叶傲霜,李逸超,胥栋,杜佳玮,张宇华,汪健辉
YE Aoshuang,LI Yichao,XU Dong,DU Jiawei,ZHANG Yuhua,WANG Jianhui

• 1:国网上海市电力公司浦东供电公司
• 2:上海电力大学

摘要(Abstract)：

为提升智能楼宇运行的经济性和低碳性，提出基于AQPSO（自适应量子粒子群优化）算法的智能楼宇微电网经济优化调度策略。首先，建立了包含光伏、风电和EV（电动汽车）的智能楼宇微电网模型，考虑功率平衡、温度舒适性和EV集群充放电总功率等约束条件，以楼宇运行周期内用能成本最小为目标。其次，通过自适应参数控制改进QPSO（量子粒子群优化）算法，并利用改进后的AQPSO算法对模型进行求解。最后，设置4种场景进行算例分析，结果表明，AQPSO算法在收敛速度和寻优能力上均优于传统算法，所提出的模型和优化调度策略能够有效降低楼宇运行成本、碳排放量及碳排放成本，提升了清洁能源利用率。
To enhance the economic efficiency and low-carbon performance of smart building operations, this paper proposes an optimal economic scheduling strategy for smart building microgrids based on the adaptive quantum particle swarm optimization(AQPSO). Firstly, a smart building microgrid model incorporating photovoltaic, wind power, and electric vehicle(EV) is established, considering constraints such as power balance, thermal comfort, and the total charging/discharging power of EV clusters, with the objective of minimizing energy costs over the building's operational cycle. Secondly, the improved quantum particle swarm optimization(QPSO) is improved through adaptive parameter control, and the AQPSO is employed to solve the model. Finally, four scenarios are set up for case analysis. The results demonstrate that the AQPSO outperforms traditional methods in terms of convergence speed and optimization capability. The proposed model and optimal scheduling strategy effectively reduce building operating costs, carbon emissions, and carbon emission costs, while improving the utilization rate of clean energy.

关键词(KeyWords)： 智能楼宇微电网;车一网互动;自适应量子粒子群优化;优化调度
smart building microgrid;V2G;AQPSO;optimal scheduling

Abstract:

Keywords:

基金项目(Foundation): 上海市科委科技创新计划项目（22010501400）

作者(Author): 叶傲霜,李逸超,胥栋,杜佳玮,张宇华,汪健辉
YE Aoshuang,LI Yichao,XU Dong,DU Jiawei,ZHANG Yuhua,WANG Jianhui

DOI: 10.19585/j.zjdl.202506008

参考文献(References)：

• [1]住房和城乡建设部科技发展促进中心.中国建筑节能发展报告-2016年-建筑节能运行管理[M].北京：中国建筑工业出版社，2016.
• [2]严敬义.计及多元灵活资源响应的智能楼宇优化调度策略[D].北京：北京交通大学，2022.YAN Jingyi. Optimal scheduling strategy for intelligent buildings considering multiple and flexible resource responses[D].Beijing:Beijing Jiaotong University,2022.
• [3]靳小龙，穆云飞，贾宏杰，等.集成智能楼宇的微电网系统多时间尺度模型预测调度方法[J].电力系统自动化，2019,43(16):25-33.JIN Xiaolong,MU Yunfei,JIA Hongjie,et al.Model predictive control based multiple-time-scheduling method for microgrid system with smart buildings integrated[J].Automation of Electric Power Systems,2019,43(16):25-33.
• [4]冀瑞强，胡健，张晓杰，等.基于第三方服务的楼宇集群共享储能成本优化[J].高电压技术，2024,50(6):2523-2536.JI Ruiqiang,HU Jian,ZHANG Xiaojie,et al.Cost optimization of shared energy storage in building cluster based on service of a third-party agent[J].High Voltage Engineering,2024,50(6):2523-2536.
• [5]陈厚合，李泽宁，靳小龙，等.集成智能楼宇的主动配电网建模及优化方法[J].中国电机工程学报，2018,38(22):6550-6563.CHEN Houhe,LI Zening,JIN Xiaolong,et al. Modeling and optimization of active distribution network with integrated smart buildings[J]. Proceedings of the CSEE,2018,38(22):6550-6563.
• [6] AMIN U,HOSSAIN M J,FERNANDEZ E. Optimal price based control of HVAC systems in multizone office buildings for demand response[J].Journal of Cleaner Production,2020,270:122059.
• [7] SáNCHEZ RAMOS J, PAVóN MORENO M,GUERRERO DELGADO M,et al. Potential of energy flexible buildings:evaluation of DSM strategies using building thermal mass[J]. Energy and Buildings,2019,203:109442.
• [8] JIN X L,MU Y F,JIA H J,et al.Dynamic economic dispatch of a hybrid energy microgrid considering building based virtual energy storage system[J].Applied Energy,2017,194:386-398.
• [9]陈厚合，李泽宁，姜涛，等.基于模型预测控制的智能楼宇用能灵活性调控策略[J].电力系统自动化，2019,43(16):116-124.CHEN Houhe,LI Zening,JIANG Tao,et al.Flexible energy scheduling strategy in smart buildings based on model predictive control[J].Automation of Electric Power Systems,2019,43(16):116-124.
• [10]李尚.2030年中国电动汽车保有量或超1亿辆[EB/OL].(2019-01-13)[2024-08-27]. https：//m. cnr. cn/auto/20190114/t20190114_524481771.html.
• [11]蒋卓臻，向月，刘俊勇，等.集成电动汽车全轨迹空间的充电负荷建模及对配电网可靠性的影响[J].电网技术，2019,43(10):3789-3800.JIANG Zhuozhen,XIANG Yue,LIU Junyong,et al.Charging load modeling integrated with electric vehicle whole trajectory space and its impact on distribution network reliability[J]. Power System Technology,2019,43(10):3789-3800.
• [12]孙辉，沈钟浩，周玮，等.电动汽车群响应的主动配电网阻塞调度研究[J].中国电机工程学报，2017,37(19):5549-5559.SUN Hui,SHEN Zhonghao,ZHOU Wei,et al. Congestion dispatch research of active distribution network with electric vehicle group response[J]. Proceedings of the CSEE,2017,37(19):5549-5559.
• [13] WAN M C,YU H Y,HUO Y N,et al. Feasibility and challenges for vehicle-to-grid in electricity market:a review[J].Energies,2024,17(3):679.
• [14]胡澄.面向智能楼宇的电动汽车优化调度策略研究[D].南京：东南大学，2020.HU Cheng. Research on optimal scheduling strategy of electric vehicles for smart buildings[D]. Nanjing:Southeast University,2020
• [15] VENAYAGAMOORTHY G K,MITRA P,CORZINE K,et al.Real-time modeling of distributed plug-in vehicles for V2G transactions[C]//2009 IEEE Energy Conversion Congress and Exposition. September 20-24,2009,San Jose,CA,USA.IEEE,2009:3937-3941.
• [16]余苏敏，杜洋，史一炜，等.考虑V2B智慧充电桩群的低碳楼宇优化调度[J].电力自动化设备，2021,41(9):95-101.YU Sumin,DU Yang,SHI Yiwei,et al.Optimal scheduling of low-carbon building considering V2B smart charging pile groups[J].Electric Power Automation Equipment,2021,41(9):95-101.
• [17]李波，王宁，吕叶林，等.考虑环境因素的电动汽车充电站实时负荷预测模型[J].同济大学学报（自然科学版），2024,52(6):962-969.LI Bo,WANG Ning,LüYelin,et al.Real-time load prediction model of electric vehicle charging station considering environmental factors[J].Journal of Tongji University(Natural Science),2024,52(6):962-969.
• [18]胡澄，刘瑜俊，徐青山，等.面向含风电楼宇的电动汽车优化调度策略[J].电网技术，2020,44(2):564-572.HU Cheng,LIU Yujun,XU Qingshan,et al. Optimal scheduling strategy for electric vehicles in buildings with wind power[J].Power System Technology,2020,44(2):564-572.
• [19]闫丽梅，曾家威，徐建军，等.含电动汽车和光伏的配电网演化动态博弈调度策略[J].太阳能学报，2024,45(5):316-323.YAN Limei,ZENG Jiawei,XU Jianjun,et al.Scheduling strategy of distribution grid with electric vehicles and photolatic based on evolutionary dynamics game[J].Acta Energiae Solaris Sinica,2024,45(5):316-323.
• [20]张志义，余涛，王德志，等.基于集成学习的含电气热商业楼宇群的分时电价求解[J].中国电机工程学报，2019,39(1):112-125.ZHANG Zhiyi,YU Tao,WANG Dezhi,et al.Optimal solution of time-of-use price based on ensemble learning for electricity-gas-heat commercial building[J].Proceedings of the CSEE,2019,39(1):112-125.
• [21]中华人民共和国住房和城乡建设部.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.