Research on load regulation strategy of integrated energy systems considering meteorological factors and time-of-use tariffs

doi:10.3969/j.issn.2097-0706.2024.01.003

Abstract

Abstract:

Being affected by meteorological factors，the power consumption of an integrated energy system （IES） coupling multiple sources is prone to surge during peak load periods， resulting in intensifying contradictions between power supply and demand. Meanwhile， since an IES need to convert different types of energy and power equipment， such as ice storage air conditioner， with its energy storage unit， the system's energy cost is affected by time-of-use tariffs. Taking the cooling and electricity demands of an IES during the peak period as regulation objectives， a two-stage progressive regulation strategy including self-regulation on cooling load and active control on cooling load is proposed， aiming at expanding the balance scope of the supply and demand from a multi-energy system and reducing the system's energy cost. The regulation period is selected by fuzzy C-means clustering， comprehensively considering the influence of meteorological factors and time-of-use prices. In the first stage， the load regulation model with the optimal temperature and humidity， and the lowest demand on cold energy as objectives is constructed. In the second stage， a cooling output active control strategy with the lowest energy cost and the minimal operating energy consumption as its objectives is proposed. Finally， the method is verified by the simulation of an IES for a campus. The multi-objective models are solved by the ε-constraint method and multi-dimensional preference linear programming. The operation energy consumption and energy cost varying with the comfort before and after the regulation on peak-period power consumption are compared. The calculation results show that the proposed method can reduce the load demand by about 13% and the cost by about 1.9%， while satisfying the comfort of energy users. It can effectively alleviate the contradiction between the supply and demand of multi-energy systems， and enhance the flexibility of system operation.

Key words: integrated energy system, load regulation, multi-objective optimization, comprehensive meteoro-logical factors, time-of-use tariff

CLC Number:

TK019：TM714

ZHANG Li, JIN Li, REN Juguang, LIU Xiaobing. Research on load regulation strategy of integrated energy systems considering meteorological factors and time-of-use tariffs[J]. Integrated Intelligent Energy, 2024, 46(1): 18-27.

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References 31

[1]	杨颖, 刘友波, 黄媛, 等. 电能替代下考虑需求响应的综合园区风光储配置方法[J]. 电力建设, 2021, 42(10):9-18. doi: 10.12204/j.issn.1000-7229.2021.10.002
	YANG Ying, LIU Youbo, HUANG Yuan, et al. Allocation of wind-solar-battery under electric energy substitution in integrated park considering demand response[J]. Electric Power Construction, 2021, 42(10):9-18. doi: 10.12204/j.issn.1000-7229.2021.10.002
[2]	陈忠华, 高振宇, 陈嘉敏, 等. 考虑不确定性因素的综合能源系统协同规划研究[J]. 电力系统保护与控制, 2021, 49(8):32-40.
	CHEN Zhonghua, GAO Zhenyu, CHEN Jiamin, et al. Research on cooperative planning of an integrated energy system considering uncertainty[J]. Power System Protection and Control, 2021, 49(8):32-40.
[3]	UPSHAW C R, RHODES J D, WEBBER M E. Modeling electric load and water consumption impacts from an integrated thermal energy and rainwater storage system for residential buildings in Texas[J]. Applied Energy, 2017, 186:492-586. doi: 10.1016/j.apenergy.2016.02.130
[4]	曾艾东, 邹宇航, 郝思鹏, 等. 考虑阶梯式碳交易机制的园区工业用户综合需求响应策略[J]. 高电压技术, 2022, 48(11):4352-4363.
	ZENG Aidong, ZOU Yuhang, HAO Sipeng, et al. Comprehensive demand response strategy of industrial users in the park considering the stepped carbon trading mechanism[J] High Voltage Engineering, 2022, 48(11): 4352-4363.
[5]	杨海柱, 李梦龙, 江昭阳, 等. 考虑需求侧电热气负荷响应的区域综合能源系统优化运行[J]. 电力系统保护与控制, 2020, 48(10):30-37.
	YANG Haizhu, LI Menglong, JIANG Zhaoyang, et al. Optimal operation of regional integrated energy system considering demand side electricity heat and natural-gas loads response[J]. Power System Protection and Control, 2020, 48(10): 30-37.
[6]	李政洁, 撖奥洋, 周生奇, 等. 计及综合需求响应的综合能源系统优化调度[J]. 电力系统保护与控制, 2021, 49(21):36-42.
	LI Zhengjie, HAN Aoyang, ZHOU Shengqi, et al. Optimization of an integrated energy system considering integrated demand response[J]. Power System Protection and Control, 2021, 49(21): 36-42.
[7]	REN Juguang, ZHANG Li, JIN Li, et al. Operation optimization and analysis of office building integrated energy system considering comprehensive demand response of heat and electricity[C]// Proceedings of 2022 Asian Conference on Frontiers of Power and Energy, 2022: 237-241.
[8]	任炬光, 张力, 金立, 等. 考虑可再生能源消纳的建筑综合能源系统日前经济调度模型[J]. 工程科学与技术, 2023, 55(2):160-170.
	REN Juguang, ZHANG Li, JIN Li, et al. Day-ahead economic dispatch model of building integrated energy systems considering the renewable energy consumption[J]. Advanced Engineering Sciences, 2023, 55(2): 160-170.
[9]	唐文虎, 申悦晴, 钱瞳, 等. 双碳目标下城市楼宇群能源系统灵活性量化分析与调控技术研究现状与展望[J]. 高电压技术, 2022, 48(9):3423-3436.
	TANG Wenhu, SHEN Yueqing, QIAN Tong, et al. Research review and prospects of quantitative analysis and regulation technique for flexible resources in urban energy system embedded with building clusters under dual carbon target[J]. Advanced Engineering Sciences, 2022, 48(9): 3423-3436.
[10]	刘蓉晖, 马天天, 高远, 等. 考虑需求侧协同响应的社区综合能源系统低碳经济调度[J]. 上海电力大学学报, 2020, 36(5):421-430.
	LIU Ronghui, MA Tiantian, GAO Yuan, et al. Low carbon economic dispatch of community integrated energy system based on demand side cooperative response J]. Journal of Shanghai University of Electric Power, 2020, 36(5): 421-430.
[11]	张宏业, 吴杰康, 蔡锦健, 等. 考虑空调负荷和柔性热负荷响应的综合能源系统储能鲁棒优化配置[J]. 电网技术, 2022, 46(7):2733-2744.
	ZHANG Hongye, WU Jie, CAI Jinjian, et al. Robust optimal allocation of energy storage in integrated energy system considering demand response of air conditioning load and flexible heating load[J]. Power System Technology, 2022, 46(7): 2733-2744.
[12]	周丽红, 于浩, 李鹏. 考虑居民热负荷主动需求响应的园区综合能源系统分布式优化运行方法[J]. 电网技术, 2023, 47(5):1989-2000.
	ZHOU Lihong, YU Hao, LI Peng. Distributed optimal operation method of park-level integrated energy system considering active demand response of residential heat loads[J]. Power System Technology: 2023, 47(5):1989-2000.
[13]	孙轶恺, 漆淘懿, 张利军, 等. 市场环境下含冰蓄冷空调的综合能源系统优化运行[J]. 南方电网技术, 2022, 16(4): 95-104.
	SUN Yikai, QI Taoyi, ZHANG Lijun, et al. Optimal operation of integrated energy system including ice-storage air-conditioning in power market[J]. Southern Power System Technology, 2022, 16(4): 95-104.
[14]	万典典, 刘智伟, 陈语, 等. 基于DDPG算法的冰蓄冷空调系统运行策略优化[J]. 控制工程, 2022, 29(3):441-446.
	WAN Diandian, LIU Zhiwei, CHEN Yu, et al. Operation strategy optimization of ice storage air conditioning system based on DDPG algorithm[J]. Control Engineering of China, 2022, 29(3): 441-446.
[15]	赵阳, 李兵, 刘文, 等. 计及冰蓄冷的冷热电联供能源系统非线性优化调度方法[J]. 节能, 2022, 41(5):47-52.
	ZHAO Yang, LI Bing, LIU Wen, et al. Nonlinear optimal scheduling method for cooling heating and power system considering ice storage[J]. Energy Conservation, 2022, 41(5):47-52.
[16]	ASU. Campus metabolism[EB/OL].(2021-03-21) [2023-08-05]. https://cm.asu.edu/.
[17]	任延欢. 基于群智能的冰蓄冷空调负荷预测及运行优化研究[D]. 西安: 西安建筑科技大学, 2020.
	REN Yanhuan. Research on ice storage air conditioning load forecastingand strategy optimization based on insect intelligent[D]. Xi'an: Xi'an University of Architecture and Technology, 2020.
[18]	杨迎军. 基于负荷预测的冰蓄冷空调系统的运行优化研究[D]. 西安: 西安建筑科技大学, 2016.
	YANG Yingjun. Optimal operation research based on cooling load prediction in storage air conditioning system[D]. Xi'an: Xi'an University of Architecture and Technology, 2016.
[19]	金立, 张力, 任炬光, 等. 针对气象敏感型综合能源负荷的收敛交叉映射因果关系分析[J]. 综合智慧能源, 2023, 45(1):23-30. doi: 10.3969/j.issn.2097-0706.2023.01.003
	JIN Li, ZHANG Li, REN Juguang, et al. Causality analysis of climate sensitive loads in integrated energy system based on convergence cross-mapping method[J]. Integrated Intelligent Energy, 2023, 45(1):23-30. doi: 10.3969/j.issn.2097-0706.2023.01.003
[20]	金立, 张力, 唐杨, 等. 考虑温湿指数与耦合特征的综合能源负荷短期预测[J]. 综合智慧能源, 2023, 45(7):70-77. doi: 10.3969/j.issn.2097-0706.2023.07.008
	JIN Li, ZHANG Li, TANG Yang, et al. Short-term prediction on integrated energy loads considering temperature-humidity index and coupling characteristics[J]. Integrated Intelligent Energy, 2023, 45(7):70-77. doi: 10.3969/j.issn.2097-0706.2023.07.008
[21]	NADERI S, HESLOP S, DONG C, et al. Consumer cost savings, improved thermal comfort, and reduced peak air conditioning demand through pre-cooling in Australian housing[J]. Energy & Buildings, 2022, 271:112172.
[22]	毕锐, 丁明, 徐志成, 等. 基于模糊C均值聚类的光伏阵列故障诊断方法[J]. 太阳能学报, 2016, 37(3):730-736.
	BI Rui, DING Ming, XU Zhicheng, et al. PV array fault diagnosis based on FCM[J]. Acta Energiae Solaris Sinica, 2016, 37(3):730-736.
[23]	王煜尘, 窦银科, 孟润泉, 等. 基于模糊C均值聚类-变分模态分解和群智能优化的多核神经网络短期负荷预测模型[J]. 高电压技术, 2022, 48(4):1308-1319.
	WANG Yuchen, DOU Yinke, et al. Forecasting model for multicore neural network short-term load based on fuzzy C-mean clustering-variational modal decomposition and chaotic swarm intelligence optimization[J]. High Voltage Engineering, 2022, 48(4):1308-1319.
[24]	岳本江, 郭玉涛, 李强, 等. 2000—2018年神东矿区气候舒适度评价[J]. 陕西林业科技, 2022, 50(2):44-49,55.
	YUE Benjiang, GUO Yutao, LI Qiang, et al. Evaluation of climate comfort in Shendong mining area during 2000—2018[J]. Shanxi Forest Science and Technology, 2022, 50(2):44-49,55.
[25]	孙悦, 吴琼, 李琦芬, 等. 计及成本与能耗的冰蓄冷系统多目标运行优化[J]. 低温与超导, 2021, 49(8):58-65.
	SUN Yue, WU Qiong, LI Qifen, et al. Multi-objective operation optimization of ice storage air conditioning system considering cost and energy consumption[J]. Cryogenics/Refrigeration, 2021, 49(8):58-65.
[26]	叶婧, 林涛, 徐遐龄, 等. 考虑稳态频率约束的含高渗透率风电的孤立电网机组组合[J]. 高电压技术, 2018, 44(4):1311-1318.
	YE Jing, LIN Tao, XU Xialing, et al. Research on unit commitment considering steady state frequency constraints of isolated grids with high permeability wind power[J]. High Voltage Engineering, 2018, 44(4):1311-1318.
[27]	NSRDB. NSRDB data viewer[EB/OL].(2021-04-10) [2021-08-13]. https://maps.nrel.gov/nsrdb-viewer/.
[28]	UES. UniSource energy services[EB/OL].(2022-09-06)[2023-08-13]. https://www.uesaz.com/time-of-use/.
[29]	中华人民共和国住房和城乡建设部. 民用建筑供暖通风与空气调节设计规范: GB50736—2012[S]. 北京: 中国建筑工业出版社, 2012.
[30]	周天宇, 王升, 张治平, 等. 永磁同步变频离心式冰蓄冷双工况机组运行能效分析[J]. 制冷与空调, 2019, 19(8):69-74,79.
	ZHOU Tianyu, WANG Sheng, ZHANG Zhiping, et al. Analysis on operational energy efficiency of dual-mode permanent-magnetic synchronous frequency-convertible centrifugal water chiller for ice storage[J] Refrigeration and Air-conditioning, 2019, 19(8):69-74,79.
[31]	REN Z G, CHEN D. Modelling study of the impact of thermal comfort criteria on housing energy use in Australia[J]. Applied Energy, 2018, 210:152-166. doi: 10.1016/j.apenergy.2017.10.110

设备	参数	额定耗电功率/MW
双工况离心式冷水机	制冷工况:45.503 MW·h	7.460
双工况离心式冷水机	制冰工况:30.38 MW·h	7.009
冷却水泵	q_V=930 m³/h，H=30 m	1.280
循环水泵	q_V=950 m³/h，H=40 m	1.280
蓄冰槽	433.554 MW·h

场景	运行策略	THI偏离量	是否满足舒适度	冷负荷削减量/（MW·h）
Ⅰ	冷负荷最低	19.2	是	33.285
	舒适度最高	0.0	是	10.773
	多目标调控	9.4	是	22.342
Ⅱ	冷负荷最低	19.2	是	33.285
Ⅲ	冷负荷最低	24.4	是	39.965
	接近中性温度	6.5	否	0
	多目标调控	16.3	否	19.553

场景	运行策略	运行能耗/（MW·h）	用能成本/美元	调控时段冷负荷替代量/（MW·h）
Ⅰ	运行能耗最低	270.952	25 143	12.693
	用能成本最低	287.359	24 173	173.286
	多目标调控	277.514	24 659	83.739
Ⅱ	用能成本最低	285.317	23 895	170.574