| Title |
Techno-Economic Assessment of an AI-Based Railway Station Energy Management System |
| Authors |
박종영(Jong-young Park) ; 홍수민(Sumin Hong) ; 허재행(Jae-Haeng Heo) ; 정호성(Hosung Jung) |
| DOI |
https://doi.org/10.5370/KIEE.2026.75.9.2282 |
| Keywords |
Techno-Economic Analysis; Railway Station Energy Management; Reinforcement Learning; Regenerative Braking Energy; Energy Storage System |
| Abstract |
Previous studies have demonstrated the technical effectiveness of artificial intelligence (AI)-based energy management systems (EMS) for railway stations using an artificial neural network (ANN) and a Deep Q-Network (DQN) to optimize energy operation while utilizing photovoltaic (PV) and regenerative braking energy. This study evaluates the techno-economic feasibility of the AI-based EMS by considering capital expenditure (CAPEX), operating expenditure (OPEX), and annual economic benefits. Economic performance is assessed using the payback period, net present value (NPV), internal rate of return (IRR), benefit-cost ratio (B/C), and sensitivity analyses of ESS price, electricity-price escalation, and regenerative energy utilization. The results indicate that the proposed EMS is economically feasible and provides a practical basis for evaluating AI-based railway station energy management systems. |