Day-Ahead Multi-Objective Microgrid Dispatch Optimization Based on Demand Side Management Via Particle Swarm Optimization

被引:11
作者
Hou, Sicheng [1 ]
Fujimura, Shigeru [1 ]
机构
[1] Waseda Univ, Grad Sch Informat Prod & Syst, Wakamatsu Ku, 2-7 Hibikino, Kitakyushu, Fukuoka 8080135, Japan
关键词
day-ahead multi-objective micro grid dispatch optimization; demand side management; combined economic and environmental dispatch optimization; particle swarm optimization; SHORT-TERM LOAD; EMISSION DISPATCH;
D O I
10.1002/tee.23711
中图分类号
TM [电工技术]; TN [电子技术、通信技术];
学科分类号
0808 ; 0809 ;
摘要
The rapid growth of electricity demands for recent years leads to many global concerns, including greenhouse, energy risk, and large size brownouts, which require modern energy management system to provide environmental-friendly power supply service with less cost and higher reliability. However, most of previous studies separately focus on improving prediction accuracy or reducing cost and emission of power supply solution by dispatch optimization. To exploit the benefits of microgrid system furthermore, this paper firstly proposes a comprehensive day-ahead multi-objective microgrid optimization framework that combines forecasting technology, demand side management (DSM) with economic and environmental dispatch (EED) together. Then, two versions of particle swarm optimization are implemented for obtaining load control plan from DSM model and power supply plan from EED model, respectively. Moreover, two different microgrids' applied scenarios are simulated with detailed sensitivities analysis on key parameters. Experiment results demonstrate effectiveness of the proposed framework, which can obtain load demands profile with better reliability, as well as power supply solution with less cost and lower emission. Meanwhile, beneficial decision supports are provided to manager for their references. (c) 2022 Institute of Electrical Engineers of Japan. Published by Wiley Periodicals LLC.
引用
收藏
页码:25 / 37
页数:13
相关论文
共 23 条
[1]   Artificial bee colony algorithm with dynamic population size to combined economic and emission dispatch problem [J].
Aydin, Dogan ;
Ozyon, Serdar ;
Yasar, Celal ;
Liao, Tianjun .
INTERNATIONAL JOURNAL OF ELECTRICAL POWER & ENERGY SYSTEMS, 2014, 54 :144-153
[2]   A quarter century of particle swarm optimization [J].
Cheng, Shi ;
Lu, Hui ;
Lei, Xiujuan ;
Shi, Yuhui .
COMPLEX & INTELLIGENT SYSTEMS, 2018, 4 (03) :227-239
[3]   Handling multiple objectives with particle swarm optimization [J].
Coello, CAC ;
Pulido, GT ;
Lechuga, MS .
IEEE TRANSACTIONS ON EVOLUTIONARY COMPUTATION, 2004, 8 (03) :256-279
[4]   Deep Learning for Household Load Forecasting-A Novel Pooling Deep RNN [J].
Shi, Heng ;
Xu, Minghao ;
Li, Ran .
IEEE TRANSACTIONS ON SMART GRID, 2018, 9 (05) :5271-5280
[5]   Neural networks for short-term load forecasting: A review and evaluation [J].
Hippert, HS ;
Pedreira, CE ;
Souza, RC .
IEEE TRANSACTIONS ON POWER SYSTEMS, 2001, 16 (01) :44-55
[6]   A particle swarm optimization to identifying the ARMAX model for short-term load forecasting [J].
Huang, CM ;
Huang, CJ ;
Wang, ML .
IEEE TRANSACTIONS ON POWER SYSTEMS, 2005, 20 (02) :1126-1133
[7]  
Kennedy J, 1995, 1995 IEEE INTERNATIONAL CONFERENCE ON NEURAL NETWORKS PROCEEDINGS, VOLS 1-6, P1942, DOI 10.1109/icnn.1995.488968
[8]   Short-Term Residential Load Forecasting Based on LSTM Recurrent Neural Network [J].
Kong, Weicong ;
Dong, Zhao Yang ;
Jia, Youwei ;
Hill, David J. ;
Xu, Yan ;
Zhang, Yuan .
IEEE TRANSACTIONS ON SMART GRID, 2019, 10 (01) :841-851
[9]   Intelligent demand side management for optimal energy scheduling of grid connected microgrids [J].
Kumar, R. Seshu ;
Raghav, L. Phani ;
Raju, D. Koteswara ;
Singh, Arvind R. .
APPLIED ENERGY, 2021, 285
[10]   Economic Optimal Dispatching Method of Power Microgrid under Environmental Uncertainties [J].
Li Lanqing ;
Murata, Tomohiro .
PROCEEDINGS 2016 5TH IIAI INTERNATIONAL CONGRESS ON ADVANCED APPLIED INFORMATICS IIAI-AAI 2016, 2016, :1036-1041