In line with the strategic plan for emerging industries in China, renewable energy sources like wind power and photovoltaic power are experiencing vigorous growth, and the number of electric vehicles in use is on a continuous upward trend. Alongside the optimization of the distribution network structure and the extensive application of energy storage technology, the active distribution network has evolved into a more flexible and interactive “source–grid–load–storage” diversified structure. When electric vehicles are plugged into charging piles for charging and discharging, it inevitably exerts a significant impact on the control and operation of the power grid. Therefore, in the context of the extensive integration of electric vehicles, delving into the charging and discharging behaviors of electric vehicle clusters and integrating them into the optimization of the active distribution network holds great significance for ensuring the safe and economic operation of the power grid. This paper adopts the two-stage “constant-current and constant-voltage” charging mode, which has the least impact on battery life, and classifies the electric vehicle cluster into basic EV load and controllable EV load. The controllable EV load is regarded as a special “energy storage” resource, and a corresponding model is established to enable its participation in the coordinated control of the active distribution network. Based on the optimization and control of the output behaviors of gas turbines, flexible loads, energy storage, and electric vehicle clusters, this paper proposes a two-layer coordinated control model for the scheduling layer and network layer of the active distribution network and employs the improved multi-target beetle antennae search optimization algorithm (MTTA) in conjunction with the Cplex solver for solution. Through case analysis, the results demonstrate that the “source–grid–load–storage” coordinated control of the active distribution network can fully tap the potential of resources such as flexible loads on the “load” side, traditional energy storage, and controllable EV clusters; realize the economic operation of the active distribution network; reduce load and voltage fluctuations; and enhance power quality.

This study proposes an improved multi-objective black-winged kite algorithm (MOBKA-QL) integrating Q-learning with adaptive mutation strategies for optimizing multi-objective scheduling in integrated energy systems (IES). The algorithm dynamically selects mutation strategies through Q-learning to enhance solution diversity and accelerate convergence. First, an optimal scheduling model is established, incorporating a carbon capture system (CCS), power-to-gas (P2G), solar thermal, wind power, and energy storage to minimize economic costs and carbon emissions while maximizing energy efficiency. Second, the heat-to-power ratio of the cogeneration system is dynamically adjusted according to load demand, enabling flexible control of combined heat and power (CHP) output. The integration of CCS+P2G further reduces carbon emissions and wind curtailment, with the produced methane utilized in boilers and cogeneration systems. Hydrogen fuel cells (HFCs) are employed to mitigate cascading energy losses. Using forecasted load and renewable energy data from a specific region, dispatch experiments demonstrate that the proposed system reduces economic costs and CO₂ emissions by 14.63% and 13.9%, respectively, while improving energy efficiency by 28.84%. Additionally, the adjustable heat-to-power ratio of CHP yields synergistic economic, energy, and environmental benefits.