Optimal Power Flow Incorporating FACTS Devices and Stochastic Wind Power Generation Using Krill Herd Algorithm

被引:35
作者
Abdollahi, Arsalan [1 ]
Ghadimi, Ali Asghar [2 ]
Miveh, Mohammad Reza [3 ]
Mohammadi, Fazel [4 ]
Jurado, Francisco [5 ]
机构
[1] Payam E Golpayegan Higher Educ Inst, Fac Elect & Comp Engn, Golpayegan, Iran
[2] Arak Univ, Fac Engn, Dept Elect Engn, Arak 3815688349, Iran
[3] Tafresh Univ, Dept Elect Engn, Tafresh 3951879611, Iran
[4] Univ ofWindsor, Elect & Comp Engn ECE Dept, Windsor, ON N9B 1K3, Canada
[5] Univ Jaen, Dept Elect Engn, Linares 23700, Spain
关键词
flexible AC transmission systems (FACTS) devices; krill herd algorithm (KHA); optimal power flow (OPF); stochastic wind power generation; PARTICLE SWARM OPTIMIZATION;
D O I
10.3390/electronics9061043
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
This paper deals with investigating the Optimal Power Flow (OPF) solution of power systems considering Flexible AC Transmission Systems (FACTS) devices and wind power generation under uncertainty. The Krill Herd Algorithm (KHA), as a new meta-heuristic approach, is employed to cope with the OPF problem of power systems, incorporating FACTS devices and stochastic wind power generation. The wind power uncertainty is included in the optimization problem using Weibull probability density function modeling to determine the optimal values of decision variables. Various objective functions, including minimization of fuel cost, active power losses across transmission lines, emission, and Combined Economic and Environmental Costs (CEEC), are separately formulated to solve the OPF considering FACTS devices and stochastic wind power generation. The effectiveness of the KHA approach is investigated on modified IEEE-30 bus and IEEE-57 bus test systems and compared with other conventional methods available in the literature.
引用
收藏
页码:1 / 18
页数:18
相关论文
共 28 条
[1]   Optimal location of FACTS devices in a power system solved by a hybrid approach [J].
Baskaran, J. ;
Palanisamy, V. .
NONLINEAR ANALYSIS-THEORY METHODS & APPLICATIONS, 2006, 65 (11) :2094-2102
[2]   Multi-objective optimal power flow with FACTS devices [J].
Basu, M. .
ENERGY CONVERSION AND MANAGEMENT, 2011, 52 (02) :903-910
[3]  
Behshad M., 2009, P 2 INT C POW EL INT
[4]   UPFC with series and shunt FACTS controllers for the economic operation of a power system [J].
Bhattacharyya, Biplab ;
Gupta, Vikash Kumar ;
Kumar, Sanjay .
AIN SHAMS ENGINEERING JOURNAL, 2014, 5 (03) :775-787
[5]   Capacity assessment for wind power integration considering transmission line electro-thermal inertia [J].
Dong, Xiaoming ;
Zhang, Ruiqi ;
Wang, Mengxia ;
Wang, Jinyu ;
Wang, Chengfu ;
Wang, Yong ;
Wang, Peng .
INTERNATIONAL JOURNAL OF ELECTRICAL POWER & ENERGY SYSTEMS, 2020, 118
[6]  
Easwaramoorthy N.K., 2012, INT J COMPUT APPL TE, V55, P38
[7]   Krill herd: A new bio-inspired optimization algorithm [J].
Gandomi, Amir Hossein ;
Alavi, Amir Hossein .
COMMUNICATIONS IN NONLINEAR SCIENCE AND NUMERICAL SIMULATION, 2012, 17 (12) :4831-4845
[8]   Power flow control and power flow studies for systems with FACTS devices [J].
Gotham, DJ ;
Heydt, GT .
IEEE TRANSACTIONS ON POWER SYSTEMS, 1998, 13 (01) :60-65
[9]   Impacts of photovoltaic distributed generation and energy efficiency measures on the electricity market of three representative Brazilian distribution utilities [J].
Heideier, Raphael ;
Bajay, Sergio Valdir ;
Jannuzzi, Gilberto M. ;
Gomes, Rodolfo D. M. ;
Guanais, Luan ;
Ribeiro, Izana ;
Paccola, Angelo .
ENERGY FOR SUSTAINABLE DEVELOPMENT, 2020, 54 :60-71
[10]   A comparative study of multi-objective optimal power flow based on particle swarm, evolutionary programming, and genetic algorithm [J].
Kahourzade, Solmaz ;
Mahmoudi, Amin ;
Bin Mokhlis, Hazlie .
ELECTRICAL ENGINEERING, 2015, 97 (01) :1-12