Shayeghi Hossein, Davoudkhani Iraj Faraji
Energy Management Research Center, University of Mohaghegh Ardabili, Ardabil, Iran.
Sci Rep. 2025 Jul 21;15(1):26374. doi: 10.1038/s41598-025-12328-5.
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.
本文提出了一种用于微电网（MG）能源管理的随机优化框架，该框架将电动自行车（E-Bike）和电动汽车（EV）充电站与绿色证书市场（GCM）相结合，以提高可持续性和经济效率。利用两点估计法（TPEM）对可再生能源发电（太阳能和风能）以及负荷需求中的不确定性进行建模，从而能够稳健地处理其变化性。开发了一种新颖的元启发式算法——基于登山队的优化算法（MTBO），以解决一个三目标优化问题：最小化运营成本、最小化排放量以及最大化绿色证书市场收益。将MTBO与粒子群优化算法进行基准测试，在确定性场景下，运营成本降低了21.6%，排放量降低了13.12%。分析了三种情况：（I）无移动储能，（II）确定性移动储能，以及（III）随机管理。在情况II中，与情况I相比，将具有车辆到电网（V2G）能力的移动储能（电动汽车和电动自行车）集成后，运营成本降低了18.6%，排放量降低了10.9%。情况III纳入了随机管理，相对于情况I，成本进一步降低了1.5%，排放量降低了19.8%，但由于可再生能源的波动，需求响应成本增加了7.5%，绿色证书市场收益降低了38.9%。这些结果突出了在微电网中将电动自行车和电动汽车移动储能与绿色证书市场交易相结合的实际益处，证明了MTBO在高维、考虑不确定性的能源调度方面具有卓越的探索和利用能力。
