A distributed charging pricing strategy for highway microgrid considering carbon trading: A Stackelberg evolutionary joint game approach

China's forest carbon sinks and mitigation potential from carbon sequestration trading perspective

Research on Coordinated Control Strategy of Electric Vehicle Pile Group Load based on Virtual Aggregation

The low-carbon ecological city aims to harmonise sustainable urban development with low-emission planning approaches. Emissions from business production processes are central to low-carbon planning. This paper explores three closed-loop supply chain recycling models - manufacturer, retailer, and third-party -considering carbon trading and emission reduction technology investment. Respective Stackelberg game models are developed incorporating carbon emission reduction costs, recycling costs, carbon trading price, emission intensity, and recycling price. The influence of these variables on carbon emission reduction and profit is examined through numerical analysis. Results indicate the government’s free carbon quota does not impact per-unit carbon reduction or manufacturer profit, nor optimal recycling mode selection. Under specific remanufacturing emission intensity and production cost saving conditions, carbon quota trading can substantially incentivise manufacturers to invest in emission reduction and recycling. With carbon trading and emission reduction technology investment, manufacturer recycling optimises economic and environmental benefits when remanufactured products provide high production cost savings. This fosters sustainable development supporting low-carbon planning.

This paper concentrates on the pricing design in electric vehicle (EV) platoon charging networks with hybrid traffic flows, where there exist different sizes of EV platoons, and charging stations have different amounts of available renewable energy. Two pricing schemes are proposed in terms of behavior awareness and congestion awareness to strike a balance between network revenue, renewable energy utilization, and overloading avoidance. Specifically, the proposed behavior-aware pricing scheme is a probabilistic approach to address the effects of driving preferences, which determines the targeted charging stations based on the charging costs. Therefore, the charging price is optimized to maximize the network revenue while improving renewable energy utilization. The proposed congestion-aware pricing scheme seeks to mitigate the overloading at each charging station by introducing the piecewise pricing mechanism, where the charging price at the charging station depends on the charging demand. The results demonstrate that our pricing schemes can boost network revenue and renewable energy utilization well. The behavior-aware pricing scheme can achieve larger network revenue than the congestion-aware pricing counterpart, and the congestion-aware pricing scheme is a better option for overcoming the load imbalance.

The development of electric vehicles(EV) has been widely established as an important way to ensure energy security and transform low-carbon economy in the world. However, the construction and operation cost of EV charging facilities is high and the profit channel is single, which makes operators in a long-term loss state. It is necessary to study the participation of EV charging facilities in carbon trading in order to find new profit growth points. On this background, firstly, the calculation method of carbon emission of whole life cycle of EV charging facilities is studied from the construction stage and the operation stage, which shows that the carbon emission accounting has technical feasibility. Secondly, the economic benefits of EV charging facilities participating in carbon trading market is analyzed, which shows that participating in carbon trading has economic rationality. Finally, a case study of the carbon emission accounting and economic benefits of EV charging facilities participating in carbon trading is carried out. The result shows that the economic benefits of EV charging facilities participating in carbon trading increase, and with the increase of charging amount and carbon trading price, the economic benefits increase continuously.

The high share of electric vehicles (EVs) in the transportation sector is one of the main pillars of sustainable development. Availability of a suitable charging infrastructure and an affordable electricity cost for battery charging are the main factors affecting the increased adoption of EVs. The installation location of fixed charging stations (FCSs) may not be completely compatible with the changing pattern of EV accumulation. Besides, their power withdrawal location in the network is fixed, and also, the time of receiving the power follows the EVs’ charging demand. The EV charging demand pattern conflicts with the network peak period and causes several technical challenges besides high electricity prices for charging. A mobile battery energy storage (MBES) equipped with charging piles can constitute a mobile charging station (MCS). The MCS has the potential to target the challenges mentioned above through a spatio-temporal transfer in the required energy for EV charging. Accordingly, in this paper, a new method for modeling and optimal management of mobile charging stations in power distribution networks in the presence of fixed stations is presented. The MCS is powered through its internal battery utilizing a self-powered mechanism. Besides, it employs a self-driving mechanism for lowering transportation costs. The MCS battery can receive the required energy at a different time and location regarding EVs accumulation and charging demand pattern. In other words, the mobile station will be charged at the most appropriate location and time by moving between the network buses. The stored energy will then be used to charge the EVs in the fixed stations’ vicinity at peak EV charging periods. In this way, the energy required for EV charging will be stored during off-peak periods, without stress on the network and at the lowest cost. Implementing the proposed method on a test case demonstrates its benefits for both EV owners and network operator.

China launched a nationwide unified Emissions Trading System (ETS) in 2021 and issued the Interim Regulations on the Administration of Carbon Emissions Trading in 2024 to regulate trading activities. To examine the effectiveness of China’s ETS policies, this study collected dynamic high-frequency data from 915 trading days, spanning from 16 July 2021 to 29 April 2025, and constructed a policy evaluation model based on the double machine learning framework. The findings indicate that China’s ETS policies have significantly increased the total trading volume of carbon emission allowances. This result has passed a series of robustness tests. Mechanism tests show that China’s ETS trading policies have significantly increased the transaction price and trading volume of carbon emission allowances. Compared with ETS in other countries and regions, China’s ETS policies are characterized by effectiveness, stability, and incrementalism, which can promote the orderly and efficient operation of the carbon emissions trading market. By refining the time span of the data and introducing cutting-edge causal inference methods, this study summarizes the successful experiences in the supervision and development of China’s carbon emissions trading market, providing precise evidence and feasible insights for global climate action and the development of ETS in developing countries.

In order to explore the impact of using electric vehicles on the cost and environment of logistics enterprises, this paper studies the optimization of vehicle routing problems with the consideration of carbon trading policies. Both the electric vehicle routing model and the traditional fuel vehicle routing model are constructed aiming at minimizing the total costs, which includes the fixed costs of vehicles, depreciation costs, penalty costs for violating customer time window, energy costs and carbon trading costs. Then a hybrid genetic algorithm (HGA) is proposed to address these two models, the advantages of greedy algorithm and random full permutation are combined to set the initial population, at the same time, the crossover operation is improved to retain the excellent gene fragments effectively and the hill climbing algorithm is embedded to enhance the local search ability of HGA. Furthermore, a case data is used with HGA to carry out computational experiments in these two models and the results indicate that first using electric vehicles for distribution can indeed reduce the carbon emissions, but results in a low customer satisfaction compared with using fuel vehicles. Besides, the battery capacity and charge rate have a great influence on total costs of using electric vehicles. Second, carbon price plays an important role in the transformation of logistics companies. As the carbon price changes, the total costs, carbon trading costs, and carbon emissions of using electric vehicles and fuel vehicles are affected accordingly, yet the trends are different. The changes of carbon quota have nothing to do with the distribution scheme and companies’ transformation but influence the total costs of using electric and fuel vehicles for distribution, and the trends are the same. These reasonable proposals can support the government on carbon trading policy, and also the logistics companies on dealing the relationship between economic and social benefits.

A two-stage robust optimization model for a virtual power plant considering responsiveness-based electric vehicle aggregation

This article constructs a closed-loop supply chain system including a single manufacturer and a single retailer and studies the optimal decision problem of enterprises under three different carbon reduction policies. Three supply chain game models were constructed to research the impact of different carbon reduction policies on the decisions of supply chain members. The results revealed that:(1) The carbon emission reduction level of the manufacturer is negatively correlated with the recycling price of used products and the abatement cost factor;(2) When the carbon trading price is equal to the tax rate, the carbon cap and trade policy is more conducive to promoting the manufacturer to improve the level of carbon emission reduction;(3) When the carbon trading price is low, mixed carbon policy is more favorable for the manufacturer to increase its carbon emission reduction. The research results of this study can provide certain suggestions for the government to implement a reasonable carbon reduction policy.

In this study, we consider robust emission reduction strategies for a monopolistic manufacturer facing demand uncertainty under governments’ cap-and-trade regulations. We model the manufacturer’s decision making and associated profits under four different emission reduction strategies: no mitigation measure, undertaking remanufacturing, improving the greening level, and both remanufacturing and improving the greening level. We find that the cap-and-trade regulation enhances the manufacturer’s motivation to be engaged in reducing carbon emissions. Furthermore, the manufacturer’s optimal choice of emissions reduction strategy depends on the level of carbon trading price and the degree of demand uncertainty. Specifically, there exists a threshold of carbon trading price at which the manufacturer’s optimal emissions reduction strategy will change. When the carbon trading price is low (below the threshold), the best strategy for the manufacturer to reduce emissions is to improve the greening level of the products. When the carbon trading price is high (above the threshold), the manufacturer should consider both remanufacturing and improving the greening level. Moreover, the threshold of the carbon trading price is further impacted by the demand uncertainty. With market demand uncertainty rising, the threshold of carbon trading price increases as well. Finally, we find raising the carbon trading price may not necessarily benefit the environment. Overpriced carbon trading may hurt the manufacturer’s production instead of encouraging them to take emission reduction measures.

Aggregated optimization scheduling of electric vehicles in high-speed railway station areas considering carbon trading

Electric Vehicle Charging Pricing Design for Agent-Based Traffic Microsimulation

SummaryIn recent days, electric vehicles (EVs) are seen as a viable transportation alternative by industry people to minimize the greenhouse gas emissions problems of regular fuel vehicles. While EVs' performance is limited due to their restricted battery power, charging wait for service, and high resource costs, they are still a viable option. To enhance the EV's performance, this work introduced an optimized priority‐based charge scheduling with dynamic pricing model. For this, a novel modified tunicate swarm algorithm method, which is the enhanced form of the standard tunicate swarm algorithm, is proposed. When an EV arrives at the charging station, the EV user is permitted to choose the type of charging based on his/her requirements. In general, when charging EVs in public stations or during peak hours, customers must be patient and wait for significant periods of time. The introduced model ignores this negative effect. EV users have also mentioned the issue of controlled charging rates as a key barrier. This is a difficult task, but it is accomplished in this study, which optimizes total charge costs. Finally, the results acquired are discussed under overall cost and energy consumption.

Deep recurrent Q-network algorithm for carbon emission allowance trading strategy