针对风光等可再生能源(renewable energy，RE)出力的不确定给配电网规划运行带来的巨大挑战，该文提出一种基于改进生成对抗网络与碳足迹的配电网多目标双层规划模型。首先，采用带梯度惩罚的 Wasserstein 生成对抗网络(wasserstein generative adversarial network with gradient penalty，WGAN-GP)来模拟大量风光出力场景，通过K-medoids聚类算法进行场景缩减;其次，通过全生命周期评价方法确定各发电单元的碳足迹系数;接着，建立考虑碳足迹的配电网双层规划模型，上层以年综合成本最小为目标函数，优化分布式电源(distributed generation，DG)、储能系统(energy storage system，ESS)及补偿电容(capacitor banks，CB)的规划方案;下层以运行成本、电压偏移量以及碳排放量最小为目标函数，考虑有载调压变压器(on-load tap changer，OLTC)、可削减负荷、补偿电容、储能及分布式电源制定在典型场景下的配电网运行策略。然后，对模型的上、下层进行关联统一，将其转换为单层模型，再采用归一化法向约束法(normalized normal constraint，NNC)求解单层多目标模型。最后，通过IEEE33节点配电系统进行仿真分析，验证模型的有效性。
A Multi-objective Bi-level Planning of Distribution Network Based on Improved Generative Adversarial Network and Carbon Footprint
In view of the great challenges brought by the uncertainty of renewable energy(RE)output such as wind and solar to the planning and operation of distribution networks,a multi-objective bi-level planning model of distribution network based on improved generative adversarial network and carbon footprint is proposed.First,Wasserstein generative adversarial network with gradient penalty(WGAN-GP)model is used to generate a large number of scenes of wind and solar outputs,and scenes reduction is carried out by K-medoids clustering algorithm.Next,the life cycle approach is used to determine the carbon footprint of all kinds of power generation technologies.Then,this paper constructs a bi-level planning model of the distribution network considering the carbon footprint.In the upper layer,the planning scheme of distributed generation(DG),energy storage system(ESS)and capacitor banks(CB)are considered to minimize the annual comprehensive cost.In the lower layer,operation strategy of on-load tap changer(OLTC),reducible load,CB,ESS and DG are considered to minimize operation costs,voltage offset and carbon emissions.Then,the bi-level model is converted to a single layer model by associating and unifying the upper and lower layers of the model,and normalized normal constraint(NNC)method is used to solve the multi-objective model.Finally,the effectiveness of the model is verified by the IEEE 33 node system.
renewable energygenerative adversarial networkcarbon footprintbi-level planning modelnormalized normal constraint
万方数据
维普
