| 引用本文: | 苟 竞,刘 方,刘嘉蔚,等.考虑高铁负荷和风光不确定性的输电网规划方法研究[J].电力系统保护与控制,2023,51(9):156-164.[点击复制] |
| GOU Jing,LIU Fang,LIU Jiawei,et al.A transmission network planning method considering high-speed railway load andwind and solar uncertainty[J].Power System Protection and Control,2023,51(9):156-164[点击复制] |
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| 摘要: |
| 为对复杂山区含高铁负荷和风光电站的输电网进行合理规划，提出了考虑高铁负荷和风光不确定性的输电网随机规划方法。首先，对具有间歇性和冲击性的高铁负荷，通过拉普拉斯混合模型结合二项分布对其进行建模。其次，针对直流随机潮流不能计及电压分布，而交流随机潮流模型较为复杂的问题，以解耦线性潮流计算为基础，提出一种基于解耦线性化的半不变量随机潮流计算方法，并基于此构建考虑电压偏差的输电网规划模型。然后，针对输电网规划求解问题中决策变量维度高、约束复杂的特性，通过自适应地动态调整进化过程中交叉、变异概率对遗传算法进行改进。最后，对某高海拔山区铁路沿线电网进行仿真研究，验证了本模型和求解算法的正确性与有效性。 |
| 关键词: 高铁负荷 随机规划 解耦线性潮流 半不变量随机潮流 遗传算法 |
| DOI：10.19783/j.cnki.pspc.221278 |
| 投稿时间：2022-08-08修订日期：2022-10-13 |
| 基金项目:国网四川省电力公司经济技术研究院科技项目资助(SGSCJY00GHJS2100041) |
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| A transmission network planning method considering high-speed railway load andwind and solar uncertainty |
| GOU Jing1,LIU Fang1,LIU Jiawei1,SUN Wenhao2,ZHANG Qiao2,LIU Zhigang2 |
| (1. State Grid Sichuan Economic Research Institute, Chengdu 610041, China; 2. School of Electrical Engineering,
Southwest Jiaotong University, Chengdu 611756, China) |
| Abstract: |
| To reasonably plan a transmission network with high-speed railway load and wind and solar power stations in complex mountainous areas, this paper proposes a stochastic planning method for the network considering the uncertainty of that load and wind and solar power. First, a Laplace mixed model combined with a binomial distribution is used to model the intermittent and shock high-speed loads. Second, for the problem that the DC random power flow cannot take into account the voltage distribution, and the AC random power flow model is relatively complex, this paper proposes a semi-invariant random power flow calculation method based on a decoupling linear power flow calculation. From this, a transmission network planning model considering voltage deviation is constructed. Then, from the characteristics of high dimension of decision variables and complex constraints in the transmission network planning and problem solution, a genetic algorithm is improved by adaptively and dynamically adjusting the probability of crossover and mutation in the evolution process. Finally, a simulation study of the power grid along a railway in a high-altitude mountainous area is carried out to verify the correctness and effectiveness of the model and the solution algorithm. |
| Key words: high-speed railway load stochastic programming decoupled linear power flow semi-invariant stochastic power flow genetic algorithm |
