楼宇型综合能源服务系统智能优化运行研究

doi:10.3969/j.issn.2097-0706.2022.02.007

摘要/Abstract

摘要：

随着“双碳”目标的推进落实,综合能源项目加速推广。针对楼宇型综合能源服务系统耦合性强、边界约束条件多、用户负荷变化大、运行方式复杂等特点,提出了一种智能优化运行控制算法模型。通过采集源、网、负荷侧数据,考虑天气的影响,建立负荷预测模型;根据运行边界条件,确立多目标优化函数,在各种能源需求、能源价格的约束下,基于混合粒子群算法实现综合能源服务系统智能优化运行。以某科技园区楼宇型综合能源服务系统为例,采用智能优化运行方案后,能源综合效率提升了6.51百分点,自耗电减少了2.24%,有效提高了系统运行的经济性。

关键词: 综合能源服务, 智能优化运行, 混合粒子群算法, 负荷预测, 能源综合效率, 碳中和

Abstract:

With the implementation of the goals of carbon peak and carbon neutrality, the integrated energy projects are booming.To deal with the strong coupling, numerous boundary constraints, volatile user load and complex operation modes of integrated energy service systems, an intelligent optimized operation and control algorithm is proposed for the systems. A load forecasting model is established by taking the collected data from source, network and load side as well as weather conditions into consideration. According to the boundary conditions,a multi-objective optimization function is established. It realizes the optimized operation of building-type integrated energy service systems under the constraints of demand and price by taking mixed particle swarm optimization algorithm. Taking a demonstrative building-type integrated energy service system in an industrial park as an example, the economy of the system has been effectively improved by the optimized operation strategy with a 6.51 per cent increase of the comprehensive energy efficiency and a 2.24% decrease of the auxiliary power ratio.

Key words: integrated energy service, intelligent optimized operation, mixed particle swarm optimization algorithm, load forecasting, comprehensive energy efficiency, carbon neutrality

中图分类号:

TK01⁺8

张爱平, 赵利兴, 刘静. 楼宇型综合能源服务系统智能优化运行研究[J]. 综合智慧能源, 2022, 44(2): 42-48.

ZHANG Aiping, ZHAO Lixing, LIU Jing. Research on optimized operation of building-type integrated energy service systems[J]. Integrated Intelligent Energy, 2022, 44(2): 42-48.

图/表 9

图1

图2

图3

图4

图5

图6

图7

图8

表1

参考文献 20

[1]	张运洲, 代红才, 吴潇雨, 等. 中国综合能源服务发展趋势与关键问题[J]. 中国电力, 2021,54(2):1-9.
	ZHANG Yunzhou, DAI Hongcai, WU Xiaoyu, et al. Development trends and key issues of China's integrated energy services[J]. Electric Power, 2021,54(2):1-9.
[2]	张宁, 薛美美, 吴潇雨, 等. 国内外能源转型比较与启示[J]. 中国电力, 2021,54(2):113-115.
	ZHANG Ning, XUE Meimei, WU Xiaoyu, et al. Comparison and enlightenment of energy transition between domestic and international[J]. Electric Power, 2021,54(2):113-115.
[3]	韩峰, 张衍国, 严矫平, 等. 综合能源服务业务和合作模式[J]. 华电技术, 2019,41(11):1-4.
	HAN Feng, ZHANG Yanguo, YAN Jiaoping, et al. Integrated energy service and cooperation modes[J]. Huadian Technology, 2019,41(11):1-4.
[4]	张运洲. 我国综合能源服务一体化发展模式研究[J]. 中国电力企业管理, 2019(13):37-41.
[5]	何鑫, 尹璐, 涂彬, 等. 我国综合能源服务的现状与发展趋势[J]. 中国电力企业管理, 2020(11):51-53.
[6]	FU Y, LIN H Y, MA C P, et al. Effects of uncertainties on the capacity and operation of an integrated energy system[J]. Sustainable Energy Technologies and Assessments, 2021,48.
[7]	LU Q, GUO Q S, ZENG W. Optimization scheduling of an integrated energy service system in community under the carbon trading mechanism: A model with reward-penalty and user satisfaction[J]. Journal of Cleaner Production, 2021,323:129171. doi: 10.1016/j.jclepro.2021.129171
[8]	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.
[9]	YOU M, WANG Q, SUN H, et al. Digital twins based day-ahead integrated energy system scheduling under load and renewable energy uncertainties[J]. Applied Energy, 2022,305:117899. doi: 10.1016/j.apenergy.2021.117899
[10]	LI Peng, ZHANG Fan, MA Xiyuan, et al. Multi-Time scale economic optimization dispatch of the park integrated energy system[J/OL]. Frontiers in Energy Research, 2021(9):743619[2021-09-28]https://www.frontiersin.org/articles/10.3389/fenrg.2021.743619/full.
[11]	隋军, 金红光, 汝林谋, 等. 分布式供能及其系统集成[J]. 科技导报, 2007,25(24):58-62.
	SUI Jun, JIN Hongguang, LIN Rumou, et al. Distributed energy system and its integration[J]. Science & Technology Review, 2007,25(24):58-62.
[12]	朱晓红, 解蕾, 张延迟, 等. 天然气分布式供能系统的配置与优化[J]. 节能技术, 2008,26(1):87-91.
	ZHU Xiaohong, XIE Lei, ZHANG Yanchi, et al. Configuration and optimization of the distributed power system using natural gas as the fuel in advanced training center of CPIP[J]. Energy Conservation Technology, 2008,26(1):87-91.
[13]	俞新详, 翁一武, 杨锦成. 分布式供能系统控制的设计和应用[J]. 热力发电, 2006,10(10):11-16.
	YU Xinxiang, WENG Yiwu, YANG Jincheng. Design and application of control system for distributed energy supply[J]. Thermal Power Generation, 2006,10(10):11-16.
[14]	丁明, 包敏, 吴红斌. 分布式供能系统的经济调度[J]. 电力科学与技术学报, 2008,23(1):13-17.
	DING Ming, BAO Min, WU Hongbin. Economic dispatching on distributed energy supply system[J]. Journal of Electric Power Science and Technology, 2008,23(1):13-17.
[15]	陈卉, 刘颖, 赵龙生. 楼宇型分布式能源系统在数据中心的应用[J]. 华电技术, 2021,43(3):76-81.
	CHEN Hui, LIU Ying, ZHAO Longsheng. Application of building-type distributed energy systems for data centers[J]. Huadian Technology, 2021,43(3):76-81.
[16]	乔克, 吴培, 田传波, 等. 面向能源互联网的综合能源仪控平台一体化设计与研究[J]. 仪器仪表用户, 2021,28(7):101-106.
	QIAO Ke, WU Pei, TIAN Chuanbo, et al. Integrated design and research of integrated energy instrument and control platform for energy internet[J]. Instrumentation Customer, 2021,28(7):101-106.
[17]	赵鑫, 郑文禹, 侯智华, 等. 基于粒子群优化算法的多能互补系统经济调度研究[J]. 华电技术, 2021,43(4):14-20.
	ZHAO Xin, ZHENG Wenyu, HOU Zhihua, et al. Research on economic dispatch of multi-energy complementary system based on particle swarm optimization[J]. Huadian Technology, 2021,43(4):14-20.
[18]	侯云鹤, 鲁丽娟, 熊信艮, 等. 改进粒子群算法及其在电力系统经济负荷分配中的应用[J]. 中国电机工程学报, 2004,24(7):95-100.
	HOU Yunhe, LU Lijuan, XIONG Xingen, et al. Enhanced particle swarm optimization algorithm and its application on economic dispatch of power systems[J]. Proceeding of the CSEE, 2004,24(7):95-100.
[19]	王哲, 王喆, 曾伟. 基于改进粒子群算法的主动配电网有功无功协调优化控制[J]. 制造业自动化, 2021,43(7):105-108.
	WANG Zhe, WANG Zhe, ZENG Wei. Optimal control active power and reactive power coordination in active distribution network based on improved particle swarm optimization[J]. Manufacturing Automation, 2021,43(7):105-108.
[20]	徐岩, 靳伟佳, 朱晓荣. 基于遗传粒子群算法的光伏并网逆变器参数辨识[J]. 太阳能学报, 2021,42(7):103-109.
	XU Yan, JIN Weijia, ZHU Xiaorong. Parameter identification of photovoltaic grid-connected inverter based on GAPSO[J]. Acta Energiae Solaris Sinica, 2021,42(7):103-109.

年份	发电总量/(GW·h)	供能总量/GJ	总耗气量/m³	自耗电/(GW·h)	能源综合效率/%
2019	21.584 3	107 141	6.578 6×10⁶	4.081 9	76.59
2021	22.013 5	118 657	6.581 2×10⁶	3.670 7	83.10