Optimal operation of new coastal power systems with seawater desalination based on grey wolf optimization

被引:4
|
作者
Gao Yujie [1 ]
Yang Hao [1 ]
Zhou Bowen [1 ,2 ]
Chen Xinyi [1 ]
Hu Zhijun [3 ]
机构
[1] Northeastern Univ, Sch Informat Sci & Engn, Shenyang 110819, Peoples R China
[2] Northeastern Univ, Key Lab Integrated Energy Optimizat & Secure Oper, Shenyang 110819, Peoples R China
[3] Northeastern Univ, Sch Resources & Civil Engn, Shenyang 110819, Peoples R China
关键词
Seawater desalination; Detail model; Optimal operation; Grey wolf optimization algorithm;
D O I
10.1016/j.egyr.2023.04.299
中图分类号
TE [石油、天然气工业]; TK [能源与动力工程];
学科分类号
0807 ; 0820 ;
摘要
Seawater desalination is one of the effective means to efficiently consume renewable energy and to improve the flexibility of system control in the new power system. In the optimal operation of new power systems, seawater desalination is usually considered as an entirety, and a motor equivalent model is used to simulate the overall external characteristics of seawater desalination. The characteristics of multiple sets of motors with multiple equipment processes inside the seawater desalination are neglected, which makes operation control characteristics inaccurate. In order to solve the above-mentioned problems, this paper firstly studies a seawater desalination system composed of 12 motors, and introduces the specific parameters of these 12 motors in detail. Secondly, after comprehensively considering the constraints of water flow, water pressure, and other factors, three control strategies are proposed for the 12 motors, respectively. Then, a detailed mathematical model of seawater desalination units and an optimal operation model of a new power system with seawater desalination are established. The optimization model aims at the minimum operating cost of seawater desalination, and considers the constraints of practical problems comprehensively such as motor power requirements and water storage capacity. Finally, the grey wolf optimization algorithm (GWO) is used in this paper to solve the optimization problem. The simulation results show that the model proposed in this paper can precisely control each unit in the seawater desalination system and improve the economic benefits of the system. Compared with the Particle Swarm Optimization algorithm (PSO) and the Moth-flame Optimization algorithm (MFO), the algorithm used in this paper can find the global optimal solution, and has both faster convergence speed and wider practicability. (c) 2023 Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).
引用
收藏
页码:391 / 402
页数:12
相关论文
共 50 条
  • [31] Potential Contribution of the Grey Wolf Optimization Algorithm in Reducing Active Power Losses in Electrical Power Systems
    Abbas, Mohamed
    Alshehri, Mohammed A.
    Barnawi, Abdulwasa Bakr
    APPLIED SCIENCES-BASEL, 2022, 12 (12):
  • [32] Optimal Sizing and Placement of Capacitors in Radial Distribution Systems Based on Grey Wolf, Dragonfly and Moth–Flame Optimization Algorithms
    Ahmed A. Zaki Diab
    Hegazy Rezk
    Iranian Journal of Science and Technology, Transactions of Electrical Engineering, 2019, 43 : 77 - 96
  • [33] Stochastic optimization using grey wolf optimization with optimal computing budget allocation
    Fu, Yaping
    Xiao, Hui
    Lee, Loo Hay
    Huang, Min
    APPLIED SOFT COMPUTING, 2021, 103
  • [34] Optimal operation of combined heat and power systems: An optimization-based control strategy
    Diaz C, Jenny L.
    Ocampo-Martinez, Carlos
    Panten, Niklas
    Weber, Thomas
    Abele, Eberhard
    ENERGY CONVERSION AND MANAGEMENT, 2019, 199
  • [35] A New Hybrid Model for Energy Consumption Prediction Based on Grey Wolf Optimization
    Wahba, Asmaa
    El-khoribi, Reda
    Taie, Shereen
    IAENG International Journal of Computer Science, 2022, 49 (02)
  • [36] Optimal reactive power planning using oppositional grey wolf optimization by considering bus vulnerability analysis
    Babu, Rohit
    Raj, Saurav
    Dey, Bishwajit
    Bhattacharyya, Biplab
    Energy Conversion and Economics, 2022, 3 (01): : 38 - 49
  • [37] Optimal Allocation of Water Resources in Canal Systems Based on the Improved Grey Wolf Algorithm
    Zheng, Qiuli
    Yue, Chunfang
    Zhang, Shengjiang
    Yao, Chengbao
    Zhang, Qin
    SUSTAINABILITY, 2024, 16 (09)
  • [38] Hybrid Grey Wolf Optimizer Based Optimal Capacitor Placement in Radial Distribution Systems
    Jayabarathi, T.
    Raghunathan, T.
    Sanjay, R.
    Jha, Aditya
    Mirjalili, S.
    Cherukuri, S. Hari Charan
    ELECTRIC POWER COMPONENTS AND SYSTEMS, 2022, 50 (08) : 413 - 425
  • [39] Oppositional based grey wolf optimization algorithm for economic dispatch problem of power system
    Pradhan, Moumita
    Roy, Provas Kumar
    Pal, Tandra
    AIN SHAMS ENGINEERING JOURNAL, 2018, 9 (04) : 2015 - 2025
  • [40] Daily Power Load Curves Analysis Based on Grey Wolf Optimization Clustering Algorithm
    Gao, Chong
    Wu, Yaxiong
    Tang, Junxi
    Cao, Huazhen
    Chen, Lvpeng
    PROCEEDINGS OF 2019 INTERNATIONAL FORUM ON SMART GRID PROTECTION AND CONTROL (PURPLE MOUNTAIN FORUM), VOL II, 2020, 585 : 661 - 671