The imperative for deep decarbonization of the energy and transportation sectors is undeniable. As major contributors to global greenhouse gas emissions, their synergistic transformation is critical for achieving climate goals. While battery electric vehicles (BEVs) represent a significant step towards cleaner transport, challenges related to charging time and range anxiety persist. Furthermore, their uncoordinated integration poses new strains on power grid stability. The emergence of the Fuel-Cell Hybrid Electric Vehicle (FCHEV), a hybrid car utilizing both electrochemical batteries and hydrogen fuel cells, presents a compelling solution. This dual-energy architecture offers not only enhanced driving range and faster refueling but also introduces unprecedented flexibility for grid operators. By intelligently managing their charging and hydrogenation cycles, fleets of these hybrid cars can act as distributed energy resources, absorbing excess renewable generation and providing power during peak demand. This paper explores the integration of FCHEV fleets into a coupled electricity-carbon market framework. We develop a novel scheduling methodology that optimizes both economic and environmental objectives, demonstrating how this technology can be a cornerstone for a resilient, low-carbon energy-transportation nexus.
The operational paradigm of the FCHEV is central to its value proposition. Unlike a pure BEV or a conventional fuel-cell vehicle, the hybrid car possesses two distinct energy storage mediums: a battery pack and a hydrogen tank. This allows it to draw energy from the electrical grid or from a hydrogen fueling station. The hydrogen, in turn, can be produced via Power-to-Gas (P2G) facilities, ideally using surplus electricity from renewable sources like wind and solar. This creates a closed-loop, flexible energy system: renewable over-generation is converted to storable hydrogen, which then fuels transportation or can be reconverted to electricity via the vehicle’s fuel cell when needed by the grid. To model the state of such a hybrid car, we propose an Equivalent Mileage Model. This model unifies the battery’s State of Charge (SOC) and the mass of stored hydrogen into a single metric: the remaining drivable distance. The total equivalent mileage \( S_{v,t} \) for vehicle \( v \) at time \( t \) is approximated by:
$$ S_{v,t} = \zeta_{E-R} \cdot E_{v,t} + \zeta_{M-R} \cdot M_{v,t} $$
where \( E_{v,t} \) is the stored electrical energy (kWh), \( M_{v,t} \) is the stored hydrogen mass (kg), \( \zeta_{E-R} \) is the battery-to-range coefficient (km/kWh), and \( \zeta_{M-R} \) is the hydrogen-mass-to-range coefficient (km/kg). This formulation is crucial for simplifying the scheduling problem, as it allows us to track the vehicle’s readiness through a single, intuitive variable.
The core of our analysis is a day-ahead optimal scheduling model for an integrated energy system comprising thermal power plants, wind farms, P2G facilities, hydrogen storage, and a fleet of FCHEVs. The model operates within a carbon emission trading scheme (ETS), where the system receives an initial carbon allowance and must pay for (or profit from) emissions exceeding (or below) this cap. The objective is to minimize the total system cost, which includes fuel costs for thermal generators, start-up/shut-down costs, and the net cost from carbon trading.
Objective Function:
$$ \min \sum_{t=1}^{N_T} \left[ \sum_{i=1}^{N_I} \left( C_i^S x_{i,t} + C_i^C y_{i,t} + C_i^G(P_{i,t}^G) \right) + \lambda_E (E_{s,t} – E_{cap}) \right] $$
Here, \( C_i^S, C_i^C, C_i^G \) are start-up, shut-down, and generation costs for thermal unit \( i \). \( x_{i,t}, y_{i,t} \) are binary status variables. \( P_{i,t}^G \) is the power output. \( \lambda_E \) is the carbon price (\$/ton), \( E_{s,t} \) is the system’s actual emissions at time \( t \), and \( E_{cap} \) is the total daily carbon allowance, typically allocated based on a benchmark emission rate \( r_{ref} \):
$$ E_{cap} = r_{ref} \cdot \sum_{t=1}^{N_T} \sum_{i=1}^{N_I} P_{i,t}^G $$
The actual emissions are calculated as \( E_{s,t} = \sum_{i=1}^{N_I} r_i P_{i,t}^G \), where \( r_i \) is the carbon intensity of unit \( i \).
The optimization is subject to a comprehensive set of constraints that model the physics and economics of each component:
1. FCHEV Fleet Constraints:
The aggregate charging/discharging power at the charging station must equal the sum of individual vehicle powers:
$$ P_{t}^{cha, sta} = \sum_{v \in \Phi_{EV}} P_{v,t}^{cha}, \quad P_{t}^{dis, sta} = \sum_{v \in \Phi_{EV}} P_{v,t}^{dis} $$
Each vehicle’s battery SOC evolution is governed by:
$$ SOC_{v,t} = SOC_{v,t-1} + \left( \eta_{cha} P_{v,t}^{cha} – P_{v,t}^{dis} / \eta_{dis} \right) \Delta t / E_v^{max} $$
A vehicle can only leave the station if its equivalent mileage meets its trip requirement \( S_v^{dep} \):
$$ \zeta_{E-R} E_v^{max} SOC_{v,T_v^{dep}} + \zeta_{M-R} M_{v,T_v^{dep}} \geq S_v^{dep} $$
The hydrogen dispensed at the station must equal the total hydrogen mass received by vehicles during their stay.
2. Power-to-Gas and Hydrogen Supply Chain Constraints:
The hydrogen production from P2G unit \( j \) is linked to its power consumption:
$$ M_{j,t}^{H_2} = \eta_{j}^{P2G} P_{j,t}^{P2G} \Delta t / L_{H_2} $$
where \( \eta_{j}^{P2G} \) is the conversion efficiency and \( L_{H_2} \) is the lower heating value of hydrogen. The total hydrogen produced must be balanced with station demand and storage inventory levels.
3. Power System Constraints:
These include standard unit commitment constraints (minimum on/off times, ramping limits), generator output limits, and the crucial system power balance at each time \( t \):
$$ \sum_{i=1}^{N_I} P_{i,t}^G + \sum_{r=1}^{N_R} P_{r,t}^{RG} + P_{t}^{dis, sta} = \sum_{d=1}^{N_D} P_{d,t}^D + \sum_{j=1}^{N_J} P_{j,t}^{P2G} + P_{t}^{cha, sta} $$
This equation ensures that the sum of thermal power, renewable (wind) power, and discharging from hybrid cars equals the sum of demand, power used for P2G, and charging of hybrid cars.
We evaluate the proposed model using data from a provincial power system. The system includes multiple thermal generators, a significant wind penetration, and a fleet of 1.6 million vehicles which are gradually replaced by FCHEVs in our scenarios. The key performance indicators analyzed are total system cost, carbon dioxide emissions, and wind curtailment rate.
The scheduling results vividly illustrate the “peak-shaving and valley-filling” capability of the FCHEV fleet. During late-night hours with high wind output and low load, the hybrid car fleet charges its batteries and the P2G system produces hydrogen, absorbing excess renewable energy. During evening peak hours, the fleet can discharge power back to the grid, alleviating stress on thermal generators. This dual role significantly enhances system flexibility.
The impact of FCHEV penetration is profound. The following table summarizes the system’s response as the share of hybrid cars increases, displacing conventional internal combustion engine vehicles (ICEVs).
| FCHEV Share (%) | Total System Cost (Million $) | CO₂ from Power Sector (tons) | CO₂ from Transport (tons) | Wind Curtailment Rate (%) |
|---|---|---|---|---|
| 0 (All ICEV) | 61.13 | 110,351 | 151,200 | 58.98 |
| 20 | 61.09 | 107,729 | 120,960 | 10.01 |
| 40 | 61.10 | 109,534 | 90,720 | 0.00 |
| 60 | 61.25 | 115,732 | 60,480 | 0.00 |
| 100 (All FCHEV) | 61.94 | 129,119 | 0 | 0.00 |
The analysis reveals a non-linear relationship. Initially, introducing FCHEVs reduces overall emissions and costs by utilizing otherwise-curtailed wind. At around 40% penetration, wind curtailment is eliminated. Beyond this point, to satisfy the growing hydrogen demand for the hybrid car fleet, the system must increase thermal generation to power P2G, leading to a rise in power sector emissions and total cost. This highlights an optimal penetration level that balances grid flexibility with carbon footprint.
Another critical design parameter for the hybrid car is the split between its electric and hydrogen driving ranges. We analyze this by varying the hydrogen-equivalent mileage ratio while keeping the total vehicle range constant.
| Hydrogen Mileage Ratio (%) | Total System Cost (Million $) | Wind Curtailment Rate (%) |
|---|---|---|
| 0 (Pure BEV) | 62.15 | 32.5 |
| 20 | 61.65 | 12.1 |
| 40 | 61.08 | 0.0 |
| 60 | 61.40 | 0.0 |
| 100 (Pure FCV) | 62.30 | 0.0 |
The results show a clear optimum near a 40% hydrogen range share. This configuration leverages the hydrogen buffer to achieve full wind integration at the lowest system cost, outperforming both pure BEV and pure FCV scenarios. It validates the economic and environmental rationale behind the hybrid car architecture.
The carbon market is a powerful policy lever. Our sensitivity analysis on carbon price (\( \lambda_E \)) demonstrates its effectiveness. As the carbon price escalates, the model increasingly prioritizes low-carbon resources. The relationship between carbon price multiplier and total system CO₂ emissions can be expressed as:
$$ E_{total} = E_{base} – \kappa \cdot \ln(1 + \alpha \lambda_E) $$
where \( E_{base} \) is baseline emissions, and \( \kappa, \alpha \) are positive constants derived from the system’s cost-emission trade-off curve. We observe diminishing returns; increasing the carbon price beyond a certain point (e.g., 50x the base price) yields minimal additional emission reductions, as the system has already shifted to its least-cost low-carbon configuration.
Finally, we project the system’s evolution with technology advancements. The combined effect of scaling up wind capacity (\( C_{wind} \)) and increasing the battery capacity (\( C_{bat} \)) in FCHEVs is synergistic in driving down emissions. A multi-variable regression from our scenario analysis suggests:
$$ \Delta E_{total} \approx -0.4 \cdot \frac{\Delta C_{wind}}{C_{wind,0}} – 0.25 \cdot \frac{\Delta C_{bat}}{C_{bat,0}} $$
This indicates that a 10% increase in wind capacity paired with a 10% increase in hybrid car battery capacity could lead to approximately a 6.5% reduction in total CO₂ emissions.
The integration of Fuel-Cell Hybrid Electric Vehicle fleets into a coupled electricity-carbon market framework presents a transformative pathway for deep decarbonization. The hybrid car is not merely a vehicle but a key flexibility asset for the modern grid. Our study demonstrates that an optimally scheduled FCHEV fleet can eliminate renewable curtailment, reduce peak-load stress on generators, and lower overall system costs and emissions. There exists an optimal market penetration level (around 40% in our case study) and an optimal vehicle design (~40% hydrogen range) that maximize these benefits. The carbon market is essential to internalize environmental costs and guide this transition effectively. Future policy should therefore focus on: 1) Encouraging the development and deployment of FCHEVs through targeted subsidies and infrastructure investment; 2) Expanding carbon pricing mechanisms to strengthen the economic signal for low-carbon technologies; 3) Supporting the scaling up of both renewable generation and P2G capacity to create a sustainable hydrogen supply for the growing fleet of hybrid cars. By viewing the energy and transportation systems as an integrated whole, we can unlock the full potential of hydrogen-electric hybrid technology to build a resilient, efficient, and carbon-neutral future.