上海交通大学学报

上海交通大学学报 >

2025 , Vol. 59 >Issue 11: 1732 - 1741

DOI: https://doi.org/10.16183/j.cnki.jsjtu.2024.009

新型电力系统与综合能源

城市电力系统演化路径的系统动力学建模分析

  • 叶骏 ,
  • 何一冰 ,
  • 李先锋 ,
  • 严国灿 ,
  • 燕磊
展开
  • 1 杭州市电力设计院有限公司, 杭州 310012
    2 浙江大学 电气工程学院, 杭州 310058
叶 骏(1990—),工程师,从事220 kV及以下变电站二次设计研究.
燕 磊,副研究员;E-mail:yan_lei@zju.edu.cn.

收稿日期: 2024-01-05

  修回日期: 2024-04-02

  录用日期: 2024-04-30

  网络出版日期: 2024-05-17

收起

System Dynamics Modeling Analysis of Urban Power System Evolution Path

  • YE Jun ,
  • HE Yibing ,
  • LI Xianfeng ,
  • YAN Guocan ,
  • YAN Lei
Expand
  • 1 Hangzhou Electric Power Design Institute, Hangzhou 310012, China
    2 College of Electrical Engineering, Zhejiang University, Hangzhou 310058, China

Received date: 2024-01-05

  Revised date: 2024-04-02

  Accepted date: 2024-04-30

  Online published: 2024-05-17

Fold

摘要

为研究城市电力系统电源结构的演化路径,分析城市电力系统各类电源装机容量的影响因素,建立城市电力系统电源结构的系统动力学模型,包括电量平衡、火电发展、分布式光伏发展、集中式风光发展和新能源承载能力模块.在建模过程中,采用符号回归方法准确地设置模型参数,并着重考虑低碳政策对各类电源的影响,以及新能源承载能力对新能源装机容量的反馈作用.通过差异化低碳政策场景仿真,得到未来城市电力系统各类电源装机容量、新能源接入比例和新能源承载能力的变化趋势.仿真结果表明:低碳政策有助于新能源发展和“双碳”目标实现,助力城市能源体系的绿色低碳转型,但过高的政策强度可能导致城市电力系统新能源承载能力不足,引发新能源消纳问题.

关键词： 系统动力学; 符号回归; 城市电力系统; 电源结构; 新能源承载能力

本文引用格式

叶骏 , 何一冰 , 李先锋 , 严国灿 , 燕磊 . 城市电力系统演化路径的系统动力学建模分析[J]. 上海交通大学学报, 2025 , 59(11) : 1732 -1741 . DOI: 10.16183/j.cnki.jsjtu.2024.009

Abstract

To investigate the evolution path of urban power system supply structures, an analysis is conducted on the factors influencing the installed capacity of various power source types in urban power systems, and a system dynamics model for the urban power system supply structure is developed, which includes modules for power balance, thermal power development, distributed photovoltaic development, centralized wind and photovoltaic development, and new energy carrying capacity. In the modeling process, the symbolic regression method is used to accurately set the model parameters, with a focus on the impact of low-carbon policies on various types of power sources, as well as the feedback effect of renewable energy carrying capacity on the installed capacity of renewable energy.The changing trends of various types of power capacity, the proportion of new energy access, and the new energy carrying capacity in future urban power systems are obtained by differentiated simulations in low-carbon policy scenarios. The simulation results indicate that low-carbon policies contribute to the development of new energy and the achievement of “dual-carbon” goals, promoting the green and low-carbon transformation of urban energy systems. However, excessive policy intensity may lead to insufficient renewable energy carrying capacity in urban power systems, causing problems in the absorption of new energy.

Key words： system dynamics; symbolic regression; urban power system; power supply structure; renewable energy carrying capacity

参考文献

[1] 阮前途, 梅生伟, 黄兴德, 等. 低碳城市电网韧性提升挑战与展望[J]. 中国电机工程学报, 2022, 42(8): 2819-2830.
  RUAN Qiantu, MEI Shengwei, HUANG Xingde, et al. Challenges and research prospects of resilience enhancement of low-carbon power grid[J]. Proceedings of the CSEE, 2022, 42(8): 2819-2830.
[2] 钟永光, 贾晓菁, 钱颖, 等. 系统动力学[M]. 2版. 北京: 科学出版社, 2013.
  ZHONG Yongguang, JIA Xiaojing, QIAN Ying. System dynamics[M]. 2nd ed. Beijing: Science Press, 2013.
[3] 赵盛楠, 王蓓蓓, 李扬, 等. 基于系统动力学的分布式可再生能源激励机制分析[J]. 电力系统自动化, 2017, 41(24): 97-104.
  ZHAO Shengnan, WANG Beibei, LI Yang, et al. System dynamics based analysis on incentive mechanism of distributed renewable energy resources[J]. Automation of Electric Power Systems, 2017, 41(24): 97-104.
[4] 曾博, 穆宏伟, 董厚琦, 等. 考虑5G基站低碳赋能的主动配电网优化运行[J]. 上海交通大学学报, 2022, 56(3): 279-292.
  ZENG Bo, MU Hongwei, DONG Houqi, et al. Optimization of active distribution network operation considering decarbonization endowment from 5G base stations[J]. Journal of Shanghai Jiao Tong University, 2022, 56(3): 279-292.
[5] YU X Y, DONG Z J, ZHOU D Q, et al. Integration of tradable green certificates trading and carbon emissions trading: How will Chinese power industry do?[J]. Journal of Cleaner Production, 2021, 279: 123485.
[6] 陈文溆乐, 向月, 彭光博, 等. “双碳” 目标下电力系统供给侧形态发展系统动力学建模与分析[J]. 上海交通大学学报, 2021, 55(12): 1567-1576.
  CHEN Wenxule, XIANG Yue, PENG Guangbo, et al. System dynamic modeling and analysis of power system supply side morphological development with “dual carbon” targets[J]. Journal of Shanghai Jiao Tong University, 2021, 55(12): 1567-1576.
[7] TANG L, GUO J, ZHAO B Y, et al. Power generation mix evolution based on rolling horizon optimal approach: A system dynamics analysis[J]. Energy, 2021, 224: 120075.
[8] 彭光博, 向月, 陈文溆乐, 等. “双碳” 目标下电力系统风电装机与投资发展动力学推演及分析[J]. 电力自动化设备, 2022, 42(11): 70-77.
  PENG Guangbo, XIANG Yue, CHEN Wenxule, et al. Kinetic deduction and analysis of installed capacity and investment development for wind power in power system under “dual carbon” target[J]. Electric Power Automation Equipment, 2022, 42(11): 70-77.
[9] ZHAO X G, WANG W, WU L. A dynamic analysis of research and development incentive on China’s photovoltaic industry based on system dynamics model[J]. Energy, 2021, 233: 121141.
[10] 张汀荟, 谢明成, 王蓓蓓, 等. 分布式光伏的共享价值及其对配电网影响的系统动力学仿真[J]. 电力系统自动化, 2021, 45(18): 35-44.
  ZHANG Tinghui, XIE Mingcheng, WANG Beibei, et al. System dynamics simulation of shared value of distributed photovoltaic and its impact on distribution network[J]. Automation of Electric Power Systems, 2021, 45(18): 35-44.
[11] FU X Q, GUO Q L, SUN H B. Statistical machine learning model for stochastic optimal planning of distribution networks considering a dynamic correlation and dimension reduction[J]. IEEE Transactions on Smart Grid, 2020, 11(4): 2904-2917.
[12] ORZECHOWSKI P, LA CAVA W, MOORE J H. Where are we now? A large benchmark study of recent symbolic regression methods[C]// Proceedings of the Genetic and Evolutionary Computation Conference. Kyoto, Japan: ACM, 2018: 1183-1190.
[13] 周雅, 李永平, 黄国和. 深圳市可持续电力规划模型及节能减排分析[J]. 电力建设, 2014, 35(6): 75-80.
  ZHOU Ya, LI Yongping, HUANG Guohe. Sustainable electric power planning model and energy-saving & emission-reduction analysis in Shenzhen[J]. Electric Power Construction, 2014, 35(6): 75-80.
[14] 黄强, 郭怿, 江建华, 等. “双碳” 目标下中国清洁电力发展路径[J]. 上海交通大学学报, 2021, 55(12): 1499-1509.
  HUANG Qiang, GUO Yi, JIANG Jianhua, et al. Development pathway of China’s clean electricity under carbon peaking and carbon neutrality goals[J]. Journal of Shanghai Jiao Tong University, 2021, 55(12): 1499-1509.
[15] 鲁宗相, 林弋莎, 乔颖, 等. 极高比例可再生能源电力系统的灵活性供需平衡[J]. 电力系统自动化, 2022, 46(16): 3-16.
  LU Zongxiang, LIN Yisha, QIAO Ying, et al. Flexibility supply-demand balance in power system with ultra-high proportion of renewable energy[J]. Automation of Electric Power Systems, 2022, 46(16): 3-16.
[16] CRANMER M. SymbolicRegression. Interpretable machine learning for science with PySR and jl[EB/OL]. (2023-05-02) [2023-11-30]. https://arXiv.org/abs/2305.01582v3.
[17] 刘志强, 赵毅, 潘荔. 中外火电节能减排效率分析与比较[J]. 热力发电, 2021, 50(3): 9-18.
  LIU Zhiqiang, ZHAO Yi, PAN Li. Analysis and comparison of energy saving efficiency and emission reduction efficiency of thermal power between China and foreign countries[J]. Thermal Power Generation, 2021, 50(3): 9-18.
[18] IRENA. Renewable power generation costs in 2022[R]. Abu Dhabi, UAE: International Renewable Energy Agency, 2023.
Options
文章导航

/

〈 〉