Coordinated Optimization for Integrated Electricity and Gas Systems with Power-to-Gas Stations

doi:10.12204/j.issn.1000-7229.2021.03.001

Abstract

Abstract:

Currently, wind power development is mainly constrained by the phenomenon of wind power curtailment. The power to gas (P2G) technology can convert electrical energy into gas fuel, which can be used to promote the accommodation of wind power. The uncertainty of wind power may influence the decision result of the integrated system as well. In this paper, the basic structure of integrated system with P2G stations is constructed and a generation method of the scenario set is proposed, then a detailed two-stage coordinated optimal model for integrated systems with P2G stations and gas/power storage devices is presented, which includes the network and coupling device constraints. Moreover, a piecewise linear method is used to linearize the natural gas flow which makes the model into a mixed integer programming problems. At last, numerical results on the IEEE 39-bus system and a modified 6-node natural gas systems verify the effectiveness of the proposed model.

Key words: integrated electricity and gas system, coordinated optimization, wind power, power-to-gas, scenario set

CLC Number:

TM73

YIN Zuoyang, ZHAO Yinbo, YANG Jingxi, LI Shun, GAO Hongjun, LIU Junyong. Coordinated Optimization for Integrated Electricity and Gas Systems with Power-to-Gas Stations[J]. ELECTRIC POWER CONSTRUCTION, 2021, 42(3): 1-9.

Figures/Tables 12

Fig.1 Structure of the integrated power and gas system

Fig.2 6-node natural gas system

Fig.3 Daily curves of electricity load, gas load and wind power

Fig.4 Curtailment of wind power in Scenario 1 and 2

Fig.5 P2G output in Scenario 2 and 3

Fig.6 Gas-fired unit output in Scenario 4 and 5

Fig.7 Gas supply in Scenario 4 and 5

Fig.8 Gas-fired unit output in Scenario 6 and 7

Fig.9 Node pressure in Scenario 7

Table 1 State of the generators

Table 2 Generation cost of the two different models

Table A1 Parameters of generation unit

References 17

[1]	赵军, 王妍, 王丹, 等. 能源互联网研究进展: 定义、指标与研究方法[J]. 电力系统及其自动化学报, 2018,30(10):1-14.
	ZHAO Jun, WANG Yan, WANG Dan, et al. Research progress in energy Internet: Definition, indicator and research method[J]. Proceedings of the CSU-EPSA, 2018,30(10):1-14.
[2]	石雪靖. 能源互联网技术成熟度等级评估[J]. 电力系统及其自动化学报, 2020,32(3):129-134.
	SHI Xuejing. Evaluation on technology readiness levels of energy Internet[J]. Proceedings of the CSU-EPSA, 2020,32(3):129-134.
[3]	葛磊蛟, 汪宇倩, 戚嘉兴, 等. 面向城市能源互联网的电力物联网内涵、架构和关键技术[J]. 电力建设, 2019,40(9):91-98.
	GE Leijiao, WANG Yuqian, QI Jiaxing, et al. The content, frameworks and key technologies of power Internet of Things for urban energy Internet[J]. Electric Power Construction, 2019,40(9):91-98.
[4]	陈健, 林咨良, 赵浩然, 等. 考虑信息耦合的电-气综合能源系统韧性优化方法[J]. 中国电机工程学报, 2020,40(21):6854-6864.
	CHEN Jian, LIN Ziliang, ZHAO Haoran, et al. Optimization method for resilience of integrated electric-gas system with consideration of cyber coupling[J]. Proceedings of the CSEE, 2020,40(21):6854-6864.
[5]	董雷, 王春斐, 李烨, 等. 多时间断面电-气综合能源系统状态估计[J]. 电网技术, 2020,44(9):3458-3465.
	DONG Lei, WANG Chunfei, LI Ye, et al. Multi-snapshot state estimation of electric-gas integrated energy system[J]. Power System Technology, 2020,44(9):3458-3465.
[6]	龚晓琴, 王进, 王珑, 等. 含电转气的电-气互联综合能源系统低碳经济运行[J]. 电力科学与技术学报, 2020,35(2):76-83.
	GONG Xiaoqin, WANG Jin, WANG Long, et al. Low-carbon economic operation for integrated electricity and natural-gas energy system with power-to-gas[J]. Journal of Electric Power Science and Technology, 2020,35(2):76-83.
[7]	邹玙琦, 杨国华, 郑豪丰, 等. 含P2G的综合能源系统经济调度研究[J]. 电工电气, 2020(3):12-15,35.
	ZOU Yuqi, YANG Guohua, ZHENG Haofeng, et al. Research on economic dispatch of integrated energy system with P2G[J]. Electrotechnics Electric, 2020(3):12-15,35.
[8]	牛启帆, 武鹏, 张菁, 等. 考虑电转气的电-气耦合系统协同优化规划方法[J]. 电力系统自动化, 2020,44(3):24-31.
	NIU Qifan, WU Peng, ZHANG Jing, et al. Collaborative optimal planning method for electricity-gas coupling system considering power to gas[J]. Automation of Electric Power Systems, 2020,44(3):24-31.
[9]	张军六, 樊伟, 谭忠富, 等. 计及需求响应的气电互联虚拟电厂多目标调度优化模型[J]. 电力建设, 2020,41(2):1-10.
	ZHANG Junliu, FAN Wei, TAN Zhongfu, et al. Multi-objective optimization model of gas-electricity interconnected virtual power plant considering demand response[J]. Electric Power Construction, 2020,41(2):1-10.
[10]	黄国日, 刘伟佳, 文福拴, 等. 具有电转气装置的电-气混联综合能源系统的协同规划[J]. 电力建设, 2016,37(9):1-13.
	HUANG Guori, LIU Weijia, WEN Fushuan, et al. Collaborative planning of integrated electricity and natural gas energy systems with power-to-gas stations[J]. Electric Power Construction, 2016,37(9):1-13.
[11]	林威, 靳小龙, 穆云飞, 等. 区域综合能源系统多目标最优混合潮流算法[J]. 中国电机工程学报, 2017,37(20):5829-5839.
	LIN Wei, JIN Xiaolong, MU Yunfei, et al. Multi-objective optimal hybrid power flow algorithm for integrated local area energy system[J]. Proceedings of the CSEE, 2017,37(20):5829-5839.
[12]	徐宪东. 电/气/热微型能源系统的建模、仿真与能量管理研究[D]. 天津: 天津大学, 2014.
	XU Xiandong. Modelling, simulation, and energy management research for electricity, gas, and heat based micro energy system[D]. Tianjin: Tianjin University, 2014.
[13]	陈曦, 徐青山, 杨永标. 考虑风电不确定性的CCHP型微网日前优化经济调度[J]. 电力建设, 2020,41(6):107-113.
	CHEN Xi, XU Qingshan, YANG Yongbiao. Day-ahead optimized economic dispatch of CCHP microgrid considering wind power uncertainty[J]. Electric Power Construction, 2020,41(6):107-113.
[14]	周浩洁, 吴任博, 马云飞, 等. 考虑风电不确定性的电-气能源系统数据驱动鲁棒优化调度[J]. 电网技术, 2020,44(10):3752-3761.
	ZHOU Haojie, WU Renbo, MA Yunfei, et al. A data-driven robust unit commitment model of integrated electricity and gas system considering wind power uncertainty[J]. Power System Technology, 2020,44(10):3752-3761.
[15]	彭春华, 于蓉, 孙惠娟. 基于K-均值聚类多场景时序特性分析的分布式电源多目标规划[J]. 电力自动化设备, 2015,35(10):58-65.
	PENG Chunhua, YU Rong, SUN Huijuan. Multi-objective DG planning based on K-means clustering and multi-scenario timing characteristics analysis[J]. Electric Power Automation Equipment, 2015,35(10):58-65.
[16]	刘小聪, 王蓓蓓, 李扬, 等. 计及需求侧资源的大规模风电消纳随机机组组合模型[J]. 中国电机工程学报, 2015,35(14):3714-3723. doi: 10.13334/j.0258-8013.pcsee.2015.14.028 URL
	LIU Xiaocong, WANG Beibei, LI Yang, et al. Stochastic unit commitment model for high wind power integration considering demand side resources[J]. Proceedings of the CSEE, 2015,35(14):3714-3723.
[17]	高红均, 刘俊勇, 魏震波, 等. 基于极限场景集的风电机组安全调度决策模型[J]. 电网技术, 2013,37(6):1590-1595.
	GAO Hongjun, LIU Junyong, WEI Zhenbo, et al. A security-constrainted dispatching model for wind generation units based on extreme scenario set optimization[J]. Power System Technology, 2013,37(6):1590-1595.