Multi-Time Scale Optimization Scheduling for Microgrids Containing Electric Vehicle Clusters

YANG Kang,SHI Lushan,ZHOU Hang,WANG Zhaoyang,WANG Bolun,ZHOU Xia,TANG Hao

Distributed Energy ›› 2024, Vol. 9 ›› Issue (3) : 21-30.

PDF(5903 KB)
PDF(5903 KB)
Distributed Energy ›› 2024, Vol. 9 ›› Issue (3) : 21-30. DOI: 10.16513/j.2096-2185.DE.2409303
Basic Research

Multi-Time Scale Optimization Scheduling for Microgrids Containing Electric Vehicle Clusters

Author information +
History +

Abstract

When discharging, electric vehicles can serve as distributed energy storage units of the power grid to alleviate the power supply pressure of microgrids with high proportions of new energy integration. Capitalizing on the characteristics of time-of-use tariffs across multi-time scales, this study proposes a multi-time scale optimization scheduling method for microgrids that takes into account clusters of electric vehicles. In day-ahead scheduling phase, the equipment output such as internal energy storage, interruptible loads and transferable loads in the microgrid is optimized based on time of use tariffs; During intra-day optimization scheduling phase, electric vehicle clusters will be included in the energy scheduling of microgrids, and reasonable charging and discharging can be achieved by analyzing the scheduling potential of each electric vehicle cluster. To verify the effectiveness of the proposed scheme, electric vehicle clusters are selected to participate in microgrid energy scheduling based on variable time of use tariffs during peak, flat, and valley periods. The results show that the multi-time scale optimization scheduling for microgrids considering the participation of electric vehicle clusters can make full use of the energy storage resources of electric vehicle clusters and improve the flexibility and economy of microgrid scheduling operation.

Key words

microgrids / multi-time scale / electric vehicle cluster / time of use tariffs / optimization scheduling

Cite this article

Download Citations
Kang YANG , Lushan SHI , Hang ZHOU , et al . Multi-Time Scale Optimization Scheduling for Microgrids Containing Electric Vehicle Clusters[J]. Distributed Energy Resources. 2024, 9(3): 21-30 https://doi.org/10.16513/j.2096-2185.DE.2409303

References

[1]
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, 55.
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, 55.
[2]
高赐威,张亮. 电动汽车充电对电网影响的综述[J]. 电网技术2011, 35(2): 127-131.
GAO Ciwei, ZHANG Liang. A survey of influence of electrics vehicle charging on power grid [J]. Power System Technology, 2011, 35(2): 127-131.
[3]
朱辉,吕红芳,阳晓明. 基于多代理系统的微电网多目标优化调度[J]. 发电技术2019, 40(6): 527-534.
ZHU Hui, Hongfang, YANG Xiaoming. Multi-objective optimization scheduling of microgrid based on multi-agent system [J]. Power Generation Technology, 2019, 40(6): 527-534.
[4]
MICHELA M, ADIB A M M, EMANUELE C, et al. Decentralized charging of plug-in electric vehicles and impact on transmission system dynamics[J]. IEEE Transactions on Smart Grid, 2021, 12(2): 1772-1781.
[5]
杨洁,吴志强,范宏. 基于实时电价的含储能可再生能源系统协同调度策略[J]. 智慧电力2023, 51(4): 46-53.
YANG Jie, WU Zhiqiang, FAN Hong. Collaborative scheduling strategy for renewable energy systems with energy storage based on real time price [J]. Smart Power, 2023, 51(4): 46-53.
[6]
GAN L, CHEN X, YU K, et al. A probabilistic evaluation method of household EVs dispatching potential considering users multiple travel needs[J]. IEEE Transactions on Industry Applications, 2020, 56(5): 5858-5867.
[7]
LU S, GU W, ZHANG C, et al. Hydraulic-thermal cooperative optimization of integrated energy systems: A convex opti-mization approach[J]. IEEE Transactions on Smart Grid, 2020, 11(6): 4818-4832.
[8]
孙黎霞,鞠平,白景涛,等. 计及蓄电池寿命的冷热电联供型微电网多目标经济优化运行[J]. 发电技术2020, 41(1): 64-72.
SUN Lixia, JU Ping, BAI Jingtao, et al. Multi-objective economic optimal operation of microgrid based on combined cooling, heating and power considering battery life [J]. Power Generation Technology, 2020, 41(1): 64-72.
[9]
林顺富,张琪,沈运帷,等. 考虑灵活性互济的跨区电网灵活性资源优化调度策略[J]. 电力建设2023, 44(8): 71-81.
LIN Shunfu, ZHANG Qi, SHEN Yunwei, et al. Optimal dispatching strategy of flexible resources considering flexible mutual aid among regional grid [J]. Electric Power Construction, 2023, 44(8): 71-81.
[10]
王敏,吕林,向月. 计及V2G价格激励的电动汽车削峰协同调度策略[J]. 电力自动化设备2022, 42(4): 27-33, 85.
WANG Min, Lin, XIANG Yue. Coordinated scheduling strategy of electric vehicles for peak shaving considering V2G price incentive [J]. Electric Power Automation Equipment, 2022, 42(4): 27-33, 85.
[11]
XU Z, SU W, HU Z, et al. A hierarchical framework for coordinated charging of plug-in electric vehicles in China[J]. IEEE Transactions on Smart Grid, 2016, 7(1): 428-438.
[12]
JIN J, XU Y. Optimal policy characterization enhanced actor-critic approach for electric vehicle charging scheduling in a power distribution network[J]. IEEE Transactions on Smart Grid, 2021, 12(2): 1416-1428.
[13]
蔡黎,张权文,代妮娜,等. 规模化电动汽车接入主动配电网研究进展综述[J]. 智慧电力2021, 49(6): 75-82.
CAI Li, ZHANG Quanwen, DAI Nina, et al. Review on research progress of large-scale electric vehicle access to active distribution network [J]. Smart Power, 2021, 49(6): 75-82.
[14]
檀勤良,代美,梅书凡. 考虑电动汽车碳配额及需求响应的电力系统调度研究[J]. 电网与清洁能源2021, 37(7): 79-86.
TAN Qinliang, DAI Mei, MEI Shufan. Research on electric vehicle carbon quota and demand response in electric power system dispatching [J]. Power System and Clean Energy, 2021, 37(7): 79-86.
[15]
赵星宇,胡俊杰. 集群电动汽车充电行为的深度强化学习优化方法[J]. 电网技术2021, 45(6): 2319-2327.
ZHAO Xingyu, HU Junjie. Deep reinforcement learning based optimization for charging of aggregated electric vehicles[J]. Power System Technology, 2021, 45(6): 2319-2327.
[16]
卢芳,王晓梅,李勇钢,等. 考虑多种储能的微能源网多时间尺度协同调度[J]. 现代电力2019, 36(5): 39-46.
LU Fang, WANG Xiaomei, LI Yonggang, et al. Multi-time scale optimal coordinated dispatch of micro-energy grid with multi-energy storage[J]. Modern Electric Power, 2019, 36(5): 39-46.
[17]
崔明勇,王楚通,王玉翠,等. 独立模式下微网多能存储系统优化配置[J]. 电力系统自动化2018, 42(4): 30-38, 54.
CUI Mingyong, WANG Chutong, WANG Yucui, et al. Optimal configuration of multi-energy storage system in standalone microgrid [J]. Automation of Electric Power Systems, 2018, 42(4): 30-38, 54.
[18]
赵磊,曾芬钰,王霜,等. 基于经济性与环保性的微电网多目标优化调度研究[J]. 高压电器2015, 51(6): 127-132.
ZHAO Lei, ZENG Fenyu, WANG Shuang, et al. Research on multi-objective optimal operation of microgrid based on economic and environmental protection[J]. High Voltage Apparatus, 2015, 51(6): 127-132.
[19]
陈奎,马子龙,周思宇,等. 电动汽车两阶段多目标有序充电策略研究[J]. 电力系统保护与控制2020, 48(1): 65-72.
CHEN Kui, MA Zilong, ZHOU Siyu, et al. Charging control strategy for electric vehicles based on two-stage multi-target optimization [J]. Power System Protection and Control, 2020, 48(1): 65-72.
[20]
杨国清,罗航,王德意,等. 分时电价与电动汽车优化调度的主从博弈模型[J]. 电力系统及其自动化学报2018, 30(10): 55-60.
YANG Guoqing, LUO Hang, WANG Deyi, et al. Leader-follower game model of time-of-use electricity price and optimized plug-in electric vehicle dispatching [J]. Proceedings of the CSU-EPSA, 2018, 30(10): 55-60.
[21]
朱心月,岳云涛,李炳华,等. 电动汽车有序充放电分群调度策略[J]. 科学技术与工程2021, 21(19): 8023-8030.
ZHU Xinyue, YUE Yuntao, LI Binghua, et al. Orderly charging and discharging group scheduling strategy for electric vehicles[J]. Science Technology and Engineering, 2021, 21(19): 8023-8030.
[22]
郭思琪,袁越,张新松,等. 多时间尺度协调控制的独立微网能量管理策略[J]. 电工技术学报2014, 29(2): 122-129.
GUO Siqi, YUAN Yue, ZHANG Xinsong, et al. Energy management strategy of isolated microgrid based on multi-time scale coordinated control[J]. Transactions of China Electro-technical Society, 2014, 29(2): 122-129.
[23]
康振南,程杉. 采用双层优化模型的电动汽车有序充放电策略研究[J]. 东北电力技术2019, 40(3): 1-6.
KANG Zhennan, CHENG Shan. Research on coordinated charge and discharge strategy of electric vehicle based on double-layer optimization model [J]. Northeast Electric Power Technology, 2019, 40(3): 1-6.
[24]
刘柏岩. 基于需求响应的含换电站微电网经济调度策略研究[D]. 吉林:东北电力大学,2021.
LIU Boyan. Study on economic scheduling strategy of microgrid containing battery swapping station based on demand response [D]. Jilin: Northeast Electric Power University, 2021.
[25]
卢少平,应黎明,王霞,等. 基于用户出行模拟的电动汽车快充站负荷预测及其优化调度[J]. 电力建设2020, 41(11): 38-48.
LU Shaoping, YING Liming, WANG Xia, et al. Charging load prediction and optimized scheduling of electric vehicle quick charging station according to user travel simulation[J]. Electric Power Construction, 2020, 41(11): 38-48.
[26]
李志伟,赵书强,刘应梅. 电动汽车分布式储能控制策略及应用[J]. 电网技术2016, 40(2): 442-450.
LI Zhiwei, ZHAO Shuqiang, LIU Yingmei. Control strategy and application of distributed electric vehicle energy storage[J]. Power System Technology, 2016, 40(2): 442-450.

Funding

National Natural Science Foundation of China(52377085)
PDF(5903 KB)

Accesses

Citation

Detail

Sections
Recommended

/