AI SearchOptimal power flow incorporating wind and solar power unclertainly cost using particle SWARM optimization with mutation
Titipong Samakpong
Optimal power flow incorporating wind and solar power unclertainly cost using particle SWARM optimization with mutation - Pathum Thani, Thailand : Asian Institute of Technology, 2020 - 88 leaves : ill. - Dissertation ; no. ET-20-03 . - Asian Institute of Technology. Dissertation ; no. ET-20-03 .
A dissertation submitted in partial fulfillment of the requirements for the degree of Doctor of Engineering in Energy, School of Environment, Resources and Development
Thesis (Ph.D.) - Asian Institute of Technology, 2020
Wind and solar power generations have been rapidly increasing in the last couple decades. large quantity of wind-solar power has sophisticated implications on power system operations due to unpredictable and intermittent nature of wind, and solar energy has significant impacts on system operations. Integrating wind-solar into to power systems cause several challenges. In this research, the 2Optimal Power Flow3 (OPF) solution method integrating the cost of wind-solar power uncertainty using 2Particle Swarm Optimization3 (PSO) techniques is proposed. A Monte-Carlo approach is used to simulate wind and solar power uncertainty representing by Weibull and Normal distribution, respectively. Wind generation power is then determined using a wind turbine mathematical model, while the solar power is calculated using PV and inverter models. The simulated renewable power is used to determines costs of wind and solar uncertainty, which comprises of the uncertainty cost of renewable power excess and the uncertainty cost of renewable power deficit. These costs arrived from the additional spinning reserve and loss of benefit cause by the intermittent characteristic of wind and solar power. These uncertainty costs are integrated into conventional Optimal Power Flow problems. The problem hence solved by four types of PSO algorithms developed in this research. An modified 2New England IEEE 39-bus system, with ten multi-valve turbine generators, is used as a case study to analyze the effect of uncertainty of the integrated wind and solar farm on the OPF problem and to verify rationality of the proposed 2Optimal Power Flow3 model. The simulation results from different PSO techniques are compared. In this research, PSO with time-variant inertia and acceleration coefficients and PSO mutation based provide better results. The PSO algorithms developed in this research are also used to solve other optimization problems: 1). OPF with nonlinear generation cost implemented on IEEE 30-bus test system; 2). Capacitive compensator optimization using PSOs to minimize voltage drop in a 10node distribution network. The simulation result shows the effectiveness and performance of the developed PSOs which are superior to the other optimization algorithms as well.
Power resources
Wind power--Costs
Solar energy--Costs
Optimal power flow incorporating wind and solar power unclertainly cost using particle SWARM optimization with mutation - Pathum Thani, Thailand : Asian Institute of Technology, 2020 - 88 leaves : ill. - Dissertation ; no. ET-20-03 . - Asian Institute of Technology. Dissertation ; no. ET-20-03 .
A dissertation submitted in partial fulfillment of the requirements for the degree of Doctor of Engineering in Energy, School of Environment, Resources and Development
Thesis (Ph.D.) - Asian Institute of Technology, 2020
Wind and solar power generations have been rapidly increasing in the last couple decades. large quantity of wind-solar power has sophisticated implications on power system operations due to unpredictable and intermittent nature of wind, and solar energy has significant impacts on system operations. Integrating wind-solar into to power systems cause several challenges. In this research, the 2Optimal Power Flow3 (OPF) solution method integrating the cost of wind-solar power uncertainty using 2Particle Swarm Optimization3 (PSO) techniques is proposed. A Monte-Carlo approach is used to simulate wind and solar power uncertainty representing by Weibull and Normal distribution, respectively. Wind generation power is then determined using a wind turbine mathematical model, while the solar power is calculated using PV and inverter models. The simulated renewable power is used to determines costs of wind and solar uncertainty, which comprises of the uncertainty cost of renewable power excess and the uncertainty cost of renewable power deficit. These costs arrived from the additional spinning reserve and loss of benefit cause by the intermittent characteristic of wind and solar power. These uncertainty costs are integrated into conventional Optimal Power Flow problems. The problem hence solved by four types of PSO algorithms developed in this research. An modified 2New England IEEE 39-bus system, with ten multi-valve turbine generators, is used as a case study to analyze the effect of uncertainty of the integrated wind and solar farm on the OPF problem and to verify rationality of the proposed 2Optimal Power Flow3 model. The simulation results from different PSO techniques are compared. In this research, PSO with time-variant inertia and acceleration coefficients and PSO mutation based provide better results. The PSO algorithms developed in this research are also used to solve other optimization problems: 1). OPF with nonlinear generation cost implemented on IEEE 30-bus test system; 2). Capacitive compensator optimization using PSOs to minimize voltage drop in a 10node distribution network. The simulation result shows the effectiveness and performance of the developed PSOs which are superior to the other optimization algorithms as well.
Power resources
Wind power--Costs
Solar energy--Costs