Capacity planning method with high reliability for integrated energy systems with low-carbon emissions based on scenario expansion

doi:10.3969/j.issn.2097-0706.2024.04.004

Abstract

Abstract:

In the context of addressing frequent extreme weather around the world and urgent needs for low-carbon energy supply， an integrated energy system （IES） that can couple multiple local energy sources is considered to be an effective paradigm for improving energy efficiency and reducing carbon emissions. Due to the limit of carbon emissions，the uncertain outputs of various renewable energy sources in an IES ，and unstable demands on cold， heat and electricity， an optimal capacity planning method aiming at optimizing the economic cost， local energy supply reliability and carbon emission cost of the IES is proposed. The planning method can expand the IES operation scenarios with the help of data-driven denoising diffusion model. It improves the reliability of the optimization model under uncertain conditions. Based on the simulation experiment on actual case data， the results show that compared with the traditional planning method， the proposed planning method reduces the operating cost by 34.5%， and reduces the carbon emission by 39.4%.

Key words: integrated energy system, multi-objective optimization, denoising diffusion probabilistic model, scenario generation, data-driven model, low-carbon energy supply

CLC Number:

TK01

CHEN Yong, XIAO Leiming, WANG Jingnan, WU Jian. Capacity planning method with high reliability for integrated energy systems with low-carbon emissions based on scenario expansion[J]. Integrated Intelligent Energy, 2024, 46(4): 24-33.

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References 30

[1]	李鹏, 王瑞, 冀浩然, 等. 低碳化智能配电网规划研究与展望[J]. 电力系统自动化, 2021, 45(24):10-21.
	LI Peng, WANG Rui, JI Haoran, et al. Research and prospect of planning for low-carbon smart distribution network[J]. Automation of Electric Power Systems, 2021, 45(24):10-21.
[2]	张希良, 姜克隽, 赵英汝, 等. 促进能源气候协同治理机制与路径跨学科研究[J]. 全球能源互联网, 2021, 4(1):1-4.
	ZHANG Xiliang, JIANG Kejun, ZHAO Yingru, et al. Cross disciplinary research on mechanisms and pathways for promoting coordinated governance of energy and climate[J] Global Energy Internet, 2021, 4(1):1-4.
[3]	宋国君, 王语苓, 姜艺婧. 基于“双碳”目标的碳排放控制政策设计[J]. 中国人口·资源与环境, 2021, 31(9):55-63.
	SONG Guojun, WANG Yuling, JIANG Yijing. Carbon emission control policy design based on the targets of carbon peak and carbon neutrality[J]. China Population Resources and Environment, 2021, 31(9):55-63.
[4]	廖柏睿, 吴晓南, 苏要港, 等. 综合能源系统运行优化设计与运行策略研究[J]. 热能动力工程, 2023(3):82-90.
	LIAO Birui, WU Xiaonan, SU Yaogang, et al. Research on operation optimization design and operation strategy of integrated energy system[J]. Journal of Engineering for Thermal Energy and Power, 2023(3):82-90.
[5]	李宜哲, 王丹, 贾宏杰, 等. 综合能源系统能量枢纽多样性建模和典型适用性研究[J]. 综合智慧能源, 2023, 45(7):22-29. doi: 10.3969/j.issn.2097-0706.2023.07.003
	LI Yizhe, WANG Dan, JIA Hongjie, et al. Diverse modeling methods for energy hubs in integrated energy systems and their typical applications[J]. Integrated Intelligent Energy, 2023, 45(7): 22-29. doi: 10.3969/j.issn.2097-0706.2023.07.003
[6]	ALABI T M, AGHIMIEN E I, AGBAJOR F D, et al. A review on the integrated optimization techniques and machine learning approaches for modeling, prediction, and decision making on integrated energy systems[J]. Renewable Energy, 2022, 194:822-849. doi: 10.1016/j.renene.2022.05.123
[7]	刘海涛, 朱海南, 李丰硕, 等. 计及碳成本的电-气-热-氢综合能源系统经济运行策略[J]. 电力建设, 2021, 42(12):21-29. doi: 10.12204/j.issn.1000-7229.2021.12.003
	LIU Haitao, ZHU Hainan, LI Fengshuo, et al. Economic operation strategy of the electricity gas heat hydrogen comprehensive energy system considering carbon costs[J]. Electric Power Construction, 2021, 42 (12): 21-29. doi: 10.12204/j.issn.1000-7229.2021.12.003
[8]	董海鹰, 贠韫韵, 马志程, 等. 计及多能转换及光热电站参与的综合能源系统低碳优化运行[J]. 电网技术, 2020, 44(10):3689-3699.
	DONG Haiying, YUN Yunyun, MA Zhicheng, et al. Low carbon optimized operation of comprehensive energy systems involving multi energy conversion and solar thermal power plants[J]. Power System Technology, 2020, 44(10):3689-3699.
[9]	蓝静, 朱继忠, 李盛林, 等. 考虑碳惩罚的电化学储能消纳风光与调峰研究[J]. 综合智慧能源, 2022, 44(1): 9-17. doi: 10.3969/j.issn.2097-0706.2022.01.002
	LAN Jing, ZHU Jizhong, LI Shenglin, et al. Research on electrochemical energy storage to assist new energy consumption and peak load regulation considering carbon penalty[J]. Integrated Intelligent Energy, 2022, 44(1): 9-17. doi: 10.3969/j.issn.2097-0706.2022.01.002
[10]	ZHONG X Q, ZHONG W F, LIU Y, et al. Optimal energy management for multi-energy multi-microgrid networks considering carbon emission limitations[J]. Energy 2022; 246:123428. doi: 10.1016/j.energy.2022.123428
[11]	周贤正, 陈玮, 郭创新. 考虑供能可靠性与风光不确定性的城市多能源系统规划[J]. 电工技术学报, 2019, 34(17):3672-3686.
	ZHOU Xianzheng, CHEN Wei, GUO Chuangxin. An urban multi-energy system planning method incorporating energy supply reliability and wind-photovoltaic generators uncertainty[J]. Transactions of China Electrotechnical Society, 2019, 34(17): 3672-3686.
[12]	何勇萍, 刘小敏, 肖艳利, 等. 电热耦合综合能源系统双阶段多目标规划研究[J]. 运筹与管理, 2023, 32(1):27-33. doi: 10.12005/orms.2023.0005
	HE Yongping, LIU Xiaomin, XIAO Yanli, et al. Study on the two-stage and multi-objective planning of electro-thermal coupled integrated energy systems[J]. Operations Research and Management Science, 2023, 32(1): 27-33. doi: 10.12005/orms.2023.0005
[13]	郑亚锋, 魏振华, 刘思渠. 考虑风光不确定性的综合能源系统规划设计方法[J]. 科学技术与工程, 2021, 21(31):13342-13348.
	ZHENG Yafeng, WEI Zhenhua, LIU Siqu. Integrated energy system planning and design method considering uncertainty of wind power and photovoltaic system[J]. Science Technology and Engineering, 2021, 21(31): 13342-13348.
[14]	孙强, 谢典, 聂青云, 等. 含电-热-冷-气负荷的园区综合能源系统经济优化调度研究[J]. 中国电力, 2020, 53(4):79-88.
	SUN Qiang, XIE Dian, NIE Qingyun, et al. Research on economic optimization and dispatching of comprehensive energy systems in industrial parks with electricity heat cold gas load[J]. Electric Power, 2020, 53(4):79-88.
[15]	WANG H T, GU C H, ZHANG X, et al. Optimal CHP planning in integrated energy systems considering network charges[J]. IEEE Systems Journal, 2020, 14(2):2684-2693. doi: 10.1109/JSYST.4267003
[16]	HUANG Y S, SHI M S, WANG W Y, et al. A two-stage planning and optimization model for water-hydrogen integrated energy system with isolated grid[J]. Journal of Cleaner Production, 2021, 313:127889. doi: 10.1016/j.jclepro.2021.127889
[17]	MA T F, WU J Y, HAO L L, et al. The optimal structure planning and energy management strategies of smart multi energy systems[J]. Energy, 2018, 160: 122-141. doi: 10.1016/j.energy.2018.06.198
[18]	HELENO M, REN Z. Multi-energy microgrid planning considering heat flow dynamics[J]. IEEE Transactions on Energy Conversion, 2020, 36(3): 1962-1971. doi: 10.1109/TEC.2020.3041572
[19]	SHAHIDEHPOUR M, LI C B, YANG H Y, et al. Multistage expansion planning of integrated biogas and electric power delivery system considering the regional availability of biomass[J]. IEEE Transactions on Sustainable Energy, 2020, 12(2): 920-930. doi: 10.1109/TSTE.2020.3025831
[20]	杨安全, 赵清松, 岳颖, 等. 电-气-热综合能源系统多目标优化调度策略[J]. 东北电力技术, 2023, 44(12):1-8.
	ZYANG Anquan, ZHAO Qingsong, YUE Ying, et al. Research on multi-objective optimization scheduling of electricity-gas-thermal integrated energy systems based on wind/photovoltaic power accommodation[J]. Northeast Electric Power Technology, 2023, 44(12):1-8.
[21]	朱玲, 李威, 王骞, 等. 基于校正条件生成对抗网络的风电场群绿氢储能系统容量配置[J/OL]. 电工技术学报:1-17(2023-03-22)[2023-10-10].https://doi.org/10.19595/j.cnki.1000-6753.tces.222009.
	ZHU Ling, LI Wei, WANG Qian, et al. Wind farms-green hydrogen energy storage system capacity sizing method based on corrected-conditional generative adversarial network[J/OL]. Transactions of China Electrotechnical Society:1-17(2023-03-22)[2023-10-10].https://doi.org/10.19595/j.cnki.1000-6753.tces.222009.
[22]	CHEN X Q, DONG W, YANG L F, et al. Scenario-based robust capacity planning of regional integrated energy systems considering carbon emissions[J]. Renewable Energy, 2023, 207: 359-375. doi: 10.1016/j.renene.2023.03.030
[23]	HART E K, JACOBSON M Z. A Monte Carlo approach to generator portfolio planning and carbon emissions assessments of systems with large penetrations of variable renewables[J]. Renewable Energy, 2011, 36(8):2278-2286. doi: 10.1016/j.renene.2011.01.015
[24]	CHEN Y Z, WANG Y S, KIRSCHEN D, et al. Model-free renewable scenario generation using generative adversarial networks[J]. IEEE Transactions on Power Systems, 2018, 33(3):3265-3275. doi: 10.1109/TPWRS.2018.2794541
[25]	YANG D F, JIANG C, CAI G W, et al. Interval method based optimal planning of multi-energy microgrid with uncertain renewable generation and demand[J]. Applied Energy, 2020, 277: 115491. doi: 10.1016/j.apenergy.2020.115491
[26]	KINGMA D P, WELLING M. Auto-encoding variational Bayes[J/OL]. arXiv Preprint, 2013:1312.6114(2013-12-20)[2023-10-10].https://doi.org/10.48550/arXiv.1312.6114.
[27]	NICHOL A Q, DHARIWAL P. Improved denoising diffusion probabilistic models[C]// International Conference on Machine Learning. PMLR, 2021:8162-8171.
[28]	KULLBACK S, LEIBLER R A. On information and sufficiency[J]. The Annals of Mathematical Statistics, 1951, 22 (1): 79-86. doi: 10.1214/aoms/1177729694
[29]	SONG Y, ERMON S. Generative modeling by estimating gradients of the data distribution[J/OL]. arXiv Preprint, 2019:1907.05600(2019-07-12)[2023-10-10].https://doi.org/10.48550/arXiv.1907.05600.
[30]	HO J, JAIN A, ABBEEL P. Denoising diffusion probabilistic models[J]. Advances in Neural Information Processing Systems, 2020, 33:6840-6851.

恒定参数	数值
电网发电的碳排放强度/[kg·（kW·h）^-1]	0.354
天然气的碳排放强度/(kg·m^-3)	2.165
天然气燃烧的低热值/(kJ·m^-3)	38 931
碳排放惩罚/（元·t^-1)	1 800
最大的年碳排放额度/t	1 100
允许的年碳排放配额/t	600

设备	S_OC，min	S_OC，max	充/放效率
ESS	0.15	0.85	0.90/0.90
TES	0.20	0.80	0.88/0.88

时间	价格
07:00 —11:00, 18:00 —20:00	0.86
11:00 —18:00, 20:00 —23:00	1.38
00:00 —06:00, 23:00 —次日00:00	0.37

设备	运行成本/ （元·kW^-1）	效率	容量限制/kW	使用寿命/a
WT	4 500	—	0~550	20
PV	3 850	—	0~550	20
GB	3 700	0.85	0~500	15
CHP机组	4 500	—	0~400	15
APC	2 700	0.90	0~400	15
AC	1 500	4.50	0~200	15
TES	1 600	0.90	0~300	10
ESS	3 000	0.90	0~300	5

样本来源	期望	方差	偏度	峰度
实际数据	252.34	180.00	0.39	2.58
DDPM生成数据	252.84	179.34	0.36	2.55