This paper introduces a new rural microgrid model, including residents and agricultural greenhouses. Based on the new model framework, the precise energy scheduling of a rural microgrid is realized by means of load classification and load forecasting. Moreover, we also adopt a new energy-storage mode, cloud energy storage (CES), as the shared energy-storage unit of rural microgrid, and analyze the service and operation mechanism of CES in detail. The shared storage characteristic and adjustable storage capacity of CES are helpful for the precise management of power dispatching. At the same time, in order to accurately implement energy scheduling, we fully consider the load characteristics of rural areas and divide the load into residential load and agricultural load. Then the extreme gradient boosting (XGBoost) algorithm is used to predict the short-term power consumption of the two types of load respectively, which can effectively alleviate the uncertainty of load power consumption and improve the accuracy of scheduling. Finally, an illustrative example of rural energy scheduling is given. The example studies the impact of energy-storage capacity on the cost of the scheduling scheme, and designs a power-dispatching scheme based on load forecasting, which accurately solves the energy charging and discharging planning and grid energy trading planning.

Amid the global decarbonization of urban transportation, the large-scale deployment of electric buses faces major challenges, including concentrated charging demand, increased peak electricity demand, and inefficient energy utilization at transit depots. Existing studies usually optimize depot energy system configuration and bus scheduling separately, which often leads to biased system-level decisions. To address this limitation, this study proposes a collaborative optimization framework that integrates cross-line scheduling with the configuration of photovoltaic–storage–charging systems at depots to improve overall resource utilization. Specifically, this study formulates a mixed-integer linear programming (MILP) model to minimize the total daily system cost. The proposed model comprehensively captures multiple factors, including the costs of bus investment, charging infrastructure, photovoltaic deployment, energy storage deployment, and carbon emissions. In this study, Benders decomposition is used as a solution framework to handle the coupling structure of the model. Case studies show that, compared with conventional operation modes, the combination of cross-line scheduling and fast charging technology produces a significant synergistic effect. This combination reduces the required fleet size from 17 to 14 buses and substantially lowers investment in depot infrastructure, thereby minimizing the total system cost. Sensitivity analysis further shows that the deployment scale of photovoltaic systems has a clear threshold effect on electricity costs, whereas the core economic value of energy storage systems depends on peak shaving and arbitrage under time-of-use electricity pricing. Overall, this study demonstrates the critical role of integrated planning in improving the economic efficiency and operational feasibility of electric bus systems. It provides important theoretical support and practical guidance for depot design and resource scheduling in low-carbon public transportation networks.