电网交互建筑及电力协调调度优化策略研究

作者:潘毅群 王皙 尹茹昕 黄治钟 曾菲 周明春
单位:同济大学 上海市建筑科学研究院有限公司 美的集团楼宇科技研究院
摘要:随着可再生能源和储能设备的迅速发展,电网交互建筑成为当前最具潜力的节能减碳方式之一。通过整合可再生能源、储能技术和公共电网,电网交互建筑可以实现建筑与电网之间的互动和协作。本文介绍了电网交互建筑的系统框架和模型构建方法,以及相关的电力协调调度优化策略,包括需求响应、能量管理和协调调度策略。此外,本文以小型居住社区为案例进行调度策略研究,构建了以经济性最优和碳排放最优为目标的规划问题,并通过改变滚动优化预测域长度来分析多日之间的协作潜力。最后,对电网交互建筑的未来发展进行了展望,探讨了多能源协同优化和利益相关者的协作合作等方面的问题,为推动建筑能源可持续发展和电力系统的稳定运行提供参考。
关键词:电网交互建筑电力协调调度优化策略能源利用建筑减碳
作者简介:潘毅群,女,1970年生,博士研究生,教授,博士生导师201804上海市曹安公路4800号开物馆441,E-mail:yiqunpan@tongji.edu.cn;
基金:上海市2022年度“科技创新行动计划”科技支撑碳达峰碳中和专项项目资助(编号:22dz1207200);
尊敬的用户,本篇文章需要2元,点击支付交费后阅读
参考文献[1] United Nations.WMO state of the global climate 2022[EB/OL].[2023-07-15].https://unfccc.int/documents/622338 gclid=CjwKCAjw5MOlBhBTE iwAAJ8e1tkGRHW7f7lkgW1CD-E-zVKTii9mBSU3k gmKS_XrBgIkQfauAG-1xhoC3QgQAvD_BwE.

[2] British Petroleum.Statistical review of world energy 2022[R/OL].[2023-09-29].https://www.bp.com/en/global/corporate/energy-economics/statistical-revi ew-of-world-energy.html.

[3] United Nations.The paris agreement[EB/OL].[2023-07-15].https://www.un.org/en/climatechange/paris-agreement.

[4] ROBATI M,OLDFIELD P,NEZHAD A A,et al.Carbon value engineering:a framework for integrating embodied carbon and cost reduction strategies in building design[J].Building and environment,2021,192:107620.

[5] MATA É,KORPAL A K,CHENG S H,et al.A map of roadmaps for zero and low energy and carbon buildings worldwide[J].Environmental research letters,2020,15(11):113003.

[6] 郝斌.建筑“光储直柔”与零碳电力如影随形[J].建筑,2021(23):27- 29.

[7] International Energy Agency.Net zero by 2050:a roadmap for the global energy sector[EB/OL].[2023-09-29].https://www.iea.org/events/net-zero-by-2050-a-roadmap-for-the-global-energy-system.

[8] 清华大学建筑节能研究中心.中国建筑节能年度发展研究报告2022[M].北京:中国建筑工业出版社,2022:238- 251.

[9] SADEGHIAN O,MORADZADEH A,MOHAMMADI-IVATLOO B,et al.A comprehensive review on energy saving options and saving potential in low voltage electricity distribution networks:building and public lighting[J].Sustainable cities and society,2021,72:103064.

[10] MA Z,COOPER P,DALY D,et al.Existing building retrofits:methodology and state-of-the-art[J].Energy and buildings,2012,55:889- 902.

[11] LIU G,TAN Y,LI X.China's policies of building green retrofit:a state-of-the-art overview[J].Building and environment,2020,169:106554.

[12] 国务院.国务院关于印发2030年前碳达峰行动方案的通知[Z/OL].[2023-08-05].https://www.gov.cn/zhengce/zhengceku/2021-10/26/content_5644984.htm.

[13] 国家发展改革委,国家能源局.关于完善能源绿色低碳转型体制机制和政策措施的意见[Z/OL].[2023-07-26].https://www.ndrc.gov.cn/xxgk/zcfb/ tz/202202/t20220210_1314511.html.

[14] 住房和城乡建设部.住房和城乡建设部关于印发“十四五”建筑节能与绿色建筑发展规划的通知[Z/OL].[2023-07-26].https://www.gov.cn/zhengce/zhengceku/2022-03/12/content_5678698.htm.

[15] 工业和信息化部,住房和城乡建设部,交通运输部,等.智能光伏产业创新发展行动计划(2021-2025年)[Z/OL].[2023-08-05].https://www.gov.cn/zhengce/zhengceku/2022-01/05/content_5666484.htm.

[16] NEUKOMM M,NUBBE V,FARES R.Grid-interactive efficient buildings technical report series:overview of research challenges and gaps:NREL/TP-5500-75470,DOE/GO-102019-5227,1577966[R/OL].[2023-07-08].http://www.osti.gov/servlets/purl/1577966/.

[17] PERRY C,BASTIAN H,YORK D.State of the market:grid-interactive efficient building utility programs[EB/OL].[2023-07-20].https://www.aceee.org/white-paper/gebs-103019.

[18] HONG T,LANGEVIN J,SUN K.Building simulation:ten challenges[J].Building simulation,2018,11(5):871- 898.

[19] SATCHWELL A,PIETTE M,KHANDEKAR A,et al.A national roadmap for grid-interactive efficient buildings[R/OL].[2023-07-15].https://www.osti.gov/servlets/purl/1784302/.DOI:10.2172/1784302.

[20] CHEN Y,XU P,GU J,et al.Measures to improve energy demand flexibility in buildings for demand response (DR):a review[J].Energy and buildings,2018,177:125- 139.

[21] MBUNGU N T,NAIDOO R M,BANSAL R C,et al.An overview of renewable energy resources and grid integration for commercial building applications[J].Journal of energy storage,2020,29:101385.

[22] PAN Y Q,ZHU M Y,LV Y,et al.Building energy simulation and its application for building performance optimization:a review of methods,tools,and case studies[J].Advances in applied energy,2023,10:100135.

[23] WANG K,QI X,LIU H.A comparison of day-ahead photovoltaic power forecasting models based on deep learning neural network[J].Applied energy,2019,251:113315.

[24] ARABZADEH V,ALIMOHAMMADISAGVAND B,JOKISALO J,et al.A novel cost-optimizing demand response control for a heat pump heated residential building[J].Building simulation,2018,11(3):533- 547.

[25] CHABAUD A,EYNARD J,GRIEU S.A rule-based strategy to the predictive management of a grid-connected residential building in southern france[J].Sustainable cities and society,2017,30:18- 36.

[26] CHEN C,DUAN S,CAI T,et al.Online 24-h solar power forecasting based on weather type classification using artificial neural network[J].Solar energy,2011,85(11):2856- 2870.

[27] WANG H,LI G,WANG G,et al.Deep learning based ensemble approach for probabilistic wind power forecasting[J].Applied energy,2017,188:56- 70.

[28] MEENAL R,SELVAKUMAR A I.Assessment of SVM,empirical and ANN based solar radiation prediction models with most influencing input parameters[J].Renewable energy,2018,121:324- 343.

[29] RAZMARA M,BHARATI G R,HANOVER D,et al.Building-to-grid predictive power flow control for demand response and demand flexibility programs[J].Applied energy,2017,203:128- 141.

[30] PINTO G,KATHIRGAMANATHAN A,MANGINA E,et al.Enhancing energy management in grid-interactive buildings:a comparison among cooperative and coordinated architectures[J].Applied energy,2022,310:118497.

[31] FAN C,HUANG G,SUN Y.A collaborative control optimization of grid-connected net zero energy buildings for performance improvements at building group level[J].Energy,2018,164:536- 549.

[32] 董金凤.计及源荷不确定性的综合能源系统优化调度[D/OL].北京:华北电力大学(北京),2020[2023-08-05].https://kns.cnki.net/kcms2/article/abstract?v=3uoqIhG8C475KOm_zrgu4lQARvep2S AkyRJRH-nhEQBuKg4okgcHYtbcJfDDdHeRC65a7k CdeCgNGgS3meVgXbl3_45BaTta&uniplatform=NZKPT.

[33] 朱倩雯.多能互补建筑能源系统电热储能容量优化配置[D/OL].济南:山东大学,2018[2023-08-05].https://kns.cnki.net/kcms2/article/abstract?v=3uoqIhG8C475KOm_zrgu4lQARvep2SAkWfZcByc-RON98J6vxPv10Sa1KwtDoqAGYsMUKIL5bQALcD q7wXHzHpgD34YgobJa&uniplatform=NZKPT.

[34] YIN R,PAN Y,DING Z.Energy consumption baselining and benchmarking of green office buildings in Shanghai[J].E3S web of conferences,2022,356:01031.

[35] GAO D,SUN Y,LU Y.A robust demand response control of commercial buildings for smart grid under load prediction uncertainty[J].Energy,2015,93:275- 283.

[36] MENA R,RODRÍGUEZ F,CASTILLA M,et al.A prediction model based on neural networks for the energy consumption of a bioclimatic building[J].Energy and buildings,2014,82:142- 155.

[37] VRABLECOVÁ P,BOU EZZEDDINE A,ROZINAJOVÁ V,et al.Smart grid load forecasting using online support vector regression[J].Computers & electrical engineering,2018,65:102- 117.

[38] AMALOU I,MOUHNI N,ABDALI A.Multivariate time series prediction by RNN architectures for energy consumption forecasting[J].Energy reports,2022,8:1084- 1091.

[39] LU H,CHENG F,MA X,et al.Short-term prediction of building energy consumption employing an improved extreme gradient boosting model:a case study of an intake tower[J].Energy,2020,203:117756.

[40] BAY C J,CHINTALA R,CHINDE V,et al.Distributed model predictive control for coordinated,grid-interactive buildings[J].Applied energy,2022,312:118612.

[41] DONG B,LI Z,TAHA A,et al.Occupancy-based buildings-to-grid integration framework for smart and connected communities[J].Applied energy,2018,219:123- 137.

[42] TANG H,WANG S,LI H.Flexibility categorization,sources,capabilities and technologies for energy-flexible and grid-responsive buildings:state-of-the-art and future perspective[J].Energy,2021,219:119598.

[43] PELLAND S,REMUND J,KLEISSL J,et al.Photovoltaic and solar forecasting:state of the art[M].Paris:IEA PVPS,2013:1- 36.

[44] ZHANG W,YAN C,XU Y,et al.A critical review of the performance evaluation and optimization of grid interactions between zero-energy buildings and power grids[J].Sustainable cities and society,2022,86:104123.

[45] GEORGES E,BRAUN J E,LEMORT V.A general methodology for optimal load management with distributed renewable energy generation and storage in residential housing[J].Journal of building performance simulation,2017,10(2):224- 241.

[46] WU X,HU X,YIN X,et al.Stochastic optimal energy management of smart home with PEV energy storage[J].IEEE transactions on smart grid,2018,9(3):2065- 2075.

[47] SARABI S,DAVIGNY A,COURTECUISSE V,et al.Potential of vehicle-to-grid ancillary services considering the uncertainties in plug-in electric vehicle availability and service/localization limitations in distribution grids[J].Applied energy,2016,171:523- 540.

[48] 张涛,刘晓华,刘效辰.“双碳”目标下车-建筑-电网(VBG)协同互动的探索[J].暖通空调,2023,53(5):1- 11.

[49] YAO E,WONG V W S,SCHOBER R.Optimization of aggregate capacity of PEVs for frequency regulation service in day-ahead market[J].IEEE transactions on smart grid,2018,9(4):3519- 3529.

[50] YIN R,KARA E C,LI Y,et al.Quantifying flexibility of commercial and residential loads for demand response using setpoint changes[J].Applied energy,2016,177:149- 164.

[51] TURNER W J N,WALKER I S,ROUX J.Peak load reductions:electric load shifting with mechanical pre-cooling of residential buildings with low thermal mass[J].Energy,2015,82:1057- 1067.

[52] SHAFIE-KHAH M,KHERADMAND M,JAVADI S,et al.Optimal behavior of responsive residential demand considering hybrid phase change materials[J].Applied energy,2016,163:81- 92.

[53] ZHANG L,WEN J.A systematic feature selection procedure for short-term data-driven building energy forecasting model development[J].Energy and buildings,2019,183:428- 442.

[54] CHEN Y,GUO M,CHEN Z,et al.Physical energy and data-driven models in building energy prediction:a review[J].Energy reports,2022,8:2656- 2671.

[55] Federal Energy Regulatory Commission.Assessment of demand response and advanced metering[R/OL].[2023-07-20].https://www.ferc.gov/news-events/news/ferc-staff-issues-report-2022-assessment-demand-response-and-advanced-metering.

[56] GILS H C.Assessment of the theoretical demand response potential in Europe[J].Energy,2014,67:1- 18.

[57] 黄珍珍.基于BP神经网络的燃料电池汽车在线多目标能量管理策略研究[D/OL].重庆:重庆大学,2021[2023-08-05].https://kns.cnki.net/kcms2/article/abstract?v=3uoqIhG8C475KOm_zrgu4lQAR vep2SAke-wuWrktdE-tSIT2YIbQ2FkP5asQc4qe3Uv UwXxUfsCATASSxuGXpIb4-lFERd0W&uniplatform=NZKPT.

[58] WANG J,MUNANKARMI P,MAGUIRE J,et al.Carbon emission responsive building control:a case study with an all-electric residential community in a cold climate[J].Applied energy,2022,314:118910.

[59] CHABAUD A,EYNARD J,GRIEU S.A new approach to energy resources management in a grid-connected building equipped with energy production and storage systems:a case study in the south of France[J].Energy and buildings,2015,99:9- 31.

[60] ZOU B,PENG J,LI S,et al.Comparative study of the dynamic programming-based and rule-based operation strategies for grid-connected PV-battery systems of office buildings[J].Applied energy,2022,305:117875.

[61] LIU X,ZHANG P,PIMM A,et al.Optimal design and operation of PV-battery systems considering the interdependency of heat pumps[J].Journal of energy storage,2019,23:526- 536.

[62] ANTUNES C H,ALVES M J,SOARES I.A comprehensive and modular set of appliance operation MILP models for demand response optimization[J].Applied energy,2022,320:119142.

[63] LI Y,PENG J,JIA H,et al.Optimal battery schedule for grid-connected photovoltaic-battery systems of office buildings based on a dynamic programming algorithm[J].Journal of energy storage,2022,50:104557.

[64] 杨金文.智慧小区微电网“源-网-荷-车-储”协同运行策略研究[D/OL].广州:广东工业大学,2021[2023-08-05].https://kns.cnki.net/kcms2/article/abstract?v=3uoqIhG8C475KOm_zrgu4lQARvep2 SAkueNJRSNVX-zc5TVHKmDNkoVS-aVBUwKb5I OJGgD0m61HwkTuftiB04fBi4Uw53FD&uniplatform=NZKPT.

[65] HOSSAIN M A,POTA H R,SQUARTINI S,et al.Modified PSO algorithm for real-time energy management in grid-connected microgrids[J].Renewable energy,2019,136:746- 757.

[66] 张雪纯,李文升,张智晟.基于IPSO算法的建筑级综合能源系统优化调度[J].青岛大学学报(工程技术版),2019,34(1):64- 69.

[67] LI S,YANG J,SONG W,et al.A real-time electricity scheduling for residential home energy management[J].IEEE internet of things journal,2019,6(2):2602- 2611.

[68] TANG R,WANG S.Model predictive control for thermal energy storage and thermal comfort optimization of building demand response in smart grids[J].Applied energy,2019,242:873- 882.

[69] GOMES I L R,RUANO M G,RUANO A E.MILP-based model predictive control for home energy management systems:a real case study in Algarve,Portugal[J].Energy and buildings,2023,281:112774.

[70] TAMASHIRO K,OMINE E,KRISHNAN N,et al.Optimal components capacity based multi-objective optimization and optimal scheduling based MPC-optimization algorithm in smart apartment buildings[J].Energy and buildings,2023,278:112616.

[71] OLDEWURTEL F,PARISIO A,JONES C N,et al.Use of model predictive control and weather forecasts for energy efficient building climate control[J].Energy and buildings,2012,45:15- 27.

[72] GOMES I,BOT K,RUANO M G,et al.Recent techniques used in home energy management systems:a review[J].Energies,2022,15(8):2866.

[73] 徐俊杰.元启发式优化算法理论与应用研究[D/OL].北京:北京邮电大学,2007[2023-08-05].https://kns.cnki.net/kcms2/article/abstract?v=3uoqIhG8C447WN1SO36whBaOoOkzJ23ELn_-3AAg J5enmUaXDTPHrMsC0JTgYjO4KJptWb3aIU8-UUm 5tx3xNTfLTFpYOMRX&uniplatform=NZKPT.

[74] ALBERIZZI J C,ROSSI M,RENZI M.A milp algorithm for the optimal sizing of an off-grid hybrid renewable energy system in South Tyrol[J].Energy reports,2020,6:21- 26.

[75] AVCI M,ERKOC M,RAHMANI A,et al.Model predictive HVAC load control in buildings using real-time electricity pricing[J].Energy and buildings,2013,60:199- 209.

[76] ASTE N,MANFREN M,MARENZI G.Building automation and control systems and performance optimization:a framework for analysis[J].Renewable and sustainable energy reviews,2017,75:313- 330.

[77] KILLIAN M,KOZEK M.Ten questions concerning model predictive control for energy efficient buildings[J].Building and environment,2016,105:403- 412.

[78] NIU J,TIAN Z,LU Y,et al.Flexible dispatch of a building energy system using building thermal storage and battery energy storage[J].Applied energy,2019,243:274- 287.

[79] MIRZAEI M A,NAZARI-HERIS M,ZARE K,et al.Evaluating the impact of multi-carrier energy storage systems in optimal operation of integrated electricity,gas and district heating networks[J].Applied thermal engineering,2020,176:115413.

[80] 穆云飞,唐志鹏,吴志军,等.计及虚拟储能的电-水-热综合能源系统日前优化调度方法[J/OL].电力系统自动化[2023-08-05].http://kns.cnki.net/kcms/detail/32.1180.TP.20230629.1528.002.html.

[81] 徐占伯,周春翔,吴江,等.基于边云协同的建筑能源系统分布式供需协同优化[J].中国科学:信息科学,2023,53(3):517- 534.

[82] KOFINAS P,DOUNIS A I,VOUROS G A.Fuzzy Q-learning for multi-agent decentralized energy management in microgrids[J].Applied energy,2018,219:53- 67.

[83] PÉREZ-FLORES A C,ANTONIO J D M,OLIVARES-PEREGRINO V H,et al.Microgrid energy management with asynchronous decentralized particle swarm optimization[J].IEEE access,2021,9:69588- 69600.

[84] DARSHI R,SHAMAGHDARI S,JALALI A,et al.Decentralized energy management system for smart microgrids using reinforcement learning[J].IET generation,transmission & distribution,2023,17(9):2142- 2155.

[85] 程义,李更丰.基于双层模仿学习的多园区综合能源系统分布式协同优化调度[J].电力系统自动化,2022,46(24):16- 25.

[86] 朱浩昊,朱继忠,李盛林,等.电-热综合能源系统优化调度综述[J].全球能源互联网,2022,5(4):383- 397.

[87] ZHENG W,HILL D J.Distributed real-time dispatch of integrated electricity and heat systems with guaranteed feasibility[J].IEEE transactions on industrial informatics,2022,18(2):1175- 1185.

[88] YANG Q,WANG H,WANG T,et al.Blockchain-based decentralized energy management platform for residential distributed energy resources in a virtual power plant[J].Applied energy,2021,294:117026.

[89] 中国建筑科学研究院.公共建筑节能设计标准:GB 50189—2015[S].北京:中国建筑工业出版社,2015:5- 12.

[90] 上海市建筑科学研究院(集团)有限公司,上海市建筑建材业市场管理总站.居住建筑节能设计标准:DGJ 08-205—2015[S].上海:同济大学出版社,2016:5- 13.

[91] 上海市发展和改革委员会.上海市发展和改革委员会关于降低本市大工业用电价格的通知[Z/OL].[2023-07-20].https://www.shanghai.gov.cn/nw49248/20201204/82b0c749089345e1aa55f68672b9e bbd.html.

[92] 上海市发展和改革委员会.关于转发《国家发展改革委关于进一步深化燃煤发电上网电价市场化改革的通知》的通知[Z].[2023-07-20].https://fgw.sh.gov.cn/fgw_jggl/20211101/43d3d6f44dac49c594f6a0 b26732ec39.html.

[93] 上海市发展和改革委员会.关于印发《上海市可再生能源和新能源发展专项资金扶持办法(2020版)》的通知[Z].[2023-07-20].https://fgw.sh.gov.cn/fgw_zdjc/20211210/fce611ef14a143adb3972266394fa 089.html.

[94] 蒋望,赵理,李亦非,等.基于滚动时域优化和反馈校正的虚拟电厂实时优化调度方法[J].电网与清洁能源,2022,38(8):40- 50.

[95] LIU T,JIAO W,TIAN X.A framework for uncertainty and sensitivity analysis of district energy systems considering different parameter types[J].Energy reports,2021,7:6908- 6920.

[96] 李楠,柳玉宾,王恒涛,等.综合能源系统优化调度研究综述[J].能源与节能,2021(10):58- 59.

[97] 周鹏程,吴南南,曾鸣.综合能源系统建模仿真规划调度及效益评价综述与展望[J].山东电力技术,2018,45(11):1- 5.

[98] 肖伟栋,刘耀,蒋纯冰,等.面向源荷互动的建筑-电网数据共享现状与展望[J/OL].暖通空调[2023-09-01].http://kns.cnki.net/kcms/detail/11.2832.TU.20230724.2042.006.html.
Study on optimization strategies for grid-interactive building and electricity coordination scheduling
Pan Yiqun Wang Xi Yin Ruxin Huang Zhizhong Zeng Fei Zhou Mingchun
(Tongji University Shanghai Research Institute of Building Sciences Midea Group-Midea Building Technologies Division)
Abstract: With the rapid development of new energy generation and energy storage devices, utilizing renewable energy to establish grid-interactive buildings has become one of the most promising approaches for energy efficiency and carbon reduction. Grid-interactive buildings can achieve interaction and collaboration between buildings and the power grid by integrating renewable energy, energy storage technologies, and the public power grid. This paper presents the system framework and model construction method of grid-interactive buildings, as well as relevant electricity coordination and scheduling optimization strategies, including demand response, energy management, and coordinated scheduling strategies. Additionally, this paper conducts a case study on scheduling strategies in a small residential community, formulating the optimization problems with the objectives of economic optimization and carbon emission minimization, and explores coordinated interactions over multiple days by adjusting the receding optimization prediction horizon. Finally, the prospects for the future development of grid-interactive buildings are discussed, including the issues related to multi-energy collaborative optimization and stakeholder cooperation, providing the insights for promoting the sustainable building energy practices and ensuring the stability of the power system.
Keywords: grid-interactive building; electricity coordination and scheduling; optimization strategy; energy utilization; building carbon reduction;
381 0 0
文字:     A-     A+     默认 取消