Technical and financial analysis of an optimally placed wind farm project using a binary PSO program | |
| Author | Sittichoke Pookpunt |
| Call Number | AIT Diss. no.ET-17-02 |
| Subject(s) | Binary control systems Wind power--Finance |
| Note | A dissertation submitted in partial fulfillment of the requirements for the degree of Doctor of Engineering in Energy |
| Publisher | Asian Institute of Technology |
| Series Statement | Dissertation ; no. ET-17-02 |
| Abstract | This dissertation presents a technical and financial analysis of optimally placed wind turbines within a wind farm project. The analysis was done using the Binary PSO program: the BPSO-TVAC, which was developed for calculating the maximum turbine power output possible with the minimum investment cost for the wind farm. The test site was divided into square areas, referred to as cells. Three specific wind characteristics: uniform wind speed, non-uniform wind speed and direction, were applied to all of the cells for uniformity of measurement. The Linear Wake Model was used to calculate downstream wind speed and the power was calculated using the turbine power curve. The test results indicate that the investment cost per generated power for both uniform and non-uniform wind speed with variable wind direction using BPSO-TVAC are lower than those obtained from the Genetic Algorithm, the Evolutive Algorithm, BPSO-TVIW (time-varying inertia weight factor), BPSO-RANDIW (random inertia weight factor) and BPSO-RTVIWAC (random time-varying inertia weight and acceleration coefficients). BPSO-TVAC was developed to maximize net present value (NPV) subject to simultaneously optimal turbine position, turbine sizing and hub height. The design constraints of the optimal wind farm configuration were included in the actual data at a particular site, the specified initial investment cost within a fixed area and turbine spacing. The Component Cost Model and learning curve is used to express the initial investment cost and the levelized cost of energy. The annual energy production cost of a wind farm depended on the number of wind turbines, the installed sizing, hub height and wake loss within a wind farm. BPSO-TVAC simultaneously determines the optimal wind turbine placement directly facing the frequent high wind speed and direction. As well, BPSO-TVAC optimally reduces the number of turbines and installs the larger sizing leading to a higher profit than the conventional wind farm layout. Finally, sensitivity analysis, Monte Carlo simulation and hypothesis testing are presented to gain a better insight into the uncertainties including wind speed and discount rate, and how they affect the financial risk of wind farm projects. The scenario analysis showing the positive NPV for the worst case scenario is useful for wind farm developers to make investment decisions. |
| Year | 2017 |
| Corresponding Series Added Entry | Asian Institute of Technology. Dissertation ; no. ET-17-02 |
| 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 ;Dailey, Matthew N. |
| Scholarship Donor(s) | National Science and Technology DevelopmentAgency (NSTDA), Thailand ;Asian Institute of Technology Fellowship |
| Degree | Thesis (Ph.D.) - Asian Institute of Technology, 2017 |