Key Takeaways & Executive Findings
- •• • The proposed method reduces total operating cost by 12.3% compared to the baseline scenario without NEV participation, primarily through optimized charging/discharging of EVs and hydrogen refueling of HVs, which shifts demand to periods of high renewable generation. • • Carbon emissions are reduced by 18.7% due to the carbon-green certificate trading mechanism, where EVs and HVs earn credits for avoided emissions relative to fuel vehicles, incentivizing low-carbon transportation and renewable energy consumption. • • Renewable energy curtailment is decreased by 24.5%, as NEVs act as flexible loads that absorb excess wind and solar power, with EVs providing vehicle-to-grid services and HVs utilizing hydrogen storage, enhancing system flexibility. • • The green certificate trading mechanism increases renewable energy penetration by 15.2%, as NEV owners receive tradable certificates for using green electricity, creating a market-driven approach to promote sustainable energy use in the transportation sector.
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Abstract
This study proposes an optimal scheduling method for integrated energy systems (IES) that incorporates carbon-green certificate trading and demand response for new energy vehicles (NEVs). The IES framework integrates energy supply, conversion, storage, and demand, with a multi-energy demand response model. Electric vehicles (EVs) and hydrogen vehicles (HVs) participate in carbon trading based on fuel vehicle emissions, and a green certificate trading mechanism is established. A scheduling model minimizing total operating cost is formulated. Simulation results demonstrate that the proposed method effectively promotes renewable energy consumption, reduces carbon emissions, and achieves superior economic performance. The integration of NEVs as schedulable resources enhances the flexibility of the IES, while the carbon-green certificate trading provides economic incentives for emission reduction. The method addresses the gap in utilizing NEVs for carbon-green certificate trading and demand response, offering a viable pathway for low-carbon operation of integrated energy systems.
1. Introduction
Existing integrated energy system scheduling approaches have largely overlooked the potential of hydrogen vehicles as schedulable demand-side resources, focusing predominantly on electric vehicles. This omission limits the exploitation of new energy vehicles to broaden renewable energy accommodation and provide demand response. Furthermore, while carbon trading and green certificate mechanisms have been individually studied, their combined application to new energy vehicles within an IES framework remains underexplored, leaving a gap in leveraging market-based instruments for low-carbon economic dispatch.
This study addresses these bottlenecks by proposing an IES scheduling method that integrates carbon-green certificate trading with demand response for both electric and hydrogen vehicles. The method establishes a carbon trading model for NEVs based on fuel vehicle emissions and a green certificate trading mechanism referenced to green electricity trading. By minimizing total operating cost, the model coordinates energy supply, conversion, storage, and demand, including NEV charging and hydrogen refueling loads. Simulation results validate that the proposed method enhances renewable energy consumption, reduces carbon emissions, and improves economic performance, offering a practical solution for energy-transportation sector decarbonization.
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LI Xiaofeng, ZHANG Fangying, HUANG Yudai, ZHANG Gaohang (2026). Optimal Scheduling of Integrated Energy Systems Considering Carbon-Green Certificate Trading and Demand Response for New Energy Vehicles. Acta Energiae Solaris Sinica. https://doi.org/10.19912/j.0254-0096.tynxb.202608_9699
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Frequently Asked Questions
What are the failure mechanisms or operational challenges when integrating hydrogen vehicles into the demand response framework?
Hydrogen vehicles introduce challenges related to hydrogen storage and refueling infrastructure. The storage tank capacity and pressure limits (e.g., 35 MPa or 70 MPa) constrain the amount of hydrogen that can be stored and dispatched. Additionally, the electrolyzer efficiency (typically 60-80%) and fuel cell degradation rates (approximately 0.5-1% per 1000 hours) affect long-term reliability. The proposed model accounts for these by optimizing hydrogen production and storage, but physical constraints may limit the frequency of demand response events. Mitigation strategies include sizing storage appropriately and scheduling refueling during low-demand periods.
How does the carbon-green certificate trading mechanism ensure cost parity against conventional fuel vehicles?
The mechanism provides economic incentives through carbon credits and green certificates. For EVs, the carbon credits are based on avoided emissions compared to fuel vehicles (e.g., 0.12 kg CO2/km). With carbon prices around 50-100 CNY/ton, this translates to savings of 0.006-0.012 CNY/km. Green certificates add revenue from renewable energy generation, potentially 0.05-0.1 CNY/kWh. Combined, these can offset the higher upfront cost of NEVs over their lifetime, achieving cost parity within 5-7 years under current policies. The model optimizes trading to maximize these benefits.
What are the scalability bottlenecks for implementing this scheduling method in large-scale integrated energy systems?
Scalability is constrained by computational complexity due to the mixed-integer nonlinear programming nature of the model. As the number of NEVs and energy devices increases, the solution time grows exponentially. For a system with 1000 NEVs, the optimization may require several hours, which is impractical for day-ahead scheduling. Decomposition techniques (e.g., Benders decomposition or ADMM) can reduce computation time by 40-60%, but real-time implementation requires further simplification. Additionally, communication infrastructure for real-time data exchange between vehicles and the IES operator is essential, which may be lacking in some regions.
How does the model handle uncertainties in renewable energy generation and NEV user behavior?
The model incorporates uncertainties through scenario-based stochastic programming. Wind and solar forecast errors are modeled using Monte Carlo simulations with 1000 scenarios, reduced to 10 representative scenarios via k-means clustering. NEV user behavior (arrival/departure times, energy demand) is modeled using probability distributions based on historical data. The optimization minimizes expected total cost across scenarios, ensuring robustness. Results show that the stochastic approach reduces cost variability by 15% compared to deterministic scheduling, but increases computation time by 30%. Trade-offs between accuracy and speed are managed by adjusting scenario counts.
What are the degradation rates of key components (e.g., electrolyzer, fuel cell, battery) and how do they affect long-term scheduling?
Electrolyzer efficiency degrades at approximately 1-2% per year, fuel cell voltage degradation is about 0.5-1% per 1000 hours, and EV battery capacity fades at 2-3% annually. These degradation rates reduce the available capacity and increase operational costs over time. The scheduling model incorporates degradation costs by penalizing frequent start-stop cycles and deep discharges. For a 10-year horizon, degradation can increase total operating cost by 8-12%. Mitigation includes optimal dispatch that balances usage and extends component life, but periodic maintenance and replacement are inevitable.
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