Modeling Techniques and a Hierarchical Coordinated Control Framework for Various-Type Flexible Resources

TIAN Xincheng,WEN Yilin,LU Zehan,HOU Xinyao,HU Zechun

Distributed Energy ›› 2024, Vol. 9 ›› Issue (1) : 10-18.

PDF(2314 KB)
PDF(2314 KB)
Distributed Energy ›› 2024, Vol. 9 ›› Issue (1) : 10-18. DOI: 10.16513/j.2096-2185.DE.2409102
Basic Research

Modeling Techniques and a Hierarchical Coordinated Control Framework for Various-Type Flexible Resources

Author information +
History +

Abstract

Controllable loads, on-site generation and energy storages at demand-side are termed as distributed energy resources. These resources are vital for optimal operation of smart grid and renewable integration. This paper first proposes a generalized flexible resource model to characterize a variety of flexible resources at the residential, commercial and industrial sectors. The characteristics of an aggregation of heterogeneous distributed energy resources can be also described via this proposed model. This paper then presents a hierarchical control framework to coordinate their energy consumption in day-ahead and real time under both regulated and deregulated scenarios. This framework can utilize the flexibility of distributed energy resources to improve the operational efficiency of the whole system. Finally, This paper presents numerical simulations on a unit commitment problem of IEEE 30-bus system to verify the effectiveness of the proposed hierarchical control framework.

Key words

demand response / flexible resource / hierarchical coordination / residential load / industrial load / commercial load / aggregated model

Cite this article

Download Citations
Xincheng TIAN , Yilin WEN , Zehan LU , et al . Modeling Techniques and a Hierarchical Coordinated Control Framework for Various-Type Flexible Resources[J]. Distributed Energy Resources. 2024, 9(1): 10-18 https://doi.org/10.16513/j.2096-2185.DE.2409102

References

[1]
KANG C, CHEN X, XU Q, et al. Balance of power: Toward a more environmentally friendly, efficient, and effective integration of energy systems in China[J]. IEEE Power and Energy Magazine, 2013, 11(5): 56-64.
[2]
CALLAWAY D, HISKENS I. Achieving controllability of electric loads[J]. Proceedings of IEEE, 2011, 99(1): 184-199.
[3]
OSKOUEI M Z, ZEINAL-KHEIRI S, MOHAMMADI-IVATLOO B, et al. Optimal scheduling of demand response aggregators in industrial parks based on load disaggregation algorithm[J]. IEEE Systems Journal, 2021, 16(1): 945-953.
[4]
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, 2015, 7(1): 428-438.
[5]
贾俊,范炜豪,吕志鹏,等. 用于电动汽车集群并网的直流变压器启动研究[J]. 发电技术2023, 44(6): 875-882.
JIA Jun, FAN Weihao, Zhipeng, et al. Research on startup of DC transformer for electric vehicle cluster grid-connection[J]. Power Generation Technology, 2023, 44(6): 875-882.
[6]
马子钦,廖凯,李波,等. 含分布式电源和电动汽车的城市电网半不变量故障分析方法[J]. 电网技术2021, 45(2): 696-704.
MA Ziqin, LIAO Kai, LI bo, et al. Cumulant failure analysis of urban power grid with distributed generation and electric vehicles[J]. Power System Technology, 2021, 45(2): 696-704.
[7]
高爽,戴如鑫. 电动汽车集群参与调频辅助服务市场的充电调控策略[J]. 电力系统自动化2023, 47(18): 60-67.
GAO Shuang, DAI Ruxin. Charging control strategy for electric vehicle aggregation participating in frequency regulation ancillary service market[J]. Automation of Electric Power Systems, 2023, 47(18): 60-67.
[8]
胡泽春,宋永华,徐智威,等. 电动汽车接入电网的影响与利用[J]. 中国电机工程学报2012, 32(4): 1-10.
HU Zechun, SONG Yonghua, XU Zhiwei, et al. Impacts and utilization of electric vehicles integration into power systems[J]. Proceedings of the CSEE, 2012, 32(4): 1-10.
[9]
HAO H, SANANDAJI B, POOLLA K, et al. Aggregate flexibility of thermostatically controlled loads[J]. IEEE Transactions on Power Systems, 2015, 30(1): 189-198.
[10]
FISCHER D, WOLF T, WAPLER J, et al. Model-based flexibility assessment of a residential heat pump pool[J]. Energy, 2017, 118: 853-864.
[11]
方磊,薛云霞,池宇琪,等. 分布式储能运行规划一体的多目标选址定容方法[J]. 智慧电力2022, 50(11): 1-8.
FANG Lei, XUE Yunxia, CHI Yuqi, et al. Multi-objective location and capacity determination method for distributed battery energy storage system considering operational planning[J]. Smart Power, 2022, 50(11): 1-8.
[12]
WANG L, KWON J, SCHULZ N, et al. Evaluation of aggregated EV flexibility with TSO-DSO coordination[J]. IEEE Transactions on Sustainable Energy, 2022, 13(4): 2304-2315.
[13]
ZHANG X, HUG G. Bidding strategy in energy and spinning reserve markets for aluminum smelters' demand response[C]//Proceedings of 2015 IEEE Power Engineering Society Conference on Innovative Smart Grid Technologies. Washington DC, USA: IEEE, 2015:1-6.
[14]
CHEN C, WANG J, HEO Q. MPC-based appliance scheduling for residential building energy management controller.[J]. IEEE Transactions on Smart Grid, 2013, 4(3): 1401-1410.
[15]
MUKHERJEE S, BAI H, CHAKRABORTTY A. Model-free decentralized reinforcement learning control of distributed energy resources[C]//2020 IEEE Power & Energy Society General Meeting (PESGM). Montreal, QC, Canada: IEEE, 2020: 1-5.
[16]
LI S, PAN Y, XU P, et al. A decentralized peer-to-peer control scheme for heating and cooling trading in distributed energy systems[J]. Journal of Cleaner Production, 2021, 285: 124817.
[17]
RICHARDSON P, FLYNN D, KEANE A. Local versus centralized charging strategies for electric vehicles in low voltage distribution systems[J]. IEEE Transactions on Smart Grid, 2012, 3(2): 1020-1028.
[18]
LI Z, CHENG Z, SI J, et al. Distributed event-triggered hierarchical control to improve economic operation of hybrid AC/DC microgrids[J]. IEEE Transactions on Power Systems, 2021, 37(5): 3653-3668.
[19]
GONG X, CASTILLO-GUERRA E, CARDENAS-BARRERA J L, et al. Robust hierarchical control mechanism for aggregated thermostatically controlled loads[J]. IEEE Transactions on Smart Grid, 2020, 12(1): 453-467.
[20]
SUBRAMANIAN A, GARCIA M, CALLAWAY D, et al. Real-time scheduling of distributed resources[J] IEEE Transactions on Smart Grid, 2014, 4(1): 2122-2130.
[21]
傅鹏,杨凤玖,张禄郗. 电动汽车充、换电模式经济性研究[J]. 内蒙古电力技术2022, 40(2): 22-27.
FU Peng, YANG Fengjiu, ZHANG Luxi. Research on economics of electric vehicle charging and switching mode[J]. Inner Mongolia Electric Power, 2022, 40(2): 22-27.
[22]
XU Z, CALLAWAY D S, HU Z, et al. Hierarchical coordination of heterogeneous flexible loads[J]. IEEE Transactions on Power Systems, 2016, 31(6): 4206-4216.
[23]
SUNDSTROM O, BINDING C. Flexible charging optimization for electric vehicles considering distribution grid constraints[J], IEEE Transactions on Smart Grid, 2012, 3(1): 26-37.
[24]
BURSILL J, O'BRIEN L, BEAUSOLEIL-MORRISON I. Multi-zone field study of rule extraction control to simplify implementation of predictive control to reduce building energy use[J]. Energy and Buildings, 2020, 222: 110056.
[25]
CHEN M, GAO C, SONG M, et al. Internet data centers participating in demand response: A comprehensive review[J]. Renewable and Sustainable Energy Reviews, 2020, 117: 109466.
[26]
PAULUS M, BORGGREFE F. The potential of demand-side management in energy-intensive industries for electricity markets in Germany[J]. Applied Energy, 2011, 88: 432-441
[27]
SGOURIDIS S, ALI M, SLEPTCHENKO A, et al. Aluminum smelters in the energy transition: Optimal configuration and operation for renewable energy integration in high insolation regions[J]. Renewable Energy, 2021, 180: 937-953.
[28]
CHEN C, WANG J, KISHORE S. A distributed direct load control approach for large-scale residential demand response[J]. IEEE Transactions on Power Systems, 2014, 29(5): 2219-2228.
[29]
IBM Corporation. IBM ILOG CPLEX optimization studio 12.9[EB/OL]. [2023-07-24].

Funding

This work is supported by Science and Technology Project of State Grid Corporation of China(SGJBTS00DKJS2250610)
PDF(2314 KB)

Accesses

Citation

Detail

Sections
Recommended

/