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中国工业与应用数学学会会刊
主管：中华人民共和国教育部
主办：西安交通大学
ISSN 1005-3085  CN 61-1269/O1
工程数学学报

工程数学学报 ›› 2024, Vol. 41 ›› Issue (5): 838-852.doi: 10.3969/j.issn.1005-3085.2024.05.004

• • 上一篇    下一篇

风电数据的不确定性建模及在电网规划的应用

张春霞1,  金玟玎1,  崔玉昆1,   王永军2,   叶  天1   

  1. 1. 西安交通大学数学与统计学院，西安 710049
    2. 温州职业技术学院人工智能学院，温州 325035
  • 收稿日期:2023-09-28 接受日期:2024-04-14 出版日期:2024-10-15
  • 通讯作者: 王永军 E-mail: wangyjmcvti@qq.com
  • 基金资助:
    国家自然科学基金 (12371512)；温州市重大科技创新攻关项目 (ZG2022012).

Uncertainty Modeling of Wind Power Data and Its Application in Power Grid Planning

ZHANG Chunxia1,  JIN Wending1,  CUI Yukun1,  WANG Yongjun2,  YE Tian1   

  1. 1. School of Mathematics and Statistics, Xi'an Jiaotong University, Xi'an 710049
    2. School of Artificial Intelligence, Wenzhou Polytechnic, Wenzhou 325035
  • Received:2023-09-28 Accepted:2024-04-14 Online:2024-10-15
  • Contact: Y. Wang. E-mail address: wangyjmcvti@qq.com
  • Supported by:
    The National Natural Science Foundation of China (12371512); the Key Science and Technology Innovation Project of Wenzhou (ZG2022012).

摘要：

在我国经济高速发展的同时，矿物资源的使用持续增长，对环境的污染也在不断加剧，发展风力发电是我国实现低碳转型的一项重要措施。然而，由于风力发电具有较强的不稳定性，这给电网的运行带来了较大的不确定性。因此，考虑风力发电过程中的不确定性因素，并对其进行建模，开展含风力发电的电网规划研究。首先对风电出力的不确定性进行建模，建立了风电机组出力的数学模型。其次，提出了以总成本、总网损最小为目标函数的考虑风电不确定性的最优潮流模型，并给出一种采用局部模型并引入动态惯性权重系数改进的粒子群优化求解算法。经采用实际的风电数据进行实验，结果表明与传统的粒子群优化算法相比，改进的粒子群优化算法在求解速度、收敛性以及稳健性方面均具有更优性能。

关键词: 出力不确定性, 蒙特卡罗法, 最优潮流, 改进的粒子群算法, 电网规划

Abstract:

With the rapid economic development of China, the usage of mineral resources continues to grow and pollution of the environment is also increasing. As a result, it is an important measure for China to develop wind power generation so as to realize low-carbon transformation. However, due to the strong instability of wind power generation, it brings greater uncertainty to the operation of the power grid. Therefore, this paper takes into account the uncertainty in the wind power generation process and models them to carry out the power grid planning. Firstly, this paper establishes a mathematical model of wind turbine output by modeling the uncertainty. Secondly, it proposes an optimal power flow model considering the uncertainty of wind power with the objective to simultaneously minimize the total cost and the total network loss. At the same time, an improved particle swarm optimization algorithm is proposed to solve the problem by using a local model and introducing dynamic inertia weight coefficients. By comparing it with the traditional particle swarm optimization algorithm with some real-world data, the novel algorithm is verified to have better performance in terms of solving speed, convergence and robustness.

Key words: generation uncertainty, Monte Carlo method, optimal power flow, improved particle swarm optimization algorithm, power grid planning

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