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This work proposes an information security vehicleto- grid (V2G) scheduling solution, which combines Federated deep learning with distributed edge computing for V2G operation. In this framework, each charging point is equipped with an intelligent computing module to conduct distributed edge scheduling for the connected electric vehicle (EV), so that not only the computation of inference process is efficient, but also the privacy-preserving of EV users is guaranteed. Besides, the desensitized V2G data of charging points are used to train the deep neural network model in each charging station. Therefore, the accurate future data acquisition problem and the uncertainty handling challenges under traditional optimization methods is avoided. At the same time, the spatial-based and time-based clustering methods are applied to improve the accuracy of prediction. Finally, through federated learning, each charging station uploads the local model to the cloud server, and a stochastic client selection pattern is designed to improve the scalability of model aggregation in the cloud server. In this way, the digital assets of each charging station are protected, and computing and communication costs are reduced. Simulation results on real datasets show that the proposed framework has superior performance in terms of training accuracy, communication burden, and computing performance, while maintaining the privacy of EV users and the digital assets of charging stations. IEEE

Uninterruptible power battery (UPS) is an important part to ensure the stable operation of data center. Its security is related to the reliability and stability of power system. Among them, the state of health (SOH) prediction is a key issue of the valve regulated lead–acid (VRLA) battery operation and maintenance in data center. In this work, the battery SOH is predicted by the correlation between the nadir voltage value of Coup De Fouet (CDF) phenomenon and SOH. Then, the CDF phenomenon is combined with popular data-driven methods, such as linear regression, regression tree, support-vector machine, gaussian process, neural network, to predict battery SOH through 215 features. Finally, the above method is verified with the real discharge dataset of UPS battery in data center. The experimental results show that the data-driven method combining big data has higher accuracy than the simple prediction of battery SOH based on the nadir voltage value of CDF phenomenon and its variants. © 2023 The Authors

Electric vehicles (EVs) could potentially act as the distributed energy storage devices to provide vehicle-to-grid (V2G) services to benefit the electric power system. Correspondingly, EV users can earn revenue based on the provision of grid services in a market environment. However, EVs would suffer the extra battery degradation incurred by the V2G operation. As such, it is essential to investigate the feasibility of EVs to conduct the V2G operation under different charging scenarios. Compared to existing studies focusing solely on the economic evaluation of V2G operation, this paper investigates the potential of EVs providing V2G services from two perspectives: time availability and economic viability. To this end, two critical indicators, viz., time indicator and economic indicator, are introduced in this paper to model the temporal and economic feasibility of EVs providing grid services to explore the applicable V2G scenarios. In addition, three charging scenarios at home, in workplace, and in fast charging stations (FCSs) are evaluated regarding the feasibility for V2G services. The simulation results reveal that, there is sufficient idle time for those EVs in the home-based and workplace-based charging scenarios and it is profitable to provide V2G services in the electricity market according to the cost-benefit analysis. However, for those EVs with FCS-based charging, it is unlikely for EV users to spend the extra time to conduct the V2G operation due to the fact that the V2G operation will prolong the grid-connection time of EVs. © 2023 Elsevier Ltd

As the number of electric vehicles (EVs) surges, EVs are expected to supply auxiliary services for power grid through the vehicle-to-grid (V2G) operation. Specifically, vehicle-to-vehicle (V2V), as a beneficial supplement of V2G, has received increasing attention owing to the capability for flexible EVs deployment and profitable individual interaction. In this paper, a genuine V2V market mechanism aiming for maximum revenue of each EV owner is proposed to construct a distributed electricity market. It reveals the dominance of EV owners from the perspective of market and allows the trade among EV owners in local distribution grid with charging points such as charging stations and auto parks. To this end, the implementation of V2V market mechanism is analyzed in detail, including the electricity price, the process and the integration with the internet of smart charging points (ISCP), a cyber-physical architecture. Then, EVs’ energy dispatching is formulated via the non-cooperative game model, in which each EV owner anticipates maximizing individual revenue by choosing feasible strategies. The existence and uniqueness of Nash Equilibrium (NE) in a non-cooperative game is proved by means of variation inequality (VI) theory. Finally, the ISCP-based optimal algorithm is developed to solve the NE efficiently, thereby maximizing reward of each EV owner. Comprehensive case studies show that the proposed V2V market mechanism can implement distributed dealing among EV owners with reducing individual charging cost and regional load variance effectively. Specially, the costs of EV1, EV8 and EV9 decrease by 31%, 36% and 34%, respectively. And V2V charging demonstrates much lower variance (10.5209) than regular charging (14.1124). Additionally, the convergence of the algorithm is also demonstrated, reaching the NE in less than 30 iterations for each EV. © 2023 Elsevier Ltd

Electric vehicles (EVs) are believed as efficient solutions to reduce carbon emissions and fossil fuel reliance in transportation sectors. Yet, the ever-increasing penetration of EVs also poses great challenges for distribution grid planning and operation. As a research hotspot, the vehicle-to-grid (V2G) technology could not only relieve the adverse effects of large-scale uncoordinated EV charging but also offer varied auxiliary services for the utility grid via the proper charging/discharging schedule. In this paper, a comprehensive review of distribution grid architectures, grid connection infrastructures and standards, and typical applications is conducted from the perspective of EV-grid integration and V2G operation for the first time. The nanogrid, microgrid, and cluster architectures that are critical components in active distribution grids are all involved in the discussion. Considering EV's triple roles played in grid interaction, the corresponding infrastructure and grid-connection standards, as well as bidirectional/unidirectional charger topologies, are overviewed. Then, four-type nanogrid and microgrid architectures are reviewed and fully evaluated in six aspects, viz., charging demand compatibility, power quality severity, V2G availability, architecture scalability, local control complexity, and technology maturity. The assessment result reveals that the hybrid AC/DC-coupled architecture based on advanced interlinking converters has flexible power flow control and power quality compensation capability, thus facilitating the EV-grid integration and V2G operation effectively. The assessment for three-type cluster architectures indicates that the hybrid parallel-series microgrid cluster has balanced performance in complexity, cost, redundancy, and reliability. Finally, the typical applications of the nanogrid, microgrid, and cluster with EVs integration are exemplified and discussed. © 2022 Elsevier Ltd

With the popularity of electric vehicles (EVs), vehicle-to-grid (V2G) technology is attracting increasing attention due to its crucial merit of enabling bidirectional power flows between EVs and grid, so as to enhance the grid security and stability by regulated dispatching. However, the existing V2G approaches are confronted with several unrealizable challenges because of high computational complexity for large-scale EVs and impracticality for future power data acquisition. In this article, an edge computing framework is proposed in a distributed manner to ensure the dispatching efficiently and provide the raw dataset flexibly. Meanwhile, the long short-term memory network is applied to prediction merely by the past and present power data. Moreover, attention mechanism and data clustering are utilized to improve the prediction accuracy and operation robustness. Experiments involving real dataset demonstrated that the proposed V2G scheme is able to achieve very satisfactory dispatching performance with the prediction accuracy up to 98.89%. © 2005-2012 IEEE.

Most recently, DC nanogrid incorporating effective energy management has attracted widespread attention. Due to its favorability to integrate renewable energy sources and emerging power electronic loads, such as photovoltaics and electric vehicles (EVs), DC nanogrid is believed to be able to improve the energy utilization efficiency and mitigate the carbon footprint in the coming decades. Towards a compact, cost-effective, and easy-to-build energy management scheme for nanogrid, this paper presents a pseudo hierarchical management architecture built upon the smart charging point. The proposed architecture incorporates the upper-level central controller with the local power role creatively and comprises two timescale management levels with corresponding operation strategies. In the short-timescale local management level, a state-triggered droop strategy based on the decentralized control mechanism is introduced to realize the autonomous power coordination without extensive communication links. The autonomous vehicle-to-grid (V2G) operation is also implemented with providing real-time power balance capability to unpredicted and short-timescale load variation in peak periods. In the power dispatching level, a multi-mode power dispatching strategy involving six operation modes is introduced to realize the efficient power scheduling for the nanogrid. The effectiveness of the proposed architecture and operation strategy is verified in the detailed simulation model and hardware-in-loop experiment platform. The results show that the real-time, autonomous, and stabilized power coordination in nanogrid could be realized along with a self-regulated V2G operation. Additionally, the peak-shaving and valley-filling of load curve, the satisfaction of EV charging demand, and an improved operation economy are achieved under the proposed architecture and operation strategy. © 2022 Elsevier Ltd

Electric vehicles (EVs) combined with low-carbon generator sets can significantly reduce CO2 emissions in the transportation sector. EVs can provide flexible auxiliary services for the grid through proper power dispatching, which benefits both EVs and the grid. This paper discusses the optimal charging and discharging plan for EVs for the purpose of a distribution system operator (DSO) while obtaining the optimal day-ahead bidding strategy for DSO in the spot market. Considering the different charging demands of EV users, two contract models of vehicle-to-grid (V2G) and smart charging services are introduced. Then, K-means clustering is adopted to manage the spatiotemporal uncertainties of EVs, in order to maximize the expected marginal revenue of DSO. Then the optimization model is proposed by considering the real conditions campus grid in Shenzhen and the electricity market in Guangdong province. The result shows that the DSO could obtain the marginal revenue not only by the optimal day-ahead bidding strategy but also by scheduling EVs’ charging and discharging. In addition, EV users could reduce the charging cost by charging their EVs during low electricity price periods and get extra revenue by exporting power to the utility grid during high price periods. © 2022 The Author(s)

Since the road transport accounts for 15% of total carbon emissions, electric vehicles (EVs) have made great strides and large-scale uncoordinated EV charging will greatly increase the load pressure of power system. The vehicle-to-grid (V2G) technology can optimize the idle EVs to charge during the grid load valley period and feeding the grid as a power source during the load peak period. This bidirectional energy flow technology builds a bridge between the power grid and EVs. However, the user privacy-preserving and data asset protection have always been ignored in previous works. In this paper, the secure and efficient V2G scheme through edge computing and federated learning from the double layer has been proposed. Firstly, the edge computing unit is set in the data source, viz., the charging point, to avoid sensitive data leakage. And then, the desensitized charging data will be stored in charging station. Next, the federated learning is utilized among charging stations to jointly train a global model without breaching data asset. Finally, the real dataset is applied to the experiment, and the effectiveness of the proposed architecture is verified. © 2022 IEEE.

Since the number of electric vehicles (EVs) grows rapidly, the EVs charging schedule problem has attracted more attention than before. On account of continuous and random power requirement from EVs, the burden of grid will increase and thus jeopardize the stability of grid. In this paper, an accessible close-loop vehicle-to-vehicle (V2V) charging mechanism with interaction of energy and information under charging station is proposed to cope with EVs charging schedule problem. And the non-cooperative game model is adopted to analyze the V2V charging mechanism. Furthermore, the optimal strategy algorithm is introduced to get the Nash Equilibrium (NE) of the game. Simulation with 10 EVs illustrates that V2V charging with non-cooperative game can decrease the load variance (0.0701) compared to V2V charging without the game (1.7248). And the convergence of the optimal strategy algorithm can reach in less than 20 iterations. © 2022 The Author(s)

Vehicle-to-grid (V2G) is an emerging technology for grid stability and environmental protection. However, predicting the unknown data accurately in V2G optimization is difficult. And utilizing the machine learning and big data method for V2G operation becomes a feasible method. Therefore, a vehicle-to-grid dataset for commercial center and its machine learning application are conducted in this paper. Firstly, three categories of datasets, i.e., conventional load data, PV output data and PEV charging data, are selected as the trend of power grid operation. Secondly, a V2G scenario of commercial center is designed, and the maximum values of three parameters were calculated. Next, both the centralized method and the distributed method are used for data annotation. In case study, the dataset is applied through a variety of machine learning methods. The results show that the accuracy of V2G prediction is up to 94%. © 2021 IEEE

The bidirectional energy flow between plug-in electric vehicles (PEVs) and power grids enables load flatting and self-consumption of the photovoltaic (PV) output. However, two critical issues should be addressed. One is how to conduct the gap between the decision makings optimized with predictive data and the reality, and the other is how to ensure efficiency of V2G dispatching. In order to tackle these problems, this work proposes a distributed V2G dispatching via long short term memory (LSTM) network within cloud-edge collaboration framework. In the cloud side, the LSTM network is applied merely utilizing the present data to obtain the prediction models of V2G dispatching. Then, these models are sent to the edge side and updated in a regular time. In edge side, the distributed dispatching is conducted to decrease the computational complexity. The proposed framework is verified by numerical analysis, which illustrates that the effectiveness, efficiency and applicability of the V2G operation. © 2021 IEEE.

Microgrid cluster embraces huge application potential with the development of Energy Internet. This paper proposes a novel hybrid AC/DC microgrid clustering architecture that is of scalability and reconfigurability. Under this architecture, a decentralized control scheme is also introduced. First, a novel interlinking converter named by the energy networking unit (ENU) is introduced, which could access to the AC and DC power grid in individual microgrid, as well as the outer power grid. Second, the clustering architecture established on the ENU is developed. Due to the reconfigurability of ENUs' topologies, this architecture facilitates the AC/DC connection among multiple microgrids via the unified power conversion modules, thus increasing the conversion efficiency and lowering the system cost. Last, an introduced decentralized control scheme is able to achieve autonomous power coordination between heterogeneous subsystems in individual microgrid, as well as a coordinated and active power interaction within the cluster. Comprehensive cases demonstrates the validity of the proposed novel clustering architecture and control scheme. © 2021 IEEE

Considering serious environment problems and energy dilemma, the importance of electric vehicles (EVs) has attracted more and more attention. However, how to cope with large scale EVs and efficiently manage them when charging have also been a big challenge. At the same time, the architecture of internet of smart charging points (ISCP) has great potential to achieve distributed energy management towards EVs and supply foundation to implement vehicle-to-vehicle (V2V) charging mechanism. For more flexible and valuable trade, a V2V charging mechanism based on bargain model is applied in this unique framework. To verify the validity of this mechanism, case studies are conducted for 153 EVs in the whole day. We finds that the total payoff is - 687.1182¥in the case of normal charging mechanism, -36.2091 Yin the case of V2V buyer-first charging mechanism and - 36.5998¥in the case of V2V seller-first charging mechanism. The results indicate that the novel V2V charging mechanism can alleviate the grid load, protect privacy of consumers and bring economic payoff to consumers.

In order to accommodate additional plug-in electric vehicle (PEV) charging loads for existing distribution power grids, the vehicle-to-grid (V2G) technology has been regarded as a cost-effective solution. Nevertheless, it can hardly scale up to large PEVs fleet coordination due to the computational complexity issue. In this paper, a centralized V2G scheme with distributed computing capability engaging internet of smart charging points (ISCP) is proposed. Within ISCP, each smart charging point equips a computing unit and does not upload PEV sensitive information to the energy coordinator, to protect PEV users’ privacy. Particularly, the computational complexity can be decreased dramatically by employing distributed computing, viz., by decomposing the overall scheduling problem into many manageable sub-problems. Moreover, six typical V2G scenarios are analyzed deliberately, and based on that, a load peak-shaving and valley-filling scheduling algorithm is built up. The proposed algorithm can be conducted in real-time to mitigate the uncertainties in arrival time, departure time, and energy demand. Finally, the proposed scheme and its algorithm are verified under the distribution grid of the SUSTech campus (China). Compared with uncoordinated charging, the proposed scheme realizes load peak-shaving and valley-filling by 11.98% and 12.68%, respectively. The voltage values are ensured within the limitation range by engaging power flow calculation, in which the minimum voltage values are increasing and the maximum voltage values are decreasing with the expansion of PEV penetration. What is more, the computational complexity of peak-shaving and valley-filling strategy is near-linear, which verifies the proposed scheme can be carried out very efficiently. © 2020 John Wiley & Sons Ltd

Vehicle-to-grid (V2G) technology plays an important part in achieving carbon neutrality. Hence, reducing the execution time under the real-time application becomes an urgent issue. In this paper, we develop a cyber-physical co-modeling to fulfill the fundamental insights into the internet of smart charging points (ISCP), wherein the local controllers are designed near the plug-in electric vehicles (PEVs), and are coordinated with each other. In perspective of energy dispatching, a hierarchical V2G scheduling is implemented in a distributed way to decompose the optimization problem into several sub-problems. Besides, the parallel computing is applied in the V2G problem to accelerate the speed of obtaining results. Moreover, the voltage regulation is applied near the energy coordinator with high-performance computer rather than by the local controller. In perspective of network communication, the small-world network is applied to ensure the communication efficiency and decrease the wiring costs. Besides, the privacy-preserving of both the energy coordinator and the PEV users is guaranteed by processing and storing the sensitive information of the two participants nearby. Finally, the cyber-physical co-modeling is performed in Matlab and Network Simulator 2. Results show load flatting, self-consumption of photovoltaic output, voltage regulation, and up/down regulation are achieved. Moreover, the delay of small-world network is 90.94 times faster than that of lattice network, and the cost of small-world network is nearly 500 times less than that of full mesh network. Particularly, the execution time for V2G operation at one-time interval is less than 1 s. © 2021 Elsevier Ltd

Plug-in electric vehicles (PEVs) and renewable energy sources (RESs) can relief the stress on air pollution. Particularly, using RES for PEV energy requirement can integrate more RESs on the grid. In this paper, a vehicle-to-grid (V2G) scheme concerning on RES and edge computing, i.e. the internet of smart charging points with photovoltaics integration, is presented. Within the architecture of the scheme, each charging point equips computing and storage units, so as to store PEV sensitive information locally and conduct “burn after scheduling”. Besides, this architecture can transform the traditional large-scale V2G problem into several sub-problems, which are small enough to optimize. Based on the architecture of the scheme, an associated high-efficiency algorithm is designed. Six typical scenarios of PEV charging are elaborated and two indexes are presented to facilitate 1) the self-consumption of photovoltaics energy by PEV charging and 2) the peak-shaving and valley-filling of net load. Additionally, voltage regulation and real-time control are applied to ensure the security of the distribution grid and mitigate the uncertain conditions. Finally, compared with uncoordinated charging, the short-time scale simulation realizes the peak-shaving and valley-filling by 17.54% and 12.42%, respectively; and the amount of self-consumption of photovoltaics energy increases by 258.74%. Furthermore, the long-time scale simulations also present a satisfying performance for the grid energy saving and the load factor. Particularly, the proposed scheme offers high computational efficiency compared with different architecture and algorithm, and the execution time for scheduling one PEV at one-time interval shows a microsecond basis. © 2020

With an ever-increasing number of plug-in electric vehicles (PEVs), there is a fast-growing interest in PEVs' charging impact on the stability and the operating cost of power grid as well as the ecological environment. The centralized coordinated charging method is one of the promising solutions to mitigate such undesired impacts as elevating load peaks, increasing energy losses, and decreasing node voltage. However, the computational complexity is a critical issue to obtain the coordinated charging strategies especially for a large number of PEVs. In this context, it is very essential to analyze the computational performance of the centralized coordinated charging methods. In this paper, a paradigm for analyzing the computational performance is provided. Three centralized methods with different standpoint, viz., to minimize carbon emissions, to minimize load variance, and to minimize generation cost, are investigated to conduct a computational performance analysis from the grid operator perspective. First, the optimization theory is employed to transform the three engineering problems into the mathematical programming models. Then, whether the mathematical programming models are convex or nonconvex is analyzed. The results show that the first two mathematical programming models are convex, and the third mathematical programming model is nonconvex. And it demonstrates that the centralized scheduling model that is convex programming has a better computational performance theoretically. At last, simulations are carried out to verify the theoretical computational performance for different types of centralized coordinated charging methods. © 2019 John Wiley & Sons, Ltd.

The vehicle-to-grid (V2G) technology is an effective and economic solution to enable the integration of electric vehicles (EVs) into power grids. As an effort to present the state of the art in relevant fields, the power interaction mode between EVs and power grids, and the scheduling methodology for the V2G implementation are overviewed comprehensively in this paper. To be specific, technical requirements, grid impacts and battery degradations regarding each power interaction mode are discussed; the operating processes of centralized and decentralized scheduling approaches are introduced and compared. More importantly, it is noted that few attentions are given to the solving algorithms of programming models used for optimizing the scheduling strategy of EVs and few works examine their feasibilities to acquire the optimal charging strategies especially for a large number of EVs. In this context, this paper also illuminates the mathematical foundation on optimization techniques for the optimal V2G strategy, and provides new insights for evaluating these models. © 2019 Elsevier Ltd

In this paper, a distributed control framework is presented for plug-in electric vehicle (PEV) coordinated charging, and, the valley filling strategy is developed for the smart charger to obtain the optimal charging profiles of PEVs. In the proposed optimization scheduling algorithm, the index of surplus capacity of distribution networks is defined to utilize the surplus power during the load valley hours to the utmost. Results show that the proposed distributed control method has a significant superiority over uncoordinated charging for PEVs in the aspect of relieving the demand stress on power grids. © 2019 IEEE.

Coordinated charging can utilize the properties of electric vehicles (EVs) to obtain various benefits. However, there are two major challenges, viz. the uncertainty of EV charging behaviors and the overlong solving time for the optimal solutions in the charging scheduling problem of large-scale EVs. It is almost infeasible to precisely predict the charging information of EVs due to the uncertainty of their mobility as transportation tools. In order to tackle this issue, the real-time charging scheduling method is employed in this paper. Even so, the computational complexity is crucially important in the real-time scheduling methods since the charging strategies of large-scale EVs must be acquired in a short time. Hence, a high efficient methodology is proposed for EV real-time scheduling based on the definition of capacity margin index and charging priority index. Finally, the simulation results show that the proposed scheduling method has a significant superiority over the uncoordinated charging in the regard of relieving the demand stress on the power system. Moreover, the complexity analysis demonstrates that the proposed method has near-linear complexity so that it can acquire the optimal real-time charging scheme in a rather shorter time than other methods. © 2018 Elsevier Ltd

This paper presents a user independent choice decentralized charging strategy to motivate electric vehicle (EV) charging loads to achieve load valley filling in household smart micro-grid. A personal tailor price method is put forward by the power company side to guide EV charging processes accompanied by the user participation. At the EV user side, the EV user select personal tailor price trade based on the charging time cost and expense cost. In this strategy, no optimal control problems need to be solved locally so that requires low computation capability. Case study results show that although the load variance of the proposed decentralized charging is less than the centralized charging, the decentralized charging strategy can also efficiently achieve load valley filling, and moreover, this process is based on the EV user independent choice which can fully mobilize the user's enthusiasm and then promote the development of the entire EV industry. © 2017 IEEE.