计及源-荷不确定性的屋顶集成光储系统容量配置

黄大为; 殷航; 曹旭东; 杨茂

doi:10.19912/j.0254-0096.tynxb.2023-1161

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太阳能学报 ›› 2024, Vol. 45 ›› Issue (10) : 363-372. DOI: 10.19912/j.0254-0096.tynxb.2023-1161

计及源-荷不确定性的屋顶集成光储系统容量配置

黄大为¹, 殷航¹, 曹旭东², 杨茂¹

作者信息 +

CAPACITY CONFIGURATION OF ROOFTOP INTEGRATED PHOTOVOLTAIC-STORAGE SYSTEM CONSIDERING SOURCE-LOAD UNCERTAINTY

Huang Dawei¹, Yin Hang¹, Cao Xudong², Yang Mao¹

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文章历史 +

摘要

利用建筑屋顶光伏发电是提高终端能源消费结构中可再生能源占比和降低用户能源成本的有效措施。针对建筑楼宇的屋顶集成光储（RIPVS）系统容量配置问题,提出计及源-荷不确定性的容量优化配置决策方法。在对光伏出力和负荷进行源-荷不确定性建模的基础上,以气象因素构建源-荷耦合气象特征集,运用K-均值聚类方法实现源-荷场景集削减,以刻画源-荷不确定性;建立以RIPVS系统全寿命周期成本最小化为目标的多场景容量优化配置模型,通过求解该模型获得反映备选方案技术经济的多维度指标,利用改进熵权法最终确定最优方案。以某商业楼宇的RIPVS系统容量配置为例,验证了所提决策方法的有效性。

Abstract

Photovoltaic power generation on building roof is an effective measure to increase the proportion of renewable energy in terminal energy consumption structure and reduce energy costs for users. Aiming at the capacity configuration problem of rooftop integrated photovoltaic-storage （RIPVS） systems in buildings, an optimal capacity configuration decision-making method that takes into account uncertainties of source-load is proposed. Based on uncertainties of source-load modeling of photovoltaic output and load, the source-load coupled meteorological feature set is constructed with meteorological factors, and the K-means clustering method is applied to achieve the source-load scenario reduction in order to portray uncertainties of source-load. A multi-scenario optimal capacity configuration model is established with the goal of minimizing the whole-life-cycle cost of RIPVS system, and multi-dimensional indexes reflecting the techno-economics of the alternative schemes are obtained by solving the model, and the optimal scheme is finally determined by using the improved entropy weight method. The effectiveness of the proposed decision-making method is verified by taking the capacity configuration of RIPVS system in a commercial building as an example.

导出引用

黄大为, 殷航, 曹旭东, 杨茂. 计及源-荷不确定性的屋顶集成光储系统容量配置[J]. 太阳能学报. 2024, 45(10): 363-372 https://doi.org/10.19912/j.0254-0096.tynxb.2023-1161

Huang Dawei, Yin Hang, Cao Xudong, Yang Mao. CAPACITY CONFIGURATION OF ROOFTOP INTEGRATED PHOTOVOLTAIC-STORAGE SYSTEM CONSIDERING SOURCE-LOAD UNCERTAINTY[J]. Acta Energiae Solaris Sinica. 2024, 45(10): 363-372 https://doi.org/10.19912/j.0254-0096.tynxb.2023-1161

中图分类号： TM615

参考文献

[1] 中国建筑节能协会. 中国建筑能耗研究报告2020[J]. 建筑节能(中英文), 2021, 49(2): 1-6.
China Building Energy Efficiency Association.China building energy consumption annual report 2020[J]. Journal of BEE, 2021, 49(2): 1-6.
[2] 李建林, 方知进, 谭宇良, 等. 电化学储能系统在整县制屋顶光伏中应用前景分析[J]. 太阳能学报, 2022, 43(4): 1-12.
LI J L, FANG Z J, TAN Y L, et al.Application prospect analysis of electrochemical energy storage technology in county-wide rooftop photovoltaic system[J]. Acta energiae solaris sinica, 2022, 43(4): 1-12.
[3] 唐冬来, 倪平波, 李玉, 等. 基于互信共识标识的县域屋顶光伏消纳交易策略[J]. 电力系统自动化, 2022, 46(22): 41-50.
TANG D L, NI P B, LI Y, et al.Transaction strategy of roof-mounted photovoltaic accommodation for county area based on mutual trust and consensus identification[J]. Automation of electric power systems, 2022, 46(22): 41-50.
[4] 陈娟, 高江梅. 基于演化博弈的户用光伏整县开发协同机制及仿真[J]. 电网技术, 2023, 47(2): 669-684.
CHEN J, GAO J M.Coordination mechanism and simulation of household photovoltaic county development based on evolutionary game[J]. Power system technology, 2023, 47(2): 669-684.
[5] 姚春妮, 马欣伯, 罗多. 碳达峰目标下太阳能光电建筑应用发展规模预测研究[J]. 建设科技, 2021(11): 33-35.
YAO C N, MA X B, LUO D.Prediction research on mid- and long-term development goals of solar photovoltaic application in buildings under carbon peak target[J]. Construction science and technology, 2021(11): 33-35.
[6] PANICKER K, ANAND P, GEORGE A.Assessment of building energy performance integrated with solar PV: towards a net zero energy residential campus in India[J]. Energy and buildings, 2023, 281: 112736.
[7] HASSAN G, HARALD N R, DANIELA ME.Holistic economic analysis of building integrated photovoltaics (BIPV) system: case studies evaluation[J]. Energy and buildings, 2019, 203: 109461.
[8] GIGLIO E, LUZZANI G, TERRANOVA V, et al.An efficient artificial intelligence energy management system for urban building integrating photovoltaic and storage[J]. IEEE access, 2023, 11: 18673-18688.
[9] ZHAN S C, DONG B, CHONG A.Improving energy flexibility and PV self-consumption for a tropical net zero energy office building[J]. Energy and buildings, 2023, 278: 112606.
[10] 陈柯蒙, 肖曦, 田培根, 等. 一种建筑集成光储系统规划运行综合优化方法[J/OL]. 中国电机工程学报: 1-12[2023-04-18]. http://kns.cnki.net/kcms/detail/11.2107.TM.20221102.1131.011.html.
CHEN K M, XIAO X, TIAN P G, et al. A comprehensive optimization method for planning and operation of building integrated photovoltaic energy storage system[J/OL]. Proceedings of the CSEE: 1-12[2023-04-18]. http://kns.cnki.net/kcms/detail/11.2107.TM.20221102.1131.011.html.
[11] 王磊, 王昭, 冯斌, 等. 基于双层优化模型的风-光-储互补发电系统优化配置[J]. 太阳能学报, 2022, 43(5): 98-104.
WANG L, WANG Z, FENG B, et al.Optimal configuration of wind-photovoltaic-ESS complementary power generation system based on bi-level optimization model[J]. Acta energiae solaris sinica, 2022, 43(5): 98-104.
[12] 马溪原, 郭晓斌, 雷金勇. 面向多能互补的分布式光伏与气电混合容量规划方法[J]. 电力系统自动化, 2018, 42(4): 55-63.
MA X Y, GUO X B, LEI J Y.Capacity planning method of distributed PV and P2G in multi-energy coupled system[J]. Automation of electric power systems, 2018, 42(4): 55-63.
[13] 倪平波, 周丹, 朱海萍, 等. 智能楼宇中的电-热储能系统双层优化配置模型研究[J]. 电测与仪表, 2021, 58(9): 122-128.
NI P B, ZHOU D, ZHU H P, et al.Research on double-layer optimal configuration model of electric-thermal energy storage system in smart building[J]. Electrical measurement & instrumentation, 2021, 58(9): 122-128.
[14] 张继红, 阚圣钧, 化玉伟, 等. 基于氢气储能的热电联供微电网容量优化配置[J]. 太阳能学报, 2022, 43(6): 428-434.
ZHANG J H, KAN S J, HUA Y W, et al.Capacity optimization of CHP microgrid based on hydrogen energy storage[J]. Acta energiae solaris sinica, 2022, 43(6): 428-434.
[15] 肖白, 王转转. 含热泵储能的独立微网电源容量优化配置方法[J]. 电力系统自动化, 2022, 46(11): 37-46.
XIAO B, WANG Z Z.Optimal configuration method of generation capacity for stand-alone microgrid with pumped heat energy storage[J]. Automation of electric power systems, 2022, 46(11): 37-46.
[16] 南斌, 董树锋, 唐坤杰, 等. 考虑需求响应和源荷不确定性的光储微电网储能优化配置[J]. 电网技术, 2023, 47(4): 1340-1352.
NAN B, DONG S F, TANG K J, et al.Optimal configuration of energy storage in PV-storage microgrid considering demand response and uncertainties in source and load[J]. Power system technology, 2023, 47(4): 1340-1352.
[17] 司徒友, 周立德, 陈凤超, 等. 基于典型场景集的智能园区多能源微网多目标配置优化研究[J]. 太阳能学报, 2022, 43(9): 515-526.
SITU Y, ZHOU L D, CHEN F C, et al.Research on configuration of multi-energy microgrid in smart park based on typical scenarios[J]. Acta energiae solaris sinica, 2022, 43(9): 515-526.
[18] 郝婷, 樊小朝, 王维庆, 等. 阶梯式碳交易下考虑源荷不确定性的储能优化配置[J]. 电力系统保护与控制, 2023, 51(1): 101-112.
HAO T, FAN X C, WANG W Q, et al.Optimal configuration of energy storage considering the source-load uncertainty under ladder-type carbon trading[J]. Power system protection and control, 2023, 51(1): 101-112.
[19] 黄南天, 王文婷, 蔡国伟, 等. 计及复杂气象耦合特性的模块化去噪变分自编码器多源-荷联合场景生成[J]. 中国电机工程学报, 2019, 39(10): 2924-2934.
HUANG N T, WANG W T, CAI G W, et al.The joint scenario generation of multi source-load by modular denoising variational autoencoder considering the complex coupling characteristics of meteorology[J]. Proceedings of the CSEE, 2019, 39(10): 2924-2934.
[20] 林之岸, 罗欣, 魏骁雄, 等. 基于双层聚类和模糊等级评定的客户侧供电服务评价[J]. 电力系统保护与控制, 2021, 49(23): 62-71.
LIN Z A, LUO X, WEI X X, et al.Client-side power supply service evaluation based on two-level clustering and fuzzy comprehensive evaluation[J]. Power system protection and control, 2021, 49(23): 62-71.
[21] GB/T18883—2002, 室内空气质量标准[S].
GB/T18883—2002, indoor air quality standard[S].