为解决数据中心能耗多、排放高、成本大的问题,提出太阳能与自然冷源结合氢储能的数据中心综合能源系统（IES）,集成光伏组件、集热器、内燃机、吸收式制冷机以及燃料电池等设备。构建系统模型,使用双层优化方法对系统配置与运行参数进行优化,上层采用非支配排序遗传算法对系统容量进行优化,下层采用多目标灰狼优化算法优化运行过程中设备出力比例。通过全面权衡能源效率、经济效益、环保性、系统独立性以及可持续性等多维度指标,优化后系统的一次能源节约率达到46.33%、年总成本节约率为38.25%、CO2减排率为49.08%、电网独立性为56.07%、年总运行成本为3.41×105美元、碳使用率为0.62。
Abstract
Aiming to address the problems of excessive energy consumption, high emission and significant cost of data centers, this study proposes an integrated energy system for adata center with solar energy and natural cooling source combined with hydrogen storage, which integrates photovoltaic module, collector, internal combustion engine, absorption refrigeration unit, and fuel cell. System configuration and operating parameters are optimized using a two-layer optimization method. In the upper layer, a non-dominated sorting genetic algorithm is adopted to optimize the system capacity, In the lower layer, a multi-objective grey wolf optimization algorithm is used to optimize the ratio of operating equipment output. System performance is significantly improved by weighing the multi-dimensional indicators of energy efficiency, economic benefit, environmental protection, system independence, and sustainability. Through optimization, primary energy conservation rate reaches 46.33%, annual cost efficiency rate is 38.25%, CO2 emission saving rate is 49.08%, grid integration level is 56.07%, annual total operating cost is $ 3.41×105, and carbon usage effectiveness is 0.62. This study provides a new idea and method for the planning and design of data center energy systems by flexibly adopting multiple energy supply strategies.
关键词
太阳能 /
数据中心 /
多目标优化 /
综合能源系统 /
容量配置 /
自然冷却
Key words
solar energy /
data centers /
multiobjective optimization /
integrated energy system /
capacity configuration /
natural cooling
{{custom_sec.title}}
{{custom_sec.title}}
{{custom_sec.content}}
参考文献
[1] WANG J J, DENG H D, LIU Y, et al.Coordinated optimal scheduling of integrated energy system for data center based on computing load shifting[J]. Energy, 2023, 267: 126585.
[2] DEYMI D M, NORANI M.Sustainability assessment and emergy analysis of employing the CCHP system under two different scenarios in a data center[J]. Renewable and sustainable energy reviews, 2021, 150: 111511.
[3] CAI S H, GOU Z H.A comprehensive analysis of green building rating systems for data centers[J]. Energy and buildings, 2023, 284: 112874.
[4] 马晓燕, 穆云飞, 李树荣, 等. 数据中心综合能源系统优化运行研究综述[J]. 电力建设, 2022, 43(11): 1-13.
MA X Y, MU Y F, LI S R, et al. Review on the optimal operation of integrated energy system in data center[J]. Electric power construction, 2022, 43(11): 1-13.
[5] XU D, XIANG S Z, BAI Z Y, et al.Optimal multi-energy portfolio towards zero carbon data center buildings in the presence of proactive demand response programs[J]. Applied energy, 2023, 350: 121806.
[6] 孙强, 孙志凰, 潘杭萍, 等. 考虑多种储能的数据中心综合能源系统配置优化[J]. 中国电力, 2022, 55(9): 1-7.
SUN Q, SUN Z H, PAN H P, et al. Configuration optimization of data center integrated energy system considering multiple energy storage [J]. Electric power, 2022, 55(9): 1-7.
[7] 张诚, 檀志恒, 晁怀颇. “双碳”背景下数据中心氢能应用的可行性研究[J]. 太阳能学报, 2022, 43(6): 327-334.
ZHANG C, TAN Z H, CHAO H P. Feasibility study on the application of hydrogen energy in data centers under the background of "dual carbon" [J]. Acta energiae solaris sinica, 2022, 43(6): 327-334.
[8] KURTZ J, MA Z W, SAUR G, et al.Analysis of hydrogen infrastructure for the feasibility, economics, and sustainability of a fuel cell powered data center[J]. Sustainable energy technologies and assessments, 2023, 58: 103357.
[9] LIU W J, JIN B, WANG D H, et al.Performance modeling and advanced exergy analysis for low-energy consumption data center with waste heat recovery, flexible cooling and hydrogen energy[J]. Energy conversion and management, 2023, 297: 117756.
[10] 张兴, 李震. 数据中心冷却技术发展[J]. 工程热物理学报, 2017, 38(1): 226-227.
ZHANG X, LI Z. Development of cooling technology in data center[J]. Journal of engineering thermophysics, 2017, 38(1): 226-227.
[11] ATWOOD D, MINER J G. Reducing data center cost with an air economizer [EB/OL].2008, https://www.intel.com
[12] ELAHEE K, JUGOO S.Ocean thermal energy for air-conditioning: case study of a green data center[J]. Energy sources, part a: recovery, utilization, and environmental effects, 2013, 35(7): 679-684.
[13] GÜĞÜL G N, GÖKÇÜL F, EICKER U. Sustainability analysis of zero energy consumption data centers with free cooling, waste heat reuse and renewable energy systems: a feasibility study[J]. Energy, 2023, 262: 125495.
[14] 徐岩, 张建浩, 张荟.含冷、热、电、气的园区综合能源系统选址定容规划案例分析[J]. 太阳能学报, 2022, 43(1): 313-322.
XU Y, ZHANG J H, ZHANG H. A case study on site selection and capacity planning of integrated energy system in park with cold, heat, electricity and gas[J]. Acta energiae solaris sinica, 2022, 43(1): 313-322.
[15] 安源, 郑申印, 苏瑞, 等. 风光水储多能互补发电系统双层优化研究[J]. 太阳能学报, 2023, 44(12): 510-517.
AN Y, ZHENG S Y, SU R, et al. Research on two-layer optimization of wind-solar-water-storage multi energy complementary power generation system[J]. Acta energiae solaris sinica, 2023, 44(12): 510-517.
[16] HE J M, WU Y N, YONG X K, et al.Bi-level optimization of a near-zero-emission integrated energy system considering electricity-hydrogen-gas nexus: a two-stage framework aiming at economic and environmental benefits[J]. Energy conversion and management, 2022, 274: 116434.
[17] FAN G Y, LIU Z J, LIU X, et al.Two-layer collaborative optimization for a renewable energy system combining electricity storage, hydrogen storage, and heat storage[J]. Energy, 2022, 259: 125047.
[18] GB 50174—2017, 数据中心设计规范[S].
GB 50174—2017, Code for design of data centers[S].
[19] CHEUNG H, WANG S W, ZHUANG C Q, et al.A simplified power consumption model of information technology equipment in data centers for energy system real-time dynamic simulation[J]. Applied energy, 2018, 222: 329-342.
[20] YUAN G X, GAO Y, YE B.Optimal dispatching strategy and real-time pricing for multi-regional integrated energy systems based on demand response[J]. Renewable energy, 2021, 179: 1424-1446.
[21] T/DZJN 93—2022, 数据中心碳排放评价规范[S]. T/DZJN 93—2022, Carbon emission assessment specification for data centers[S].
[22] GB/T 32910.4.1—2021, 数据中心-资源利用-第四部分:可再生能源利用率[S]. GB/T 32910.4.1—2021, Data center-resource utilization-part 4: renewable energy utilization rate[S].
[23] YANG S, LIU B L, LI X L, et al.Flexibility index for a distributed energy system design optimization[J]. Renewable energy, 2023, 219: 119423.
[24] WANG Y L, LIU C, QIN Y M, et al.Synergistic planning of an integrated energy system containing hydrogen storage with the coupled use of electric-thermal energy[J]. International journal of hydrogen energy, 2023, 48(40): 15154-15178.
[25] ZHANG Y, SUN H X, TAN J X, et al.Capacity configuration optimization of multi-energy system integrating wind turbine/photovoltaic/hydrogen/battery[J]. Energy, 2022, 252: 124046.
[26] WANG J S, XUE K, GUO Y J, et al.Multi-objective capacity programming and operation optimization of an integrated energy system considering hydrogen energy storage for collective energy communities[J]. Energy conversion and management, 2022, 268: 116057.
[27] XU Y P, LIU R H, TANG L Y, et al.Risk-averse multi-objective optimization of multi-energy microgrids integrated with power-to-hydrogen technology, electric vehicles and data center under a hybrid robust-stochastic technique[J]. Sustainable cities and society, 2022, 79: 103699.
[28] YUAN H L, FENG K, LI W, et al.Multi-objective optimization of virtual energy hub plant integrated with data center and plug-in electric vehicles under a mixed robust-stochastic model[J]. Journal of cleaner production, 2022, 363: 132365.
基金
国家自然科学基金（51806021）; 湖南省教育厅科学研究重点项目（23A0238）; 湖南省自然资源厅科技计划（HBZ20240118）; 湖南省教育厅研究生科研创新项目（CX20230902）
PDF(2448 KB)
