Wind integrated optimal power flow considering power losses, voltage deviation, and emission using equilibrium optimization algorithm

被引:0
|
作者
Mohammed Amroune
机构
[1] University of Ferhat Abbas Setif 1,Department of Electrical Engineering
来源
关键词
Optimal power flow; Wind power; Equilibrium optimization; Marine predators algorithm; Artificial ecosystem-based optimization; Slime mould algorithm;
D O I
暂无
中图分类号
学科分类号
摘要
In this paper, the recently developed optimization algorithm, namely equilibrium optimization (EO), will be utilized to solve the optimal power flow problem (OPF), combining stochastic wind power with conventional thermal power generators in the system. The objectives are to minimize generation costs, including those incurred in thermal and stochastic wind power generation, active power loss, voltage deviation, and emission. To evaluate the performance of the EO algorithm in the OPF problem, modified IEEE 30-bus and IEEE 57-bus test systems with stochastic wind power generators will be used. A comparative study will be performed to show the efficiency of the EO algorithm compared with other recently developed metaheuristic algorithms such as the marine predators algorithm (MPA), artificial ecosystem-based optimization (AEO), and slime mould algorithm (SMA), as well as with other well-known algorithms. Based on the obtained results, the EO algorithm offered the best results.
引用
收藏
页码:369 / 392
页数:23
相关论文
共 50 条
  • [1] Wind integrated optimal power flow considering power losses, voltage deviation, and emission using equilibrium optimization algorithm
    Amroune, Mohammed
    [J]. ENERGY ECOLOGY AND ENVIRONMENT, 2022, 7 (04) : 369 - 392
  • [2] Multi-objective optimal power flow considering the cost, emission, voltage deviation and power losses using multi-objective modified imperialist competitive algorithm
    Ghasemi, Mojtaba
    Ghavidel, Sahand
    Ghanbarian, Mohammad Mehdi
    Gharibzadeh, Masihallah
    Vahed, Ali Azizi
    [J]. ENERGY, 2014, 78 : 276 - 289
  • [3] Probability Interval Optimization for Optimal Power Flow Considering Wind Power Integrated
    Chen, J. J.
    Wu, Q. H.
    [J]. 2016 INTERNATIONAL CONFERENCE ON PROBABILISTIC METHODS APPLIED TO POWER SYSTEMS (PMAPS), 2016,
  • [4] Optimal Power Flow Considering Intermittent Wind Power Using Particle Swarm Optimization
    Shilaja, C.
    Ravi, K.
    [J]. INTERNATIONAL JOURNAL OF RENEWABLE ENERGY RESEARCH, 2016, 6 (02): : 504 - 509
  • [5] Optimal power flow using Moth Swarm Algorithm with Gravitational Search Algorithm considering wind power
    Shilaja, C.
    Arunprasath, T.
    [J]. FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE, 2019, 98 : 708 - 715
  • [6] Minimization of voltage deviation and power losses in power networks using Pareto optimization methods
    Montoya, Francisco G.
    Banos, Raul
    Gil, Consolacion
    Espin, Antonio
    Alcayde, Alfredo
    Gomez, Julio
    [J]. ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE, 2010, 23 (05) : 695 - 703
  • [7] Optimal power flow solution of an integrated power system using elephant herd optimization algorithm incorporating stochastic wind and solar power
    Rambabu, Muppidi
    Kumar, Gundavarapu VenkataNagesh
    Rao, Bathina Venkateswara
    Kumar, Bali Sravan
    [J]. ENERGY SOURCES PART A-RECOVERY UTILIZATION AND ENVIRONMENTAL EFFECTS, 2021,
  • [8] Optimal Power Flow in Wind Power Integrated Systems using Function Optimization by Learning Automata
    Liao, H. L.
    Wu, Q. H.
    [J]. 2011 IEEE POWER AND ENERGY SOCIETY GENERAL MEETING, 2011,
  • [9] Optimal power flow solution with stochastic wind power using the Levy coyote optimization algorithm
    Kaymaz, Enes
    Duman, Serhat
    Guvenc, Ugur
    [J]. NEURAL COMPUTING & APPLICATIONS, 2021, 33 (12): : 6775 - 6804
  • [10] Optimal power flow using multi-objective glowworm swarm optimization algorithm in a wind energy integrated power system
    Salkuti, Surender Reddy
    [J]. INTERNATIONAL JOURNAL OF GREEN ENERGY, 2019, 16 (15) : 1547 - 1561