碳中和城市建筑能源系统3):负荷篇
摘要：本文是碳中和城市建筑能源系统系列文章的第三篇。碳中和城市能源系统要实现“两个替代”,即能源生产的可再生能源替代和能源消费的电力替代。因此有2个关键点对负荷分析提出了要求：一是建筑电气化，使得建筑供热供冷负荷与电力负荷更紧密地结合在一起。二是可再生能源利用的规模化，使得电力系统从原先只应对需求侧的变动负荷，变为要应对需求侧和供应侧2个方面的变动负荷；而建筑成为电网灵活性的最主要的提供者。本文总结了在这种形势下的电力负荷和冷热负荷分析的特点，阐述了电力负荷分析与建筑供热供冷负荷分析技术路径的异同。重点介绍了在建筑能耗限额背景下供热供冷负荷的反推方法，以及其重要参数“当量满负荷小时数”的概念和生成方法。最后介绍了为电网提供灵活性的供热供冷负荷预测的技术路径。
关键词：碳中和电力负荷供热供冷负荷灵活性柔性)负荷反推能耗限额当量满负荷小时数需求侧响应
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[30] ASHRAE.ASHRAE handbook:fundamentals[M].SI edition.Atlanta:ASHRAE Inc.,2021:19-27- 19-34.
[2] 国家电网公司.电网运行准则：GB/T 31464—2015[S].北京：中国标准出版社，2015:24- 25.
[3] 西安交通大学，中机生产力促进中心，中国能源建设集团广东省电力设计研究院有限公司，等.微电网第1部分：微电网规划设计导则：NB/T 10148—2019[S].北京：中国标准出版社，2019:9- 10.
[4] 国网北京经济技术研究院.电网规划设计手册[M].北京：中国电力出版社，2015:308- 309.
[5] 孙育英，王丹，王伟，等.空调运行负荷预测方法的研究综述[J].建筑科学，2016,32(6):142- 150.
[6] 林晶怡，李斌，熊敏，等.电力负荷影响因素研究[M].北京：中国电力出版社，2016:16.
[7] PRICE P N.Methods for analyzing electric load shape and its variability[R].Berkeley:Lawrence Berkeley National Laboratory,2010:3- 4.
[8] HONG T.Energy forecasting:past,present and future[J].Foresight:the international journal of applied forecasting,2014,32:43- 48.
[9] ELKARMI F,ABU-SHIKHAH N.Chapter 4 load research,power system planning technologies and applications:concepts,solutions,and management[M].[S.l.]:IGI Global,Engineering Science Reference in US,2012:47- 56.
[10] 康重庆，姚良忠.高比例可再生能源电力系统的关键科学问题与理论研究框架[J].电力系统自动化，2017,41(9):2- 11.
[11] IEA.Energy in Buildings and Communities Program Annex 67 energy flexible buildings:principles of energy flexible buildings[R/OL].[2022-05-30].https://www.annex67.org/media/1918/principles-of-energy-flexible-buildings.pdf.
[12] IEA.Energy in Buildings and Communities Program Annex 67 energy flexible buildings:characterization of energy flexibility in buildings[R/OL].[2022-05-31].https://annex67.org/media/1919/characterization-of-energy-flexibility-in-buildings.pdf.
[13] 单寄平.空调负荷实用计算法[M].北京：中国建筑工业出版社，1989:1- 6.
[14] 潘毅群，郁丛，龙惟定，等.区域建筑负荷与能耗预测研究综述[J].暖通空调，2015,45(3):33- 40.
[15] 潘毅群，梁育民，朱明亚.碳中和目标背景下的建筑碳排放计算模型研究综述[J].暖通空调，2021,51(7):37- 48.
[16] HONG T,PINSON P,WANG Y,et al.Energy forecasting:a review and outlook[J].IEEE open access journal of power and energy,2020,7:376- 388.
[17] FATHI S,SRINIVASAN R,FENNER A,et al.Machine learning applications in urban building energy performance forecasting:a systematic review[J].Renewable and sustainable energy reviews,2020,133:1- 13.
[18] HYNDMAN R J,ATHANASOPOULOS G.Forecasting:principles and practice[M/OL].[2022-06-10].https://www.OTexts.com/fpp2.
[19] ROBINSON J B.Energy backcasting:a proposed method of policy analysis[J].Energy policy,1982,10:337- 344.
[20] Wikipedia contributors.Backcasting[EB/OL].[2022-06-13].https://en.wikipedia.org/wiki/Backcasting.
[21] 龙惟定.人工智能技术在建筑能源管理中的应用场景[J].建筑科学，2021,37(2):127- 136.
[22] ASHRAE.Development of equivalent full load heating and cooling hours for GCHPs applied in various building types and locations[R].Atlanta:ASHRAE Inc.,2001:1- 66.
[23] AHMAD R,PRATYUS H,KUMAR R.Very short-term photovoltaic (PV) power forecasting using deep learning (LSTMs)[C]//2021 International Conference on Intelligent Technologies (CONIT).Karnataka,India,2021.
[24] NAKAMURA M,TAHARA S,HATAZAKI H.One-day-ahead load forecasting by Kalman filter using atmospheric temperature information[J].IFAC proceedings,1981,14(2):3067- 3072.
[25] Wikipedia.Kalman filter[EB/OL].[2022-06-30].https://encyclopedia.thefreedictionary.com/Kalman+filter.
[26] 龙惟定.用状态空间方法计算墙体反应系数[J].制冷学报，1989(4):8- 14.
[27] 龙惟定，刘魁星.城区需求侧能源规划中的几个关键问题[J].暖通空调，2017,47(4):2- 9.
[28] CHEN Y X,CASTIGLIONE J,ASTROZA R,et al.Parameter estimation of resistor-capacitor models for building thermal dynamics using the unscented Kalman filter[J/OL].Journal of building engineering.[2022-06-30].https://doi.org/10.1016/j.jobe.2020.101639.
[29] 贾文琦.基于混合数据驱动模型的区域建筑动态负荷预测方法[D].上海：同济大学，2021:1- 10.
[30] ASHRAE.ASHRAE handbook:fundamentals[M].SI edition.Atlanta:ASHRAE Inc.,2021:19-27- 19-34.
Building energy system of carbon neutrality cities(3):Load
Abstract: This article is the third in a series of articles on the building energy system of carbon neutrality cities. The carbon neutralized urban energy system should realize the “two alternatives”, that is, the renewable energy replacement in energy production and the electric substitution in energy consumption. Therefore, there are two key points on load analysis requirements. First, the electrification of the building makes the building heating and cooling load closely combined with the electrical load. Second, the large-scale application of renewable energy causes that the power system changes its focus from originally variable load is only in the demand side to be both in the demand side and the supply side, and the buildings become the most important providers of grid flexibility. The article summarizes the characteristics of electricity load and heating/cooling load analysis under this situation, and explains the similarities and differences in the technical paths of power load analysis and building heating/cooling load analysis. The article focuses on the heating/cooling load backcasting method under the background of the building energy consumption quota, as well as the concept and generating method of “equivalent full load hours”. Finally, the technical pathway of heating/cooling load forecasting for providing flexibility to the power grid is presented.
Keywords: carbon neutrality; electrical load; heating/cooling load; flexibility; load backcasting; energy quota; equivalent full load hour(EFLH); demand side response;
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