考虑设备变工况特性的园区综合能源系统两阶段规划优化方法研究

doi:10.3969/j.issn.2097-0706.2022.10.001

摘要/Abstract

摘要：

园区综合能源系统通过多能耦合互补和协同优化调度，可以显著提高能源利用率和促进可再生能源消纳，已成为用户侧满足多能供需的一种新的能源利用实现方式。以河北雄安新区某园区作为研究对象，提出了一种考虑设备变工况特性的园区综合能源系统两阶段优化方法：第1阶段基于带有精英保留策略的非支配排序遗传算法（NSGA-II）对园区能源站设备类型及容量进行优化，是一个多目标规划优化问题，其目的是实现经济成本和环境成本的协调优化；第2阶段是一个运行优化问题，针对上一阶段规划得到的多组Pareto前沿解，考虑风、光出力的不确定性，以系统运行经济、环境总成本和用户舒适度为优化目标，利用Benders分解算法优化求解各规划方案对应系统运行时设备出力计划，针对不同规划方案计算其综合成本，结果作为确定系统最佳规划方案的重要参考。算例分析结果表明所设计规划方法可以在第2阶段考虑系统运行的不确定性，充分考虑设备变工况特性实现设备精细化运行模拟，可以有效降低系统运行成本和保障负荷供给可靠性，对指导园区综合能源系统规划更具实用性。

关键词: 园区综合能源系统, 变工况模型, 规划优化, 非支配排序遗传算法, 可再生能源消纳

Abstract:

The community integrated energy system（CIES）can significantly improve energy utilization efficiency and facilitate the consumption of renewable energy through multi-energy complementary and collaborative optimization scheduling.It has become a new energy utilization approach to balance multi-energy supply and demand on user side.Taking a community in Xiong’an New District as the research object，a two-stage optimization approach for the CIES considering off-design conditions is proposed.At the first stage，non-dominated sorting genetic algorithm（NSGA-II）with elite preservation strategy is taken to optimize the equipment type and capacity of the community energy station.It is a multi-objective planning optimization problem，aiming at coordinately scheduling the economic costs and environmental costs.At the second stage，operation optimization is made.Considering the uncertainty of wind and PV outputs and taking the system economy，environmental cost and users’ comfort as optimization objects，the multiple Pareto fronts obtained at the first stage are solved by Benders decomposition optimization method and the optimal outputs of different generators in various plans are obtained.The comprehensive costs of different plans are the important indicator for determining the best planning scheme.Case studies show that the designed planning scheme takes the operation uncertainty into consideration at the second stage，and realizes the refined operation simulation by fully considering the characteristics of equipment under variable working conditions.The approach can effectively reduce the system operating cost and guarantee the reliability of power supply，which is practical for instructing the CIES planning.

Key words: CIES, off-design condition model, planning optimization, NSGA-II, renewable energy consumption

中图分类号:

TK01：TM07

温港成, 石鑫, 张怡, 房方. 考虑设备变工况特性的园区综合能源系统两阶段规划优化方法研究[J]. 综合智慧能源, 2022, 44(10): 1-11.

WEN Gangcheng, SHI Xin, ZHANG Yi, FANG Fang. Research on two-stage planning optimization approach for community integrated energy systems considering off-design conditions[J]. Integrated Intelligent Energy, 2022, 44(10): 1-11.

图/表 14

图1

图2

图3

图4

图5

图6

图7

图8

表1

CIES设备参数

设备	符号	值	设备	符号	值
光伏	$P P V, m a x$ /kW	1 000	风机	$P W T, m a x$ /kW	1 500
	$E 0$ /(W·m^-2)	1		$v i n$ /(m·s^-1)	3
	$α P V$	-0.4		$v o u t$ /(m·s^-1)	25
	T₀/℃	25		$v R -$ /(m·s^-1)	11
	$T c, N O C T$ /℃	48		$v R +$ /(m·s^-1)	12
	$T α, N O C T$ /℃	20	CHP	$P C H P, m a x e$ /kW	1 200
	$η s$	0.15	CHP	$N C H P, m i n h$ /%	30
	$E T, N O C T$ /(W·m^-2)	0.8	GB	$P G B, m a x h$ /kW	1 400
	$f P V$	0.9	GB	$N G B, m i n h$ /%	30
地源热泵	$P H P, m a x h$ /kW	600	蓄电设备	$P E S, m a x$ /kW	300
蓄热设备	$P H S, m a x$ /kW	300

表1

图9

表2

表3

图10

图11

参考文献 22

[1]	BAO Z J, ZHOU Q, YANG Z H, et al. A multi time-scale and multi energy-type co-ordinated microgrid scheduling solution—Part I:Model and methodology[J]. IEEE Transactions on Power Systems, 2015, 30(5):2257-2266. doi: 10.1109/TPWRS.2014.2367127
[2]	施锦月, 许健, 曾博, 等. 基于热电比可调模式的区域综合能源系统双层优化运行[J]. 电网技术, 2016, 40(10): 2959-2966.
	SHI Jinyue, XU Jian, ZENG Bo, et al. A bi-level optimal operation for energy hub based on regulating heat-to-electric ratio mode[J]. Power System Technology, 2016, 40(10): 2959-2966.
[3]	贾宏杰, 王丹, 徐宪东, 等. 区域综合能源系统若干问题研究[J]. 电力系统自动化, 2015, 39(7):198-207.
	JIA Hongjie, WANG Dan, XU Xiandong, et al. Research on some key problems related to integrated energy systems[J]. Automation of Electric Power Systems, 2015, 39(7):198-207.
[4]	郝然, 艾芊, 朱宇超, 等. 基于能源集线器的区域综合能源系统分层优化调度[J]. 电力自动化设备, 2017, 37(6):171-178.
	HAO Ran, AI Qian, ZHU Yuchao, et al. Hierarchical optimal dispatch based on energy hub for regional integrated energy system[J]. Electric Power Automation Equipment, 2017, 37(6):171-178.
[5]	ANA Q, ESTEBAN G, JAMES D M, et al. A multiperiod generalized network flow model of the U.S. integrated energy system:Part I—Model description[J]. IEEE Transactions on Power Systems, 2007, 22(2):829-836. doi: 10.1109/TPWRS.2007.894844
[6]	LIU C, SHAHIDEHPOUR M, FU Y, et al. Security-constrained unit commitment with natural gas transmission constraints[J]. IEEE Transactions on Power Systems, 2009, 24(3):1523-1536. doi: 10.1109/TPWRS.2009.2023262
[7]	周长城, 马溪原, 郭晓斌, 等. 基于主从博弈的工业园区综合能源系统互动优化运行方法[J]. 电力系统自动化, 2019, 43(7):74-80.
	ZHOU Changcheng, MA Xiyuan, GUO Xiaobin, et al. Leader-follower game based optimized operation method for interaction of integrated energy system in industrial park[J]. Automation of Electric Power Systems, 2019, 43(7):74-80.
[8]	赵瑾, 雍静, 郇嘉嘉, 等. 基于长时间尺度的园区综合能源系统随机规划[J]. 电力自动化设备, 2020, 40(3):68-73.
	ZHAO Jin, YONG Jing, HUAN Jiajia, et al. Stochastic planning of park-level integrated energy system based on long time-scale[J]. Electric Power Automation Equipment, 2020, 40(3):68-73.
[9]	XUE Y, GAO Y, LI Y, et al. Optimal coordinated operation of electricity and natural gas distribution networks with power-to-gas facilities[C]// 2018 IEEE Innovative Smart Grid Technologies—Asia(ISGT Asia), 2018,Singapore:294-299.
[10]	杜振东, 徐世泽, 张盈哲, 等. 电-气-储区域综合能源系统协同规划方法研究[J]. 电气自动化, 2021, 43(4):24-27.
	DU Zhendong, XU Shize, ZHANG Yingzhe, et al. Study on collaborative planning method for the electricity-gas storage regional integrated energy system[J]. Electrical Automation, 2021, 43(4):24-27.
[11]	朱海东, 郝浩, 郑剑, 等. 基于冷热电多能互补的园区综合能源系统设计[J]. 华电技术, 2021, 43(4):34-38.
	ZHU Haidong, HAO Hao, ZHENG Jian, et al. Design of integrated energy system for parks based on complementation of cold,heat and electricity[J]. Huadian Technology, 2021, 43(4):34-38.
[12]	孙浩, 傅金洲, 鄢小虎, 等. 区域综合能源仿真优化系统的研制[J]. 华电技术, 2021, 43(4):8-13.
	SUN Hao, FU Jinzhou, YAN Xiaohu, et al. Research and development of integrated community energy simulation-optimization system[J]. Huadian Technology, 2021, 43(4):8-13.
[13]	朱少杰, 刘皓明, 唐宇, 等. 含多个能源站的区域综合能源系统建模及协同优化运行策略[J]. 电力需求侧管理, 2019, 21(4):60-66.
	ZHU Shaojie, LIU Haoming, TANG Yu, et al. Modeling and collaborative optimal operation strategy for multiple energy stations of regional integrated energy system[J]. Power Demand Side Management, 2019, 21(4):60-66.
[14]	魏震波, 郭毅, 魏平桉, 等. 基于IGDT的电-气互联综合能源系统多目标扩展规划模型[J]. 高电压技术, 2022, 48(2):526-537.
	WEI Zhenbo, GUO Yi, WEI Ping’an, et al. IGDT-based multi-objective expansion planning model for integrated natural gas and electric power systems[J]. High Voltage Engineering, 2022, 48(2):526-537.
[15]	周步祥, 夏海东, 臧天磊. 考虑能量梯级利用的园区综合能源系统站网协同规划[J]. 电力自动化设备, 2022, 42(1):20-27.
	ZHOU Buxiang, XIA Haidong, ZANG Tianlei. Station and network coordinated planning of park integrated energy system considering energy cascade utilization enhanced publishing[J]. Electric Power Automation Equipment, 2022, 42(1):20-27.
[16]	刘洪, 郑楠, 葛少云, 等. 考虑负荷特性互补的综合能源系统站网协同规划[J]. 中国电机工程学报, 2021, 41(1):52-64,397.
	LIU Hong, ZHENG Nan, GE Shaoyun, et al. Station and network coordinated planning of integrated energy systems considering complementation of load characteristic[J]. Proceedings of the CSEE, 2021, 41(1):52-64,397.
[17]	秦羽飞, 葛磊蛟, 王波. 能源互联网群体智能协同控制与优化技术[J]. 华电技术, 2021, 43(9):1-13.
	QIN Yufei, GE Leijiao, WANG Bo. Swarm intelligence collaborative control and optimization technology of Energy Internet[J]. Huadian Technology, 2021, 43(9):1-13.
[18]	范宏, 袁倩倩, 邓剑. 多区域综合能源系统的两阶段容量优化配置方法[J]. 现代电力, 2020, 37(5):441-447.
	FAN Hong, YUAN Qianqian, DENG Jian. A two-stage optimal capacity configuration of multi-region integrated energy system[J]. Modern Electric Power, 2020, 37(5):441-447.
[19]	陈建华, 吴文传, 张伯明, 等. 安全性与经济性协调的鲁棒区间风电调度方法[J]. 中国电机工程学报, 2014, 34(7):1033-1040.
	CHEN Jianhua, WU Wenchuan, ZHANG Boming, et al. A robust interval wind power dispatch method considering the tradeoff between security and economy[J]. Proceedings of the CSEE, 2014, 34(7):1033-1040.
[20]	卢志刚, 杨宇, 耿丽君, 等. 基于Benders分解法的电热综合能源系统低碳经济调度[J]. 中国电机工程学报, 2018, 38(7):1922-1934,2208.
	LU Zhigang, YANG Yu, GENG Lijun, et al. Low-carbon economic dispatch of the integrated electrical and heating systems based on benders decomposition[J]. Proceedings of the CSEE, 2018, 38(7):1922-1934,2208.
[21]	尹硕, 郭兴五, 燕景, 等. 考虑高渗透率和碳排放约束的园区综合能源系统优化运行研究[J]. 华电技术, 2021, 43(4): 1-7.
	YIN Shuo, GUO Xingwu, YAN Jing, et al. Study on optimized operation on integrated energy system in parks with high permeability and carbon emission constraints[J]. Huadian Technology, 2021, 43(4): 1-7.
[22]	梁昌勇, 戚筱雯, 丁勇, 等. 一种基于TOPSIS的混合型多属性群决策方法[J]. 中国管理科学, 2012, 20(4):109-117.
	LIANG Changyong, QI Xiaowen, DING Yong, et al. A hybrid multi-criteria group decision making with TOPSIS method[J]. Chinese Journal of Management Science, 2012, 20(4):109-117.

方案	容量/kW								年投资成本/ 万元	年运行成本/ 万元	年碳排放量/ t
方案	CHP	PV	WT	EB	GSHP	GB	ES	HS	年投资成本/ 万元	年运行成本/ 万元	年碳排放量/ t
1	1 100	960	1 240	0	1 120	700	900	400	194.486	268.214 8	1 892.84
2	1 080	900	1 170	0	1 050	750	740	0	177.296	278.064 5	1 954.35
3	1 020	700	1 010	0	1 000	830	420	0	158.728	286.928 6	2 047.87

方案	类型	供电占比/%				供热占比/%			购电成本/元	购气成本/元	运行成本/元	碳排放成本/元	总成本/ 元	年综合成本/万元
方案	类型	CHP	WT	PV	电网	CHP	GB	HS	购电成本/元	购气成本/元	运行成本/元	碳排放成本/元	总成本/ 元	年综合成本/万元
1	高峰日	51.0	33.0	5.7	10.4	45.1	11.1	43.7	2 171.1	20 180.2	1 254.4	631.0	24 236.6	472.16
	工作日	49.2	33.5	5.8	11.6	43.6	11.5	44.8	2 385.1	19 333.6	1 229.8	616.5	23 565.0
	休息日	44.4	36.7	6.4	12.5	37.0	14.5	48.5	2 322.2	16 596.7	1 149.1	540.0	20 608.1
2	高峰日	53.4	31.5	5.4	9.7	46.7	13.0	40.4	2 033.8	21 138.3	1 244.3	650.5	25 066.8	464.02
	工作日	51.9	32.0	5.5	10.6	45.4	13.4	41.2	2 166.0	20 361.3	1 220.9	635.7	24 384.0
	休息日	47.6	34.8	6.1	11.5	39.1	16.2	44.7	2 103.2	17 680.1	1 139.1	560.7	21 483.2
3	高峰日	57.1	27.5	4.3	11.2	49.3	13.2	37.5	2 491.3	22 252.8	1 206.2	691.6	26 641.9	467.97
	工作日	56.0	28.0	4.3	11.7	48.4	13.6	38.0	2 461.4	21 580.4	1 185.0	676.4	25 903.1
	休息日	52.4	31.1	4.8	11.7	42.3	16.7	41.0	2 118.0	18 988.5	1 110.2	596.7	22 813.5