Research on adaptive dispatching of smart grid considering the cost of renewable energy power generation

被引:0
作者
Qin, Wenchao [1 ]
He, Jinding [1 ]
机构
[1] Yunnan Elect Power Dispatching Control Ctr, Kunming, Yunnan, Peoples R China
关键词
cost of renewable energy power generation; smart grid; adaptive scheduling; conventional power generation unit; energy storage unit; distributed reinforcement learning;
D O I
10.1504/IJGEI.2024.141924
中图分类号
X [环境科学、安全科学];
学科分类号
08 ; 0830 ;
摘要
In order to overcome the problems of poor convergence, high cost and long completion time of scheduling tasks in traditional methods, an adaptive dispatching method of smart grid considering the cost of renewable energy power generation is proposed. Firstly, the operation cost of smart grid is calculated from the total operation cost of conventional power generation unit, renewable energy power generation unit and energy storage unit. Then, combined with the benefits of flexible load, a smart grid adaptive dispatching model is built. Finally, under various constraints, the distributed reinforcement learning is used to solve the scheduling model and the adaptive scheduling results of smart grid are obtained. The experimental results show that the scheduling model solving algorithm of this method converges in 43 iterations, and the total operation cost of smart grid is 5.68 x 10(7) yuan, and the scheduling task completion time is always less than 0.48 s.
引用
收藏
页码:585 / 602
页数:19
相关论文
共 15 条
[1]   A novel privacy-preserving multi-level aggregate signcryption and query scheme for Smart Grid via mobile fog computing [J].
Li, Kunchang ;
Shi, Runhua ;
Wu, Mingxia ;
Li, Yifei ;
Zhang, Xiaoxu .
JOURNAL OF INFORMATION SECURITY AND APPLICATIONS, 2022, 67
[2]  
[罗金满 Luo Jinman], 2022, [电力系统保护与控制, Power System Protection and Control], V50, P167
[3]   Hierarchical learning optimisation method for the coordination dispatch of the inter-regional power grid considering the quality of service index [J].
Lv, Kai ;
Tang, Hao ;
Bak-Jensen, Birgitte ;
Radhakrishna Pillai, Jayakrishnan ;
Tan, Qi ;
Zhang, Qianli .
IET GENERATION TRANSMISSION & DISTRIBUTION, 2020, 14 (18) :3673-3684
[4]  
Peng D., 2020, IEEE Access., V8
[5]   A Machine Learning-Based Gradient Boosting Regression Approach for Wind Power Production Forecasting: A Step towards Smart Grid Environments [J].
Singh, Upma ;
Rizwan, Mohammad ;
Alaraj, Muhannad ;
Alsaidan, Ibrahim .
ENERGIES, 2021, 14 (16)
[6]  
Sun G.W., 2020, Foreign Electronic Measurement Technology, V39, P40
[7]   Multi-Objective Optimal Dispatching for a Grid-Connected Micro-Grid Considering Wind Power Forecasting Probability [J].
Sun, Sizhou ;
Fu, Jingqi ;
Wei, Lisheng ;
Li, Ang .
IEEE ACCESS, 2020, 8 :46981-46997
[8]  
Wang J., 2022, Expert Systems with Application, V25
[9]   Optimal dispatching of power grid integrating wind-hydrogen systems [J].
Wei, Fanrong ;
Sui, Quan ;
Li, Xuesong ;
Lin, Xiangning ;
Li, Zhengtian .
INTERNATIONAL JOURNAL OF ELECTRICAL POWER & ENERGY SYSTEMS, 2021, 125
[10]   A Stacked GRU-RNN-Based Approach for Predicting Renewable Energy and Electricity Load for Smart Grid Operation [J].
Xia, Min ;
Shao, Haidong ;
Ma, Xiandong ;
de Silva, Clarence W. .
IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS, 2021, 17 (10) :7050-7059