Optimal power flow incorporating wind and solar power unclertainly cost using particle SWARM optimization with mutation | |
| Author | Titipong Samakpong |
| Call Number | AIT Diss no.ET-20-03 |
| Subject(s) | Power resources Wind power--Costs Solar energy--Costs |
| Note | A dissertation submitted in partial fulfillment of the requirements for the degree of Doctor of Engineering in Energy, School of Environment, Resources and Development |
| Publisher | Asian Institute of Technology |
| Series Statement | Dissertation ; no. ET-20-03 |
| Abstract | 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 “Optimal Power Flow” (OPF) solution method integrating the cost of wind-solar power uncertainty using “Particle Swarm Optimization” (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 “New 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 “Optimal Power Flow” 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 10‑node distribution network. The simulation result shows the effectiveness and performance of the developed PSOs which are superior to the other optimization algorithms as well. |
| Year | 2020 |
| Corresponding Series Added Entry | Asian Institute of Technology. Dissertation ; no. ET-20-03 |
| Type | Dissertation |
| School | School of Environment, Resources, and Development (SERD) |
| Department | Department of Energy and Climate Change (Former title: Department of Energy, Environment, and Climate Change (DEECC)) |
| Academic Program/FoS | Energy Technology (ET) |
| Chairperson(s) | Weerakorn Ongsakul; |
| Examination Committee(s) | Singh, Jai Govind;Shrestha, Rajendra Prasad;Anal, Anil Kumar; |
| Scholarship Donor(s) | HM King HRD Project;Asian Institute of Technology Fellowship; |
| Degree | Thesis (Ph.D.) - Asian Institute of Technology, 2020 |