Integrated energy system operation optimization model considering double uncertainties

doi:10.3969/j.issn.2097-0706.2023.10.002

Abstract

Abstract:

Wide application of renewable energy is effective in alleviate greenhouse gas emissions， and the integrated energy system is an important way to improve its utilization efficiency. However， the randomness of distributed new energy output can lead to unstable operation of the system. Based on the study on the uncertainty of distributed energy outputs and prices，an integrated energy system optimization model considering the two uncertainties above is proposed. And the model takes the highest renewable energy accommodation rate and minimum operating cost as optimization objectives. Finally， the feasibility of the proposed model is verified by an integrated energy system of a demonstrative base located in the Yangzi River Delta， laying the foundation for pursuing carbon neutrality.

Key words: integrated energy system, carbon neutrality, uncertainty, distributed energy, low carbon economic dispatch

CLC Number:

TK01：TM715

YU Xiaobao, ZHAO Wenjing, SUN Yixin. Integrated energy system operation optimization model considering double uncertainties[J]. Integrated Intelligent Energy, 2023, 45(10): 10-17.

Figures/Tables 8

Fig.1

Table 1

Parameters and their interpretations

参数	释义	参数	释义
$r$	清洁能源消纳率/%	$P W T$	风电消纳功率/kW
$P P V$	光伏消纳功率/kW	$P ¯ W T$	风电预测出力/kW
$P ¯ P V$	光伏预测出力/kW	$T$	最大运行时间/h
$C E$	系统运行总成本/元	$C S$	电系统运行成本/元
$C L$	需求侧管理成本/元	$C H$	热系统运行成本/元
$P g r i d$	外网购电功率/kW	$q g r i d$	电价水平/元
$P S B$	储能充放电功率/kW	$k S B$	储能运维系数
$k W T$	风电运维系数	$k P V$	光伏运维系数
$f M T$	微燃机成本/元	$P M T$	微燃机功率/kW
$f F C$	燃料电池成本/元	$P F C$	燃料电池功率/kW
$c d e l a y 0$	平移负荷固定成本/元	$P d e l a y 0$	可平移负荷功率/kW
$c d e l a y$	转移可变系数	$P d e l a y$	负荷平移功率/kW
$λ d e l a y$	转移电效益损耗系数	$P H$	外网购热功率/kW
$p H$	外系统供热价格/元	$P X$	储能充放热功率/kW
$k X$	蓄能功率运维成本/元	$P G$	购气功率/kW
$C N G$	天然气单价/（元·m^-3）	$α H$	热能系统置信度水平
$C ¯ H$	热系统乐观值	$α G$	气能系统置信度水平
$C ¯ G$	气系统乐观值	$C G$	气系统运行成本/元
$P l o s s$	网损功率/kW	$P ¯ L$	需求管理负荷功率/kW
$P E, G$	热转电的功率/kW	$P E, H$	电转热的功率/kW
$d (j, t)$	负荷平移矩阵元素	$P g r i d m a x$	外购电功率最大值/kW
$R M T$	微燃机额定爬坡率	$R F C$	燃料电池额定爬坡率
$P M T m i n$	微燃机最小出力/kW	$P M T m a x$	微燃机最大出力/kW
$P F C m i n$	燃料电池最小出力/kW	$P F C m a x$	燃料电池最大出力/kW
$P E, G, m a x$	热转电最大功率/kW	$P E, H, m a x$	电转热最大功率/kW
$η d i s$	储能放电效率/%	$S S B$	储能剩余电量/（kW·h）
$S S B m i n$	储能余量最小值/（kW·h）	$η c h$	储能充电功率/kW
$P S B m i n$	储能充放功率下限/kW	$S S B m a x$	储能余量最大值/（kW·h）
$Q S B$	储能容量/（kW·h）	$P S B m a x$	储能充放功率上限/kW
$P E m a x$	最大供电容量/（kW·h）	$P^L, m a x$	电负荷偏差最大值
$P - P V$	光伏实际功率/kW	$P - ¯ P V$	光伏预测功率/kW
$P^P V$	光伏功率偏差/%	$P^P V, m i n$	光伏偏差最小值
$P^P V, m a x$	光伏偏差最大值	$P - W T$	风电实际功率/kW
$P - P V$	光伏实际功率/kW	$P - ¯ P V$	光伏预测功率/kW
$P - ¯ W T$	风电预测功率/kW	$P^W T$	风电功率偏差
$P^W T, m i n$	风电偏差最小值	$P^W T, m a x$	风电偏差最大值
$P - L$	电荷实际功率/kW	$P - ¯ L$	电荷预测功率/kW
$P^L$	电负荷功率偏差	$P^L, m i n$	电负荷偏差最小值
$P^L, m a x$	电负荷偏差最大值	$C h e$	吸收制热的制热系数
$P L H$	热负荷功率/kW	$P H m a x$	购热的最大功率/kW
$λ x$	热量自损失系数	$P m i n$	充放热功率最小值/kW
$P m a x$	充放热功率最大值/（kW·h）	$X m i n$	蓄热装置余热下限
$X m a x$	蓄热装置余热上限/（kW·h）	$P H m i n$	购热的最小功率/kW
$P H m a x$	购热的最大功率/kW	$P g s$	储气罐气体释放功率/kW
$P E, G$	气转电的功率/kW	$P L, G$	天然气负荷功率/kW
$P r s$	储气罐余量功率/kW	$P r s m a x$	储气余量功率上限/（kW·h）
$P r s m i n$	储气余量功率下限/（kW·h）	$P g s m a x$	储气释放功率上限/（kW·h）
$P g s m i n$	储气释放功率下限/（kW·h）	$P r {}$	事件发生的概率
$P i n m i n$	购气的最小功率/kW	$ξ$	随机变量
$u$	控制变量	$m$	不确定约束条件个数
$f (u, ξ)$	模型的目标	$n$	确定性约束条件个数
$β j$	置信度	$β H /$ $β G$	确定性约束条件个数热能/气能置信度
$f -$	乐观值	$P i n m a x$	购气的最大功率/kW
$δ$	不平衡功率偏差

Table 1

Fig.2

Fig.3

Fig.4

Table 2

Fig.5

Fig.6

References 20

[1]	喻小宝, 郑丹丹, 杨康, 等. “双碳”目标下能源电力行业的机遇与挑战[J]. 华电技术, 2021, 43(6):21-32.
	YU Xiaobao, ZHENG Dandan, YANG Kang, et al. Opportunities and challenges in the energy and power industry under the "dual carbon" goal[J]. Huadian Technology, 2021, 43(6):21-32.
[2]	徐文涛, 张晶, 马红明, 等. 计及多能转化效率的区域综合能源系统协同优化模型研究[J]. 电网与清洁能源, 2021, 37(10):98-106.
	XU Wentao, ZHANG Jing, MA Hongming, et al. Research on the collaborative optimization model of regional comprehensive energy system considering multi energy conversion efficiency[J]. Power Grid and Clean Energy, 2021, 37(10):98-106.
[3]	郭宴秀, 苏建军, 刘洋, 等. 考虑电热交互和共享储能的多综合能源系统运行优化[J]. 中国电力, 2023, 56(4):138-145.
	GUO Yanxiu, SU Jianjun, LIU Yang, et al. Optimization of the operation of a multi integrated energy system considering electric heating interaction and shared energy storage[J]. China Electric Power, 2023, 56(4):138-145.
[4]	胡道明, 李蛟, 杜晓东, 等. 碳中和背景下含氢综合能源系统碳排放和经济性分析[J]. 热能动力工程, 2023, 38(4):111-120.
	HU Daoming, LI Jiao, DU Xiaodong, et al. Carbon emissions and economic analysis of hydrogen containing integrated energy systems in the context of carbon neutrality[J]. Thermal Power Engineering, 2023, 38(4):111-120.
[5]	赵芳正, 王俊江, 陈斌. 考虑绿证-碳配额互认的区域综合能源系统低碳经济调度[J/OL]. 电测与仪表:1-11(2023-04-21)[2023-05-01]. http://kns.cnki.net/kcms/detail/23.1202.TH.20230421.1403.008.html.
	ZHAO Fangzheng, WANG Junjiang, CHEN Bin. Low carbon economic dispatch of regional comprehensive energy systems considering mutual recognition of green certificates and carbon quotas[J/OL]. Electrical Measurement and Instrumentation:1-11(2023-04-21)[2023-05-01]. http://kns.cnki.net/kcms/detail/23.1202.TH.20230421.1403.008.html.
[6]	MAO Y S, WU J K, WANG R D, et al. A collaborative demand-controlled operation strategy for a multi-energy system[J]. IEEE Access, 2021, 9:80571-80581. doi: 10.1109/ACCESS.2021.3083922
[7]	ASL D K, SEIFI A R, RASTEGAR M, et al. Distributed two-level energy scheduling of networked regional integrated energy systems[J]. IEEE Systems Journal, 2022, 16(4):5433-5444. doi: 10.1109/JSYST.2022.3166845
[8]	LV G Y, CAO B, JIA D X, et al. Optimal scheduling of regional integrated energy system considering integrated demand response[J]. CSEE Journal Power of and Energy Systems, 2021(99):1-10.
[9]	GAO Y, AI Q. A novel optimal dispatch method for multiple energy sources in regional integrated energy systems considering wind curtailment[J]. CSEE Journal of Powerand Energy Systems, 2022(99):1-10.
[10]	YAN M Y, HE Y B, SHAHIDEHPOUR M, et al. Coordinated regional-district operation of integrated energy systems for resilience enhancement in natural disasters[J]. IEEE Trans on Smart Grid Actions, 2019, 10(5):4881-4892.
[11]	QI F, SHAHIDEHPOUR M, LI Z Y, et al. A chance-con- strained decentralized operation of multi-area integrated electricity-natural gas systems with variable wind and solar energy[J]. IEEE Transactions on Sustainable Energy, 2020, 11(4):2230-2240. doi: 10.1109/TSTE.5165391
[12]	曾贤强, 张警卫, 王晓兰. 计及多重不确定性及光热电站参与的区域综合能源系统配置与运行联合优化[J]. 高电压技术, 2023, 49(1):353-363.
	ZENG Xianqiang, ZHANG Weiwei, WANG Xiaolan. Joint optimization of regional integrated energy system configuration and operation taking into account multiple uncertainties and participation of solar thermal power stations[J]. High Voltage Technology, 2023, 49(1):353-363.
[13]	林威, 靳小龙, 叶荣. 面向区域综合能源系统的分布式优化调度方法[J]. 电力建设, 2021, 42(11):44-53. doi: 10.12204/j.issn.1000-7229.2021.11.005
	LIN Wei, JIN Xiaolong, YE Rong. A decentralized optimal scheduling method for integrated community energy system[J]. Electric Power Construction, 2021, 42(11):44-53. doi: 10.12204/j.issn.1000-7229.2021.11.005
[14]	杨梅, 周喜超, 魏强, 等. 考虑经济效益目标的源网荷储综合能源系统动态最优能流优化研究[J]. 综合智慧能源, 2022, 44(11):63-69. doi: 10.3969/j.issn.2097-0706.2022.11.009
	YANG Mei, ZHOU Xichao, WEI Qiang, et al. Study on dynamic optimal energy flow of a source-network-load-storage integrated energy system considering its economic benefit[J]. Integrated Intelligent Energy, 2022, 44(11): 63-69. doi: 10.3969/j.issn.2097-0706.2022.11.009
[15]	刘自发, 谭雅之, 李炯, 等. 区域综合能源系统规划关键问题研究综述[J]. 综合智慧能源, 2022, 44(6):12-24. doi: 10.3969/j.issn.2097-0706.2022.06.002
	LIU Zifa, TAN Yazhi, LI Jiong, et al. Review on key points in the planning for a district-level integrated energy system[J]. Integrated Intelligent Energy, 2022, 44(6):12-24. doi: 10.3969/j.issn.2097-0706.2022.06.002
[16]	YU X B, ZHENG D D, ZHOU L Y. Credit risk analysis of electricity retailers based on cloud model and intuitionistic fuzzy analytic hierarchy process[J]. International Journal of Energy Research, 2020, 45(3):4285-4302. doi: 10.1002/er.v45.3
[17]	YU X B, ZHENG D D. Cross-regional integrated energy system scheduling optimization model considering conditional value at risk[J]. International Journal of Energy Research, 2020, 44(7):5564-5581. doi: 10.1002/er.v44.7
[18]	YU X B, GENG Y Q. Complementary configuration research of new combined cooling,heating,and power system driven by renewable energy under energy management modes[J]. Energy Technology, 2019, 7(10):1-11. doi: 10.1002/ente.v7.1
[19]	WU J, DE G F, TAN Z F, et al. Study on bilevel multi-objective collaborative optimization model for integrated energy system considering source-load uncertainty[J]. Energy Science & Engineering, 2021, 9(8):1160-1179.
[20]	TAN Z F, DE G F, LI M L, et al. Combined electricity-heat-cooling-gas load forecasting model for integrated energy system based on multi-task learning and least square support vector machine[J]. Journal of Cleaner Production, 2020, 248:119252. doi: 10.1016/j.jclepro.2019.119252

参数	取值
储能充电效率/%	94
储能放电效率/%	95
储能自放电系数	0.011
储能最大容量/(kW·h)	1 000
自损失系数	0.09
储气罐最大容量/m³	400
补偿系数	2.85