Operational efficiency and economic impact of rule-based PV-battery management in electric bus charging systems

• Rule-Based Operational Framework: The study implements and validates a transparent, priority-based dispatch logic (PV → battery → grid) for integrated energy management, proving effective even without complex optimization. • Significant Economic Benefit: The system reduces grid energy imports by ∼16%, cuts electricity costs by €863, and generates a positive net revenue of €11,016 from energy arbitrage, demonstrating a strong financial case. • Comprehensive Loss Accounting: The model incorporates realistic battery costs, accounting for both continuous self-discharge losses (0.1%/hr) and degradation expenses (€0.02/kWh), enabling accurate lifecycle economic assessment. • Operational Resilience & Synergy: Active management ensures fleet charging readiness while optimizing the use of all assets, transforming the stationary battery from a passive cost center into a strategic, revenue-contributing asset. • Foundation for Advanced Services: The proven operational and financial performance creates a direct pathway and strong economic baseline for future participation in ancillary service markets (e.g., frequency regulation), unlocking further revenue potential. The integration of stationary battery storage with electric bus fleets represents a critical opportunity for enhancing grid flexibility while reducing operational costs in urban transportation systems. However, existing research often addresses these components in isolation, neglecting the synergistic optimization potential and realistic degradation costs. This paper presents a comprehensive Python-based energy management system that unifies stationary battery operations with electric bus fleet charging through a rule-based PV-battery management framework. The developed model incorporates detailed battery degradation accounting, including self-discharge losses and cycling costs, while implementing a multi-source charging hierarchy that prioritizes photovoltaic generation, stationary storage, and grid imports based on real-time market signals. Using real-world operational data, the system demonstrates substantial economic benefits through price arbitrage, negative price utilization, and avoided grid costs, achieving net revenue improvements of 16% compared to conventional charging strategies. The open-source implementation provides extensive visualization capabilities and operational reporting, offering both researchers and practitioners a transparent, customizable tool for multi-asset energy system optimization. This work advances the field by introducing a holistic, degradation-aware rule-based approach that bridges theoretical models with practical decision support, enhancing the economic viability and operational efficiency of integrated electric bus depot systems.