ECONOMIC DISPATCH STRATEGY OF DISTRIBUTED ELECTRIC HEATING LOAD PARTICIPATION SYSTEM

Shang Lijie; Lin Hong

doi:10.19912/j.0254-0096.tynxb.2020-1429

PDF(2964 KB)

Acta Energiae Solaris Sinica ›› 2022, Vol. 43 ›› Issue (8) : 352-359. DOI: 10.19912/j.0254-0096.tynxb.2020-1429

ECONOMIC DISPATCH STRATEGY OF DISTRIBUTED ELECTRIC HEATING LOAD PARTICIPATION SYSTEM

Shang Lijie, Lin Hong

Author information +

History +

Abstract

Electric heating, a schedulable load with time-shifting characteristics, but it has the characteristics of large quantity and small capacity, which makes that centralized scheduling method is difficult to meet the timeliness of scheduling. An economic scheduling strategy of power system with distributed electric heating load participation is proposed. Firstly, the functional relationship between the variable quantity in power generation cost and in output on the power generation side and the functional relationship between the variable quantity in electric heating load benefit and in electric heating load of users are established. A multi-agent leader-follower consensus algorithm based on the incremental change of cost on the power generation side and the increment in the benefit of electric heating load is designed, which is used to solve the economic scheduling problem and obtain the economically optimal allocation plan. Finally, the validity of the dispatching strategy is verified by example simulation.

Key words

distributed / economy dispatch / agent / consistency / electric heating load

Cite this article

EndNote

Ris (Procite)

Bibtex

Download Citations

Shang Lijie, Lin Hong. ECONOMIC DISPATCH STRATEGY OF DISTRIBUTED ELECTRIC HEATING LOAD PARTICIPATION SYSTEM[J]. Acta Energiae Solaris Sinica. 2022, 43(8): 352-359 https://doi.org/10.19912/j.0254-0096.tynxb.2020-1429

References

[1] 国家发展和改革委员会能源研究所. 中国2050年高比例可再生能源发展情景暨路径研究[R]. 国家发展和改革委员会能源研究所, 2015.
Energy Research Institute National Development and Reform Commission. China 2050 high renewable energy penetration scenario and roadmap study[R]. Energy Research Institute National Development and Reform Commission, 2015.
[2] 王志强, 王珊, 张馨月, 等. 计及用户响应行为差异性的区域电采暖负荷特性建模[J]. 电力系统自动化, 2019, 43(7): 67-79.
WANG Z Q,WANG S, ZHANG X Y, et al.Modeling of regional electric heating load characteristics taking into account the differences in user response behavior[J]. Automation of electric power systems, 2019, 43(7): 67-79.
[3] 李亚平, 姚建国, 雍太有, 等. 居民温控负荷聚合功率及响应潜力评估方法研究[J]. 中国电机工程学报, 2017, 37(19): 5519-5528, 5829.
LI Y P, YAO J G, YONG T Y, et al.Research on the assessment method of the aggregate power and response potential of residential temperature control load[J]. Proceedings of the CSEE, 2017, 37(19): 5519-5528,5829.
[4] 黄亚峰, 朱玉杰, 穆钢, 等. 基于温度预报的户用电采暖负荷可调节能力评估[J]. 电网技术, 2018, 42(8): 2487-2493.
HUANG Y F, ZHU Y J, MU G, et al.Evaluation of adjustable capacity of household electric heating load based on temperature forecast[J]. Power system technology, 2018, 42(8): 2487-2493.
[5] 宋梦, 高赐威, 苏卫华. 面向需求响应应用的空调负荷建模及控制[J]. 电力系统自动化, 2016, 40(14): 158-167.
SONG M, GAO C W, SU W H.Modeling and control of air conditioning load for demand response applications[J]. Automation of electric power systems, 2016, 40(14): 158-167.
[6] 田爱娜, 李卫星, 刘道伟, 等. 基于改进状态空间模型的空调负荷控制策略[J]. 电力系统自动化, 2019, 43(8): 124-137.
TIAN A N, LI W X, LIU D W, et al.Air conditioning load control strategy based on improved state space model[J]. Automation of electric power systems, 2019, 43(8): 124-137.
[7] 刘蓉晖, 李子林, 杨秀, 等. 考虑用户侧柔性负荷的社区综合能源系统日前优化调度[J]. 太阳能学报, 2019, 40(10): 2842-2850.
LIU R H, LI Z L, YANG X, et al.Day-ahead optimal dispatch of community integrated energy system considering user-side flexible load[J]. Acta energiae solaris sinica, 2019, 40(10): 2842-2850.
[8] 张晶晶, 张鹏, 吴红斌, 等. 负荷聚合商参与需求响应的可靠性及风险分析[J]. 太阳能学报, 2019, 40(12): 3526-3533.
ZHANG J J, ZHANG P, WU H B, et al.Reliability and risk analysis of load aggregator participation in demand response[J]. Acta energiae solaris sinica, 2019, 40(12): 3526-3533.
[9] 高赐威, 李倩玉, 李扬. 基于DLC的空调负荷双层优化调度和控制策略[J]. 中国电机工程学报, 2014, 34(10): 1546-1555.
GAO C W, LI Q Y, LI Y.DLC-based optimal scheduling and control strategy for two-layer air-conditioning load[J]. Proceedings of the CSEE, 2014, 34(10): 1546-1555.
[10] 杨清志, 蒋伟, 许春雷. 基于多智能体的交直流混合微电网监控设计与分层控制研究[J]. 高电压技术, 2020, 46(7): 2327-2339.
YANG Q Z, JIANG W, XU C L.Research on monitoring design and hierarchical control of AC/DC hybrid microgrid based on multi-agent[J]. High voltage technology, 2020, 46(7): 2327-2339.
[11] 王岳, 杨国华, 董晓宁, 等. 基于多智能体一致性的微电网无功功率分配方法研究[J]. 电力系统保护与控制, 2019, 47(17): 54-60.
WANG Y, YANG G H, DONG X N, et al.Research on reactive power distribution method of microgrid based on multi-agent consistency[J]. Power system protection and control, 2019, 47(17): 54-60.
[12] 谢俊, 陈凯旋, 岳东, 等. 基于多智能体系统一致性算法的电力系统分布式经济调度策略[J]. 电力自动化设备, 2016, 36(2): 112-117.
XIE J, CHEN K X, YUE D, et al.Power system distributed economic dispatch strategy based on multi-agent system consensus algorithm[J]. Electric power automation equipment, 2016, 36(2): 112-117.
[13] 朱玉杰. 电采暖负荷可调节能力评估与集群控制策略研究[D]. 吉林: 东北电力大学, 2019.
ZHU Y J.Evaluation of electric heating load adjustability and cluster control strategy research[D]. Jilin: Northeast Electric Power University, 2019.