## 6 Conclusions  
This study develops an ESS optimization framework for a PV-ESS-Grid system with carbon-REC trading, establishing four 10 min operational modes (traction, cruising, coasting, braking). The meteorologically complex Hongqiao-Xining corridor validates framework adaptability through systematic evaluation of seasonal weather impacts. EDMOA optimizes ESS capacity/power allocation and grid dispatch to achieve dual cost-carbon reduction objectives in high-speed rail systems. Simulation results verify that the hybrid system outperforms the conventional rail grid system in terms of costs and CO\({}_{2}\) emissions. The following conclusions are drawn: (i) Through dynamic pricing and benefit evaluation frameworks, green power-integrated multi-energy systems reduce total costs by 23.90% and carbon emissions by 24.02%, synergistically enhancing economic-environmental performance of high-speed rail while boosting marginal revenue and reducing carbon intensity. (ii) A multi-source power system with energy management is proposed, employing dynamic energy-flow models and LCA to reveal renewable synergy in low-carbon rail opera \begin{table}
\begin{tabular}{l l l l} \hline \hline  & **Case 1** & **Case 2** & **Case 3** \\ \hline Total CO\({}_{2}\) emission (kg) & 2,886,213.1 & 2,194,300.6 & 2,192,760.6 \\ Grid CO\({}_{2}\) emission (kg) & 2,886,213.1 & 2,173,131.3 & 2,172,514.5 \\ PV CO\({}_{2}\) emission (kg) & 0 & 13,000.0 & 13,000.0 \\ ESS CO\({}_{2}\) emission (kg) & 0 & 8169.6 & 7246.1 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Carbon emission in 3 cases in the first year. \begin{table}
\begin{tabular}{l l l l} \hline \hline  & **Case 1** & **Case 2** & **Case 3** \\ \hline PV size (m\({}^{2}\)) & 0 & 2000 & 2000 \\ ESS capacity (kwh) & 0 & 460 & 408 \\ ESS power (kw) & 0 & 1430 & 1498 \\ Total NPC (USD) & 3,864,852 & 3,196,558 & 2,941,700 \\ Grid cost (USD) & 3,864,852 & 2,909,983 & 2,909,157 \\ PV installation cost (USD) & 0 & 89,281 & 89,281 \\ PV replacement cost (USD) & 0 & 68,898 & 68,898 \\ ESS installation cost (USD) & 0 & 64,198 & 56,940 \\ ESS replacement cost (USD) & 0 & 64,198 & 56,940 \\ Carbon emission cost (USD) & 0 & 0 & 159,413 \\ Carbon transaction cost (USD) & 0 & 0 & 119,109 \\ Carbon incentivization (USD) & 0 & 0 & 1579 \\ Carbon penalty cost (USD) & 0 & 0 & 62,968 \\ REC revenue (USD) & 0 & 0 & 579,427 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Net present cost in 3 cases in the first year. tions, establishing clean transformation pathways for transit systems. (iii) The optimization algorithm achieves 435 fewer objective function iterations and 58% faster convergence speed than the original DMOA, with significant enhancements from trust-region search, elite retention, parallel evaluation, and improved logistics circle chaotic mapping. While current carbon-electricity coupling mechanisms limit real-time price modeling fidelity, the proposed multi-scale framework enables mobile energy coordination. Future work will implement deep reinforcement learning-driven neural co-optimization across temporal scales, systematically addressing price volatility through spatiotemporal flexibility transfer mechanisms. This approach will be specifically benchmarked in maritime port and airport microgrid electrification scenarios to quantify resilience and efficiency gains in critical infrastructures. W.-N.Z. : conceptualization, methodology, investigation, formal analysis, writing--review and editing, funding acquisition. Z.X. : methodology, software, data curation, visualization, writing--original draft. Y.-Y.H. : conceptualization, investigation, resources, supervision. F.-Y.L. : investigation, supervision. Z.-Q.B. : investigation, supervision. All authors have read and agreed to the published version of the manuscript. This research was funded by the Soft Science Research Project of the Shanghai Science and Technology Innovation Action Plan, No. 23692109400. Not applicable. The raw data supporting the conclusions of this article will be made available by the authors on request.