针对“双碳”减排目标以及新能源大规模并网带来的电力系统调峰问题，提出了一种考虑阶梯型碳交易的梯级水火风光储多能联合优化调度模型。首先，采用分层优化调度，在上层模型中以负荷波动最小为目标函数，使用一种考虑了Circle映射策略、三角形游走策略、Levy飞行游走策略以及精英反向学习策略的改进沙丘猫群优化算法（modified sand cat swarm optimization，MSCSO）优化梯级水电的出力，从而得到经过平抑后的等效负荷曲线。在下层模型中以综合成本最小为目标函数对其分段线性化进行求解。最后，在修改的IEEE 30节点中进行多场景仿真计算，结果表明所提模型可以有效减少碳排放量，充分发挥火电机组的深度调峰能力，有效提升新能源的消纳能力和系统运行的低碳性以及经济性，验证了模型的有效性。

Aiming at the "Dual-Carbon" emission reduction target and the peak shaving problem of power system brought by the large-scale grid connection of new energy sources, a cascade hydro-thermal-wind-photovoltaic-storage multi-energy joint optimal scheduling model considering the stepped carbon trading is proposed. Firstly, hierarchical optimal scheduling is adopted, and the minimum load fluctuation is taken as the objective function in the upper model, using a modified sand cat swarm optimization(MSCSO)algorithm that considers Circle mapping strategy, triangular migration strategy, Levy flight migration strategy and elite reverse learning strategy to optimize the output of cascade hydropower. Thus, the equivalent load curve after smoothing is obtained. In the lower model, the minimum comprehensive cost is taken as the objective function to solve its piecewise linearization. Finally, a multi-scenario simulation is carried out in the modified IEEE 30 nodes, and the results show that the proposed model can effectively reduce carbon emissions, give full play to the deep peak shaving capacity of thermal power units, effectively improve the new energy consumption capacity and the low-carbon and economic operation of the system, which verifies the validity of the model.