Research on active distribution network planning and design strategy from the perspective of urban energy internet

doi:10.3969/j.issn.1674-1951.2021.01.010

Abstract

Abstract:

Urban energy internet links the information of power resources in network planning ,which changes the value chain of power industry. Under the new situation, the research on distribution network planning and design has become an urgent problem for the power industry. By analyzing the impact of distributed resources access, energy storage, demand response and key communication technologies of distribution network on active distribution network planning under the urban energy internet, four specific implementation strategies for the companies were put forward from the perspective of urban energy interconnection which include strengthen communication with local government,improving technology exchange between enterprises, updating enterprises' business and building an integrated value chain.The strategies ensure the orderly implementation of active distribution network planning and design.

Key words: urban energy internet, active distribution network（ADN）, planning strategy, distributed resources, energy storage, demand response, smart grid, new infrastructure

CLC Number:

TM933:TM01 ⁺8

ZHOU Binghua, WANG Yang, LI Feng, CUI Jindong, RAN Zihan. Research on active distribution network planning and design strategy from the perspective of urban energy internet[J]. Huadian Technology, 2021, 43(1): 59-65.

Figures/Tables 7

Fig.1

Tab.1

Fig.2

Fig.3

Fig.4

Fig.5

Fig.6

References 17

[1]	范明天, 张祖平, 苏傲雪, 等. 主动配电系统可行技术的研究[J]. 中国电机工程学报, 2013,33(22):12-18.
	FAN Mingtian, ZHANG Zuping, SU Aoxue, et al. Study on feasible technology of active distribution system[J]. Chinese Journal of Electrical Engineering, 2013,33(22):12-18.
[2]	邢海军, 程浩忠, 张沈习, 等. ADN规划研究综述[J]. 电网技术, 2015,39(10):2705-2711. doi: 10.13335/j.1000-3673.pst.2015.10.004
	XING Haijun, CHENG Haozhong, ZHANG Shenxi, et al. Overview of active distribution network planning[J]. Power Grid Technology, 2015,39(10):2705-2711.
[3]	LI Y, FENG B, LI G, et al. Optimal distributed generation planning in active distribution networks considering integration of energy storage[J]. Applied Energy, 2018,210:1073-1081. doi: 10.1016/j.apenergy.2017.08.008
[4]	EHSAN A, YANG Q. State-of-the-art techniques for modelling of uncertainties in active distribution network planning: A review[J]. Applied Energy, 2019,239:1509-1523. doi: 10.1016/j.apenergy.2019.01.211
[5]	MOKRYANI G, HU Y, PANAGIOTIS P, et al. Deterministic approach for active distribution networks planning with high penetration of wind and solar power[J]. Renewable Energy, 2017(113):942-951.
[6]	XIE S, HU Z, WANG J, et al. The optimal planning of smart multi-energy systems incorporating transportation,natural gas and active distribution networks[J]. Applied Energy,2020(261).https://doi.org/10.1016/j.apenergy.2019.114412.
[7]	XIE S, HU Z, WANG J. Two-stage robust optimization for expansion planning of active distribution systems coupled with urban transportation networks[J]. Applied Energy,2020,261:https://doi.org/10.1016/j.apenergy.2019.
[8]	芮松华, 刘海璇, 王洪波, 等. 基于遗传-蚁群算法的交直流配电网分布式电源优化配置[J]. 电力建设, 2019,40(4):9-17.
	RUI Songhua, LIU Haixuan, WANG Hongbo, et al. Optimal allocation of distributed generation in AC / DC distribution network based on genetic ant colony algorithm[J]. Electric Power Construction, 2019,40(4):9-17.
[9]	陈浩, 马平. 基于改进多目标粒子群算法的DG选址定容优化研究[J]. 计算机与数字工程, 2020,48(1):16-38.
	CHEN Hao, MA Ping. Study on DG location and capacity optimization based on improved multi-objective particle swarm optimization[J]. Computer and Digital Engineering, 2020,48(1):16-38.
[10]	EBRAHIMI H, MARJANI S R, TALAVAT V. Optimal planning in active distribution networks considering nonlinear loads using the MOPSO algorithm in the TOPSIS framework[J]. International Transactions on Electrical Energy Systems, 2020,30(3):1-17.
[11]	JAN-HMENKE M, BORNHORST N, BRAUN M. Distribution system monitoring for smart power grids with distributed generation using artificial neural networks[J]. International Journal of Electrical Power and Energy Systems, 2019 ( 113):472-480.
[12]	李宜修. 中国智能电网技术研究热点和前沿[J]. 中国科技信息, 2018(5):98-99.
	LI Yixiu. Research hot spots and frontiers of smart grid technology in China[J]. China Science and Technology Information, 2018 ( 5):98-99.
[13]	BACKE S, KARA G, TOMASGARD A. Comparing individual and coordinated demand response with dynamic and static power grid tariffs[J]. Energy,2020,201.https://doi.org/10.1016/j.energy.2020.117619.
[14]	魏联滨, 王伟臣, 李慧, 等. 基于AP聚类和鲁棒优化的电网规划灵活性评估[J]. 电力系统及其自动化学报, 2020,32(3):99-106.
	WEI Lianbin, WANG Weichen, LI Hui, et al. Grid planning flexibility evaluation based on AP clustering and robust optimization[J]. Journal of Power System and Automation, 2020,32(3):99-106.
[15]	DILEEP G. A survey on smart grid technologies and applications[J]. Renewable Energy, 2020,146(2):2589-2625. doi: 10.1016/j.renene.2019.08.092
[16]	李云波, 张子立, 张晋宾, 等. 基于区块链的分布式微电网交易与能源调度研究[J]. 华电技术, 2020,42(8):24-31.
	LI Yunbo, ZHANG Zili, ZHANG Jinbin, et al. Energy trading and scheduling on micro-grid based on blockchain technology[J]. Huadian Technology, 2020,42(8):24-31.
[17]	赵龙, 岳义博. 大型储能电站智能辅助调试系统的设计及应用[J]. 华电技术, 2019,41(11):53-56.
	ZHAO Long, YUE Yibo. Design and application of intelligent auxiliary debugging system for large-scale energy storage power station[J]. Huadian Technology, 2019,41(11):53-56.

阶段	FDN	AND	城市能源互联网下的ADN
规划前期	规划前期主要以负荷预测为主	综合考虑负荷预测与发电预测	综合城市整体规划与DG,EV,ESS等源性负荷进行负荷预测、发电预测,确定变电站选址定容与电源定点规划
目标规划	主要以DG设备选址定容为主	综合考虑DR、需求侧响应、EV和ESS的联合规划;考虑主动管理技术的应用	综合城市用户需求响应、信息通信、区块链技术进行城市“多源-多网-多荷-多储”一体化建模;不仅实现电源、网架规划,还因地制宜地制定了不同城市多类资源协同规划的方法
评估决策	评价主要以电网安全经济可靠性为目标函数	增加适用ADN的评估指标,如电量供储比、DG接入能力等	城市能源互联网市场开放,规划后评价保证安全可靠运行的同时,必要考虑不同能源主体利益需求,研究适用于不同城市能源互联下ADN可靠性评估体系