基于能量枢纽的沼–风–光综合能源系统双层协同优化配置

doi:10.11930/j.issn.1004-9649.202303062

中国电力 ›› 2024, Vol. 57 ›› Issue (4): 1-13.DOI: 10.11930/j.issn.1004-9649.202303062

• 综合能源系统优化配置策略 • 上一篇下一篇

基于能量枢纽的沼–风–光综合能源系统双层协同优化配置

李梓萌¹(), 王天阔²(), 胡鹏飞¹(), 于彦雪¹, 杜翼³, 蔡期塬³

1. 浙江大学电气工程学院，浙江杭州　310027
2. 华电电力科学研究院有限公司，浙江杭州　310030
3. 国网福建省电力有限公司经济技术研究院，福建福州　350012

收稿日期:2023-03-14 出版日期:2024-04-28 发布日期:2024-04-26
作者简介:李梓萌（2000—），女，硕士研究生，从事电力能源互联及其智能化研究，E-mail：22110116@zju.edu.cn
王天阔（1992—），男，工程师，从事电气工程、工程管理等研究，E-mail：wangtiankuo92999@163.com
胡鹏飞（1988—），男，通信作者，博士，研究员，从事大规模储能技术及其在电力系统的应用等研究，E-mail：hpf@zju.edu.cn
基金资助:
国家自然科学基金资助项目（52007167）；浙江省教育厅科研项目（Y202250813）；国家电网公司科技项目（52130N22000G）。

Bi-level Collaborative Configuration Optimization of Biogas-Wind-Solar Integrated Energy System Based on Energy Hub

Zimeng LI¹(), Tiankuo WANG²(), Pengfei HU¹(), Yanxue YU¹, Yi DU³, Qiyuan CAI³

1. College of Electrical Engineering, Zhejiang University, Hangzhou 310027, China
2. Huadian Electric Power Research Institute Co., Ltd., Hangzhou 310030, China
3. Power Economic Research Institute of State Grid Fujian Electric Power Company, Fuzhou 350012, China

Received:2023-03-14 Online:2024-04-28 Published:2024-04-26
Supported by:
This work is supported by National Natural Science Foundation of China (No.52007167), Scientific Research Fund of Zhejiang Provincial Education Department (No.Y202250813), Science & Technology Project of SGCC (No.52130N22000G).

摘要/Abstract

摘要：

针对沼-风-光乡村综合能源系统协同优化问题，构建了综合能源系统能量枢纽模型，并提出沼-风-光综合能源系统配置-运行协同优化双层规划模型，上层以年化总成本最小和碳排放量最小为目标，采用NSGA-II算法获取配置方案Pareto最优解集；下层以运行成本（包括碳排放成本）最小为目标获取最优运行方案，引入启发性规则，排除上层输入配置方案可能存在的设备冗余，加速上层优化配置。通过对福建某乡村沼-风-光综合能源系统进行算例验证，结果证明了所提优化配置和运行方案的多元性和优越性。

关键词: 沼-风-光综合能源系统, 经济性, 环保性, 能量枢纽, Pareto最优解集

Abstract:

In order to solve the collaborative optimization problem of rural biogas-wind-solar integrated energy system, a bi-level planning model of collaborative optimization of biogas-wind-solar integrated energy system was proposed based on energy hub. In the upper level, the Pareto optimal solutions of the configuration scheme were obtained using the NSGA-II algorithm with the goal of minimizing the annual total cost and carbon emissions. In the lower level, the optimal operation scheme was obtained with the objective of minimizing the operation cost (including carbon emission cost). The heuristic rule was introduced to eliminate the possible equipment redundancy of the configuration scheme from the upper and speed up the optimal configuration process. Finally, the proposed model was verified through a rural biogas-wind-solar integrated energy system in Fujian. The results have proved the plurality and superiority of the proposed optimal configuration and operation scheme.

Key words: biogas-wind-solar integrated energy system, economy, environmental protection, energy hub, Pareto optimal solutions

李梓萌, 王天阔, 胡鹏飞, 于彦雪, 杜翼, 蔡期塬. 基于能量枢纽的沼–风–光综合能源系统双层协同优化配置[J]. 中国电力, 2024, 57(4): 1-13.

Zimeng LI, Tiankuo WANG, Pengfei HU, Yanxue YU, Yi DU, Qiyuan CAI. Bi-level Collaborative Configuration Optimization of Biogas-Wind-Solar Integrated Energy System Based on Energy Hub[J]. Electric Power, 2024, 57(4): 1-13.

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链接本文: https://www.electricpower.com.cn/CN/10.11930/j.issn.1004-9649.202303062

https://www.electricpower.com.cn/CN/Y2024/V57/I4/1

图/表 28

图 1 沼-风-光综合能源系统能量枢纽框架示意

Fig.1 Schematic diagram of energy hub framework of biogas wind solar integrated energy system

图 2 综合能源系统协同优化双层规划模型算法流程

Fig.2 Algorithm flow chart of the integrated energy system co-optimization bi-level planning model

表 2 设备投资及运维参数表

Table 2 Equipment investment and operation & maintenance parameters

设备	单位额定容量/kW	单位额定容量成本/万元	运维系数/ 万元	最大允许安装数量/台
风机	50.000	0.600	0.019	4
PVT	50.000	1.270	0.010	4
沼气池	300.000	0.500	0.011	2
沼气罐	150.000	0.170	0.011	4
储能电池	100.000	0.290	0.003	4
CHP	100.000	0.880	0.085	10
沼气炉	100.000	0.210	0.045	10
电锅炉	50.000	0.200	0.021	5

图 3 夏季典型日电、热、气负荷曲线

Fig.3 Typical daily load curves of electricity, heat and gas in summer

图 4 冬季典型日电、热、气负荷曲线

Fig.4 Typical daily load curves of electricity, heat and gas in winter

图 5 夏季典型日可再生能源出力和外部温度曲线

Fig.5 Typical daily renewable energy output curves and ambient temperature in summer

图 6 冬季典型日可再生能源出力和外部温度曲线

Fig.6 Typical daily renewable energy output curves and ambient temperature in winter

表 1 福建电网电价表

Table 1 Electricity price of Fujian power grid

时段		电价/ (元·(kW·h)^–1)
高峰时段（08:30—11:30；14:30—16:30；18:00—20:00）		0.808
平峰时段（06:00—08:30；11:30—14:30；16:30—18:00）		0.548
低谷时段（00:00—06:00；20:00—24:00）		0.288

表 3 设备技术参数表

Table 3 Equipment technical parameters

参数	设定值	参数	设定值
λ_on,chp/(元·次^–1)	53.350	η_e,PVT	0.450
λ_off,chp/(元·次^–1)	53.350	η_h,PVT	0.550
η_e,CHP	0.400	T_in,min/ ℃	15.000
η_h,CHP	0.450	T_in,max/ ℃	55.000
P_chp,min/kW	5.000	Q_bio/((kW·h)·m^–3)	6.110
P_chp,max/kW	100.000	Pr_bio/(元·(kW·h)^–1)	0.245
η_f	0.750	S_bes,min	0.100
P_f,ma/kW	100.000	S_bes,max	1.000
η_b	0.750	V_gs,min/(m³·h^–1)	–150.000
P_b,max/kW	50.000	V_gs,max/(m³·h^–1)	150.000
G_s,grid/(g·(kW·h)^–1)	3.120	S_bes,min	0.100
G_n,grid/(g·(kW·h)^–1)	2.350	S_bes,max	0.900
G_c,grid/(g·(kW·h)^–1)	0.890	P_ch,max/kW	80.000
G_c,bio/(kg·m^–3)	1.960	P_dis,max/kW	80.000
G_bef/kg	3721.550	η_ch	0.910
r	0.050	η_dis	0.910
N_inv	8.000	$r_{\rm SO_2} $/(元·g^–1)	0.006
R	0.050	${r_{{\text{N}}{{\text{O}}_{{x}}}}} $/(元·g^–1)	0.008
P_grid,max/kW	1000.000	$ r_{\rm CO_2}$/(元·kg^–1)	0.060

表 4 协同优化方案

Table 4 Collaborative optimization scheme

方案	上层优化配置算法	下层优化运行算法	上层优化配置方法	下层优化运行方法
1	NSGA-II算法	Gurobi 求解器	基于Pareto最优解集的多目标优化方法	将环保性指标转化为经济性指标的多目标优化方法
2	遗传算法	Gurobi 求解器	单目标优化方法	单目标优化方法
3	遗传算法	Gurobi 求解器	基于权重分析法的多目标优化方法	将环保性指标转化为经济性指标的多目标优化方法

图 7 Pareto最优解集

Fig.7 Pareto optimal solution set graph

表 5 方案1优化配置方案及对应优化结果

Table 5 Scheme I optimized configuration scheme and corresponding optimized results

解集	风机/ 台	光伏/ 台	储能电池/ 台	沼气池/ 台	沼气罐/ 台	CHP/ 台	沼气炉/ 台	电锅炉/ 台	年化总成本/ (万元·年^–1)	碳排放量/ (t·年^–1)
折衷解	4	4	1	1	2	1	1	2	105.02	697.70
典型解1	4	1	1	1	1	1	1	3	90.66	823.00
典型解2	4	2	1	1	2	1	1	2	95.95	777.51
典型解3	4	3	1	1	2	1	1	2	100.33	737.73
典型解4	4	4	2	2	4	1	1	2	126.70	680.64

图 8 沼气池内部环境温度曲线

Fig.8 Ambient temperature curve inside biogas digester

表 6 方案1、2优化运行对比结果

Table 6 Comparison results of optimized operation of Scheme I and Scheme II

方案	储能电池/ 台	沼气罐/ 台	CHP/ 台	沼气炉/ 台	电锅炉/ 台	投资成本/ (万元·年^–1)	运行成本/ (万元·年^–1)	沼气产量/ (t·年^–1)	可再生能源消纳率/ %
1	1	2	1	1	2	76.60	21.95	529.70	100.00
2	4	1	3	3	3	111.46	41.60	143.00	69.88

图 9 方案1夏季典型日电功率平衡图

Fig.9 Scheme I electric load balance power diagram in summer

图 10 方案1冬季典型日电功率平衡图

Fig.10 Scheme I electric load balance power diagram in winter

图 11 方案3夏季典型日电功率平衡图

Fig.11 Scheme III electric load balance power diagram in summer

图 12 方案3冬季典型日电功率平衡图

Fig.12 Scheme III electric load balance power diagram in winter

图 13 方案1夏季典型日热功率平衡图

Fig.13 Scheme I thermal load balance power diagram in summer

图 14 方案1冬季典型日热功率平衡图

Fig.14 Scheme I thermal load balance power diagram in winter

图 15 方案3夏季典型日热功率平衡图

Fig.15 Scheme III thermal load balance power diagram in summer

图 16 方案3冬季典型日热功率平衡图

Fig.16 Scheme III thermal load balance power diagram in winter

图 17 方案1夏季典型日气功率平衡图

Fig.17 Scheme I biogas load balance power diagram in summer

图 18 方案1冬季典型日气功率平衡图

Fig.18 Scheme I biogas load balance power diagram in winter

图 19 方案3夏季典型日气功率平衡图

Fig.19 Scheme III biogas load balance power diagram in summer

图 20 方案3冬季典型日气功率平衡图

Fig.20 Scheme III biogas load balance power diagram in winter

图 21 方案1、3夏季能量存储设备SOC曲线

Fig.21 Energy storage equipment SOC curve in summer of Scheme I & III

表 7 方案1、3优化运行对比结果

Table 7 Comparison results of optimized operation of Scheme I and Scheme III

方案	碳排放量/ (t·年^–1)	运行成本/(万元·年^–1)	CHP启停成本/(万元·年^–1)	能量转换成本/(万元·年^–1)	购电成本/(万元·年^–1)	污染物排放成本/(万元·年^–1)	碳排放成本/(万元·年^–1)
1	736.7	21.95	0	7.43	9.28	0.88	4.35
3	736.3	23.30	0	7.85	10.31	0.82	4.32

参考文献 24

1	ZHOU B, XU D, LI C B, et al. Optimal scheduling of biogas-solar-wind renewable portfolio for multicarrier energy supplies[J]. IEEE Transactions on Power Systems, 2018, 33 (6): 6229- 6239. DOI
2	YU C T, LAI X Y, CHEN F, et al. Multi-time period optimal dispatch strategy for integrated energy system considering renewable energy generation accommodation[J]. Energies, 2022, 15 (12): 4329. DOI
3	王瑞, 程杉, 徐建宇, 等. 基于主从博弈和混合需求响应的能量枢纽多时间尺度优化调度策略[J]. 电力自动化设备, 2023, 43 (1): 32- 40.
	WANG Rui, CHENG Shan, XU Jianyu, et al. Multi-time scale optimal scheduling strategy of energy hub based on master-slave game and hybrid demand response[J]. Electric Power Automation Equipment, 2023, 43 (1): 32- 40.
4	王丹, 孟政吉, 贾宏杰, 等. 基于配置-运行协同优化的分布式能源站选型与定容规划[J]. 电力自动化设备, 2019, 39 (8): 152- 160.
	WANG Dan, MENG Zhengji, JIA Hongjie, et al. Siting and sizing planning for distributed energy station based on coordinated optimization of configuration and operation[J]. Electric Power Automation Equipment, 2019, 39 (8): 152- 160.
5	宋晨辉, 冯健, 杨东升, 等. 考虑系统耦合性的综合能源协同优化[J]. 电力系统自动化, 2018, 42 (10): 38- 45, 86.
	SONG Chenhui, FENG Jian, YANG Dongsheng, et al. Collaborative optimization of integrated energy considering system coupling[J]. Automation of Electric Power Systems, 2018, 42 (10): 38- 45, 86.
6	尤森槟, 程志江, 张子建, 等. 风光沼储微电网系统容量优化配置[J]. 中国沼气, 2020, 38 (1): 66- 70.
	YOU Senbin, CHENG Zhijiang, ZHANG Zijian, et al. Optimal capacity allocation of PV-wind-biogas-battery microgrid system[J]. China Biogas, 2020, 38 (1): 66- 70.
7	LI C B, YANG H Y, SHAHIDEHPOUR M, et al. Optimal planning of islanded integrated energy system with solar-biogas energy supply[J]. IEEE Transactions on Sustainable Energy, 2020, 11 (4): 2437- 2448. DOI
8	LAI C S, MCCULLOCH M D. Sizing of stand-alone solar PV and storage system with anaerobic digestion biogas power plants[J]. IEEE Transactions on Industrial Electronics, 2017, 64 (3): 2112- 2121. DOI
9	李民, 刘钦浩, 赵冠, 等. 考虑多元产业协同的乡村综合能源系统规划[J]. 中国电力, 2022, 55 (8): 14- 22.
	LI Min, LIU Qinhao, ZHAO Guan, et al. Rural integrated energy system planning considering multi-industry synergy[J]. Electric Power, 2022, 55 (8): 14- 22.
10	冯智慧, 吕林, 许立雄. 基于能量枢纽的沼–风–光全可再生能源系统日前–实时两阶段优化调度模型[J]. 电网技术, 2019, 43 (9): 3101- 3109.
	FENG Zhihui, LÜ Lin, XU Lixiong. Two-stage optimal dispatch model of day-ahead and real-time for biogas-wind-solar fully renewable energy system based on energy hub[J]. Power System Technology, 2019, 43 (9): 3101- 3109.
11	谭洪, 颜伟, 王浩. 基于建筑物热能流分析的沼–风–光孤立微能网优化调度模型[J]. 电网技术, 2020, 44 (7): 2483- 2491.
	TAN Hong, YAN Wei, WANG Hao. Optimal dispatch model of biogas-wind-solar isolated multi-energy micro-grid based on thermal energy flow analysis of buildings[J]. Power System Technology, 2020, 44 (7): 2483- 2491.
12	王瑞琪, 王新立, 郭光华, 等. 农村光-氢-沼储能综合能源系统建模与鲁棒优化调度[J]. 中国电力, 2023, 56 (5): 89- 98.
	WANG Ruiqi, WANG Xinli, GUO Guanghua, et al. Modeling and robust optimal dispatch of rural integrated energy system considering PV-hydrogen-methane energy storage characteristics[J]. Electric Power, 2023, 56 (5): 89- 98.
13	边晓燕, 史越奇, 裴传逊, 等. 计及经济性和可靠性因素的区域综合能源系统双层协同优化配置[J]. 电工技术学报, 2021, 36 (21): 4529- 4543.
	BIAN Xiaoyan, SHI Yueqi, PEI Chuanxun, et al. Bi-level collaborative configuration optimization of integrated community energy system considering economy and reliability[J]. Transactions of China Electrotechnical Society, 2021, 36 (21): 4529- 4543.
14	刘海涛, 朱海南, 李丰硕, 等. 计及碳成本的电-气-热-氢综合能源系统经济运行策略[J]. 电力建设, 2021, 42 (12): 21- 29.
	LIU Haitao, ZHU Hainan, LI Fengshuo, et al. Economic operation strategy of electric-gas-heat-hydrogen integrated energy system considering carbon cost[J]. Electric Power Construction, 2021, 42 (12): 21- 29.
15	王志贺, 刘元园, 唐沂媛, 等. 考虑二氧化碳排放的冷热电联供系统的容量配置[J]. 电力系统及其自动化学报, 2017, 29 (8): 104- 110.
	WANG Zhihe, LIU Yuanyuan, TANG Yiyuan, et al. Capacity configuration of CCHP system considering carbon dioxide emissions[J]. Proceedings of the CSU-EPSA, 2017, 29 (8): 104- 110.
16	白凯峰, 顾洁, 彭虹桥, 等. 融合风光出力场景生成的多能互补微网系统优化配置[J]. 电力系统自动化, 2018, 42 (15): 133- 141.
	BAI Kaifeng, GU Jie, PENG Hongqiao, et al. Optimal allocation for multi-energy complementary microgrid based on scenario generation of wind power and photovoltaic output[J]. Automation of Electric Power Systems, 2018, 42 (15): 133- 141.
17	刁涵彬, 李培强, 吕小秀, 等. 考虑多元储能差异性的区域综合能源系统储能协同优化配置[J]. 电工技术学报, 2021, 36 (1): 151- 165.
	DIAO Hanbin, LI Peiqiang, LÜ Xiaoxiu, et al. Coordinated optimal allocation of energy storage in regional integrated energy system considering the diversity of multi-energy storage[J]. Transactions of China Electrotechnical Society, 2021, 36 (1): 151- 165.
18	刘广, 白晓清, 刁天一. 考虑气电网络架构的沼-风-光综合能源微网优化调度[J]. 电网与清洁能源, 2020, 36 (12): 49- 58.
	LIU Guang, BAI Xiaoqing, DIAO Tianyi. Optimal scheduling of biogas-wind-solar integrated energy microgrid system considering gas-power network architecture[J]. Power System and Clean Energy, 2020, 36 (12): 49- 58.
19	WU T, BU S Q, WEI X, et al. Multitasking multi-objective operation optimization of integrated energy system considering biogas-solar-wind renewables[J]. Energy Conversion and Management, 2021, 229, 113736. DOI
20	白斌, 韩明亮, 林江, 等. 含风电和光伏的可再生能源场景削减方法[J]. 电力系统保护与控制, 2021, 49 (15): 141- 149.
	BAI Bin, HAN Mingliang, LIN Jiang, et al. Scenario reduction method of renewable energy including wind power and photovoltaic[J]. Power System Protection and Control, 2021, 49 (15): 141- 149.
21	孙惠娟, 方杜, 彭春华. 基于可拓距K-均值聚类和正弦微分进化算法的风储联合系统优化配置[J]. 电力自动化设备, 2021, 41 (10): 20- 27.
	SUN Huijuan, FANG Du, PENG Chunhua. Optimal allocation of wind-energy storage combined system based on extension distance K-means clustering and sine differential evolution algorithm[J]. Electric Power Automation Equipment, 2021, 41 (10): 20- 27.
22	吴静, 德格吉日夫, 谭忠富, 等. 计及P2G与CCHP技术的综合能源系统多目标协同优化模型[J]. 电测与仪表, 2021, 58 (5): 20- 30.
	WU Jing, DE Gejirifu, TAN Zhongfu, et al. Multi-objective coordinated optimization model for integrated energy systems with power-to-gas and combined-cooling-heating-power technologies[J]. Electrical Measurement & Instrumentation, 2021, 58 (5): 20- 30.
23	张博智, 卢妍, 谭晨, 等. 光伏光热互补发电系统多目标容量优化研究[J]. 热力发电, 2022, 51 (5): 9- 17.
	ZHANG Bozhi, LU Yan, TAN Chen, et al. Research on multi-objective capacity optimization of PV-CSP hybrid system[J]. Thermal Power Generation, 2022, 51 (5): 9- 17.
24	查永星. 综合能源系统调度优化的分解型交叉熵算法[D]. 深圳: 深圳大学, 2020.
	ZHA Yongxing. Decomposition-based cross-entropy algorithm for scheduling optimization of integrated energy systems[D]. Shenzhen: Shenzhen University, 2020.

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基于能量枢纽的沼–风–光综合能源系统双层协同优化配置

Bi-level Collaborative Configuration Optimization of Biogas-Wind-Solar Integrated Energy System Based on Energy Hub

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基于能量枢纽的沼–风–光综合能源系统双层协同优化配置

Bi-level Collaborative Configuration Optimization of Biogas-Wind-Solar Integrated Energy System Based on Energy Hub

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