考虑算力需求的港口综合能源系统分布式能源管理

doi:10.3969/j.issn.2097-0706.2025.01.006

摘要/Abstract

摘要：

为应对港口数字化、绿色化转型带来的算力需求急剧增长及新能源发电不确定性挑战，提出了一种考虑算力需求的港口综合能源系统分布式能源管理方法，旨在通过能源调度与协同利用，实现港口区域内电力、热能及算力的综合优化，以最大化经济效益。考虑到港口不同数据负荷下的时间延迟约束及余热回收潜力，建立港口数据中心耗能计算模型；以港口微电网运行成本、数据中心运行成本和热力系统运行成本最小化为目标，考虑港口电力、热能等多种能源形式的交互耦合机制与多样负荷需求约束，构建港口综合能源系统能源管理模型；利用基于对偶分解混合整数线性规划的分布式算法进行求解，以获得综合能源系统的最优能源管理方案，协同推进航运业及道路运输业脱碳行动。仿真分析表明，该方法可以提高港口综合能源系统的运行效率，有效应对算力需求的挑战。

关键词: 港口数字化, 综合能源系统, 数据中心, 算力, 微电网, 余热回收, 分布式能源管理, 混合整数线性规划

Abstract:

To address the challenges of sharp growth of computing power demands and uncertainties of renewable energy generation brought by the digital and green transformation of ports，a distributed energy management method of port integrated energy system is introduced considering computing power demands. The aim is to achieve comprehensive optimization of electricity，thermal energy，and computational power within the port areas through energy scheduling and collaborative utilization，thereby maximizing economic benefits. Considering the time delay constraints and the potential for waste heat recovery under different data loads，an energy consumption calculation model for port data centers was established. Aiming to minimize the operational costs of port microgrids，data centers，and thermal systems，and considering the interactive coupling mechanisms and diverse load demand constraints of various energy forms such as electricity and thermal energy，an energy management model of port integrated energy system was developed. In order to obtain the optimal energy management solution for the integrated energy system and collaboratively advance decarbonization efforts in shipping and road transport sectors，a distributed algorithm based on dual decomposition mixed integer linear programming was utilized to solve the problem. Simulation analysis indicated enhanced operational efficiency of the port integrated energy system which could effectively address the challenges posed by computational power demands.

Key words: port digitalization, integrated energy system, data center, computing power, microgrid, waste heat recovery, distributed energy management, mixed integer linear programming

中图分类号:

TK01

曲琪, 滕菲, 郭禹辛, 张琳雪. 考虑算力需求的港口综合能源系统分布式能源管理[J]. 综合智慧能源, 2025, 47(1): 42-50.

QU Qi, TENG Fei, GUO Yuxin, ZHANG Linxue. Distributed energy management of port integrated energy system considering computing power demands[J]. Integrated Intelligent Energy, 2025, 47(1): 42-50.

图/表 7

图1

图2

表1

设备参数

参数	数值	参数	数值
$P m i n n$ /kW	1	$L R$ /个	8 000
$P m a x n$ /kW	3	$η G$	0.8
$v n$ /（个·h^-1）	40	$K D C$ /美元	0.650
$M$ /台	600	R_pue	1.3
$L Y$ /个	10 000	$η$	0.2
$α w$ /美元	0.620	$α p v$ /美元	0.615
$α C H P - E$ /美元	0.650	$P w m i n$ /kW	0
$P w m a x$ /kW	1 300	$P C H P - E m i n$ /kW	200
$P C H P - E m a x$ /kW	1 000	$P p v m i n$ /kW	0
$P p v m a x$ /kW	1 500	$β C H P - H$ /美元	1.500
$β G$ /美元	1.200	$P C H P - H m i n$ /kW	0
$P C H P - H m a x$ /kW	300	$P G - H m i n$ /kW	0
$P G - H m a x$ /kW	300	$P S$ /kW	[3,5]
$E r e f n$ /(kW·h)	[0.55，0.80] $E m a x n$	$E m a x n$ /(kW·h)	[8,16]
$K S C$ /[美元·(kW·h)^-1]	[0.6，1.1]	$E m i n n$ /(kW·h)	1
$K S D$ /[美元·(kW·h)^-1]	1.1 $K S C$	$E i n i t n$ /(kW·h)	[0.2，0.5] $E m a x n$

表1

图3

图4

图5

图6

参考文献 23

[1]	PSARAFTIS H N. Sustainable shipping: A cross-disciplinary view[M]. Berlin: Springer, 2019.
[2]	ZHANG F, DENG X Z, PHILLIPS F, et al. Impacts of industrial structure and technical progress on carbon emission intensity: Evidence from 281 cities in China[J]. Technological Forecasting and Social Change, 2020, 154: 119949.
[3]	UNCTAD. Review of maritime transport 2020[R]. Geneva: UNCTAD, 2020.
[4]	IMO. Fourth greenhouse gas study 2020[R]. London: IMO, 2020.
[5]	MOLAVI A, LIM G J, RACE B. A framework for building a smart port and smart port index[J]. International Journal of Sustainable Transportation, 2020, 14(9): 686-700.
[6]	敦文斌, 陈业, 王大伟, 等. 零碳视角下电气化码头源网荷储协同优化研究[J]. 综合智慧能源, 2022, 44(12): 68-74. doi: 10.3969/j.issn.2097-0706.2022.12.010
	DUN Wenbin, CHEN Ye, WANG Dawei, et al. Study on source-grid-load-storage cooperative optimization of an electrified quay from the perspective of zero carbon[J]. Integrated Intelligent Energy, 2022, 44(12): 68-74. doi: 10.3969/j.issn.2097-0706.2022.12.010
[7]	IRIS Ç, LAM J S L. Optimal energy management and operations planning in seaports with smart grid while harnessing renewable energy under uncertainty[J]. Omega, 2021, 103: 102445.
[8]	GU W, WANG Z H, WU Z, et al. An online optimal dispatch schedule for CCHP microgrids based on model predictive control[J]. IEEE Transactions on Smart Grid, 2017, 8(5):2332-2342.
[9]	MAO A J, YU T T, DING Z H, et al. Optimal scheduling for seaport integrated energy system considering flexible berth allocation[J]. Applied Energy, 2022, 308: 118386.
[10]	丁肇豪, 曹雨洁, 张素芳, 等. 能源互联网背景下数据中心与电力系统协同优化(一): 数据中心能耗模型[J]. 中国电机工程学报, 2022, 42(9): 3161-3177.
	DING Zhaohao, CAO Yujie, ZHANG Sufang, et al. Coordinated operation for data center and power system in the context of energy Internet(Ⅰ):Energy demand management model of data center[J]. Proceedings of the CSEE, 2022, 42(9): 3161-3177.
[11]	曹雨洁, 丁肇豪, 王鹏, 等. 能源互联网背景下数据中心与电力系统协同优化(二): 机遇与挑战[J]. 中国电机工程学报, 2022, 42(10): 3512-3527.
	CAO Yujie, DING Zhaohao, WANG Peng, et al. Coordinated operation for data center and power system in the context of energy Internet(Ⅱ): Opportunities and challenges[J]. Proceedings of the CSEE, 2022, 42(10): 3512-3527.
[12]	丁巧宜, 王梓耀, 潘振宁, 等. 面向电量-调频-容量市场的数据中心园区算力及电力资源规划[J]. 电力系统自动化, 2024, 48(1): 59-66.
	DING Qiaoyi, WANG Ziyao, PAN Zhenning, et al. Planning of computing power and electric power resources in data center parks for electricity, frequency regulation and capacity markets[J]. Automation of Electric Power Systems, 2024, 48(1): 59-66.
[13]	马浩天, 胡俊杰, 童宇轩. 考虑灵活性的数据中心微网两阶段鲁棒规划方法[J]. 中国电机工程学报, 2023, 43(19): 7396-7409.
	MA Haotian, HU Junjie, TONG Yuxuan. A two-stage robust planning approach for data center microgrids considering flexibility[J]. Proceedings of the CSEE, 2023, 43(19): 7396-7409.
[14]	BELOGLAZOV A, ABAWAJY J, BUYYA R. Energy-aware resource allocation heuristics for efficient management of data centers for cloud computing[J]. Future Generation Computer Systems, 2012, 28(5): 755-768.
[15]	滕菲, 单麒赫, 李铁山. 智能船舶综合能源系统及其分布式优化调度方法[J]. 自动化学报, 2020, 46(9): 1809-1817.
	TENG Fei, SHAN Qihe, LI Tieshan. Intelligent ship integrated energy system and its distributed optimal scheduling algorithm[J]. Acta Automatica Sinica, 2020, 46(9): 1809-1817.
[16]	SHI W B, XIE X R, CHU C C, et al. Distributed optimal energy management in microgrids[J]. IEEE Transactions on Smart Grid, 2015, 6(3): 1137-1146.
[17]	ZHAO B W, LIU X M, SONG A, et al. PriMPSO: A privacy-preserving multiagent particle swarm optimization algorithm[J]. IEEE Transactions on Cybernetics, 2023, 53(11): 7136-7149.
[18]	LAI K X, ILLINDALA M S. Sizing and siting of distributed cloud energy storage systems for a shipboard power system[C]// Proceedings of 2020 IEEE Industry Applications Society Annual Meeting. IEEE, 2020: 1-8.
[19]	ZHONG W F, XIE K, LIU Y, et al. ADMM empowered distributed computational intelligence for Internet of energy[J]. IEEE Computational Intelligence Magazine, 2019, 14(4): 42-51.
[20]	CHEN G, REN J H, FENG E N. Distributed finite-time economic dispatch of a network of energy resources[J]. IEEE Transactions on Smart Grid, 2017, 8(2): 822-832.
[21]	陈敏, 高赐威, 陈宋宋, 等. 考虑数据中心用电负荷调节潜力的双层经济调度模型[J]. 中国电机工程学报, 2019, 39(5): 1301-1314.
	CHEN Min, GAO Ciwei, CHEN Songsong, et al. Bi-level economic dispatch modeling considering the load regulation potential of Internet data centers[J]. Proceedings of the CSEE, 2019, 39(5): 1301-1314.
[22]	余文昶, 陈永刚, 曹俊波, 等. 考虑负载响应特性的数据中心综合能源系统优化调度研究[J]. 综合智慧能源, 2023, 45(10): 25-34. doi: 10.3969/j.issn.2097-0706.2023.10.004
	YU Wenchang, CHEN Yonggang, CAO Junbo, et al. Optimal scheduling of the data center integrated energy system considering load response characteristics[J]. Integrated Intelligent Energy, 2023, 45(10): 25-34. doi: 10.3969/j.issn.2097-0706.2023.10.004
[23]	FALSONE A, MARGELLOS K, GARATTI S, et al. Dual decomposition for multi-agent distributed optimization with coupling constraints[J]. Automatica, 2017, 84: 149-158.