fluctuation under the travel constraints of private EVs, and the optimized charging power is transferred to the second stage. In the second stage, load aggregators encourage users to participate in ancillary services by setting subsidy prices. In this stage, the model constructs a user decision model based on a logistic function to describe the probability of users accepting dispatching instructions. With the goal of maximizing the revenue of load aggregators, the wolf colony algorithm is used to solve the optimal solution of the time-sharing subsidy level, and finally the economic adjustable capacity of the EV fleet considering the subjective decision of users is obtained.

Focusing on the battery-charging problem that is brought to the electric automobile users, this paper integrated the “automobile-network-path” multi-source information and presented the multi-level user experience index system which combined charging prices, driving distances, degrees of traffic congestion and other factors. The recommended algorithm and model for charging strategy was built up to improve the user experience. Meanwhile, it invoked map Application Programming Interface (API) to plan multiple paths. Consolidated by the status of charging piles, the distance between the automobile and the piles, the charging prices along with more real-time information, the multi-level user oriented experience index system was set up to recommend an optimal navigation route to the charging station for the automobile owners. Validated by the application results, the proposed algorithm that helped navigate to the charging stations or piles can effectively solve the practical problems1 such as difficulty in orienting the charging piles, waiting in lines, and high charging fees.

相比电动汽车(electric vehicle,EV)慢充用户,大规模、随机快充负荷的接入将会带来更加严重的电网安全运行问题.从整体社会经济效益最大化的角度出发,仅单纯从电网侧增加基础设施建设并非最佳解决之道,有必要从用户角度研究有效的调度策略对电动汽车的快充行为进行引导.为此,基于未来智慧城市的场景下,提出了基于节点关键度的配网供电电压偏差指标(voltage deviation index based on node importance,VDINI)的概念,指出动态调整快充站充电服务费的手段,分析电动汽车用户对于快充站选择行为的影响因素,建立电动汽车用户基于自身效益的充电位置决策模型,从而说明通过调整各充电服务费的来对快充行为进行引导的可行性.在此基础上,根据需要满足的各种约束条件,制定各快充站快充服务费的求解流程,在保证充电站效益不变的同时,将电动汽车合理地引导至各个快充站,均衡区域内的充电负荷,实现空间上的有序快充,改善了配网的电能质量.算例仿真验证了所提充电服务费制定方法的合理性以及快充负荷引导策略的优越性.

In order to meet the needs of the group users' official and private travel, an electric vehicle (EV) sharing and leasing model with the joint participation of multiple entities, including the group, employees in the group and rental platform operators, is proposed. Based on the problem analysis of existing official vehicle-using system, a group-using EV sharing and leasing business architecture and a trusted transaction architecture based on blockchain technology are established with the rental operation platform as the core. For group users' official and private vehicle-using scenarios, corresponding business processes, implementation plans, group credit for business, individual deposit schemes and car rental cost subsidies are also discussed in this paper, which can provide effective support for the construction of the EV leasing ecology.

Electric vehicles (EV) comprise one of the foremost components of the smart grid and tightly link the power system with the road network. Spatial and temporal randomness in electric charging distribution will exert negative impacts on power grid dispatch. Existing research focuses mainly on mathematical inferences from statistical data, and the dynamic movement of an individual vehicle traveling in a traffic system is rarely taken into account. Machine learning algorithm can take the EV dynamic condition into consideration. Based on machine learning algorithm, this paper proposes a charging demand simulation method based on the Agent–cellular automata model to describe the changes in location and the state of charge of a moving EV. CRUISE software is used to analyze power consumption in different scenarios. Then, the Monte Carlo algorithm models the dynamic fluctuation of EV traffic and charging demands. Case studies are conducted on a typical composite system consisting of a 54-node distribution system and a 25-node traffic network, and the simulation results demonstrate the effectiveness of the proposed method.

含有柴油发电机、光伏发电单元和储能单元的孤岛微网为偏远地区的能源供给提供了有效的解决途径,但是由于光伏出力与负荷需求的双重不确定性,目前尚缺乏行之有效的能量控制策略以同时保证孤岛微网的供电可靠性与经济性.如何在不间断供电的基础上,实现多源孤岛微网的经济运行已是电网公司亟待解决的新问题.为此,提出了一种基于双层模型预测控制(Model Predictive Control,MPC)的多源孤岛微网经济运行控制策略,以实现运行成本最小化,同时减小负荷和光伏功率波动带来的不利影响.通过仿真分析中国西北某村的经济运行成本验证了所提控制策略的有效性.

为了减少电动汽车充电负荷大规模接入对电网安全稳定运行的负面影响,针对含有多主体运营商充电站的充电市场竞争定价问题,提出了基于Hotelling模型的充电服务费双寡头联盟定价方法.首先,分析了多元充电市场中电网公司、充电站运营商和电动汽车用户各自的利益追求.其次,利用Hotelling模型刻画了充电需求受价格引导的分布特性,设计了多元活性充电市场中的充电站运营商充电服务费定价方案.最后,通过仿真算例分析,验证了所提充电服务费双寡头联盟定价方法的有效性.