Low-Carbon Planning for Buildings Considering Ladder Carbon Reward and Punishment and Integrated Demand Response

doi:10.16183/j.cnki.jsjtu.2022.527

Abstract

Abstract:

With the rapid development of commercial complexes in recent years, energy consumption and carbon emissions of buildings are growing continuously. In this context, the low-carbon planning of commercial complexes is studied including shopping, restaurants, offices, and accommodation. In addition, a low-carbon planning model for building considering ladder carbon reward and punishment with the introduction of the time-sharing carbon measurement model of superior network power purchase and an integrated demand response (IDR) considering the differentiated predicted mean vote (PMV) of each functional area of the building is established. First, the time-sharing carbon measurement model of superior network power purchase is introduced to evaluate the equivalent carbon emissions of the building. Then, a ladder carbon reward and punishment model is built to measure the carbon emissions of the building. Based on the characteristics of the commercial complex planned in this paper, the IDR considering load time shifting and energy use reduction, end-use energy equipment substitution, and energy use type conversion at the superior energy end is constructed. Afterwards, a low-carbon planning model for building is established to determine the equipment type and capacity considering the PMV for each functional area of the building. Especially, the objective function is to optimize the total annual planning cost of the building by taking into account carbon reward and punishment costs of the building. A distributionally robust optimization model based on Kullback-Leibler divergence is proposed to cope with the output volatility of the connected distributed photovoltaic and photovoltaic curtain wall. Finally, the effects of the ladder carbon reward and punishment mechanism of the time-sharing carbon measurement model of superior network power purchase, the IDR of the differentiated PMV of each functional area of the building, and the volatility of the connected photovoltaic output on the building planning are analyzed to verify the effectiveness of the planning model proposed for energy saving and emission reduction in the building.

Key words: commercial complex, building planning, ladder carbon reward and punishment, integrated demand response (IDR), low-carbon

CLC Number:

TM743

SHANG Mengqi, GAO Hongjun, HE Shuaijia, LIU Junyong. Low-Carbon Planning for Buildings Considering Ladder Carbon Reward and Punishment and Integrated Demand Response[J]. Journal of Shanghai Jiao Tong University, 2024, 58(6): 926-940.

Figures/Tables 14

Fig.1

Fig.2

Tab.1

Results of low-carbon planning of buildings

案例	C/万元	C^inv/ 万元	C^ope/ 万元	$C C O 2$ / 万元	$M C O 2$ /t
1	546.02	70.50	475.52	168.51	2755.15
2	606.47	110.78	495.69	52.55	2034.92
3	652.62	144.87	507.74	63.73	1325.80
4	510.50	55.51	454.99	154.61	2565.56
5	552.74	92.37	460.37	27.22	1248.89
6	574.75	117.15	457.60	32.25	849.21

Tab.1

Tab.2

Fig.3

Tab.3

Planning results of influence of time-sharing metering model on carbon emission from electricity purchase

方案	C/万元	C^ope/ 万元	$C C O 2$ / 万元	$M C O 2$ /t
a	578.99	461.97	40.36	986.32
b	574.75	457.60	32.25	849.21

Tab.3

Tab.4

Fig.4

Fig.5

Fig.6

Tab.5

Planning results of impacts of PMV comfort index

方案	C/万元	C^ope/ 万元	$C C O 2$ / 万元	$M C O 2$ /t
c	574.75	457.60	32.25	849.21
d	582.33	465.88	37.35	943.76

Tab.5

Tab.6

Tab.7

Planning results of different optimal planning methods considering PV output fluctuation

方法	C/万元	C^inv/ 万元	C^ope/ 万元	$C C O 2$ / 万元	$M C O 2$ /t
e	568.48	110.57	457.91	31.17	948.15
f	569.77	110.57	459.21	31.67	956.47
g	575.36	109.69	465.67	36.30	1041.16
h	570.65	109.93	460.72	32.25	965.91

Tab.7

Tab.8

References 37

[1]	中国建筑节能协会. 中国建筑能耗研究报告2020[R]. 北京: 中国建筑节能协会, 2021.
	China Association of Building Energy Efficiency. China building energy consumption annual report 2020[R]. Beijing: China Association of Building Energy Efficiency, 2021.
[2]	辛保安, 单葆国, 李琼慧, 等. “双碳”目标下“能源三要素”再思考[J]. 中国电机工程学报, 2022, 42(9): 3117-3126.
	XIN Baoan, SHAN Baoguo, LI Qionghui, et al. Rethinking of the “three elements of energy” toward carbon peak and carbon neutrality[J]. Proceedings of the CSEE, 2022, 42(9): 3117-3126.
[3]	张志义. 电力市场下区域商业楼宇日前电能交易的建模与优化研究[D]. 广州: 华南理工大学, 2020.
	ZHANG Zhiyi. Research on modeling and optimization of day-ahead electricity transaction among regional commercial buildings in electricity market[D]. Guangzhou: South China University of Technology, 2020.
[4]	徐筝, 孙宏斌, 郭庆来. 综合需求响应研究综述及展望[J]. 中国电机工程学报, 2018, 38(24): 7194-7205.
	XU Zheng, SUN Hongbin, GUO Qinglai. Review and prospect of integrated demand response[J]. Proceedings of the CSEE, 2018, 38(24): 7194-7205.
[5]	LIU J, CAO S L, CHEN X, et al. Energy planning of renewable applications in high-rise residential buildings integrating battery and hydrogen vehicle storage[J]. Applied Energy, 2021, 281: 116038.
[6]	LIU J, CHEN X, YANG H X, et al. Hybrid renewable energy applications in zero-energy buildings and communities integrating battery and hydrogen vehicle storage[J]. Applied Energy, 2021, 290: 116733.
[7]	程耀华, 张宁, 康重庆, 等. 低碳多能源系统的研究框架及展望[J]. 中国电机工程学报, 2017, 37(14): 4060-4069.
	CHENG Yaohua, ZHANG Ning, KANG Chongqing, et al. Research framework and prospects of low-carbon multiple energy systems[J]. Proceedings of the CSEE, 2017, 37(14): 4060-4069.
[8]	丁曦, 张笑演, 王胜寒, 等. 双碳目标下考虑最优建设时序的区域综合能源系统低碳规划[J]. 高电压技术, 2022, 48(7): 2584-2596.
	DING Xi, ZHANG Xiaoyan, WANG Shenghan, et al. Low-carbon planning of regional integrated energy system considering optimal construction timing under dual carbon goals[J]. High Voltage Engineering, 2022, 48(7): 2584-2596.
[9]	XIANG Y, GUO Y T, WU G, et al. Low-carbon economic planning of integrated electricity-gas energy systems[J]. Energy, 2022, 249: 123755.
[10]	陈志, 胡志坚, 翁菖宏, 等. 基于阶梯碳交易机制的园区综合能源系统多阶段规划[J]. 电力自动化设备, 2021, 41(9): 148-155.
	CHEN Zhi, HU Zhijian, WENG Changhong, et al. Multi-stage planning of park-level integrated energy system based on ladder-type carbon trading mecha-nism[J]. Electric Power Automation Equipment, 2021, 41(9): 148-155.
[11]	张晓辉, 刘小琰, 钟嘉庆. 考虑奖惩阶梯型碳交易和电-热转移负荷不确定性的综合能源系统规划[J]. 中国电机工程学报, 2020, 40(19): 6132-6142.
	ZHANG Xiaohui, LIU Xiaoyan, ZHONG Jiaqing. Integrated energy system planning considering a reward and punishment ladder-type carbon trading and electric-thermal transfer load uncertainty[J]. Proceedings of the CSEE, 2020, 40(19): 6132-6142.
[12]	王雷雷, 高红均, 刘畅, 等. 考虑分时碳计量的智能楼宇群电-碳耦合互动共享[J]. 电网技术, 2022, 46(6): 2054-2064.
	WANG Leilei, GAO Hongjun, LIU Chang, et al. Electricity carbon coupling sharing among intelligent buildings considering time-of-use carbon emission measurement[J]. Power System Technology, 2022, 46(6): 2054-2064.
[13]	王凌云, 徐健哲, 李世春, 等. 考虑电-气-热需求响应和阶梯式碳交易的综合能源系统低碳经济调度[J]. 智慧电力, 2022, 50(9): 45-52.
	WANG Lingyun, XU Jianzhe, LI Shichun, et al. Low carbon economic dispatch of integrated energy system considering electricity-gas-heat demand response and tiered carbon trading[J]. Smart Power, 2022, 50(9): 45-52.
[14]	齐先军, 蒋中琦, 张晶晶, 等. 考虑碳捕集与综合需求响应互补的综合能源系统优化调度[J]. 电力自动化设备, 2023, 43(7): 133-141.
	QI Xianjun, JIANG Zhongqi, ZHANG Jingjing, et al. Optimal dispatching of integrated energy system considering complementation of carbon capture and integrated demand response[J]. Electric Power Automation Equipment, 2023, 43(7): 133-141.
[15]	王瑞, 程杉, 刘烨, 等. 基于综合需求响应和奖惩阶梯碳交易的能源枢纽主从博弈优化调度[J]. 电力系统保护与控制, 2022, 50(8): 75-85.
	WANG Rui, CHENG Shan, LIU Ye, et al. Master-slave game optimal scheduling of energy hub based on integrated demand response and a reward and punishment ladder carbon trading mechanism[J]. Power System Protection & Control, 2022, 50(8): 75-85.
[16]	周鑫, 韩肖清, 李廷钧, 等. 计及需求响应和电能交互的多主体综合能源系统主从博弈优化调度策略[J]. 电网技术, 2022, 46(9): 3333-3346.
	ZHOU Xin, HAN Xiaoqing, LI Tingjun, et al. Master-slave game optimal scheduling strategy for multi-agent integrated energy system based on demand response and power interaction[J]. Power System Technology, 2022, 46(9): 3333-3346.
[17]	宋晓通, 李文博, 周京华, 等. 碳交易机制下计及P2G及负荷柔性特征的低碳经济调度[J/OL]. 电测与仪表. http://kns.cnki.net/kcms/detail/23.1202.TH.20221021.1503.016.html.
	SONG Xiaotong, LI Wenbo, ZHOU Jinghua, et al. Low-carbon economic dispatch considering P2G and load flexibility characteristic under carbon trading mechanism[J/OL]. Electrical Measurement & Instrumentation. http://kns.cnki.net/kcms/detail/23.1202.TH.20221021.1503.016.html.
[18]	张宏, 陈燕楠, 王洪坤, 等. 考虑源荷双重不确定性的多源系统鲁棒模糊动态综合环境经济调度[J]. 高压电技术, 2024, 50(4): 1446-1456.
	ZHANG Hong, CHEN Yannan, WANG Hongkun, et al. Robust fuzzy dynamic integrate environmental economic dispatch for multi-source system considering the double uncertainty of source-load[J]. High Voltage Engineering, 2024, 50(4): 1446-1456
[19]	米阳, 赵海辉, 付起欣, 等. 考虑风光不确定与碳交易的区域综合能源系统双层博弈优化运行[J]. 电网技术, 2023, 47(6): 2174-2188.
	MI Yang, ZHAO Haihui, FU Qixin, et al. Two-level game optimal operation of regional integrated energy system considering wind and solar uncertainty and carbon trading[J]. Power System Technology, 2023, 47(6): 2174-2188.
[20]	贺帅佳, 阮贺彬, 高红均, 等. 分布鲁棒优化方法在电力系统中的理论分析与应用综述[J]. 电力系统自动化, 2020, 44(14): 179-191.
	HE Shuaijia, RUAN Hebin, GAO Hongjun, et al. Overview on theory analysis and application of distributionally robust optimization method in power system[J]. Automation of Electric Power Systems, 2020, 44(14): 179-191.
[21]	吴孟雪, 房方. 计及风光不确定性的电-热-氢综合能源系统分布鲁棒优化[J]. 电工技术学报, 2023, 38(13): 3473-3485.
	WU Mengxue, FANG Fang. Distributionally robust optimization of electricity-heat-hydrogen integrated energy system with wind and solar uncertainties[J]. Transactions of China Electrotechnical Society, 2023, 38(13): 3473-3485.
[22]	李旭霞, 张琳娜, 郑晓明, 等. 基于KL散度的储能电站分布鲁棒规划方法[J]. 太阳能学报, 2022, 43(4): 46-55. doi: 10.19912/j.0254-0096.tynxb.2021-0049
	LI Xuxia, ZHANG Linna, ZHENG Xiaoming, et al. Kl divergence-based distributionally robust planning method for energy storage plants[J]. Acta Energiae Solaris Sinica, 2022, 43(4): 46-55. doi: 10.19912/j.0254-0096.tynxb.2021-0049
[23]	魏梅芳, 吴燕, 黎跃龙, 等. 基于分布鲁棒优化的微电网日前经济运行模型与求解方法[J]. 电力系统及其自动化学报, 2022, 34(12): 81-90.
	WEI Meifang, WU Yan, LI Yuelong, et al. Day-ahead economic operation model of microgrid and its solving method based on distributed robust optimization[J]. Proceedings of the CSU-EPSA, 2022, 34(12): 81-90.
[24]	CHERNYAVS'KA L, GULLì F. Marginal CO₂ cost pass-through under imperfect competition in power markets[J]. Ecological Economics, 2008, 68(1): 408-421.
[25]	崔杨, 曾鹏, 仲悟之, 等. 考虑阶梯式碳交易的电-气-热综合能源系统低碳经济调度[J]. 电力自动化设备, 2021, 41(3): 10-17.
	CUI Yang, ZENG Peng, ZHONG Wuzhi, et al. Low-carbon economic dispatch of electricity-gas-heat integrated energy system based on ladder-type carbon trading[J]. Electric Power Automation Equipment, 2021, 41(3): 10-17.
[26]	孙毅, 张辰, 李泽坤, 等. 计及多区域用户差异化PMV的柔性负荷多功率级调控策略[J]. 中国电机工程学报, 2021, 41(22): 7574-7586.
	SUN Yi, ZHANG Chen, LI Zekun, et al. Flexible load multi-power level control strategy taking into account the differentiated PMV of multi-region users[J]. Proceedings of the CSEE, 2021, 41(22): 7574-7586.
[27]	任文诗, 高红均, 刘友波, 等. 智能建筑群电能日前优化共享[J]. 电网技术, 2019, 43(7): 2568-2577.
	REN Wenshi, GAO Hongjun, LIU Youbo, et al. Optimal day-ahead electricity scheduling and sharing for smart building cluster[J]. Power System Technology, 2019, 43(7): 2568-2577.
[28]	靳小龙, 穆云飞, 贾宏杰, 等. 融合需求侧虚拟储能系统的冷热电联供楼宇微网优化调度方法[J]. 中国电机工程学报, 2017, 37(2): 581-591.
	JIN Xiaolong, MU Yunfei, JIA Hongjie, et al. Optimal scheduling method for a combined cooling, heating and power building microgrid considering virtual storage system at demand side[J]. Proceedings of the CSEE, 2017, 37(2): 581-591.
[29]	梁锦来, 胡福金. 基于电力负荷历史数据挖掘的负荷预测算法研究[J]. 能源与环保, 2021, 43(11): 267-272.
	LIANG Jinlai, HU Fujin. Research on load forecasting algorithm based on electric load historical data mining[J]. China Energy & Environmental Protection, 2021, 43(11): 267-272.
[30]	李驰宇, 高红均, 刘友波, 等. 多园区微网优化共享运行策略[J]. 电力自动化设备, 2020, 40(3): 29-36.
	LI Chiyu, GAO Hongjun, LIU Youbo, et al. Optimal sharing operation strategy for multi park-level microgrid[J]. Electric Power Automation Equipment, 2020, 40(3): 29-36.
[31]	刘文霞, 姚齐, 王月汉, 等. 基于阶梯型需求响应机制的供需主从博弈电源规划模型[J]. 电力系统自动化, 2022, 46(20): 54-63.
	LIU Wenxia, YAO Qi, WANG Yuehan, et al. Generation planning model of stackelberg game between supply and demand based on stepped demand response mechanism[J]. Automation of Electric Power Systems, 2022, 46(20): 54-63.
[32]	国家发展改革委, 价格司. 关于2020年光伏发电上网电价政策有关事项的通知[R]. 北京: 国家发展改革委, 2020.
	National Development and Reform Commission, Price Department. Notice on matters related to the feed-in tariff policy for photovoltaic power generation in 2020[R]. Beijing: National Development and Reform Commission, 2020.
[33]	刘自发, 周翰泽. 计及多主体能源交易的综合能源系统规划方法研究[J]. 电网技术, 2022, 46(9): 3524-3536.
	LIU Zifa, ZHOU Hanze. Research on comprehensive energy system planning method considering multi agent energy transaction[J]. Power System Technology, 2022, 46(9): 3524-3536.
[34]	钟鹏元, 杨晓宏, 寇建玉. 含储氢结构的园区综合能源系统优化配置研究[J]. 综合智慧能源, 2022, 44(9): 11-19. doi: 10.3969/j.issn.2097-0706.2022.09.002
	ZHONG Pengyuan, YANG Xiaohong, KOU Jianyu. Research on the optimal configuration of integrated energy systems for parks with hydrogen storage devices[J]. Integrated Intelligent Energy, 2022, 44(9): 11-19. doi: 10.3969/j.issn.2097-0706.2022.09.002
[35]	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. doi: 10.19912/j.0254-0096.tynxb.2020-1235
	SITU You, ZHOU Lide, CHEN Fengchao, 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. doi: 10.19912/j.0254-0096.tynxb.2020-1235
[36]	张涛, 胡泽春, 张丹阳. 楼宇综合能源系统容量配置优化[J]. 电力建设, 2019, 40(8): 3-11. doi: 10.3969/j.issn.1000-7229.2019.08.001
	ZHANG Tao, HU Zechun, ZHANG Danyang. Study on optimal capacity planning of building integrated energy system[J]. Electric Power Construction, 2019, 40(8): 3-11. doi: 10.3969/j.issn.1000-7229.2019.08.001
[37]	夏雪薇, 魏霞, 陈洁, 等. 风电-P2G与燃气采暖多能耦合系统规划分析[J]. 太阳能学报, 2021, 42(6): 356-363.
	XIA Xuewei, WEI Xia, CHEN Jie, et al. Planning and analysis of wind power-P2G and gas heating multi-energy coupling system[J]. Acta Energiae Solaris Sinica, 2021, 42(6): 356-363.

案例	P/kW							E/(kW·h)
案例	DPV	PVCW	EB	GB	P2G	GT	HB	ESS
1	450.56	526.50	150	150	0	200	160	750
2	605.44	614.25	150	170	0	200	160	1000
3	689.92	666.90	100	220	200	160	120	1000
4	450.56	421.20	180	130	0	200	160	500
5	605.44	526.50	130	150	0	160	120	750
6	661.76	614.25	130	180	100	120	100	750

方案	P/kW							E/(kW·h)
方案	DPV	PVCW	EB	GB	P2G	GT	HB	ESS
a	661.76	614.25	60	180	80	140	120	750
b	661.76	614.25	130	180	100	120	100	750

方案	P/kW							E/(kW·h)
方案	DPV	PVCW	EB	GB	P2G	GT	HB	ESS
c	661.76	614.25	130	180	100	120	100	750
d	661.76	614.25	100	160	80	160	120	750

方法	P/kW							E/(kW·h)
方法	DPV	PVCW	EB	GB	P2G	GT	HB	ESS
e	633.6	614.25	130	170	80	140	120	750
f	633.6	614.25	130	170	80	140	120	750
g	633.6	614.25	130	170	50	140	120	750
h	633.6	614.25	130	170	80	120	100	750