面向区域综合能源系统的分布式优化调度方法

doi:10.12204/j.issn.1000-7229.2021.11.005

摘要/Abstract

摘要：

为保障区域综合能源系统(integrated community energy system, ICES)中多元能源主体的信息安全与涉密隐私,满足终端用户电/热等多维度用能需求,提出了一种面向ICES的分布式优化调度方法。首先,分别对ICES中配电系统、配气系统及耦合系统进行建模,并采用合适的数学方法对模型进行线性化处理;其次,以系统运行成本最小化为目标,通过引入共识变量实现对ICES优化调度问题解耦,并给出适用于ICES多元能源主体接入的分布式优化调度算法;最后,采用一个典型的ICES算例对所提方法进行验证。结果表明,基于文章所提方法得到的优化调度方案与采用集中式优化调度方法所得方案一致,能够在满足不同能源系统的运行约束,实现ICES经济最优运行的同时,保障ICES中多元能源主体的信息安全与涉密隐私。

关键词: 区域综合能源系统(ICES), 分布式优化调度, 交替乘子算法(ADMM), 共识变量, 多能耦合

Abstract:

In order to guarantee the information safety and confidential privacy of diverse energy entities among the integrated community energy system (ICES), and satisfy the multiple energy demand for electricity and heat, a decentralized optimal scheduling method is proposed in this paper. Firstly, the mathematical models of electric distribution system, natural gas distribution system and coupling system are established respectively and linearized by certain mathematical method. Secondly, the optimal scheduling model of ICES is developed with the operation cost set as the objective function. By introducing the consensus variable into the scheduling model, the optimal operation can be decoupled on the basis of the decentralized scheduling framework, which is suitable for the multiple entities integration for ICES. Finally, a typical ICES case is utilized to verify the proposed method. It is illustrated that the proposed decentralized scheduling method is able to provide the scheduling scheme same as that by centralized method, which is capable of the satisfaction for operation constraints, the realization of optimal operation and the protection of information safety and confidential privacy.

Key words: integrated community energy system (ICES), decentralized optimal scheduling, alternating direction method of multipliers (ADMM), consensus variable, multi-energy coupling

中图分类号:

TM73

林威, 靳小龙, 叶荣. 面向区域综合能源系统的分布式优化调度方法[J]. 电力建设, 2021, 42(11): 44-53.

LIN Wei, JIN Xiaolong, YE Rong. A Decentralized Optimal Scheduling Method for Integrated Community Energy System[J]. ELECTRIC POWER CONSTRUCTION, 2021, 42(11): 44-53.

图/表 13

图1 配电系统模型结构

Fig.1 Structure of electric distribution system

图2 修正Weymouth方程分段线性化

Fig.2 Piecewise linearization of modified Weymouth equation

图3 两类耦合系统结构

Fig.3 Structure of two types of coupling systems

图4 引入共识变量的ICES解耦

Fig.4 ICES decoupling by introducing the consensus variables

图5 分布式优化调度流程框图

Fig.5 Flowchart of decentralized optimal scheduling

图6 ICES算例结构

Fig.6 Structure of the ICES

表1 耦合系统电/热负荷

Table 1 Electricity and heat load of coupling systemskW

图7 耦合系统优化调度方案

Fig.7 Optimal scheduling scheme of coupling systems

图8 不同优化调度方案下配电系统电压分布

Fig.8 Voltage magnitude of electric distribution system under different optimal scheduling schemes

图9 不同优化调度方案下配气系统气压分布

Fig.9 Node pressure of natural gas distribution system under different optimal scheduling schemes

表2 时段17不同分配系数下EH1优化调度方案

Table 2 Optimal scheduling scheme of EH1 under different partition coefficient for time period 17

图10 时段17内EH1耦合变量收敛过程

Fig.10 Convergence process of coupling variables of EH1 for time period 17

图11 时段17内ICES优化结果残差收敛过程

Fig.11 Convergence process of residual of ICES for time period 17

参考文献 23

[1]	WANG C S, LV C, LI P, et al. Modeling and optimal operation of community integrated energy systems: A case study from China[J]. Applied Energy, 2018, 230: 1242-1254. doi: 10.1016/j.apenergy.2018.09.042 URL
[2]	周孝信, 曾嵘, 高峰, 等. 能源互联网的发展现状与展望[J]. 中国科学: 信息科学, 2017, 47(2): 149-170.
	ZHOU Xiaoxin, ZENG Rong, GAO Feng, et al. Development status and prospects of the Energy Internet[J]. Scientia Sinica (Informationis), 2017, 47(2): 149-170.
[3]	周灿煌, 郑杰辉, 荆朝霞, 等. 面向园区微网的综合能源系统多目标优化设计[J]. 电网技术, 2018, 42(6): 1687-1697.
	ZHOU Canhuang, ZHENG Jiehui, JING Zhaoxia, et al. Multi-objective optimal design of integrated energy system for park-level microgrid[J]. Power System Technology, 2018, 42(6): 1687-1697.
[4]	LONG S, MARJANOVIC O, PARISIO A. Generalised control-oriented modelling framework for multi-energy systems[J]. Applied Energy, 2019, 235: 320-331. doi: 10.1016/j.apenergy.2018.10.074 URL
[5]	GEIDL M, ANDERSSON G. Optimal power flow of multiple energy carriers[J]. IEEE Transactions on Power Systems, 2007, 22(1): 145-155. doi: 10.1109/TPWRS.2006.888988 URL
[6]	徐宪东, 贾宏杰, 靳小龙, 等. 区域综合能源系统电/气/热混合潮流算法研究[J]. 中国电机工程学报, 2015, 35(14): 3634-3642.
	XU Xiandong, JIA Hongjie, JIN Xiaolong, et al. Study on hybrid heat-gas-power flow algorithm for integrated community energy system[J]. Proceedings of the CSEE, 2015, 35(14): 3634-3642.
[7]	靳小龙, 穆云飞, 贾宏杰, 等. 考虑配电网重构的区域综合能源系统最优混合潮流计算[J]. 电力系统自动化, 2017, 41(1): 18-24.
	JIN Xiaolong, MU Yunfei, JIA Hongjie, et al. Calculation of optimal hybrid power flow for integrated community energy system considering electric distribution network reconfiguration[J]. Automation of Electric Power Systems, 2017, 41(1): 18-24.
[8]	LIN W, JIN X L, MU Y F, et al. A two-stage multi-objective scheduling method for integrated community energy system[J]. Applied Energy, 2018, 216: 428-441. doi: 10.1016/j.apenergy.2018.01.007 URL
[9]	JIN X L, WU Q W, JIA H J. Local flexibility markets: Literature review on concepts, models and clearing methods[J]. Applied Energy, 2020, 261: 114387. doi: 10.1016/j.apenergy.2019.114387 URL
[10]	LIU G D, JIANG T, OLLIS T B, et al. Distributed energy management for community microgrids considering network operational constraints and building thermal dynamics[J]. Applied Energy, 2019, 239: 83-95. doi: 10.1016/j.apenergy.2019.01.210 URL
[11]	HE C, WU L, LIU T Q, et al. Robust co-optimization scheduling of electricity and natural gas systems via ADMM[J]. IEEE Transactions on Sustainable Energy, 2017, 8(2): 658-670. doi: 10.1109/TSTE.2016.2615104 URL
[12]	HE Y B, YAN M Y, SHAHIDEHPOUR M, et al. Decentralized optimization of multi-area electricity-natural gas flows based on cone reformulation[J]. IEEE Transactions on Power Systems, 2018, 33(4): 4531-4542. doi: 10.1109/TPWRS.2017.2788052 URL
[13]	WEN Y F, QU X B, LI W Y, et al. Synergistic operation of electricity and natural gas networks via ADMM[J]. IEEE Transactions on Smart Grid, 2018, 9(5): 4555-4565. doi: 10.1109/TSG.2017.2663380 URL
[14]	BARAN M, WU F F. Optimal sizing of capacitors placed on a radial distribution system[J]. IEEE Transactions on Power Delivery, 1989, 4(1): 735-743. doi: 10.1109/61.19266 URL
[15]	SHAO C C, WANG X F, SHAHIDEHPOUR M, et al. An MILP-based optimal power flow in multicarrier energy systems[J]. IEEE Transactions on Sustainable Energy, 2017, 8(1): 239-248. doi: 10.1109/TSTE.2016.2595486 URL
[16]	URBINA M, LI Z Y. A combined model for analyzing the interdependency of electrical and gas systems[C]// 2007 39th North American Power Symposium. IEEE, 2007: 468-472.
[17]	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.
[18]	LOFBERG J. YALMIP: A toolbox for modeling and optimization in MATLAB[C]// 2004 IEEE International Conference on Robotics and Automation (IEEE Cat. No. 04CH37508). IEEE, 2004: 284-289.
[19]	SU W C, WANG J H, ZHANG K L, et al. Model predictive control-based power dispatch for distribution system considering plug-in electric vehicle uncertainty[J]. Electric Power Systems Research, 2014, 106: 29-35. doi: 10.1016/j.epsr.2013.08.001 URL
[20]	KERSTING W H. Radial distribution test feeders[J]. IEEE Transactions on Power Systems, 1991, 6(3): 975-985. doi: 10.1109/59.119237 URL
[21]	骆柏锋, 穆云飞, 赵波, 等. 基于统一潮流模型的电-气耦合综合能源系统静态灵敏度分析[J]. 电力系统自动化, 2018, 42(13): 29-35.
	LUO Bofeng, MU Yunfei, ZHAO Bo, et al. Static sensitivity analysis of integrated electricity and gas system based on unified power flow model[J]. Automation of Electric Power Systems, 2018, 42(13): 29-35.
[22]	JIN X L, MU Y F, JIA H J, et al. Optimal day-ahead scheduling of integrated urban energy systems[J]. Applied Energy, 2016, 180: 1-13. doi: 10.1016/j.apenergy.2016.07.071 URL
[23]	HUANG Y, MCILVEEN-WRIGHT D R, REZVANI S, et al. Comparative techno-economic analysis of biomass fuelled combined heat and power for commercial buildings[J]. Applied Energy, 2013, 112: 518-525. doi: 10.1016/j.apenergy.2013.03.078 URL