Under the low carbon background, a multi‐objective scheduling model of electrical‐thermal integrated energy system which aims at minimizing the integrated operating costs and minimizing the carbon trading costs is established. The uncertainty of wind power brings new challenge to integrated energy systems. In recent years, light robust optimization has been effectively applied to solve the uncertainty of wind power. However, traditional light robust optimization has the shortcomings of over‐conservative decision or excessive violation of constraints. Therefore, light robustness coefficient is introduced to adjust the deterioration tolerance of the objective function. To constrain the relaxation, the relaxation threshold and the ladder penalty coefficient are defined to generate the ladder penalty cost. Then, the ladder light robust optimization is used to deal with the uncertainty of the source‐load sides and a multi‐objective scheduling model based on the ladder light robust optimization is proposed. Finally, the Multi‐objective Optimization Bacterial Colony Chemotaxis (MOBCC) algorithm is used to solve the model. Simulations results show that the proposed model can reduce the total costs by 5.56% and the wind power curtailment rate to 0.45%. Furthermore, different schemes are compared to verify that the ladder light robust optimization can provide a trade‐off between economy and conservation of the system.

中文翻译：

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基于梯形光鲁棒优化的集成能源系统低碳多目标调度

在低碳背景下，建立了电热综合能源系统的多目标调度模型，其目标是使综合运行成本最小化，并将碳交易成本最小化。风力发电的不确定性给集成能源系统带来了新的挑战。近年来，光鲁棒性优化已有效地应用于解决风力发电的不确定性。但是，传统的鲁棒性优化的缺点是过于保守的决策或过度违反约束条件。因此，引入光鲁棒系数以调节目标函数的劣化容限。为了约束松弛，定义松弛阈值和阶梯惩罚系数以产生阶梯惩罚成本。然后，利用梯形光鲁棒优化算法处理源负载侧的不确定性，提出了基于梯形光鲁棒优化的多目标调度模型。最后，使用多目标优化细菌菌落趋化性（MOBCC）算法对模型进行求解。仿真结果表明，提出的模型可以使总成本降低5.56％，风电削减率达到0.45％。此外，还比较了不同的方案，以验证梯形灯的鲁棒性优化可以在系统经济性与保护性之间做出权衡。使用多目标优化细菌菌落趋化性（MOBCC）算法求解模型。仿真结果表明，提出的模型可以使总成本降低5.56％，风电削减率达到0.45％。此外，还比较了不同的方案，以验证梯形灯的鲁棒性优化可以在系统经济性与保护性之间做出权衡。使用多目标优化细菌菌落趋化性（MOBCC）算法求解模型。仿真结果表明，提出的模型可以使总成本降低5.56％，风电削减率达到0.45％。此外，还比较了不同的方案，以验证梯形灯的鲁棒性优化可以在系统经济性与保护性之间做出权衡。