基于 i-C&CG求解算法的数据中心与储能协同规划

doi:10.19799/j.cnki.2095-4239.2024.0828

摘要/Abstract

摘要：

随着人工智能对算力需求的激增，数据中心(internet data center, IDC)作为数据处理与存储的机构，其能耗需求远超预期，使用新能源是其可持续发展的需要。然而，可再生能源具有出力不确定性，仅依靠数据中心参与需求响应难以实现消纳，可配置储能提高系统灵活性。因此，本工作建立了以规划总成本最优为目标的数据中心与电池储能(battery energy storage，BES)协同规划两阶段鲁棒模型，为防止规划结果过于乐观，引入了储能寿命约束。同时针对在求解所建模型过程中，传统C&CG(column-and-constraint generation)算法存在难以平衡求解速度与精度间关系的问题，本工作提出了一种不精确列和生成约束算法i-C&CG(inexact column-and-constraint generation)进行求解。基于IEEE30节点与IEEE118节点算例系统进行优化解算，仿真结果表明，与仅配置单一储能相比，本工作所提模型储能年等效建设成本下降39785元，数据中心年等效建设成本下降289080元；且本工作所提算法与传统C&CG相比，采用0.18低精度下的i-C&CG，与采用0.16较高精度的C&CG相比较，i-C&CG最多可缩短3632 s的单次迭代求解所需时间，且最终计算结果的相对误差为0.46%，两者收敛间隙与相对最优间隙近似。

关键词: 数据中心, 储能寿命, 不精确列和约束生成算法

Abstract:

The skyrocketing demand for AI computing has fueled a surge in energy consumption within internet data centers (IDCs), surpassing expectations. However, the unpredictable nature of renewable energy poses significant challenges for stable grid operation, which demand response from IDCs alone cannot fully solve. Configurable energy storage enhances flexibility. This paper presents a two-stage robust model for IDCs and battery energy storage (BES) planning, minimizing operational costs amidst wind power uncertainty. A lifespan constraint ensures realistic planning. The traditional C&CG algorithm's speed-accuracy dilemma is tackled with an Inexact C&CG (i-C&CG) algorithm, avoiding exact column/constraint generation. Simulations on IEEE 30-node and 118-node systems highlight the benefits of this approach. The proposed energy storage configuration reduces annual costs by 39785 CNY for storage systems and 289080 CNY for IDCs. The i-C&CG algorithm shortens iteration time by 3632 seconds, achieving a 0.18 precision and 0.46 relative error. These results match the traditional C&CG's convergence and optimality gaps.

Key words: internet data centers, energy storage life, inexact column-and-constraint generation

中图分类号:

TM 715

王述祯. 基于 i-C&CG求解算法的数据中心与储能协同规划[J]. 储能科学与技术, 2025, 14(2): 671-687.

Shuzhen WANG. i-C&CG solving algorithm-driven collaborative planning of data center and battery energy storage[J]. Energy Storage Science and Technology, 2025, 14(2): 671-687.

图/表 17

图1

表1

表 2

储能装置参数"

参数	数值
储能功率 $P c h u p$ / P $d c h u p$	5 MW
基准容量EE	10 MWh/台
效率 η_ch/ η_dch	0.95/1
单位建设成本 C_E	18250元/MWh
N₀	1591
k_p	2.09
贴现率 r	5%
SOCE_min	0.25
SOCE_max	1
e_Emax	3台
T_flo	10年

表 2

表 3

图2

图3

表4

表5

图4

表6

图5

表7

图6

表8

图7

图8

图9

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机组编号	出力上限/MW	出力下限/MW	爬坡速率/滑坡速率	最小离线	最小在线	固定成本/元	运行成本/元	启动成本/元
1	157	50	37.5	4	4	244.2	20.26	3200
2	100	25	30	4	4	244.2	20.26	3200
3	60	15	15	3	3	176.8	24.68	3000
4	80	20	20	3	3	176.8	24.68	3000
5	40	10	15	2	2	120	40	7000
6	40	10	15	2	2	120	40	7000

参数	数值
β₁/ β₂	0.1/0.1
Y_IDC	10年
α₁	1.5
P^ser-peak	15 kW
P^ser-idle	7.5 kW
T_del	0.1s
τ	500个/s
c	0.01元/Gbit
θ^I-ser	8030元

项目	总成本	机组固定成本	机组可变成本	储能年投资成本	数据中心激励成本	数据中心年投资建设成本
方案一	176725	20468	151196	—	—	1846900
方案二	166134	19324	134600	606995	—	1846900
方案三	169008	21000	145943	—	1845.8	1558915
方案四	161591	18970	132812	567210	1830	1557820

模型	总成本/元	机组固定成本/元	机组可变成本/元	储能投建总量/个
CMP	161932	17732	131478	18
EQU	162592	18970	132812	22

成本	确定性模型	风力发电的不确定度 ϕ
成本	ϕ=0	ϕ=1	ϕ=2	ϕ=3
总成本	157526.8	158333.1	159110.3	159813.7
火电运行成本	150660.6	151286.6	152199.1	152890.1
储能投资成本	654265.0	728270.4	733667.1	721345.3
数据中心建设	1479013.5	1445351.8	1447702	1449559.5
数据中心激励	1828.3	1598.2	1827.6	1675.5