综合智慧能源 ›› 2025, Vol. 47 ›› Issue (2): 29-40.doi: 10.3969/j.issn.2097-0706.2025.02.003

• 交能融合系统 • 上一篇    下一篇

高速公路微网的储能容量配置与调度优化策略

陈晓祺1,4(), 张敏2, 孙周3, 刘斌3, 毛勇4, 陶永晋1   

  1. 1.四川欣智造科技有限公司, 成都 610200
    2.蜀道投资集团有限责任公司, 成都 610095
    3.四川蜀道清洁能源集团有限公司, 成都 610041
    4.四川蜀兴智慧能源有限责任公司, 成都 610041
  • 收稿日期:2024-10-08 修回日期:2024-10-25 出版日期:2025-02-25
  • 作者简介:陈晓祺(1986),男,工程师,博士,从事交能融合规划及设备研发,综合能源系统建模、稳定性分析及控制策略方面的研究,178252639@qq.com
  • 基金资助:
    国家重点研发计划项目(2021YFB2601400);四川省交通运输科技项目(2023-H-06);四川省交通运输科技项目(2023-D-07)

Energy storage capacity configuration and scheduling optimization strategy for the expressway microgrids

CHEN Xiaoqi1,4(), ZHANG Min2, SUN Zhou3, LIU Bin3, MAO Yong4, TAO Yongjin1   

  1. 1. Sichuan X-intelligent Manufacturing Technology Company Limited, Chengdu 610200, China
    2. Shudao Investment Group Company Limited, Chengdu 610095, China
    3. Sichuan Shudao Clean Energy Group Company Limited,Chengdu 610041, China
    4. Sichuan Shuxing Intelligent Energy Company Limited,Chengdu 610041,China
  • Received:2024-10-08 Revised:2024-10-25 Published:2025-02-25
  • Supported by:
    National Key Research and Development Program of China(2021YFB2601400);Sichuan Transportation Science and Technology Project(2023-H-06);Sichuan Transportation Science and Technology Project(2023-D-07)

摘要:

为提高高速公路清洁能源利用率,实现储能设施科学经济配置与弹性优化调度,提出一种高速公路光储充微网的储能容量配置与调度优化模型,采用新型求解算法求解并进行仿真分析。基于路域气象信息及高速公路服务区负荷,建立了高速公路光储充微网数学模型,通过蒙特卡洛模拟分析服务区电动汽车充电负荷,基于高速公路服务区、管理中心、收费站、隧道的负荷特性,建立了高速公路微网负荷模型。从高速公路微网的经济性角度出发,建立了双层优化模型以综合实现微网储能系统的优化配置与优化调度,采用指数分布算法-混合整数规划算法(EDO-MILP)对模型进行求解。以攀大高速(四川境内)分布式光储示范项目为例,进行8 760 h的模拟与优化。结果表明,面向光伏装机容量2 MW、最大负荷约为800 kW的实际微网,引入1 131 kW·h/283 kW的储能设备,可实现系统年增收38.4万元,比无储能方案提升了42.8%,较经验方案提高了4.3%,实现了经济性的有效提升。此外,该配置方案还提升了微网系统对光伏绿电的消纳能力,较无储能方案,消纳能力提高了5.7%,较传统方案,提升了3.4%。

关键词: 交能融合, 双层优化模型, 指数分布算法, 混合整数规划

Abstract:

To improve the utilization of clean energy for highways and achieve the scientific and economical allocation and flexible scheduling optimization of energy storage facilities, an energy storage capacity allocation and scheduling optimization model for highway photovoltaic-storage-charging microgrids is proposed. A novel solution algorithm was used to solve the model and conduct simulation analysis. Based on meteorological information along the highways and load conditions of highway service areas, a mathematical model for the highway photovoltaic-storage-charging microgrids was established. Monte Carlo simulations were conducted to analyze the charging loads of electric vehicles in the service areas. Additionally, based on the load characteristics of highway service areas, management centers, toll stations, and tunnels, a highway microgrid load model was developed. From the perspective of the economic efficiency of highway microgrids, a bi-level optimization model was established to achieve integrated optimization of energy storage system allocation and scheduling. The (Exponential Distribution Optimizer-Mixed-Integer Linear Programming, EDO-MILP) algorithm was applied to solve the model. Taking the distributed photovoltaic-storage demonstration project on the Panzhihua-Dali Expressway (Sichuan section) as an example, simulation and optimization were conducted over a period of 8 760 h. The simulation results showed that for microgrids with a photovoltaic installed capacity of 2 MW and a maximum load of approximately 800 kW, the introduction of 1 131 kW·h/283 kW of energy storage devices led to an annual increase in system revenue of 384 000 yuan, effectively improving the economic efficiency. This was a 42.8% improvement compared to the non-energy storage scheme and a 4.3% improvement over the empirical scheme. Additionally, the allocation scheme increased the microgrid system's consumption capacity for photovoltaic green electricity. Compared to the non-energy storage scheme, the consumption capacity increased by 5.7%, and compared to the traditional scheme, it improved by 3.4%.

Key words: energy integration, bi-level optimization model, exponential distribution algorithm, mixed-integer linear programming algorithm

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