摘要
随着能源互联网技术、分布式发电供能技术以及新能源交易方式的快速发展和广泛应用,综合能源服务在全球范围内发展迅速。综合能源园区(integrated energy park, IEP)作为综合能源服务的主战场日益受到关注。文章提出了基于地理分区的IEP用能特性评价模型及供能分区方法。首先,基于地理分区,对电、热、冷负荷用能信息进行分析,选取负荷的共性指标和个性指标。然后,将园区内各地块的各指标值合理量化,再采用结合熵权法的层次分析法求取各指标的权重,并将各指标值及其权重进行加权叠加以得到IEP内各地块电、热、冷负荷用能特性的共性和个性得分。最后,以各区域综合负荷分布最均匀为目标,通过电、热、冷负荷综合化、初选核心地块、初步分区和确定最终核心地块及分区,最终得到IEP最终的分区结果。采用某IEP进行算例分析,结果表明所提出的评价模型和分区方法具有较好的有效性和实用性。
Abstract
With the rapid development and wide application of energy internet technology, distributed generation technology and new energy trading mode, integrated energy services are developing rapidly in the world. Integrated energy park (IEP), as the main battlefield of integrated energy services, has attracted more and more attention. This paper puts forward the evaluation model of energy consumption characteristics and the method of energy supply partition of integrated energy parks based on geographical partition. Firstly, on the basis of geographical partition, the energy consumption information of electric, thermal and cooling loads is analyzed, and the commonality and individuality indexes of electric, thermal and cooling loads are selected. Secondly, the index values of each plot in the park are quantified reasonably. Thirdly, the weights of each index are calculated by the analytic hierarchy process combined with the entropy weight method, and the commonality score and individuality score of the energy consumption characteristics of electric, thermal and cooling loads in each plot of IEP can be obtained by weighted overlaying the quantized values of each index and their corresponding weights. Finally, aiming at the most uniform distribution of integrated load in each area, the final partition result of IEP are obtained through the steps of electric, thermal and cooling load integration, primary core plot selection, preliminary partition and determination of final core plot and partition. Taking an integrated energy park as an example, the results show that the evaluation model and partition method proposed in this paper are effective and practical.
关键词
综合能源园区(IEP) /
地理分区 /
评价模型 /
负荷综合化 /
供能分区
Key words
integrated energy park(IEP) /
geographical partition /
evaluation model /
load integration /
energy supply partition
陈非凡,高亚静,梁海峰,段杰.
基于地理分区的综合能源园区用能特性评价模型及供能分区方法[J]. 电力建设. 2019, 40(6): 23-32 https://doi.org/10.3969/j.issn.1000-7229.2019.06.003
CHEN Feifan, GAO Yajing, LIANG Haifeng, DUAN Jie.
Energy Consumption Characteristic Evaluation Model and Energy Supply Partition Method of Integrated Energy Park Based on Geographical Partition[J]. Electric Power Construction. 2019, 40(6): 23-32 https://doi.org/10.3969/j.issn.1000-7229.2019.06.003
{{custom_sec.title}}
{{custom_sec.title}}
{{custom_sec.content}}
参考文献
[1]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.
[2]肖云鹏,王锡凡,王秀丽,等.面向高比例可再生能源的电力市场研究综述[J].中国电机工程学报,2018,38(3):663-674.
XIAO Yunpeng, WANG Xifan, WANG Xiuli, et al. Review on electricity market towards high proportion of renewable energy[J]. Proceedings of the CSEE, 2018,38(3):663-674.
[3]白建华,辛颂旭,刘俊,等.中国实现高比例可再生能源发展路径研究[J].中国电机工程学报,2015,35(14):3699-3705.
BAI Jianhua, XIN Songxu, LIU Jun, et al. Roadmap of realizing the high penetration renewable energy in China[J]. Proceedings of the CSEE, 2015,35(14):3699-3705.
[4]程浩忠,李隽,吴耀武,等.考虑高比例可再生能源的交直流输电网规划挑战与展望[J].电力系统自动化,2017,41(9):19-27.
CHENG Haozhong, LI Jun, WU Yaowu, et al. Challenges and prospects for AC/DC transmission expansion planning considering high proportion of renewable energy[J]. Automation of Electric Power Systems, 2017,41(9):19-27.
[5]邓莉荣, 孙宏斌, 陈润泽, 等.面向能源互联网的热电联供系统节点能价研究[J].电网技术, 2016, 40(11): 3375-3382.
DENG Lirong, SUN Hongbin, CHEN Runze, et al. Research on nodal energy price of combined heat and power system for energy internet[J]. Power System Technology, 2016, 40(11): 3375-3382.
[6]GALBUSERA L,THEODORIDIS G,GIANNOPOULOS G.Intelligent energy systems: Introducing power-ICT interdependency in modeling and control design[J]. IEEE Transactions on Industrial Electronics, 2015, 62(4): 2468-2477.
[7]曾鸣,刘英新,周鹏程,等.综合能源系统建模及效益评价体系综述与展望[J].电网技术,2018,42(6):1697-1708.
ZENG Ming, LIU Yingxin, ZHOU Pengcheng, et al. Review and prospects of integrated energy system modeling and benefit evaluation[J]. Power System Technology, 2018,42(6):1697-1708.
[8]SOROUDI A, EHSAN M. A distribution network expansion planning model considering distributed generation options and techo-economical issues[J]. Energy, 2010, 35(8):3364-3374.
[9]张世翔,吕帅康.面向园区微电网的综合能源系统评价方法[J].电网技术,2018,42(8):2431-2439.
ZHANG Shixiang, L Shuaikang. Evaluation method of park-level integrated energy system for microgrid[J]. Power System Technology, 2018,42(8):2431-2439.
[10]黄玉雄,李更丰,别朝红,等. 分布式综合能源系统可靠性评估[J]. 智慧电力,2017,45(7):43-50.
HUANG Yuxiong, LI Gengfeng, BIE Chaohong, et al. Reliability evaluation of distributed integrated energy systems[J]. Smart Power, 2017,45(7):43-50.
[11]陈柏森,廖清芬,刘涤尘,等. 区域综合能源系统的综合评估指标与方法[J]. 电力系统自动化,2018,42(4):174-182.
CHEN Baisen, LIAO Qingfen, LIU Dichen, et al. Comprehensive evaluation indices and methods for regional integrated energy system[J]. Automation of Electric Power Systems, 2018,42(4):174-182.
[12]CHEN F, GAO Y, DUAN J, et al. The research on energy partition of multi-energy complementary park based on the improved SMMC algorithm and AHP[C]// 2018 13th IEEE Conference on Industrial Electronics and Applications (ICIEA). Wuhan: IEEE,2018:940-945.
[13]黄存强,贺永龙,安娟,等. 结合供电区域特点的供电分区细分方法研究[J]. 湖北电力, 2017(4): 40-44.
HUANG Cunqiang, HE Yonglong, AN Juan, et al. Study on characteristics of power supply regions and subdivision method of power supply subarea in Qinghai[J]. Hubei Electric Power, 2017(4): 40-44.
[14]赵树军,杨普,郝鹏飞,等. 配电网负荷分区及其规划研究[J]. 电网与清洁能源, 2016, 32(8): 79-85.
ZHAO Shujun, YANG Pu, HAO Pengfei, et al. Load partition and planning research of distribution networks[J]. Power System and Clean Energy,2016,32(8): 79-85.
[15]赵婧琦. 配电网供电分区划分方法研究[D].北京:华北电力大学,2017.
ZHAO Jingqi. Research on classification method of distribution network partition[D]. Beijing:North China Electric Power University, 2017.
[16]CHEN F, GUO S, GAO Y, et al. Evaluation model of demand-side energy resources in urban power grid based on geographic information[J]. Applied Sciences,2018,8(9):1491.
[17]杨楠,崔家展,周峥,等.基于模糊序优化的风功率概率模型非参数核密度估计方法[J].电网技术,2016,40(2):335-340.
YANG Nan, CUI Jiazhan, ZHOU Zheng, et al. Research on nonparametric kernel density estimation for modeling of wind power probability characteristics based on fuzzy ordinal optimization[J]. Power System Technology, 2016,40(2):335-340.
[18]刘思,傅旭华,叶承晋,等.应用聚类分析与非参数核密度估计的空间负荷分布规律[J].电网技术,2017,41(2):604-609.
LIU Si, FU Xuhua, YE Chengjin, et al. Spatial load distribution based on clustering analysis and non-parametric kernel density estimation[J]. Power System Technology, 2017,41(2):604-609.
[19]焦立新. 评价指标标准化处理方法的探讨[J]. 安徽农业技术师范学院学报,1999(3): 9-12.
JIAO Lixin. Discussion on standardized processing method of evaluation index[J]. Journal of Anhui Agro Technical Teachers College, 1999(3): 9-12.
[20]GOYAL R K, KAUSHAL S. Deriving crisp and consistent priorities for fuzzy AHP-based multicriteria systems using non-linear constrained optimization[J]. Fuzzy Optimization and Decision Making,2018, 17(2): 195-209.
[21]NAVEED Q N, QURESHI M R N, ALSAYED A O, et al. Prioritizing barriers of E-learning for effective teaching-learning using fuzzy analytic hierarchy process (FAHP)[C]// 2017 4th IEEE International Conference on Engineering Technologies and Applied Sciences (ICETAS). Salmabad: IEEE,2017.
[22]HAMID T, AL-JUMEILY D, HUSSAIN A, et al. Cyber security risk evaluation research based on entropy weight method[C]// 2016 9th International Conference on Developments in eSystems Engineering (DeSE). Liverpool, United Kingdom: IEEE, 2016. DOI: 10.1109/DeSE.2016.18.
[23]宋蒙, 刘健, 刘巩权. 基于优化分区的城市配电网架规划[J]. 电力系统保护与控制, 2005, 33(23):31-35.
SONG Meng, LIU Jian, LIU Gongquan. Urban distribution network planning based on optimal partitioning[J]. Power System Protection and Control, 2005, 33(23):31-35.
基金
国家自然科学基金项目(51607068);中央高校基本科研业务费专项资金(2017MS090,2018MS082)