## 5 Simulation Results and Discussion
### Economic Benefit Analysis  
This study investigates the integration of solar energy and braking regenerative energy into high-speed railway power systems via PV devices and ESS while incorporating carbon trading mechanisms and REC trading mechanisms into the model. The analysis evaluates the impacts of operational load variations under diverse conditions, PV device performance, and ESS configurations on the high-speed railway power system, thereby validating the efficacy of the proposed EDMOA as an optimization framework.  
Case 1: Cost analysis considering solely grid energy.  
Case 2: Objective optimization incorporating grid energy, PV devices, and ESS.  
Figure 8: The summer energy interaction diagram.  
Figure 7: The winter energy interaction diagram.  
Case 3: Objective optimization incorporating grid energy, PV devices, and ESS, while integrating carbon trading and REC trading mechanisms.  
From Table 3, the following can be observed:  
(a) Both Case 2 and Case 3 exhibit lower Net present cost compared to Case 1, with reductions of USD 668,294 and USD 923,152, respectively. This demonstrates that the hybrid power system incorporating PV and ESS significantly reduces operational costs.  
(b) Using the proposed EDMOA optimization algorithm to solve the two models, the ESS in Case 2 is configured with a rated capacity of 460 kWh and a maximum output power of 1430 kW, while in Case 3, the ESS has a rated capacity of 408 kWh and a maximum output power of 1498 kW. Quantitative analysis indicates that after integrating life-cycle carbon emission accounting and internalizing carbon trading costs, the system optimization direction shifts significantly. Compared to a focus on maximizing storage capacity, the algorithm prioritizes ESS configurations with higher power density (\(\geq\)3.67 kW/kWh). This shift in technical selection arises from the sensitivity of marginal carbon abatement costs to the rapid charging and discharging capabilities of high-power ESS. Such systems can achieve multiple daily cycles, optimizing carbon footprint reduction and reflecting the influence of carbon trading mechanisms on ESS technology selection. Case 3 achieves USD 826 lower grid-related expenditure compared to Case 2 due to the higher power density of the ESS.  
(c) After connecting the hybrid power system to the carbon trading market and implementing daily carbon emission quotas, the system incurs additional annual carbon credit purchase costs of USD 119,109. This indicates that the current energy supply structure remains grid-dependent, with a significant proportion of electricity sourced from coal-fired power generation. The carbon intensity transmission effect of this energy mix ultimately necessitates the purchase of supplementary carbon credits to meet operational requirements.  
(d) By incorporating the REC trading mechanism, Case 3 achieves annual revenue of USD 579,427. This highlights the substantial potential for renewable energy development along high-speed railway corridors. The integration of distributed renewable energy systems into the high-speed railway power supply network, combined with participation in the REC trading market, enables the creation of auxiliary revenue streams through market-based compensation mechanisms. These results confirm that the EDMOA-optimized hybrid power system achieves significant cost reduction compared to traditional single-source architectures. Despite the carbon trading-related emission rights acquisition costs, the revenue generated through REC trading enables Case 3 to achieve a lower net cost compared to Case 2, with a reduction of USD 254,858 (7.97%). Economic analysis demonstrates that the algorithm-driven ESS configuration shifts toward higher power density. These findings validate the economic feasibility of the carbon-REC synergistic trading mechanism in high-speed railway operations and highlight the value of the EDMOA algorithm in optimizing hybrid power systems for high-speed railways.