区域综合能源系统双层多目标模糊优化模型预测控制方法

doi:10.12204/j.issn.1000-7229.2020.12.012

电力建设 ›› 2020, Vol. 41 ›› Issue (12): 123-132.doi: 10.12204/j.issn.1000-7229.2020.12.012

区域综合能源系统双层多目标模糊优化模型预测控制方法

吕振华¹, 李强¹, 韩华春¹, 王大朔², 马瑞²

1.国网江苏省电力有限公司电力科学研究院,南京市 211103
2.长沙理工大学电气与信息工程学院,长沙市 410114

收稿日期:2020-04-11 出版日期:2020-12-01 发布日期:2020-12-04
通讯作者: 马瑞
作者简介:吕振华(1988),男,硕士,工程师,研究方向为新能源并网技术;|李强(1981),男,博士,正高级工程师,研究方向为新能源并网、交直流配用电技术;|韩华春(1988),女,博士,高级工程师,研究方向为新能源并网、储能电池技术;|王大朔(1995),男,硕士研究生,研究方向为综合能源系统优化。
基金资助:
国家自然科学基金项目(51677007);国网江苏省电力有限公司科技项目(J2019047)

Model Predictive Control Method of Bi-level Multi-objective Fuzzy Optimization for Regional Integrated Energy System

Lü Zhenhua¹, LI Qiang¹, HAN Huachun¹, WANG Dashuo², MA Rui²

1. Electric Power Research Institute of State Grid Jiangsu Electric Power Co., Ltd., Nanjing 211103, China
2. School of Electrical & Information Engineering, Changsha University of Science & Technology, Changsha 410114, China

Received:2020-04-11 Online:2020-12-01 Published:2020-12-04
Contact: MA Rui
Supported by:
National Natural Science Foundation of China(51677007);Science and Technology Project of State Grid Jiangsu Electric Power Co., Ltd.(J2019047)

摘要/Abstract

摘要：

针对区域综合能源系统需满足电网调度要求的特点和自身优化问题,提出一种区域综合能源系统与电网协同的双层多目标模糊优化模型预测控制方法。首先,在考虑区域综合能源源荷储特性模型及其约束基础上,建立双层优化模型,上层模型以电网调度关心的综合能效最大及清洁能源消纳最大等多目标为优化目标;下层模型以运行及用能成本等多目标最小为优化目标。然后,基于模型预测控制,求解单层模型获取上下层目标函数区间,将其模糊化,提出以最大满意度为新目标的模型预测控制方法并求解,从而获得兼顾上下层多目标的优化结果。最后,采用算例仿真验证,表明所提方法正确有效。

关键词: 区域综合能源系统, 综合能效, 可再生能源消纳, 双层多目标优化, 模型预测控制

Abstract:

Aimed at the characteristics that the regional integrated energy system needs to meet the requirements of power grid dispatch and the system optimization problem, a model predictive control method of bi-level multi-objective fuzzy optimization for the coordination of the regional integrated energy system and the power grid is proposed. Firstly, on the basis of the regional integrated energy "source-load-storage" characteristics model and its constraints, a bi-level multi-objective optimization model is established. The upper-level model takes the multi-objective maximum of comprehensive energy efficiency and maximum clean energy accommodation, which both are concerned by grid dispatching, as the optimization objective. The lower-level model takes the multi-objective minimum of operation and energy cost as the optimization objective. Solving the single-level model by model predictive control, the upper and lower objective function intervals are obtained. Then the intervals are blurred to establish a model predictive control method with maximum satisfaction as the new objective, so as to get an optimization result that takes both the upper and lower level into account. Finally, a simulation example is used to verify that the method in the paper is correct and effective.

Key words: regional integrated energy system, comprehensive energy efficiency, renewable energy accommodation, bi-level multi-objective optimization, model predictive control

中图分类号:

TM73

吕振华, 李强, 韩华春, 王大朔, 马瑞. 区域综合能源系统双层多目标模糊优化模型预测控制方法[J]. 电力建设, 2020, 41(12): 123-132.

Lü Zhenhua, LI Qiang, HAN Huachun, WANG Dashuo, MA Rui. Model Predictive Control Method of Bi-level Multi-objective Fuzzy Optimization for Regional Integrated Energy System[J]. ELECTRIC POWER CONSTRUCTION, 2020, 41(12): 123-132.

图/表 12

图1 区域综合能源系统结构

Fig.1 Schematic diagram of the structure of a regional integrated energy system

图2 模型预测控制的滚动优化求解方法

Fig.2 Schematic diagram of rolling optimization solution for model predictive control

图3 区域综合能源系统的多能负荷需求

Fig.3 Multi-energy load demand of regional integrated energy system

图4 分时电价曲线

Fig.4 Time-of-use time electricity price curve

图5 预测环节输出的风电出力预测结果

Fig.5 Prediction results of wind power output in the prediction link

图6 电力设备最优调度决策对比

Fig.6 Comparison of optimal dispatch decisions of power equipment

图7 天然气设备最优调度决策对比

Fig.7 Comparison of optimal dispatch decisions for natural gas equipment

图8 热力设备最优调度决策对比

Fig.8 Comparison of optimal dispatch decisions for thermal equipment

表1 场景1、2部分设备出力策略对比

Table 1 Comparison of the output strategies of part of devices in scenarios 1 and 2

表2 场景1、2综合能效对比

Table 2 Comparison of comprehensive energy efficiency in scenarios 1 and 2

图9 风电出力消纳情况对比

Fig.9 Comparison of wind power output accommodation

图10 场景2区域综合能源系统成本成分分析

Fig.10 Analysis of cost components of regional integrated energy system in scenario 2

参考文献 25

[1]	郭创新, 王惠如, 张伊宁, 等. 面向区域能源互联网的“源-网-荷”协同规划综述[J]. 电网技术, 2019,43(9):3071-3080.
	GUO Chuangxin, WANG Huiru, ZHANG Yining, et al. Review of “source-grid-load” co-planning orienting to regional energy Internet[J]. Power System Technology, 2019,43(9):3071-3080.
[2]	薛友, 李杨, 高滢, 等. 计及风电出力随机特性的电-气综合能源系统随机优化[J]. 电力建设, 2018,39(12):2-12.
	XUE You, LI Yang, GAO Ying, et al. Stochastic optimization in integrated electricity-gas energy systems considering stochastic characteristics of wind power outputs[J]. Electric Power Construction, 2018,39(12):2-12.
[3]	夏新华, 高宗和, 李恒强, 等. 考虑时空互补特性的风光水火多能源基地联合优化调度[J]. 电力工程技术, 2017,36(5):59-65.
	XIA Xinhua, GAO Zonghe, LI Hengqiang, et al. Combined optimization dispatching of multi-source hybrid power bases considering the time-space complementary characteristics[J]. Electric Power Engineering Technology, 2017,36(5):59-65.
[4]	蔡超, 窦晓波, 曹水晶, 等. 基于模型预测控制的园区热电联供微电网能量优化[J]. 电力建设, 2019,40(3):27-33.
	CAI Chao, DOU Xiaobo, CAO Shuijing, et al. Energy optimization based on model predictive control for combined heating and power microgrid in industrial park[J]. Electric Power Construction, 2019,40(3):27-33.
[5]	梅建春, 卫志农, 张勇, 等. 电-气互联综合能源系统多时间尺度动态优化调度[J]. 电力系统自动化, 2018,42(13):36-42.
	MEI Jianchun, WEI Zhinong, ZHANG Yong, et al. Dynamic optimal dispatch with multiple time scale in integrated power and gas energy systems[J]. Automation of Electric Power Systems, 2018,42(13):36-42.
[6]	OUAMMI A, DAGDOUGUI H, DESSAINT L, et al. Coordinated model predictive-based power flows control in a cooperative network of smart microgrids[J]. IEEE Transactions on Smart Grid, 2015,6(5):2233-2244. doi: 10.1109/TSG.2015.2396294 URL
[7]	肖浩, 裴玮, 孔力. 基于模型预测控制的微电网多时间尺度协调优化调度[J]. 电力系统自动化, 2016,40(18):7-14.
	XIAO Hao, PEI Wei, KONG Li. Multi-time scale coordinated optimal dispatch of microgrid based on model predictive control[J]. Automation of Electric Power Systems, 2016,40(18):7-14.
[8]	董雷, 陈卉, 蒲天骄, 等. 基于模型预测控制的主动配电网多时间尺度动态优化调度[J]. 中国电机工程学报, 2016,36(17):4609-4616.
	DONG Lei, CHEN Hui, PU Tianjiao, et al. Multi-time scale dynamic optimal dispatch in active distribution network based on model predictive control[J]. Proceedings of the CSEE, 2016,36(17):4609-4616.
[9]	靳小龙, 穆云飞, 贾宏杰, 等. 集成智能楼宇的微网系统多时间尺度模型预测调度方法[J]. 电力系统自动化, 2019,43(16):25-33.
	JIN Xiaolong, MU Yunfei, JIA Hongjie, et al. Model predictive control based multiple-time-scheduling method for microgrid system with smart buildings integrated[J]. Automation of Electric Power Systems, 2019,43(16):25-33.
[10]	马瑞, 王京生. 智慧社区多能流随机响应面模型预测控制方法[J]. 电力系统自动化, 2018,42(4):121-127.
	MA Rui, WANG Jingsheng. Model predictive control method for multi-energy flow of smart community combined with stochastic response surface method[J]. Automation of Electric Power Systems, 2018,42(4):121-127.
[11]	张彦, 张涛, 刘亚杰, 等. 基于模型预测控制的家庭能源局域网最优能量管理研究[J]. 中国电机工程学报, 2015,35(14):3656-3666. doi: 10.13334/j.0258-8013.pcsee.2015.14.021 URL
	ZHANG Yan, ZHANG Tao, LIU Yajie, et al. Optimal energy management of a residential local energy network based on model predictive control[J]. Proceedings of the CSEE, 2015,35(14):3656-3666.
[12]	LÜ J, ZHANG S X, CHENG H Z, et al. Optimal day-ahead operation of user-level integrated energy system considering dynamic behaviour of heat loads and customers’ heat satisfaction[J]. IET Smart Grid, 2019,2(3):320-326. doi: 10.1049/stg2.v2.3 URL
[13]	王成山, 吕超贤, 李鹏, 等. 园区型综合能源系统多时间尺度模型预测优化调度[J]. 中国电机工程学报, 2019,39(23):6791-6803.
	WANG Chengshan, LÜ Chaoxian, LI Peng, et al. Multiple time-scale optimal scheduling of community integrated energy system based on model predictive control[J]. Proceedings of the CSEE, 2019,39(23):6791-6803.
[14]	丁涛, 牟晨璐, 别朝红, 等. 能源互联网及其优化运行研究现状综述[J]. 中国电机工程学报, 2018,38(15):4318-4328.
	DING Tao, MU Chenlu, BIE Zhaohong, et al. Review of energy Internet and its operation[J]. Proceedings of the CSEE, 2018,38(15):4318-4328.
[15]	张彦, 张涛, 孟繁霖, 等. 基于模型预测控制的能源互联网系统分布式优化调度研究[J]. 中国电机工程学报, 2017,37(23):6829-6845.
	ZHANG Yan, ZHANG Tao, MENG Fanlin, et al. Model predictive control based distributed optimization and scheduling approach for the energy Internet[J]. Proceedings of the CSEE, 2017,37(23):6829-6845.
[16]	廖小兵, 刘开培, 乐健, 等. 基于双层模型预测结构的跨区域AGC机组协同控制策略[J]. 中国电机工程学报, 2019,39(16):4674-4685.
	LIAO Xiaobing, LIU Kaipei, LE Jian, et al. Coordinated control strategy for AGC units across areas based on bi-level model predictive control[J]. Proceedings of the CSEE, 2019,39(16):4674-4685.
[17]	郭尊, 李庚银, 周明, 等. 考虑网络约束和源荷不确定性的区域综合能源系统两阶段鲁棒优化调度[J]. 电网技术, 2019,43(9):3090-3100.
	GUO Zun, LI Gengyin, ZHOU Ming, et al. Two-stage robust optimal scheduling of regional integrated energy system considering network constraints and uncertainties in source and load[J]. Power System Technology, 2019,43(9):3090-3100.
[18]	张沈习, 袁加妍, 程浩忠, 等. 主动配电网中考虑需求侧管理和网络重构的分布式电源规划方法[J]. 中国电机工程学报, 2016,36(S1):1-9.
	ZHANG Shenxi, YUAN Jiayan, CHENG Haozhong, et al. Optimal distributed generation planning in active distribution network considering demand side management and network reconfiguration[J]. Proceedings of the CSEE, 2016,36(S1):1-9.
[19]	杨经纬, 张宁, 王毅, 等. 面向可再生能源消纳的多能源系统: 述评与展望[J]. 电力系统自动化, 2018,42(4):11-24.
	YANG Jingwei, ZHANG Ning, WANG Yi, et al. Multi-energy system towards renewable energy accommodation: Review and prospect[J]. Automation of Electric Power Systems, 2018,42(4):11-24.
[20]	崔杨, 姜涛, 仲悟之, 等. 考虑风电消纳的区域综合能源系统源荷协调经济调度[J]. 电网技术, 2020,44(7):2474-2483.
	CUI Yang, JIANG Tao, ZHONG Wuzhi, et al. Source-load coordination economic dispatch method for regional integrated energy system considering wind power consumption[J]. Power System Technology, 2020,44(7):2474-2483.
[21]	HE C, LIU T Q, WU L, et al. Robust coordination of interdependent electricity and natural gas systems in day-ahead scheduling for facilitating volatile renewable generations via power-to-gas technology[J]. Journal of Modern Power Systems and Clean Energy, 2017,5(3):375-388. doi: 10.1007/s40565-017-0278-z URL
[22]	LIU N, WANG J, WANG L F. Hybrid energy sharing for multiple microgrids in an integrated heat-electricity energy system[J]. IEEE Transactions on Sustainable Energy, 2019,10(3):1139-1151. doi: 10.1109/TSTE.5165391 URL
[23]	WANG Y L, WANG Y D, HUANG Y J, et al. Optimal scheduling of the regional integrated energy system considering economy and environment[J]. IEEE Transactions on Sustainable Energy, 2019,10(4):1939-1949. doi: 10.1109/TSTE.5165391 URL
[24]	马瑞. 电力市场中兼顾环境保护和经济效益的双目标模糊优化短期交易计划新模型[J]. 中国电机工程学报, 2002,22(4):104-108.
	MA Rui. A novel bi-objective fuzzy optimal model of short-term trade planning considering environmental protection and economic profit in deregulated power system[J]. Proceedings of the CSEE, 2002,22(4):104-108.
[25]	鲍海波, 韦化. 考虑风电的电压稳定概率评估的随机响应面法[J]. 中国电机工程学报, 2012,32(13):77-85.
	BAO Haibo, WEI Hua. A stochastic response surface method for probabilistic evaluation of the voltage stability considering wind power[J]. Proceedings of the CSEE, 2012,32(13):77-85.

区域综合能源系统双层多目标模糊优化模型预测控制方法

Model Predictive Control Method of Bi-level Multi-objective Fuzzy Optimization for Regional Integrated Energy System

RichHTML

PDF

可视化

摘要/Abstract

引用本文

使用本文

图/表 12

参考文献 25

相关文章 15

编辑推荐

Metrics

本文评价

[1]	朱旭, 杨军, 李高俊杰, 董旭柱, 刘首文. 计及虚拟储能系统的区域综合能源系统优化调度策略[J]. 电力建设, 2020, 41(8): 99-110.
[2]	魏震波，任小林，黄宇涵. 考虑综合需求侧响应的区域综合能源系统多目标优化调度[J]. 电力建设, 2020, 41(7): 92-99.
[3]	郇嘉嘉，肖勇，陆文升，刘洪，彭家颖，潘险险，李吉峰. 基于复杂网络理论的区域综合能源系统可靠性评估[J]. 电力建设, 2020, 41(4): 1-9.
[4]	董凌1， 2，年珩3，范越2，赵建勇3，张智3，林振智3. 能源互联网背景下共享储能的商业模式探索与实践[J]. 电力建设, 2020, 41(4): 38-44.
[5]	张亚迪, 孙欣, 谢敬东, 孙波, 何志涛. 考虑电转气和可平移负荷的区域综合能源系统优化运行[J]. 电力建设, 2020, 41(12): 102-111.
[6]	王上行，贾学翠，王立华，闫士杰，李相俊. 混合储能系统的功率变换器电流预测控制方法[J]. 电力建设, 2020, 41(1): 71-79.
[7]	耿琪，胡炎，何建宗，周永言，赵伟. 基于纳什谈判的区域综合能源系统运行优化[J]. 电力建设, 2020, 41(1): 114-125.
[8]	张涌新，沈弘，文俊. 面向综合需求响应的综合能源系统储能控制面向综合需求响应的综合能源系统储能控制[J]. 电力建设, 2019, 40(9): 43-51.
[9]	肖海伟，孙久严2，李章溢，肖浩，张亮，黄娟，裴玮. 基于综合储能设备的工业园区联络线功率实时MPC控制方法[J]. 电力建设, 2019, 40(8): 34-42.
[10]	陈启超，李晖，王帅，史锐，张宁，戚庆茹，王菲. 促进可再生能源消纳的多能互补时序运行模拟技术[J]. 电力建设, 2019, 40(7): 18-25.
[11]	黄伟，郭兆蕊，华亮亮. 多主体利益协调的区域综合能源系统综合评价[J]. 电力建设, 2019, 40(4): 81-89.
[12]	蔡超，窦晓波，曹水晶，陈曦，王娜. 基于模型预测控制的园区热电联供微电网能量优化[J]. 电力建设, 2019, 40(3): 27-33.
[13]	董雷，明捷，孟天骄，陈乃仕，蒲天骄. 基于模型预测控制的含柔性直流装置的主动配电网电压控制[J]. 电力建设, 2018, 39(7): 123-128.
[14]	徐少华1，李建林1，宋新甫2，李绚绚3，王定美4. 大规模储能系统提升西北地区可再生能源消纳能力分析[J]. 电力建设, 2018, 39(4): 67-.
[15]	李盟盟1，王育飞1，薛花1，张宇2，方陈2. 不平衡电网下无锁相环逆变器模型预测控制[J]. 电力建设, 2018, 39(4): 100-.