Journal of Shanghai Jiaotong University >
Optimal Dispatch of Integrated Energy System Based on Flexibility of Thermal Load
Received date: 2021-12-28
Revised date: 2022-02-18
Accepted date: 2022-04-02
Online published: 2023-03-28
The flexibility of thermal loads of buildings is a valuable balancing resource for operation of the heat and electricity integrated energy system (HE-IES). Considering the characteristics of large scale and small single load capacity of the themal load, the non-intrusive data-driven method has become an effective means to quantify the flexibility of building thermal load. However, due to the inaccuracy of the model or the lack of data, this method inevitably produces errors and brings epistemic uncertainty to the optimal dispatch of the HE-IES. An optimal dispatch model of the HE-IES that is compatible with the epistemic uncertainty of demand flexibility in the thermal loads of buildings is proposed. First, a data-driven flexible demand assessment method for building thermal load is described. The measurement errors are modeled as epistemic uncertainty and the multiple error sources are combined by using the D-S evidence theory. Then, the representative scenarios are selected to represent the epistemic uncertainty of the demand flexibility based Latin hypercube sampling(LHS) method, and the scenarios are reduced by the fuzzy clustering method. Finally, the representative scenarios are embedded in the coordinated and optimized dispatch of the HE-IES to realize the comprehensive consideration of the thermal load flexibility and related epistemic uncertainty of the building. The results demonstrate that considering the epistemic uncertainties of the thermal load demand is crucial for reducing the wind power curtailments and improving the operational flexibility of HE-IES.
HU Bo, CHENG Xin, SHAO Changzheng, HUANG Wei, SUN Yue, XIE Kaigui . Optimal Dispatch of Integrated Energy System Based on Flexibility of Thermal Load[J]. Journal of Shanghai Jiaotong University, 2023 , 57(7) : 803 -813 . DOI: 10.16183/j.cnki.jsjtu.2021.534
| [1] | 夏越, 陈颖, 杜松怀, 等. 综合能源系统多时间尺度动态时域仿真关键技术[J]. 电力系统自动化, 2022, 46(10): 97-110. |
| [1] | XIA Yue, CHEN Ying, DU Songhuai, et al. Key technologies for multi-time-scale dynamic time-domain simulation of integrated energy system[J]. Automation of Electric Power Systems, 2022, 46(10): 97-110. |
| [2] | 武梦景, 万灿, 宋永华, 等. 含多能微网群的区域电热综合能源系统分层自治优化调度[J]. 电力系统自动化, 2021, 45(12): 20-29. |
| [2] | WU Mengjing, WAN Can, SONG Yonghua, et al. Hierarchical autonomous optimal dispatching of district integrated heating and power system with multi-energy microgrids[J]. Automation of Electric Power Systems, 2021, 45(12): 20-29. |
| [3] | LU S, GU W, MENG K, et al. Economic dispatch of integrated energy systems with robust thermal comfort management[J]. IEEE Transactions on Sustainable Energy, 2021, 12(1): 222-233. |
| [4] | DAI Y H, CHEN L, MIN Y, et al. Dispatch model of combined heat and power plant considering heat transfer process[J]. IEEE Transactions on Sustainable Energy, 2017, 8(3): 1225-1236. |
| [5] | 李健, 李雪峰, 张娜, 等. 计及储热备用效益的电热综合能源系统优化调度模型[J]. 电网技术, 2021, 45(10): 3851-3859. |
| [5] | LI Jian, LI Xuefeng, ZHANG Na, et al. Optimal dispatch model of electricity-heat integrated energy system considering reserved benefits of heat storage[J]. Power System Technology, 2021, 45(10): 3851-3859. |
| [6] | 崔杨, 姜涛, 仲悟之, 等. 考虑风电消纳的区域综合能源系统源荷协调经济调度[J]. 电网技术, 2020, 44(7): 2474-2483. |
| [6] | CUI Yang, JIANG Tao, ZHONG Wuzhi, et al. Source-load coordination economic dispatch method for regional integrated energy system considering wind power accommodation[J]. Power System Technology, 2020, 44(7): 2474-2483. |
| [7] | 孙娟, 卫志农, 孙国强, 等. 计及P2H的电-热互联综合能源系统概率能量流分析[J]. 电力自动化设备, 2017, 37(6): 62-68. |
| [7] | SUN Juan, WEI Zhinong, SUN Guoqiang, et al. Analysis of probabilistic energy flow for integrated electricity-heat energy system with P2H[J]. Electric Power Automation Equipment, 2017, 37(6): 62-68. |
| [8] | DAI Y H, CHEN L, MIN Y, et al. Dispatch model for CHP with pipeline and building thermal energy storage considering heat transfer process[J]. IEEE Transactions on Sustainable Energy, 2019, 10(1): 192-203. |
| [9] | NIU J D, TIAN Z, LU Y K, et al. Flexible dispatch of a building energy system using building thermal storage and battery energy storage[J]. Applied Energy, 2019, 243: 274-287. |
| [10] | CHEN Y B, XU P, CHEN Z, et al. Experimental investigation of demand response potential of buildings: Combined passive thermal mass and active storage[J]. Applied Energy, 2020, 280: 115956. |
| [11] | LI W L, YANG L, JI Y, et al. Estimating demand response potential under coupled thermal inertia of building and air-conditioning system[J]. Energy and Buildings, 2019, 182: 19-29. |
| [12] | XUE P N, JIANG Y, ZHOU Z G, et al. Multi-step ahead forecasting of heat load in district heating systems using machine learning algorithms[J]. Energy, 2019, 188: 116085. |
| [13] | 朱继忠, 董瀚江, 李盛林, 等. 数据驱动的综合能源系统负荷预测综述[J]. 中国电机工程学报, 2021, 41(23): 7905-7924. |
| [13] | ZHU Jizhong, DONG Hanjiang, LI Shenglin, et al. Review of data-driven load forecasting for integrated energy system[J]. Proceedings of the CSEE, 2021, 41(23): 7905-7924. |
| [14] | QI N, CHENG L, XU H L, et al. Smart meter data-driven evaluation of operational demand response potential of residential air conditioning loads[J]. Applied Energy, 2020, 279: 115708. |
| [15] | YAN L, LIU M. A simplified prediction model for energy use of air conditioner in residential buildings based on monitoring data from the cloud platform[J]. Sustainable Cities and Society, 2020, 60: 102194. |
| [16] | TENG Y, SUN P, OUYANG L, et al. Optimal operation strategy for combined heat and power system based on solid electric thermal storage boiler and thermal inertia[J]. IEEE Access, 2019, 7: 180761-180770. |
| [17] | 王琎. 基于数据驱动的区域供热系统热负荷预测方法研究[D]. 天津: 天津理工大学, 2020. |
| [17] | WANG Jin. Research on heat load forecasting method of district heating system based on data drive[D]. Tianjin: Tianjin University of Technology, 2020. |
| [18] | 刘尔佳. 数据驱动下的综合能源预测及需求响应优化运行研究[D]. 北京: 华北电力大学(北京), 2021. |
| [18] | LIU Erjia. Data-driven integrated energy forecasting and research on optimal operation of integrated demand response[D]. Beijing: North China Electric Power University, 2021. |
| [19] | CHEN X Y, WANG J X, XIE J, et al. Demand response potential evaluation for residential air conditioning loads[J]. IET Generation, Transmission & Distribution, 2018, 12(19): 4260-4268. |
| [20] | LI Z G, WU W C, WANG J H, et al. Transmission-constrained unit commitment considering combined electricity and district heating networks[J]. IEEE Transactions on Sustainable Energy, 2016, 7(2): 480-492. |
| [21] | 邵腾. 东北严寒地区乡村民居节能优化研究[D]. 哈尔滨: 哈尔滨工业大学, 2018. |
| [21] | SHAO Teng. The energy-saving optimization for rural house in northeast severe cold regions[D]. Harbin: Harbin Institute of Technology, 2018. |
| [22] | 胡志豪, 冯忠楠, 魏繁荣, 等. 计及热力不确定性的智能建筑电-热联合鲁棒经济调度[J]. 中国电机工程学报, 2020, 40(12): 3907-3919. |
| [22] | HU Zhihao, FENG Zhongnan, WEI Fanrong, et al. Robust dispatch for electrical-thermal combined intelligent building considering impacts of uncertainties on thermal side[J]. Proceedings of the CSEE, 2020, 40(12): 3907-3919. |
| [23] | ZU T P, KANG R, WEN M L. Graduation formula: A new method to construct belief reliability distribution under epistemic uncertainty[J]. Journal of Systems Engineering and Electronics, 2020, 31(3): 626-633. |
| [24] | 时洪会, 蒋文保. D-S证据理论综述[J]. 信息化建设, 2015(11): 331. |
| [24] | SHI Honghui, JIANG Wenbao. A summary of D-S evidence theory[J]. Informatization Construction, 2015(11): 331. |
| [25] | 李昌玺, 周焰, 王盛超, 等. 多源信息融合中一种新的证据合成算法[J]. 上海交通大学学报, 2016, 50(7): 1125-1131. |
| [25] | LI Changxi, ZHOU Yan, WANG Shengchao, et al. A novel combination rule of evidence theory in multi-source information fusion[J]. Journal of Shanghai Jiao Tong University, 2016, 50(7): 1125-1131. |
| [26] | 徐洪富, 吴根秀, 许才. 基于大焦元的子焦元的信任函数逼近方法[J]. 江西师范大学学报(自然科学版), 2019, 43(3): 282-286. |
| [26] | XU Hongfu, WU Genxiu, XU Cai. The approximation method of the belief function based on the sub focal elements of the large focal elements[J]. Journal of Jiangxi Normal University (Natural Science Edition), 2019, 43(3): 282-286. |
| [27] | 程子成, 吴根秀, 宋姝婷. 基于融合信息熵性质的信任函数概率逼近[J]. 江西师范大学学报(自然科学版), 2014, 38(5): 534-538. |
| [27] | CHENG Zicheng, WU Genxiu, SONG Shuting. A probability approximations of belief function based on fusion of the properties of information entropy[J]. Journal of Jiangxi Normal University (Natural Science Edition), 2014, 38(5): 534-538. |
| [28] | SCOTT I J, CARVALHO P M S, BOTTERUD A, et al. Clustering representative days for power systems generation expansion planning: Capturing the effects of variable renewables and energy storage[J]. Applied Energy, 2019, 253: 113603. |
| [29] | ZHANG S X, CHENG H Z, ZHANG L B, et al. Probabilistic evaluation of available load supply capability for distribution system[J]. IEEE Transactions on Power Systems, 2013, 28(3): 3215-3225. |
| [30] | JIRUTITIJAROEN P, SINGH C. Comparison of simulation methods for power system reliability indexes and their distributions[J]. IEEE Transactions on Power Systems, 2008, 23(2): 486-493. |
| [31] | 高新波, 谢维信. 模糊聚类理论发展及应用的研究进展[J]. 科学通报, 1999, 44(21): 2241-2251. |
| [31] | GAO Xinbo, XIE Weixin. Research progress on the development and application of fuzzy clustering theory[J]. Chinese Science Bulletin, 1999, 44(21): 2241-2251. |
| [32] | YANG W H, CAO M S, GE P J, et al. Risk-oriented renewable energy scenario clustering for power system reliability assessment and tracing[J]. IEEE Access, 2020, 8: 183995-184003. |
| [33] | SHAO C Z, SHAHIDEHPOUR M, DING Y. Market-based integrated generation expansion planning of electric power system and district heating systems[J]. IEEE Transactions on Sustainable Energy, 2020, 11(4): 2483-2493. |
| [34] | LIU X Z, WU J Z, JENKINS N, et al. Combined analysis of electricity and heat networks[J]. Applied Energy, 2016, 162: 1238-1250. |
/
| 〈 |
|
〉 |
