Citation

Hossen, Md Sabbir and Ramasamy, Gobbi and Sarker, Md Tanjil and Ngu, Eng Eng (2026) Real-World Tariff-Aware Safe Reinforcement Learning for Grid-Stable OCPP EV Charging Networks. IEEE Access, 14. pp. 18530-18545. ISSN 2169-3536

Text Real-World Tariff-Aware Safe Reinforcement Learning for Grid-Stable OCPP EV Charging Networks.pdf - Published Version Restricted to Repository staff only Download (4MB)

Abstract

The rapid growth of electric-vehicle (EV) charging networks introduces coupled challenges in grid safety, tariff responsiveness, and protocol interoperability. This paper presents a real-world, tariffaware reinforcement learning (RL) framework for grid-safe load management in Open Charge Point Protocol (OCPP) networks. The approach combines a projection-based safety layer with a tariff-sensitive policy that optimizes operating cost, peak demand, fairness, and tail delay under flat, time-of-use (ToU), and real-time pricing (RTP) regimes. Using operational OCPP logs from a Malaysian multi-site deployment, we instantiate a multi-scenario evaluation in which the proposed proximal policy optimization (PPO) controller reduces aggregate peak demand by about 25% versus proportional fairness and nearly 40% versus a rule-based baseline, while maintaining comparable or intentionally reduced energy throughput when required by cost–peak trade-offs, without compromising grid safety. Under ToU and RTP, the controller achieves cost reductions up to 30% without violating feeder or station caps; improvements in fairness (lower Gini index) and queueing performance (lower w95 delay) confirm that safety projection and tariff awareness jointly enhance equity and grid compliance. The methodology includes a formal projection operator that enforces hard constraints at dispatch, a multi-objective reward with tariff terms, and statistical validation based on sitelevel medians, confidence intervals, and Wilcoxon tests. To our knowledge, this is the first demonstration of a tariff-aware, safety-constrained RL controller validated on real OCPP data across multiple tariff scenarios, providing a practical path to scalable, economically adaptive, and grid-stable smart charging

Item Type:	Article
Uncontrolled Keywords:	Electric vehicle
Subjects:	T Technology > TK Electrical engineering. Electronics Nuclear engineering
Divisions:	Faculty of Artificial Intelligence & Engineering (FAIE)
Depositing User:	Ms Rosnani Abd Wahab
Date Deposited:	11 Feb 2026 01:17
Last Modified:	11 Feb 2026 01:17
URII:	http://shdl.mmu.edu.my/id/eprint/15335

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