MULTI-LOAD MASTER-SLAVE GAME OPTIMIZATION STRATEGY CONSIDERING PEAK SHAVING REWARDS AND FINE LOAD REGULATION

Yang Xu; Zhang Yixin; Zhang Tao; Gao Shihang; Tu Rang

doi:10.19912/j.0254-0096.tynxb.2024-1112

PDF(1167 KB)

Acta Energiae Solaris Sinica ›› 2025, Vol. 46 ›› Issue (10) : 526-535. DOI: 10.19912/j.0254-0096.tynxb.2024-1112

MULTI-LOAD MASTER-SLAVE GAME OPTIMIZATION STRATEGY CONSIDERING PEAK SHAVING REWARDS AND FINE LOAD REGULATION

Yang Xu^1,2, Zhang Yixin^1,2, Zhang Tao^1,2, Gao Shihang², Tu Rang³

Author information +

History +

Abstract

To address the issues of the inability of a single electricity price strategy to meet the problem of adequate dispatch of multiple flexible loads, this paper proposed a multi-load master-slave game optimization strategy considering peak shaving rewards and fine load regulation. firstly, according to the maximum peak cutting contribution of each flexible load, differentiated ladder reward and punishment mechanism was established. secondly, a multiple flexible master-slave game optimization model was constructed in which the microgrid operator is taken as the leader, and the customer aggregator, and the air conditioner aggregator with fine temperature control, and the electric vehicle aggregator with travel stochasticity are taken as the follower. finally, a numerical example shows that the proposed strategy can effectively tap the peaking potential of a variety of flexible loads, and based on this, achieve multi-benefit improvement for microgrid and a variety of flexible loads.

Key words

demand response / master-slave game / peak shaving / optimization decision / air conditioner

Cite this article

EndNote

Ris (Procite)

Bibtex

Download Citations

Yang Xu, Zhang Yixin, Zhang Tao, Gao Shihang, Tu Rang. MULTI-LOAD MASTER-SLAVE GAME OPTIMIZATION STRATEGY CONSIDERING PEAK SHAVING REWARDS AND FINE LOAD REGULATION[J]. Acta Energiae Solaris Sinica. 2025, 46(10): 526-535 https://doi.org/10.19912/j.0254-0096.tynxb.2024-1112

References

[1] 李英量, 董志伟, 白博旭, 等. 含风电并网的源荷双层机组优化组合模型[J]. 太阳能学报, 2024, 45(3): 399-407.
LI Y L, DONG Z W, BAI B X, et al.Optimal combination model of two-layer source-load unit with wind power grid connection[J]. Acta energiae solaris sinica, 2024, 45(3): 399-407.
[2] 姜恩宇, 陈周, 史雷敏, 等. 计及多重不确定性与综合贡献率的多微网合作运行策略[J]. 太阳能学报, 2023, 44(10): 80-89.
JIANG E Y, CHEN Z, SHI L M, et al.Cooperative operation strategy of multi-microgrids based on multiple uncertainties and comprehen sive contribution rate[J]. Acta energiae solaris sinica, 2023, 44(10): 80-89.
[3] 魏子强, 温鹏, 梁志, 等. 计及需求响应比例的园区综合能源系统低碳经济调度方法[J]. 太阳能学报, 2023, 44(10): 38-45.
WEI Z Q, WEN P, LIANG Z, et al.Low carbon economic dispatching method of park integrated energy system considering proportion of demand response[J]. Acta energiae solaris sinica, 2023, 44(10): 38-45.
[4] 贾海清, 林延平, 刘茹, 等. 考虑需求侧无功不确定性的负荷聚合商运营优化模型[J]. 太阳能学报, 2022, 43(6): 17-23.
JIA H Q, LIN Y P, LIU R, et al.load aggregator operation optimization model considering demand response uncertainty[J]. Acta energiae solaris sinica, 2022, 43(6): 17-23.
[5] WANG T H, HUA H C, SHI T Y, et al.A bi-level dispatch optimization of multi-microgrid considering green electricity consumption willingness under renewable portfolio standard policy[J]. Applied energy, 2024, 356: 122428.
[6] 张衡, 张沈习, 程浩忠, 等. Stackelberg博弈在电力市场中的应用研究综述[J]. 电工技术学报, 2022, 37(13): 3250-3262.
ZHANG H, ZHANG S X, CHENG H Z, et al.A state-of-the-art review on stackelberg game and its applications in power market[J]. Transactions of China Electrotechnical Society, 2022, 37(13): 3250-3262.
[7] 张青苗, 陈来军, 李笑竹, 等. 基于主从博弈的工业园区运营商与分布式光储用户购售电优化决策[J]. 太阳能学报, 2024, 45(1): 450-456.
ZHANG Q M, CHEN L J, LI X Z, et al.Optimization decision of purchasing and selling power between industrial park operators and distributed optical storage users based on stackelberg game[J]. Acta energiae solaris sinica, 2024, 45(1): 450-456.
[8] 马越, 蔺红. 多代理技术下基于主从博弈的多微网系统经济优化调度[J]. 太阳能学报, 2024, 45(1): 574-582.
MA Y, LIN H.Economic optimization scheduling of multi-microgrid system based on master-slave game under multi-agent technology[J]. Acta energiae solaris sinica, 2024, 45(1): 574-582.
[9] LI Y C, HONG S H.Real-time demand bidding for energy management in discrete manufacturing facilities[J]. IEEE transactions on industrial electronics, 2017, 64(1): 739-749.
[10] 吴桐, 惠红勋, 张洪财. 商业建筑空调系统参与城市电网负荷调控综述[J]. 中国电力, 2023, 56(7): 1-11.
WU T, HUI H X, ZHANG H C.Review of commercial air conditioners for participating in urban grid regulation[J]. Electric power, 2023, 56(7): 1-11.
[11] 朱宇超, 王建学, 曹晓宇. 中央空调负荷直接控制策略及其可调度潜力评估[J]. 电力自动化设备, 2018, 38(5): 227-234.
ZHU Y C, WANG J X, CAO X Y. direct control strategy of central air-conditioning load and its schedulable potential evaluation[J]. Electric power automation equipment, 2018, 38(5): 227-234.
[12] 徐刚, 安启启, 杨杰, 等. 考虑个体差异性的PMV-PPD人体热舒适性评估模型及应用[J]. 西安科技大学学报, 2021, 41(1): 55-61.
XU G, AN Q Q, YANG J, et al.Evaluation and its application of an improved PMV-PPD model based on individual differences[J]. Journal of Xi’an University of Science and Technology, 2021, 41(1): 55-61.
[13] 范德金, 张姝, 王杨, 等. 考虑用户调节行为多样性的空调负荷聚合商日前调度策略[J]. 电力系统保护与控制, 2022, 50(17): 133-142.
FAN D J, ZHANG S, WANG Y, et al.Day ahead scheduling strategy for air conditioning load aggregators considering user regulation behavior diversity[J]. Power system protection and control, 2022, 50(17): 133-142.
[14] 张美霞, 孙铨杰, 杨秀. 考虑多源信息实时交互和用户后悔心理的电动汽车充电负荷预测[J]. 电网技术, 2022, 46(2): 632-645.
ZHANG M X, SUN Q J, YANG X.Electric vehicle charging load prediction considering multi-source information real-time interaction and user regret psychology[J]. Power system technology, 2022, 46(2): 632-645.
[15] 郑远硕. 面向V2G车电互联的电动汽车充放电灵活性调控与优化研究[D]. 北京: 北京科技大学, 2023.
ZHENG Y S.Flexible dispatch and optimization of electric vehicle charging and discharging for V2G system[D]. Beijing: University of Science and Technology Beijing, 2023.
[16] 马苗苗, 王浩, 董利鹏, 等. 基于主从博弈的微电网分布式能量管理策略[J]. 系统仿真学报, 2024, 36(4): 834-843.
MA M M, WANG H, DONG L P, et al.Distributed energy management strategy of microgrid based on master-slave game[J]. Journal of system simulation, 2024, 36(4): 834-843.