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Accurate carbon emission accounting for electric vehicles (EVs) is particularly important, especially for those participating in the carbon market. However, the participation of numerous EVs in vehicle-to-grid (V2G) scheduling complicates the precise accounting of individual EV emissions. This paper presents a novel approach to carbon accounting and benefits distribution for EVs. It includes a low-carbon dispatch model for a distribution system (DS), aimed at reducing total emissions through strategic EV charging scheduling. Further, an improved carbon emission flow accounting model is proposed to calculate the carbon reduction of EVs before and after low-carbon dispatch. It enables real-time carbon flow tracking during EV charging and discharging, then accurately quantifies the carbon reduction amount. Additionally, it employs the Shapley value method to ensure equitable distribution of carbon revenue, balancing low-carbon operation costs and carbon reduction contributions. A case study based on a 31-node campus distribution network demonstrated that effective scheduling of 1296 EVs can significantly reduce system carbon emissions. This method can accurately account for the carbon emissions of EVs under different charging states, and provides a balanced analysis of EV carbon reduction contributions and costs, advocating for fair revenue allocation. [ABSTRACT FROM AUTHOR]

• A cost-effective and high-efficient shared fast charging scheme is proposed. • The enabled DC power is used to supply the terminal charging points. • More EV penetration is to be achieved in old residential communities. • The DC power supply achieves a 1.3 % average transfer efficiency improvement. Charging infrastructures are being deployed widely in urban and rural areas to solve the so-called "last one-kilometer" charging problem. However, for old residential communities, the existing difficult-to-charge issue is not easily solved solely by the widespread deployment of charging infrastructure owing to limited spaces, complex reconstruction, and huge costs. In this context, this paper introduces a cost-effective and high-efficient shared fast charging scheme for old residential communities. The comprised registration-based queuing charging mechanism is proposed to schedule electric vehicle (EV) charging. An integrated and multi-stage power electronic device called smart interlinking unit (SIU) is introduced for grid reconstruction. The SIU-based charging station architecture features flexible control capability, enabling DC power supply for terminal EV chargers. For this architecture, a novel hierarchical coordinated operation strategy is designed, comprising the upper-level real-time vehicle-to-grid (V2G) dispatching and lower-level coordination strategies. Case studies demonstrate that the proposed scheme could accommodate 40 % EV penetration, while exhibiting a significant decline in system investment and occupied area compared to the slow charging scheme. Additionally, a 1.3 % improvement in average transfer efficiency is verified under this architecture. The proposed strategy is proven to realize a prominent power peak-shaving and valley-filling, effective transformer load balance, and DC voltage restoration. [ABSTRACT FROM AUTHOR]

• A novel carbon emission reduction(CER) methodology is proposed. • The Black-Scholes mode is introduced to measure carbon emission option pricing. • Case studies of campus grid CER assessment are conducted in Shenzhen. • Methods and policy suggestions of CER are given for grid integration of EVs. China, as the world's largest emitter of carbon emissions, has implemented carbon markets to reduce carbon emissions, but the economic feasibility of integrating electric vehicles (EVs) into these markets remains uncertain. To address this challenge, we propose a novel methodology for grid integration of EVs based on the Shenzhen carbon inclusive management measures. Our proposed methodology can optimize EV charging schedules based on carbon emission prices and improve the economic feasibility of low-carbon transitions through EVs in China. We incorporate the Black-Scholes model to measure carbon emission option pricing and develop an optimal bidding strategy that considers spatiotemporal uncertainties of EVs and electricity markets. We use numerical simulations with real-world data from Shenzhen carbon emission market and Guangdong electricity market to demonstrate the effectiveness of our proposed methodology. Results reveal that scheduling 100 EVs' charging/discharging behavior properly can generate a revenue of 2379.4 yuan for the distribution system operator, while the revenue for carbon emission reduction (CER) is only 9.35 yuan. This paper demonstrates that the potential revenue of CER may be substantially based on the future scenario for the carbon emission market. These findings provide valuable insights for policymakers and stakeholders seeking to reduce carbon emissions in China's electricity sector. [ABSTRACT FROM AUTHOR]

• A novel classification method for the EV fleet is proposed to predict the total load. • A modified Bass model is presented to predict the ownership of private EVs. • The regression model is used for the ownership prediction of other types of EVs. • The probability theory is adopted to model the charging profiles of EVs. More and more adoptions of electric vehicles (EVs) would bring a potential threat on the existing electric grid. In this context, a systematic methodology is presented in this paper to predict the additional loads resulting from EV charging in the mid-and-long term. It includes probabilistic models for describing the EV charging profiles and forecast models for predicting the future EV ownership. It is impractical to develop a method to simulate the charging profiles of the entire EV fleet due to the diversity of EV charging behaviors. As a consequence, the entire EV fleet is divided into four categories viz. private EV, electric taxi, electric bus and official EV so as to predict their charging loads respectively. The proposed method is conducted in the city of Shenzhen, which currently has the largest electric bus and electric taxi fleet in the world. Results indicate that the maximum value of the predicted EV charging profile in 2025 would occur at 21:30, reaching 1,760 MW under high oil price, which could elevate the existing load peak by 11.08 %. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

This paper addresses the significant challenges faced by distribution system operators (DSOs) in competitive electricity markets due to uncertainties arising from electricity markets, electric vehicles (EVs), and conventional loads. To tackle these challenges, this paper proposes a pseudo-local reserve market between the DSO and EVs, which enables the DSO to integrate EV flexibility into the overall market framework while compensating for deviations in the conventional load across the wholesale electricity market. Furthermore, a two-stage framework is developed to facilitate the participation of EVs in electricity markets. In the first stage, the reserve capacity of EVs is integrated into the decision-making process for day-ahead bidding, taking into account uncertainties of the load and electricity. In the second stage, the day-ahead results are coordinated to enable the efficient dispatch of EVs for real-time market participation. To assess the efficacy of the proposed framework, a case study is conducted within a 31-node distribution system situated on a university campus located in Guangdong, China. This paper investigates the economic impact of load deviation constraints on EV reserves, revealing a penalty reduction of 3100 CNY for a daily reservation of 1000 EVs and a net profit increase of 1300 CNY for DSOs utilizing reserves. It further introduces a method for quantifying reserve capacity, enhancing DSO decision-making accuracy. • A pseudo-local reserve market is established considering EV's reserve potential. • Optimal bidding strategy for DSO in China's electricity markets is constructed. • A framework for EV coordinated V2G operation is developed. • The proposed method can limit load deviation cost for more profits. [ABSTRACT FROM AUTHOR]

• The day-ahead bidding strategy of electric vehicle (EV) aggregator is explored. • Two typical agent modes between EV users and EV aggregator are introduced. • The market uncertainty is handled by analyzing potential market clearing scenarios. • The Guangdong electricity market is first investigated for case study. The electric vehicle (EV) aggregator is a new entity that coordinates the charging of an EV fleet to exploit the business opportunities in the electricity market. This paper explores the optimal bidding strategy for the EV aggregator participating in day-ahead markets. Two typical agent modes between the EV aggregator and the EV user are introduced based on the reality need of the EV user, viz. the centralized protocol management mode (CPMM) and the decentralized demand response mode (DDRM). After that, the stochastic optimization model is developed to maximize the expected profits of EV aggregator. The uncertainties from the electricity market and the EV charging are incorporated into the stochastic optimization model. The Guangdong electricity market, as the pioneer of China's power reform, is first investigated as the target market to verify the rationality of the proposed approach. The possible real-time clearing scenarios are extracted from the historical market clearing data using the multi-class support vector machine (MSVM). Results reveal that the EV aggregator can make an optimal bidding strategy for the potential clearing scenarios when multiple agent modes are involved. Moreover, the comparison of two agent modes indicates that the EVs with the CPMM are more sensitive to the electricity price than those with the DDRM. [ABSTRACT FROM AUTHOR]

• An easy-to-implement hybrid microgrid reconstruction scheme is proposed for old residential communities. • The existing transformers and power lines could be reused in the proposed hybrid microgrid architecture. • A significant maximum 297% of EV penetration could be obtained with the proposed architecture and operation strategy. • Real-time voltage regulation, desirable operation efficiency, and improved load ratio are to be implemented. The widespread popularity of electric vehicles (EVs) imposes great challenges for distribution grids, such as feeder congestion, voltage deviation, and so forth. Although building new electrical facilities is feasible to release more capacity for EV integration, this is not a wise scheme to be applied in old residential communities owing to huge costs and limited spaces. In light of this, the paper proposes an easy-to-implement and cost-effective hybrid AC/DC microgrid reconstruction scheme for low-voltage distribution grids of old residential communities. The advanced power electronic device called smart interlinking unit (SIU) is incorporated in prior power distribution rooms of residential communities, featuring integrated structure, dedicated interfaces, and flexible control capability. The SIU-based architecture reuses existing redundant transformers and power lines to form AC/DC subgrids. An outstanding transfer capacity improvement and power loss reduction via the DC renovation are quantified. Considering the varied efficiency characteristics of multiple power converters and transformers, a hierarchical coordinated operation strategy is proposed for this architecture to realize the overall energy-saving operation. The optimal operation point tracking and AC/DC voltage regulation are included in the strategy design. Regarding the target scenario in the case study, the adopted reconstruction scheme would achieve a significant 297% of the maximum EV penetration. The real-time voltage regulation for AC and DC feeders is also realized to avoid possible voltage violations. Additionally, the proposed architecture and operation strategy could obtain about 10% of load ratio improvement and 1.5% of average efficiency promotion compared to the traditional AC architecture. [ABSTRACT FROM AUTHOR]

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. • The emerging DC-powered converter topologies to interface EVs are identified and discussed. • Extensive nanogrid, microgrid, and cluster are contained in the architecture discussion. • Extensive six indexes are developed to evaluate four-type nanogrid and microgrid architectures. • Comprehensive assessment of varied power architectures is completed in view of the EV-grid integration and V2G operation. [ABSTRACT FROM AUTHOR]