[Objective] To enhance the flexibility and low-carbon performance of integrated energy systems （IES）， this study proposes an optimal scheduling strategy that accounts for various electric vehicle （EV） charging modes and supply-demand flexibility. [Methods] First， from the demand perspective， an ordered charging strategy is developed based on the dynamic state of charge. This applies to three charging modes： conventional slow charging， fast charging， and power exchange. Second， flexibility-enhancement strategies are further explored in coordination with integrated energy demand. On the supply side， the Kalina cycle （KC） is introduced as an improvement over the organic Rankine cycle. The KC enables thermoelectric decoupling and supports flexible， efficient output from the CHP unit. Finally， an IES low-carbon economic optimization model is constructed. It incorporates stepwise carbon trading， EV charging modes， demand response， and the KC， while considering carbon emission constraints. [Results] By comparing and analyzing multiple scenarios， the proposed strategy reduces the total system cost by 16.22%. It also improves the flexibility of IES supply and demand， enables orderly charging across various EV charging modes， and lowers both economic costs and carbon emissions. [Conclusions] The key innovations include sequential charging tailored to different EV charging modes and strategies to enhance supply and demand flexibility. These findings offer new insights into improving IES flexibility and can be integrated with other flexibility resources to further maximize system performance.

[Objective] China encourages the construction and development of integrated comprehensive transportation and energy service stations to accommodate the rapid growth and widespread adoption of new-energy vehicles. This study proposes an economic operation method for electric-hydrogen integrated energy stations that incorporates dynamic pricing strategies. [Methods] First， the study analyzes when new-energy vehicle users arrive at energy stations and how they charge their vehicles. Based on this， a charging demand prediction model is developed using the Monte Carlo method. Second， the study considers several factors. These include the power load ratio for charging， the absorption rate of new energy， changes in station storage capacity， and external purchasing costs. Based on these， a dynamic pricing strategy is formulated. The strategy includes both electricity and hydrogen pricing. This strategy encourages new-energy vehicle users to participate in demand response programs. It also helps manage the energy loads of hydrogen buses and sanitation vehicles. Based on this， an economic operation method for electric-hydrogen integrated energy stations is proposed. [Results] Simulation results show that dynamic pricing strategies significantly improve station revenue compared to fixed pricing strategies. Specifically， electricity sales， hydrogen sales， and total energy sales revenue increased by 24.13%， 4.57%， and 14.59%， respectively. Meanwhile， the total operating cost decreased by 10.3%， further boosting net revenue. [Conclusions] The proposed method overcomes the limitations of fixed pricing strategies and enables more economical operation of electric-hydrogen integrated energy stations.

[Objective] Factors such as road networks， temperature， and electric vehicle （EV） type affect the spatial and temporal distribution of EV charging loads. To improve prediction accuracy， a spatiotemporal EV charging load prediction model is developed by integrating multiparty information. [Methods] By introducing the model of temperature and vehicle speed on energy consumption， the impact of the external environment on EV range is quantified. A charging demand gravity model is also used， incorporating factors such as station size， electricity price， time cost， and gravity parameters. These are used to dynamically adjust user behavior in choosing charging stations. Additionally， the Dijkstra algorithm is improved to plan charging paths more effectively by including real-time road condition data. Finally， the total charging load is accumulated and superimposed. [Results] The MATLAB simulation results showed a significant difference between the charging load distributions of private cars and cabs. The charging loads of private cars in residential， working， and commercial areas are concentrated during nighttime， daytime working hours， and off-duty hours， respectively. In contrast， the charging loads of cabs are characterized by morning and evening peaks， valleys， and small peaks at noon due to operational demand. The proposed improved Dijkstra's algorithm improves the efficiency of path planning by dynamically adjusting road section weights， reducing driving time by 3.9% for the same destination. The proposed charging demand gravity model optimizes users' charging station selection behavior by integrating factors such as charging station size， electricity price， and user time cost， resulting in a more reasonable spatial and temporal distribution of the charging load[Conclusions] This study constructed a spatial and temporal distribution prediction model for electric vehicle charging loads by integrating information from multiple sources. It reveals the differences in the charging behaviors of different types of EVs， their temperature sensitivity， and the dynamic characteristics of user decision-making. The results provide theoretical support for grid load scheduling， charging station planning， and the development of an orderly charging strategy.

[Objective] To fully exploit the flexible and adjustable potential of the charging load of a taxi battery swapping station， a charging optimization scheduling strategy is proposed. This strategy aims to ease the conflict between the charging load， peak and valley pressures on the power grid， and new energy consumption. It considers the coupling between the power market and new energy sources. [Methods] The strategy is based on two main goals： providing auxiliary services in the power market and addressing the abandonment and consumption of new energy. A coordinated operation framework is constructed， linking battery swapping stations， power grids， and new energy stations. An optimization mechanism is designed， incorporating peak response， time-of-use tariff matching， and dynamic tracking of battery SOC. Taking 96 time slots as the scheduling granularity， a dual-objective model—maximizing economic benefits and optimizing new energy consumption—was established， and an improved Harris Hawk optimization algorithm was introduced to solve the problem. [Results] Results from a case study show that the proposed strategy increases the economic benefit of the battery swapping station by 25%. It also raises new energy consumption by 16.5%. Additionally， the charging load during grid peak hours is significantly reduced. This helps achieve peak shaving and valley filling. [Conclusions] By dynamically matching new energy abandonment with time-of-use tariffs， the proposed strategy enhances both economic efficiency and the station's ability to consume new energy. It also reduces grid pressure during peak periods. The proposed market-new energy synergy framework offers a new approach for battery swapping stations to participate in power system regulation.

[Objective] With the increasing number of electric vehicles （EVs）， rational planning of charging stations （CSs） has become a key challenge in meeting charging demand. A multistage planning method for EVCSs considering expansion is proposed to adapt to dynamic changes in the ownership of EVs and improve the rationality and economic efficiency of CS planning. [Methods] First， based on the system dynamics （SD） method， dynamic prediction of EV ownership is conducted. Then， to maximize the investment return of CSs and minimize the queuing time of EV users， a multi-stage planning model for EVCSs considering expansion is constructed. Finally， the model is solved using the immune genetic algorithm. [Results] Simulation results based on case studies demonstrate that the queuing time is reduced significantly when staged planning is used compared with the one-time planning approach. In addition， by incorporating the increase in EV ownership predicted through SD prediction as opposed to relying on a fixed ownership model， the proposed planning method leads to higher costs and total income. Moreover， planning results that consider expansion strategies have more advantages in terms of total revenue than those that do not. [Conclusions] The proposed EV ownership prediction model is more accurate and suitable for meeting future planning needs， and can effectively improve the accuracy of the planning results. The proposed planning model， which incorporates both staged and expansion strategies， reduces the initial investment cost and significantly improves the economic viability of the planning results. It also increases the flexibility and sustainability of CS planning.