Electric vehicles (EVs) are expected to play a critical role in future transportation systems. A number of countries have published roadmaps aiming to facilitate the adoption of EVs on the road. It is estimated that existing charging facilities will not be able to satisfy the tremendous charging demands of a dramatically increasing number of EVs. Following the rapid development of artificial intelligence and mobile communication technology, certain charging pricing mechanism is expected to influence the charging behavior of EV drivers. In order to maximize the working efficiency of highway charging facilities and the consumption of the renewable energy near charging facilities, this paper proposes a pricing methodology taking into account the charging facility service ratio, traffic flow and renewable energy generation. To support the adoption of the proposed pricing methodology, forecasts of hour-by-hour traffic flow and renewable generation as well as calculation of the shortest paths to different charging stations (CSs) are investigated. A road network testbed based on the Dublin traffic network is established to evaluate the proposed pricing methodology. It is discovered that for certain wind-rich CSs, the proposed pricing methodology can increase the consumption rate of wind energy by up to 82.97%, with an average improvement of 30.73%; for certain solar-rich CSs, it can improve the level of solar energy consumption by up to 59.50% and an average increase of 29.28% is achieved. The proposed pricing methodology can also reduce traffic jams to some extent at both peak and off-peak times.

The development of hydrogen‐enriched compressed natural gas (HCNG) can make full use of the existing natural gas infrastructure, thereby alleviating the economic and technical pressure on the development of pure hydrogen. This paper proposes a multi‐stage flexible planning method for the regional electricity‐HCNG‐integrated energy system (E‐HCNG‐IES), which considers the medium and long‐term dynamic characteristics of the E‐HCNG‐IES. For each sub‐regional energy station, an energy‐hub type model including hydrogen/HCNG‐related equipment and facilities is established. Considering the influence of hydrogen blending on the energy delivery capacity and line pack of pipelines, a hydrogen‐resistant retrofit model for existing natural gas pipelines between sub‐regions is proposed. Meanwhile, the model for newly constructed hydrogen pipelines is also presented. Subsequently, a series of flexibility evaluation indicators are proposed to support the performance analysis of planning schemes. The lateral comparison of sub‐cases in Changzhou, China demonstrates that considering the hydrogen‐resistant retrofit of natural gas pipelines can reduce the construction cost of hydrogen pipelines by about 37.12%. The peak shaving capacity of underground hydrogen storage (UHS), the energy recycling ability of turbo expander (TE), and the space‐time decoupling ability of hydrogen in energy supply and demand have also been confirmed.

Transporting green hydrogen by existing natural gas networks has become a practical means to accommodate curtailed wind and solar power. Restricted by pipe materials and pressure levels, there is an upper limit on the hydrogen blending ratio of hydrogen-enriched compressed natural gas (HCNG) that can be transported by natural gas pipelines, which affects whether the natural gas network can supply energy safely and reliably. To this end, this paper investigates the effects of the intermittent and fluctuating green hydrogen produced by different types of renewable energy on the dynamic distribution of hydrogen concentration after it is blended into natural gas pipelines. Based on the isothermal steady-state simulation results of the natural gas network, two convection–diffusion models for the dynamic simulation of hydrogen injections are proposed. Finally, the dynamic changes of hydrogen concentration in the pipelines under scenarios of multiple green hydrogen types and multiple injection nodes are simulated on a seven-node natural gas network. The simulation results indicate that, compared with the solar-power-dominated hydrogen production-blending scenario, the hydrogen concentrations in the natural gas pipelines are more uniformly distributed in the wind-power-dominated scenario and the solar–wind power balance scenario. To be specific, in the solar-power-dominated scenario, the hydrogen concentration exceeds the limit for more time whilst the overall hydrogen production is low, and the local hydrogen concentration in the natural gas network exceeds the limit for nearly 50% of the time in a day. By comparison, in the wind-power-dominated scenario, all pipelines can work under safe conditions. The hydrogen concentration overrun time in the solar–wind power balance scenario is also improved compared with the solar-power-dominated scenario, and the limit-exceeding time of the hydrogen concentration in Pipe 5 and Pipe 6 is reduced to 91.24% and 91.99% of the solar-power-dominated scenario. This work can help verify the day-ahead scheduling strategy of the electricity-HCNG integrated energy system (IES) and provide a reference for the design of local hydrogen production-blending systems.

With the development of distributed energy resources and intelligent energy management technologies, park‐level integrated energy systems (PIESs) are essential for multi‐energy flow conversion and utilization. However, coordinating the upstream energy sources, internal energy stations, distribution networks, and downstream loads within the PIES framework remains a challenge. Developing a station‐network coordinated planning scheme for PIES confronts challenges, such as overly complex models that are difficult to solve and the difficulty of implementing standard methods for medium and long‐term planning. This paper proposes a multi‐stage coordinated planning approach for PIES, containing energy stations, multi‐energy networks, and load aggregation nodes. The energy equipment and energy networks are precisely modelled to enhance the reliability of the planning scheme. The chance‐constrained programming (CCP) method is adopted to address uncertainties arising from renewable energy generation. Additionally, the improved big‐M method and second‐order cone (SOC) relaxation technique are utilized to manage non‐linear elements, transforming the model into a mixed‐integer second‐order cone programming (MISOCP) form to enhance solution efficiency. A case study illustrates that the proposed method enables effective allocation of equipment and networks, outperforming single‐stage planning by reducing investment costs, enhancing facility utilization rates, and facilitating renewable energy integration.

The rising penetration of electric vehicles is driving huge demand for lithium batteries, making low-carbon manufacturing a critical objective. This goal is challenged by insufficient production scheduling flexibility and the neglect of carbon-reduction technologies. To address these challenges, this paper develops a low-carbon planning methodology for lithium battery plant energy systems by leveraging manufacturing chain flexibilities. First, a lithium battery energy–carbon material modeling approach is developed that accounts for process production delays and intermediate product storage to capture schedulable process energy consumption patterns. A nitrogen–oxygen coupling production framework is introduced to facilitate oxygen-enriched combustion technology application, while energy recovery pathways are incorporated given the high energy consumption of the formation stage. Subsequently, a process scheduling-driven planning model for lithium battery industrial integrated energy systems (IIES) is developed. Finally, the planning model is validated through four contrasting case studies and systematically evaluated using multi-criteria decision analysis (MCDA). The results demonstrate three principal conclusions: (1) incorporating process scheduling effectively enhances process energy flexibility and reduces total system costs by 19.4%, with MCDA closeness coefficient improving from 0.257 to 0.665; (2) oxygen-enriched combustion increases maximum combustion and carbon capture (CCS) rates from 90% to 95%, reducing carbon tax to 40.5% of the baseline; (3) energy recovery on the basis of process scheduling further reduces costs and carbon emissions, with battery recovery achieving an additional 30.2% cost reduction compared to 24.1% for heat recovery, and MCDA identifies this integrated approach as the optimal solution with a closeness coefficient of 0.919.