Uncertainty aware energy management in microgrids with integrated electric bicycle charging stations and green...

This paper presents a stochastic optimization framework for microgrid (MG) energy management, integrating electric bicycle (E-Bike) and electric vehicle (EV) charging stations with a green certificate market (GCM) to enhance sustainability and economic efficiency. Uncertainties in renewable energy generation (solar and wind) and load demand are modeled using the Two-Point Estimation Method (TPEM), enabling robust handling of variability. A novel metaheuristic algorithm, Mountaineering Team-Based Optimization (MTBO), is developed to solve a three-objective optimization problem: minimizing operational costs, minimizing emissions, and maximizing GCM revenue. MTBO is benchmarked against particle swarm optimization, achieving a 21.6% reduction in operational costs and a 13.12% reduction in emissions in deterministic scenarios. Three cases are analyzed: (I) no mobile storage, (II) deterministic mobile storage, and (III) stochastic management. In case II, integrating mobile storage (EVs and E-Bikes) with V2G capabilities reduces operational costs by 18.6% and emissions by 10.9% compared to case I. Case III, incorporating stochastic management, further lowers costs by 1.5% and by 19.8% relative to case I, but increases demand response costs by 7.5% and reduces GCM revenue by 38.9% due to renewable fluctuations. These results highlight the practical benefits of combining E-Bike and EV mobile storage with GCM trading in microgrids, demonstrating MTBO's superior exploration and exploitation capabilities for high-dimensional, uncertainty-aware energy scheduling.