中国电力 ›› 2025, Vol. 58 ›› Issue (7): 15-23.DOI: 10.11930/j.issn.1004-9649.202410031
郭政麟1(), 陈舒童2(
), 田嘉雯1(
), 陈峦2(
), 郭中杰2(
), 胡维昊2(
), 付强1(
)
收稿日期:
2024-10-10
发布日期:
2025-07-30
出版日期:
2025-07-28
作者简介:
基金资助:
GUO Zhenglin1(), CHEN Shutong2(
), TIAN Jiawen1(
), CHEN Luan2(
), GUO Zhongjie2(
), HU Weihao2(
), FU Qiang1(
)
Received:
2024-10-10
Online:
2025-07-30
Published:
2025-07-28
Supported by:
摘要:
综合能源系统(integrated energy system,IES)是实现“双碳”目标的重要载体,而经济、高效的多元资源协同配置是实现综合能源行业持续健康发展的前提。目前,IES配置模型多以成本最小为优化目标,而对投资回报率的考量较少,这可能导致成本回收期延长,进而降低资金周转效率并增加财务风险。有鉴于此,首先,提出IES容量价值函数,该函数以待配置资源容量为自变量,以系统运行成本降低量为因变量,实现多元资源容量价值的解析表达。然后,建立一种基于分式规划理论的多元资源协同配置模型,该模型以最大化投资收益率为目标,旨在构建具有最佳成本效益的配置策略。进一步地,通过变量替换技术将难以求解的分式规划问题线性化,并提出一种分解迭代算法,实现容量价值函数的外逼近与多元资源协同配置策略的高效求解。最后,通过仿真分析以及与成本最小法的对比,验证所提方法的有效性。
郭政麟, 陈舒童, 田嘉雯, 陈峦, 郭中杰, 胡维昊, 付强. 基于容量价值函数的综合能源系统多元资源协同配置[J]. 中国电力, 2025, 58(7): 15-23.
GUO Zhenglin, CHEN Shutong, TIAN Jiawen, CHEN Luan, GUO Zhongjie, HU Weihao, FU Qiang. Allocation of Multiple Resources for Integrated Energy System Based on Capacity Value Function[J]. Electric Power, 2025, 58(7): 15-23.
时段 | 电价/(元·(kW·h)–1) | |
高峰(08:00—23:00) | 1.18 | |
低谷(23:00—次日08:00) | 0.43 |
表 1 电价设置
Table 1 Tariff setting
时段 | 电价/(元·(kW·h)–1) | |
高峰(08:00—23:00) | 1.18 | |
低谷(23:00—次日08:00) | 0.43 |
回收天数/天 | ||||||||||
32.4 | 17.0 | 26.0 | 5.4 | |||||||
32.3 | 17.1 | 26.1 | 5.5 | |||||||
30.9 | 22.2 | 34.8 | 7.1 | |||||||
17.3 | 28.3 | 45.6 | 9.9 | |||||||
14.5 | 34.9 | 84.8 | 15.5 |
表 2 $ {\kappa _{\text{w}}} $的影响
Table 2 Impact of $ {\kappa _{\text{w}}} $
回收天数/天 | ||||||||||
32.4 | 17.0 | 26.0 | 5.4 | |||||||
32.3 | 17.1 | 26.1 | 5.5 | |||||||
30.9 | 22.2 | 34.8 | 7.1 | |||||||
17.3 | 28.3 | 45.6 | 9.9 | |||||||
14.5 | 34.9 | 84.8 | 15.5 |
回收天数/天 | ||||||||||
30.9 | 22.2 | 34.8 | 7.1 | |||||||
30.9 | 22.2 | 34.8 | 7.1 | |||||||
32.3 | 17.1 | 26.1 | 5.5 | |||||||
32.4 | 17.0 | 26.0 | 5.4 | |||||||
30.8 | 12.9 | 19.2 | 4.3 |
表 3 $ {\kappa _{\text{p}}} $的影响
Table 3 Impact of $ {\kappa _{\text{p}}} $
回收天数/天 | ||||||||||
30.9 | 22.2 | 34.8 | 7.1 | |||||||
30.9 | 22.2 | 34.8 | 7.1 | |||||||
32.3 | 17.1 | 26.1 | 5.5 | |||||||
32.4 | 17.0 | 26.0 | 5.4 | |||||||
30.8 | 12.9 | 19.2 | 4.3 |
(元·(kW·h)–1) | (kW·h) | kW | 回收天 数/天 | |||||||
30.9 | 22.2 | 34.8 | 7.1 | |||||||
32.3 | 17.1 | 26.1 | 5.5 | |||||||
32.5 | 16.9 | 8.0 | 0.4 | |||||||
32.4 | 16.9 | 8.5 | 0.4 | |||||||
31.7 | 14.1 | 0 | 0 |
表 4 $ {\kappa _{\text{e}}} $的影响
Table 4 Impact of $ {\kappa _{\text{e}}} $
(元·(kW·h)–1) | (kW·h) | kW | 回收天 数/天 | |||||||
30.9 | 22.2 | 34.8 | 7.1 | |||||||
32.3 | 17.1 | 26.1 | 5.5 | |||||||
32.5 | 16.9 | 8.0 | 0.4 | |||||||
32.4 | 16.9 | 8.5 | 0.4 | |||||||
31.7 | 14.1 | 0 | 0 |
回收天数/天 | ||||||||||
200 | 30.9 | 22.0 | 34.2 | 8.8 | ||||||
300 | 30.9 | 22.2 | 34.8 | 7.1 | ||||||
400 | 32.3 | 17.1 | 26.1 | 5.5 | ||||||
500 | 32.4 | 17.0 | 26.0 | 5.4 | ||||||
600 | 32.5 | 16.9 | 8.3 | 0.4 |
表 5 $ {\kappa _{{\text{ep}}}} $的影响
Table 5 Impact of $ {\kappa _{{\text{ep}}}} $
回收天数/天 | ||||||||||
200 | 30.9 | 22.0 | 34.2 | 8.8 | ||||||
300 | 30.9 | 22.2 | 34.8 | 7.1 | ||||||
400 | 32.3 | 17.1 | 26.1 | 5.5 | ||||||
500 | 32.4 | 17.0 | 26.0 | 5.4 | ||||||
600 | 32.5 | 16.9 | 8.3 | 0.4 |
算法 | 采样点数 | 计算时间/s | ||||
迭代算法 | 112.7 | 0.23 | ||||
均匀分布 采样法 | 24 | 13.0 | 10.87 | |||
34 | 67.4 | 6.61 | ||||
44 | 435.8 | 1.19 | ||||
54 | 0.83 |
表 6 迭代算法与均匀分布采样法效果对比
Table 6 Comparison of the effectiveness of the iterative algorithm and the uniformly distributed sampling method
算法 | 采样点数 | 计算时间/s | ||||
迭代算法 | 112.7 | 0.23 | ||||
均匀分布 采样法 | 24 | 13.0 | 10.87 | |||
34 | 67.4 | 6.61 | ||||
44 | 435.8 | 1.19 | ||||
54 | 0.83 |
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