Search

• 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]

• The concept of developing a user-oriented vehicle-to-grid (V2G) scheme is presented. • The peak shaving capacity of a user-oriented V2G scheme for power grids is assessed. • The global/divided model is developed for centralized/decentralized V2G strategies. • The city of Shenzhen is taken as a case study for the peak shaving assessment. The vehicle-to-grid (V2G) technology creates a cost-effective alternative solution for the integration of electric vehicles (EVs) into smart grids. Two mainstreams of operation modes exist for the V2G implementation, viz. the centralized and decentralized V2G operation mode. In previous studies, researchers used to assume that EV users involved would participate in a special mode for the coordinated V2G strategy. In fact, the requirements of EV users on the V2G operation vary from individual to individual, depending on the type of EV usage, the charging preference of EV users, and the urgency degree of EV charging. Therefore, it is essential to develop a user-oriented V2G scheme with multiple operation modes to make more EVs participate in the V2G operation for coordinated charging. In this paper, the peak shaving capacity of a user-oriented V2G scheme with multiple operation modes is surveyed compared to the conventional charging mode. The city of Shenzhen, China, is taken as a case study to evaluate the power demand of EV charging during peak hours for different scenarios. In order to obtain the coordinated EV charging strategy, the global/divided scheduling optimization model is developed for the centralized/decentralized V2G strategy. The global scheduling method devotes to minimizing the total EV charging costs whereas the divided scheduling method aims to minimize the individual EV charging costs based on the time of use pricing system. Results reveal that the power demand during peak periods is decreased by 0.93 GW or 5.89 % for a modest case in a user-oriented V2G scheme in contrast to the conventional charging mode. [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]